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Beau Martonik sits back down with Dr. Karl Miller for the second half of their conversation, and this half is entirely about what a deer sees. Karl was part of the original work that got inside a whitetail's eye to determine whether deer perceive color at all, and he later worked with Sitka on a camo pattern developed around deer vision rather than human vision. He starts with the hardware. A deer's pupil opens about three times wider than ours, which alone is roughly nine times the light-gathering ability, and a reflective layer behind the retina called the tapetum bounces that light back across the rods and cones a second time to roughly double it again. The pupil is a horizontal slit rather than a circle, and it rotates to stay level even when the deer drops its head to feed, so a deer eating acorns is still watching the horizon. They see roughly 310 degrees, whereas we see 120. Then the tradeoff. Deer have no fovea centralis, they can't accommodate for distance, and their visual acuity at a stationary object comes in somewhere around 20/40 to 20/60. Karl's line is that they'd almost need corrective lenses to drive. What they do have is motion. Their eyes stay still and let the image travel across the retina engaging new cones, and they process that visual information roughly four to five times faster than a human does, which Karl suggests means your movement looks to them something like slow motion. That leads into a long back and forth on jumping the string, whether a deer is reacting to the sound at all, and whether it might be seeing the bow flex or even the arrow itself. Beau brings his axis deer footage from Hawaii into it, where the deer were on the ground before the arrow arrived at 25 yards. The back half is camo, and it's the most useful stretch of either episode. Karl lays out the two jobs camo has to do, explains why pattern detail matters far less than most hunters think and why outline and spectral match matter far more, and gets into whiteners and brighteners in fabrics and detergents that take ultraviolet light and re-radiate it into exactly the part of the spectrum a deer sees 20 times better than we do. He explains why he stopped wearing blue jeans in the woods, why a whitetail's tail is white in the first place, why old army camo is actually good camo, and why a shiny fabric can beat you even when the color is right. He also walks through how Sitka's Cover pattern was developed and what it was actually designed to solve. He closes with what he calls the take home: hunt the deer's brain first. There are only three questions in a deer's head, and everything it does traces back to one of them. Part one of this conversation is episode 506. Topics: 00:00:00 — Part two intro 00:07:16 — A deer's eye is built for two jobs, and neither one is looking at you 00:11:32 — Pupil size, and nine times your light gathering ability 00:13:31 — Their eyes rotate to stay level when their head goes down 00:14:23 — The tapetum, and 18 times the light 00:21:21 — Blue cones across the whole retina, and 310 degrees of vision 00:24:49 — 20/40 to 20/60: they'd need corrective lenses to drive 00:27:39 — Flicker fusion, and why your movement looks like slow motion to them 00:28:40 — Jumping the string: the sound, the arrow, or the bow 00:34:29 — Drawing on a quartering deer, and why they don't look up 00:37:20 — What camo has to do, and what it doesn't 00:40:39 — Whiteners, brighteners, and camo that glows 00:42:53 — How Cover got built around deer eyes instead of ours 00:50:08 — Fabric shine, old army camo, and plastic blaze orange 00:54:03 — Hunt the deer's brain first Dr. Karl Miller's published research: Google Scholar, search “Karl Miller deer UGA” Part one of this conversation is Ep. 506 Instagram:   ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@eastmeetswesthunt⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@beau.martonik⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook:   ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠East Meets West Outdoors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Shop Hunting Gear and Apparel: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.eastmeetswesthunt.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ YouTube: Beau Martonik - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/channel/UCQJon93sYfu9HUMKpCMps3w⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Partner Discounts and Affiliate Links: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.eastmeetswesthunt.com/partners⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Amazon Influencer Page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.amazon.com/shop/beau.martonik⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. 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Everyone lives through time, but we continue to struggle to fully understand it. We remain unsure whether time is fundamental or emergent, why it has an arrow pointing from past to future, and how that arrow connects to our experience of time's passage. Jim Al-Khalili's new book is On Time: The Physics that Makes the Universe Tick. We discuss what time is at the most basic level, and how it connects to open questions in biology, cosmology, and quantum mechanics. Mindscape listeners can try StatsKey Pro free for one month with code MINDSCAPE. After that, it's $4.99 per month unless canceled. #ad Blog post with transcript: https://preposterousuniverse.com/podcast/2026/08/31/366-jim-al-khalili-on-time-quantum-biology-and-cosmology/ Support Mindscape on Patreon. Jim Al-Khalili received his Ph.D. in physics from the University of Surrey. He is currently Distinguished Emeritus Professor of Physics at Surrey. He is a Fellow of the Royal Society, and has served as past president of the British Science Association. He has been awarded the Michael Faraday Prize by the Royal Society, and has been appointed an appointed Officer of the Order of the British Empire. Web site University of Surrey web page Google Scholar publications Amazon author page Wikipedia YouTube

In this episode, Dr. Boris Vinatzer, 2026 APS Fellow and Professor of Microbial Genetics and Genomics in the School of Plant and Environmental Sciences at Virginia Tech in Blacksburg, Virginia joins host Matt Kasson to discuss his career working on plant biotechnology and plant pathogenic bacteria with an emphasis on Pseudomonas syringae and Rolstonia solanacearum. He also talks about his work on ice nucleation activity in bacteria and fungi and genome-based approaches for plant disease diagnostics including multi-locus sequence analysis for species level identification of plant pathogenic bacteria. show notes The full transcript for this episode can be found here: Dr. Boris Vinatzer's Virginia Tech faculty webpage: https://spes.vt.edu/faculty-staff/faculty/vinatzer-boris.html Dr. Boris Vinatzer's Google Scholar profile: https://scholar.google.com/citations?user=STcUUYcAAAAJ&hl=en Dr. Boris Vinatzer's 2026 APS Fellow Profile: https://www.apsnet.org/members/give-awards/Pages/awardees.aspx This episode is produced by Association Briefings.Special Guest: Boris Vinatzer.

Beau Martonik sits down with Dr. Karl Miller of the University of Georgia, where he spent 40 years leading deer research and mentoring close to 100 graduate students. Karl grew up hunting the Pennsylvania big woods before he went south for his PhD, and much of what hunters take for granted about rubs and scrapes came out of his lab. The core of this conversation is signpost communication. Karl walks through why bucks make rubs in the first place, and what one of his students found when he ran a chemical analysis on forehead gland secretions: 67 different compounds, and not one of them in higher concentration in dominant bucks than in subordinate ones. No dominance signal at all. What varies is the mix, deer to deer, which means every buck is leaving something closer to a name than a rank. He also explains why the loser of a sparring match will walk up and lick the winner's forehead. Then he gets into scrapes, and the finding he clearly loves most. Scraping activity peaks about two weeks before peak breeding in almost every study ever run on it. Karl's hypothesis is that this isn't coincidence or anticipation. It's that buck urine deposited in scrapes may be acting as a priming hormone on the does, pulling their cycles into synchrony, and the two-week lag matches the length of a doe's follicular wave. He and a graduate student demonstrated the same mechanism in Eld's deer using a control group and a group exposed to male urine, and the treated group entered estrus together with enhanced hormone profiles. Then he flips it and explains flehmen, the vomeronasal organ, and how a buck taking doe urine into his mouth routes it past the smelling part of his brain entirely and straight to the part that runs his reproductive physiology. Both sides priming each other. Also covered: 3,600 rubs per square mile on an unhunted Georgia herd, roughly six per acre; the interdigital gland and the 11 compounds that actually do track with maturity; why a tarsal gland's smell comes from bacteria and not from the gland; the staph and listeria living on it; the castrated buck that ran an entire rut in two weeks on a testosterone dose they overshot; young bucks making 15 percent as many scrapes as mature bucks; and a stretch where he and Beau land in different places on whether there's any point in doctoring a scrape. This is part one of two. Part two is deer vision and camo, and it's out Tuesday, September 1st. Topics: 00:00:00 — Intro 00:19:25 — What everybody had wrong about rubs in the seventies 00:25:04 — 67 compounds, and why a rub isn't a dominance display 00:26:09 — Why the loser of a sparring match licks the winner's forehead 00:38:32 — 3,600 rubs per square mile, and how many one buck makes in a year 00:49:08 — The three things a buck does at a scrape 00:52:56 — Why a tarsal gland actually stinks 01:01:45 — 39 bucks on one scrape, and 85 percent of visits after dark 01:04:30 — Scrapes peak two weeks before the rut 01:06:24 — Priming hormones, and synchronized estrus 01:10:09 — Flehmen, and the does priming the bucks right back 01:21:59 — Adding scent to a scrape, and whether it does anything 01:35:00 — What a broken age structure does to the rut you're hunting Dr. Karl Miller's published research: Google Scholar, search “Karl Miller deer UGA” Instagram:   ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@eastmeetswesthunt⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@beau.martonik⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook:   ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠East Meets West Outdoors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Shop Hunting Gear and Apparel: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.eastmeetswesthunt.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ YouTube: Beau Martonik - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/channel/UCQJon93sYfu9HUMKpCMps3w⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Partner Discounts and Affiliate Links: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.eastmeetswesthunt.com/partners⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Amazon Influencer Page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.amazon.com/shop/beau.martonik⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Have you ever wondered how trolls, bots, and paid influencers are quietly manipulating what you see online and why even smart, skeptical people fall for it? Today, disinformation expert Dr. Darren Linvill pulls back the curtain on the tactics that bad actors are using to try and shape our beliefs.    In this episode, Therese Markow and Dr. Darren Linvill discuss the various forms of digital deception and the strategies used by state actors to manipulate global discourse. Dr. Linvill distinguishes between misinformation, disinformation, and malinformation and gives examples of each, including tactics used by different international organizations from Iran, China, Israel, North Korea, and others. He also discusses the psychological impact that this disinformation has on humanity.     Key Takeaways: Fraud is the oldest form of disinformation, and it's rampant across social media. It's a multi-billion-dollar industry.  The main way that AI is being used right now to disseminate false information, disinformation, whether it's fraud or political disinformation, is the scale at which this content is created.  Sometimes there is not enough information to attribute disinformation accounts to a specific person or group. Sometimes it can be traced either through the content being posted or similar information on other websites.  At the end of the day, people will believe what they want to believe, regardless of whether it is true or has been fact-checked as false.    "At the end of the day, all of us humans are emotional animals. And we are interpreting the world around us, including the information we find on social media, through an emotional lens, especially when it comes to the communities that we're a part of, and our emotional attachments to those communities and our emotional needs that we derive from those communities, that absolutely affects the lens through which we interpret information." —  Dr. Darren Linvill   Episode References:  Clemson University Media Forensics Hub: Spot the Troll - https://spotthetroll.org/    Connect with Dr. Darren Linvill: Professional Bio: https://www.clemson.edu/cbshs/about/profiles/darrenl  Twitter: https://x.com/DarrenLinvill?lang=en   LinkedIn: https://www.linkedin.com/in/darren-linvill-6673655b  Google Scholar: https://scholar.google.com/citations?user=gQl4PlQAAAAJ&hl=en    Connect with Therese: Website:  www.criticallyspeaking.net Bluesky: @CriticallySpeaking.bsky.social Instagram: @criticallyspeakingpodcast Email: theresemarkow@criticallyspeaking.net   Audio production by Turnkey Podcast Productions. You're the expert. Your podcast will prove it. 

Black holes, as Stephen Hawking discovered, do grow old: they emit radiation, lose mass, and eventually evaporate away. But our fascination with black holes never grows old. This is especially true today, as we are seeing a flood of new data and intriguing theoretical ideas, which both tests the limits of Einstein's general relativity and teach us new things about the astrophysical universe. At the Center of Gravity at the University of Copenhagen, they are currently celebrating Black Hole Week, which provides an excellent opportunity to talk with Center director Vitor Cardoso about what we've been learning about these singular cosmic objects. Use code MINDSCAPE at https://monarch.com/ to get your first year of Monarch Core half off at just $50. #ad Upgrade your everyday and get free shipping and 365-day returns at https://quince.com/MINDSCAPE. #ad See what ElevenAgents can do for your specific workflows at https://elevenlabs.io/MINDSCAPE. #ad Blog post with transcript: https://preposterousuniverse.com/podcast/2026/08/24/365-vitor-cardoso-on-why-black-holes-are-special/ Support Mindscape on Patreon. Vitor Cardoso received his Ph.D. in physics from the Instituto Superior Técnico in Portugal. He is currently a Villum Investigator and Director of the Center of Gravity at the Niels Bohr Institute in Copenhagen, and a Distinguished Professor at Técnico. Web site University of Copenhagen web page Simons Collaboration page Google Scholar publications

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

One of the paradoxes of science is that it seeks objectively true understanding of the world, but its methodology is driven by ignorance, failure, and uncertainty. Some phenomena we understand pretty well, but interesting research happens at the boundary of what we do and don't know. And there is no foolproof algorithm for moving in the right direction; we need to make conjectures and test them against the world. Biologist Stuart Firestein has been advocating for a better public understanding of the true methods of science, most recently in his new book It Could Be Otherwise: Science In the Age of Uncertainty. Stop piecing your software together. Go to Odoo.com/mindscape to learn more. #ad Mindscape listeners can try StatsKey Pro free for one month with code MINDSCAPE. After that, it's $4.99 per month unless canceled. #ad Blog post with transcript: https://preposterousuniverse.com/podcast/2026/08/17/364-stuart-firestein-on-how-science-relies-on-ignorance-and-failure/ Support Mindscape on Patreon(opens in new tab). Stuart Firestein received his Ph.D. in neurobiology from the University of California, Berkeley. He is currently Professor of Neuroscience at in the Department of Biological Sciences at Columbia University, and Fractal Faculty at the Santa Fe Institute. He is a fellow of the American Association for the Advancement of Science, the Sloan Foundation, and the Guggenheim Foundation. His research studies cellular mechanisms of signal transduction and olfaction. His previous books include Ignorance: How It Drives Science and Failure: Why Science Is So Successful. Columbia web page Google Scholar publications Amazon author page Wikipedia

A wind-driven ember shower is a key wildfire exposures a building can face, but we have not yet fully understood or accounted for them in making our communities wildfire resilient. Today we sit down with Samuel L. Manzello of Tohoku University and Reax Engineering to unpack how in the last 20 years the firebrand science went from not being able to characterize the features of them well to being able to procur controlled experiments and standardized generators that can actually shape building codes and product design. We dig into what firebrands really are, why vegetation embers and structure-generated embers behave differently, and why lab tests with single particles often fail to explain the ignitions seen after major WUI disasters. From there, we follow the chain reaction that led to the Dragon firebrand generator: the need for wind, the lack of suitable facilities, and the breakthrough of creating a continuously feedable device that can produce repeatable firebrand showers. Samuel explains how airflow settings can shift firebrands from glowing to flaming, and why that control is essential for meaningful wildfire exposure testing. The second half moves into standardization and real-world impact. Samuel breaks down ISO TC 92 and the work of the IAFSS LOFBE group on large outdoor fires in the built environment, including why the "Baby Dragon" became an ISO methodology and what it unlocks next: better roof tests, vent penetration tests, facade and opening vulnerabilities, and more consistent ways to compare hazards across regions and vegetation types. If you care about wildfire resilience, WUI fire safety, ember intrusion, and the future of practical standards, this conversation maps the path forward. As promised, here are some links:The world is burning: What exactly are firebrands and why should anyone care?Progress in creating international standardsExperimentally producing various firebrandsNIST - Dragon firebrand generator (2014)Samuel's classic paper from 2006 - on ignition of mulch with firebrandsBut there is much, much more in the literature on the firebrands! I recommend going to Scopus or Google Scholar and looking for Samuel's record there: https://scholar.google.com/citations?user=4oAvxXMAAAAJ&hl=pl&oi=ao----The Fire Science Show is produced by the Fire Science Media in collaboration with OFR Consultants. Thank you to the podcast sponsor for their continuous support towards our mission.

Large Language Models display an uncanny ability to construct human-sounding speech, and can synthesize concepts in novel ways. Is this because they are truly thinking like human beings in some way, or have they found a way to be human-like without reproducing the internal mechanisms of human thought? Chandra Sripada argues that LLM cognition is more human-like than we suppose, and offers evidence from the ways that cognitive scientists study actual humans.   Blog post with transcript: https://preposterousuniverse.com/podcast/2026/08/10/363-chandra-sripada-on-how-llms-and-humans-are-cognitive-cousins/ Support Mindscape on Patreon. Chandra Sripada received an M.D. from the University of Texas and a Ph.D. in philosophy from Rutgers University. He is currently a professor of philosophy and psychiatry at the University of Michigan, where he holds the Theophile Raphael Research Professorship and directs the Weinberg Institute for Cognitive Science. He writes Cognition, Decoded, a Substack newsletter about AI, cognitive science, and philosophy. Web site Google Scholar publications PhilPeople profile Babbel is offering listeners up to 60% off. Go to https://babbel.com/MINDSCAPE. #ad Get 60% off an annual plan from Incogni using code MINDSCAPE at https://incogni.com/mindscape. #ad

Education is changing quickly. Today, technology is doing much more than bringing lessons online. It is helping students and professionals learn by doing, making mistakes safely, and building real-world skills before they step into a classroom or workplace.In today's episode, I'm excited to welcome Professor Charlott Sellberg from the University of Gothenburg. She is an expert in simulation-based learning and studies how technology can make education more practical, engaging, and effective.Google Scholar: https://scholar.google.com/citations?user=EktLntMAAAAJ&hl=enLike the show? Please subscribe, review, download and share.Want to know more about me and my work go to: https://gargisarkar1611.wixsite.com/gargi-sarkarConnect with me: https://www.linkedin.com/in/gargi-sarkar1611/Follow me on Instagram: https://www.instagram.com/gargispeaks/Contact me: gargisarkar1611@gmail.com

Bron tackles a listener question: how do you actually stay evidence-based once you're out of university, time-poor, and no longer have easy access to academic databases? She walks through the full range of free and low-cost options, from AI search tools to library memberships, so you can keep up-to-date with research without it becoming a second job. Bron covers:

On episode 263, we welcome J. Aaron Simmons and Benjamin McGraw to discuss the philosophy of heavy metal, the satanic panic and the Judas Priest trial, metal as an expression of liberty and individuality, the intellectual diversity in metal, challenging racist and misogynistic ideologies in the sphere, music as an expression of rebellion and critical thinking, the battle between reactionary and progressive beliefs and how it manifests in the war against the devil, the paradox of Christian metal, and transcending the need to transgress as an authentic expression of metal fandom. J. Aaron Simmons is a Professor of Philosophy who has published over a dozen books, the most recent is Camping with Kierkegaard. Simmons is the former President of the Søren Kierkegaard Society (USA) and writes regularly for his Substack, Philosophy in the Wild. Benjamin W. McCraw is Instructor of Philosophy at the University of South Carolina Upstate and Research Associate at the African Centre for Epistemology and Philosophy of Science (ACEPS), University of Johannesburg. He works in epistemology and philosophy of religion and has published in journals including Social Epistemology, Faith and Philosophy, Acta Analytica, and Religious Studies. Their new book, available now, is called The Heaviest Ideas in the Universe: A Philosophy of Heavy Metal. | J. Aaron Simmons & Benjamin W. McCraw| ► Website | https://jaaronsimmons.com, https://philpeople.org/profiles/benjamin-mccraw  ► Youtube | https://www.youtube.com/channel/UCtktPVW5IrLx0772vl8znWQ ► Substack | https://jaaronsimmons.substack.com ► Twitter |  https://twitter.com/jaaronsimmons ► Facebook | https://www.facebook.com/profile.php?id=100090093484022 ► Instagram | https://www.instagram.com/simmonsphilosopher ► Google Scholar | https://scholar.google.com/citations?user=kUbkQ8EAAAAJ&hl=en ► The Heaviest Ideas in the Universe | https://amzn.to/4hI1IED Where you can find us: | Seize The Moment Podcast | ► Facebook | https://www.facebook.com/SeizeTheMomentPodcast ► Twitter | https://twitter.com/seize_podcast ► Instagram | https://www.instagram.com/seizethemomentPodcast ► TikTok | https://www.tiktok.com/@seizethemomentpodcast ► Patreon |  patreon.com/user?u=32208666  

There's a photo on Tim Ireland's Google Scholar profile of a toddler standing on the floor of an open pit gold mine in Australia's Northern Territory, wearing a hard hat and — because it was the early 1980s — flip flops. That toddler is now the Principal Geologist for Exploration at **First Quantum Minerals**, one of the world's major copper producers, and in this episode Jesse sits down with him to talk about how mineral exploration actually works.Tim grew up with two geologist fathers, but the path in wasn't as direct as it sounds. He took a gap year as a field technician and mostly hated it. He considered leaving to design jewelry. He turned down a PhD because being told he'd become "one of the world experts on sedimentary rock–hosted zinc" sounded uncomfortably narrow — then spent a few years alone in the desert with a drill rig and 22,000 square kilometers of ground before the university called back with something better.From there we get into the machinery of exploration. Tim describes the job as **reducing search space** — starting with a continent and narrowing until you're willing to spend real money drilling — and why that philosophy holds whether you're chasing porphyry copper in Chile, sediment-hosted copper in the DRC, or orthomagmatic nickel in Finland. We also get into the part they don't teach in a mineral deposits course: three or four people weighing in over email on whether to spend millions on relatively scant information, and why what a company really pays a principal geologist for is a **calibrated gut**.The scientific heart of the episode is what Tim calls the **quality question**. Deposit models are good at telling a geologist whether they're getting warmer. They're not good at telling you whether the thing you're walking toward will ever be a mine — and the industry is full of "technical successes" nobody publishes, where the geologist did the job right and the deposit simply wasn't good enough. We also cover critical minerals and why the West is late (Tim was in the room in Oslo in 2007 when China announced the plan out loud), the 22-year lag from discovery to production, and why he's skeptical of AI prospectivity tools — if there are only ten porphyry deposits on Earth with more than a hundred million tons of contained copper, that's not a training set.It closes with Tim's best day as a geologist: alone in a gorge in the northern Chilean desert, a ten-mile traverse, a spire of rock he probably shouldn't have climbed, and one outcrop that put his hand on a fault he'd only been able to argue about on paper.In this episode- The Google Scholar baby photo — hard hat, flip flops, floor of an open pit- A gap year he didn't enjoy, and the jewelry business that never happened- Why he turned down a PhD on sedimentary zinc — and what he asked for instead- Industry-funded research: "cut-price consultants," or the best training pipeline there is?- Reducing search space: the one philosophy that holds across every deposit type- Spending millions on scant information, and what a calibrated gut is worth- Critical minerals, the 2007 Oslo warning, and who's still in denial- 22 years from discovery to production — and why averages lie- The quality question: why our models can't tell a mine from a "technical success"- AI in exploration: the useful camp, the black-box camp, and why ten deposits isn't a training set- Big company vs. junior — kudos versus shares, and the trade-off nobody spells out- Tim's best day as a geologist, alone in a Chilean gorgeAbout the guestDr. Tim Ireland is Principal Geologist for Exploration at First Quantum Minerals, a global copper-focused mining company. He trained at the University of Tasmania, with an honors project on the MacArthur River zinc deposit and a PhD on porphyry copper systems in northern Chile. His career has run through Newmont in Turkey, a junior working sediment-hosted copper in the DRC, and thirteen years at First Quantum.Memorable quotes- "If it were mathematical and there was a definite yes/no answer, we wouldn't be needed."- "You still have to do the work. You can't just sit at your desk and think about the model."- "If you've only got ten porphyry deposits in the world with more than a hundred million tons of contained copper, ten's not a great enough training set."- "I'm still kind of hanging out for that day that I walk up a hill and crack the first rock."Download the CampGeo app now at this link. On the app you can get tons of free content, exclusive images, and access to our Geology of National Parks series. You can also learn the basics of geology at the college level in our FREE CampGeo content series - get learning now!Like, Subscribe, and leave us a Rating!——————————————————Instagram: @planetgeocastTwitter: @planetgeocastFacebook: @planetgeocastSupport us: https://planetgeocast.com/support-usEmail: planetgeocast@gmail.comWebsite: https://planetgeocast.com/

In this episode, Dr. Rodrigo Almeida, 2026 APS Fellow, Professor of Emerging Infectious Disease Ecology, and Hildebrand-Laumeister Chair in Plant Pathology in the Department of Environmental Science, Policy and Management at the University of California Berkeley joins host Matt Kasson to discuss his career working on insect-transmitted plant pathogens inside and outside California with an emphasis on the bacterial plant pathogen Xylella fastidiosa, the causal agent of Pierce's Disease of grape. He helps untangle the tricky taxonomy of Xylella and talks about some of the modern tools and techniques his lab has developed to study this fastidious organism. Show Notes Dr. Rodrigo Almeida's UC Berkeley faculty webpage: https://sites.google.com/berkeley.edu/almeida-lab/home Dr. Rodrigo Almeida's Google Scholar profile: https://scholar.google.com/citations?user=LMsjc2cAAAAJ&hl=en 2026 APS Fellow Profile: https://www.apsnet.org/members/give-awards/Pages/awardees.aspx This episode is produced by Association Briefings.Special Guest: Rodrigo Almeida.

Political conflicts related to immigration are thorny enough on their own. The issues and challenges intensify once we add to the mix our warming planet and its relationship to migration, immigration, and human displacement. Two 2024-25 CASBS fellows – Maureen Eger, a sociologist of immigration, and Alice Farmer, a refugee lawyer specializing in climate change and displacement – discuss scholarly and policy dimensions of the climate migration landscape in conversation with Pulitzer Prize-winning journalist John Markoff (CASBS fellow, 2017-18). Maureen Eger: USC faculty page | personal website | Google Scholar page | Alice Farmer: LinkedIn page |  Read John Markoff's latest book, Whole Earth: The Many Lives of Stewart Brand  (Penguin Random House, 2022). John's next book, expected in 2027, will be published by MIT Press. Referenced in the episode or related works L. Yang, M. Eger, and Link, eds. Migration Stigma: Understanding Prejudice, Discrimination, and Exclusion (MIT Press, 2024) M. Eger and S Valdez, "From Radical Right to Neo-nationalist," European Political Scientist (2019) M. Eger, M. Hjerm, and P. Velasquez, "What is the Liberalizing Potential of Higher Education? An Analysis of Academic Fields and Anti-Immigrant Sentiment Across 32 Countries," The British Journal of Sociology (2025) M. O'Brien and M. Eger, "Suppression, Spikes, and Stigma: How COVID-19 Will Shape International Migration and Hostilities toward It," International Migration Review (2020) L. Yang, M. Eger, and B. Link, "The Human Cost of Politicizing Immigration: Migration Stigma, US Politics, and Health," JAMA (2024) A. Farmer and K. Linos, "Tools to Finance Climate Mobility: An Introduction to the Symposium," American Journal of International Law (2025) A. Farmer, "Disasters in Slow Motion: Why Climate Displacement Tests International Legal Frameworks, and What to Do about it," University of Pennsylvania Journal of International Law (2026) J. Bittle, The Great Displacement: Climate Change and the Next American Migration (Simon & Schuster, 2024) G. Vince, Nomad Century: How Climate Migration will Reshape our World (Macmillan, 2022) A. Lustgarten, On the Move: The Overheating Earth and the Uprooting of America (Macmillan, 2024) T. Hale, Long Problems: Climate Change and the Challenge of Governing Across Time (Princeton Univ. Press, 2024) Center for Advanced Study in the Behavioral Sciences (CASBS) at Stanford UniversityExplore CASBS: website | Bluesky | X | YouTube |LinkedIn | podcast | latest newsletter | signup | outreach​Human CenteredProducer: Mike Gaetani | Audio engineer & co-producer: Joe Monzel

People who live in large cities produce more patents per person than those who live in small towns. They also walk faster down the street, and use less infrastructure per person. Indeed, the relationships between these quantities and city population obey power laws with apparently universal exponents, reminiscent of scaling laws is biological organisms? Luis Bettencourt argues that they can be explained in similar ways, using ideas from complex systems and network theory. Blog post with transcript: https://preposterousuniverse.com/podcast/2026/07/27/362-luis-bettencourt-on-the-universal-properties-of-self-organizing-cities/ Support Mindscape on Patreon. Luis Bettencourt received a Ph.D. in theoretical physics from Imperial College London. He is currently the Lorna Puttkammer Straus Professor Department of Ecology and Evolution & Data Sciences Institute, as well as Associate Faculty and Special Friend in the Department of Sociology, at the University of Chicago. He is the author of Introduction to Urban Science: Evidence and Theory of Cities as Complex Systems. Web site University of Chicago web page Google Scholar publications Wikipedia

Welcome to Session 336 The Behavioral Observations Podcast. Well technically, this is actually Session 199, and I wanted to take a minute to explain why you're hearing a rebroadcast. This conversation with Dr. Jason Travers originally aired back in September of 2022, but I've decided it's worth bringing back because, unfortunately, the issues surrounding Facilitated Communication haven't gone away. In fact, if anything, they've become more relevant. Over the past several years, we've continued to see heartwarming news stories and viral social media posts featuring non-speaking autistic individuals who appear to be communicating through methods such as Facilitated Communication, the Rapid Prompting Method, or Spelling to Communicate. These stories are understandably compelling. We all want people with significant communication challenges to have access to effective ways to express themselves. The problem is that hope, by itself, isn't evidence. Facilitated Communication has been studied extensively for decades, and the scientific record is remarkably consistent. The evidence has repeatedly shown that the messages produced through FC originate with the facilitator, not the individual. Despite that, these practices continue to find new audiences under different names and in different settings. As behavior analysts—and really, as anyone who cares about helping people with disabilities—we have an obligation to evaluate interventions based on evidence, even when the conclusions are uncomfortable. That's especially true when the potential harms are so significant, not only for the individuals using these methods, but also for their families, educators, and support teams. I also think this episode serves as a useful foundation because I plan to cover this topic in greater depth over the coming months, along with other interventions that have gained popularity despite lacking scientific support. Rather than assuming everyone has heard this discussion before, I thought it made sense to revisit what I think is one of the clearest introductions to the topic. So whether you're hearing this conversation for the first time or giving it another listen, I hope you'll pay particular attention to the history of Facilitated Communication, how it was scientifically evaluated, why those findings matter, and how Dr. Travers recommends discussing these issues with colleagues and families in a way that's both accurate and compassionate. Please note that I edited out the "how did you get into ABA segment," so you can get right to the heart of the topic. I'll include links to the original recording if you want to go back and hear more about Jason's background. Here are the shownotes from the original broadcast: If your social media consumption is anything like mine, you've likely seen some feel-good stories in the media as of late that report on non-speaking students - generally students with Autism - who are graduating from college, giving valedictorian speeches, and so forth.  Unfortunately, what's often underpinning many of these cases is a form of Facilitated Communication, or FC for short. What is FC? Glad you asked! In today's episode, Dr. Jason Travers, Associate Professor at Temple University, joins me today to answer this very question (follow him on Twitter here).  We covered the history of Facilitated Communication, the early scientific investigations that discredited this practice, FC's variants like the Rapid Prompting Method and Spelling to Communicate, where the practice of FC stands today, the harms that Facilitated Communication causes both users and caregivers, and how Behavior Analysts should both view and talk about these practices.  Jason also provides the audience with a treasure trove of additional resources: Jason's Research Gate and Google Scholar pages. The original Session 199 broadcast. Jason's on-demand CEU on Spelling2Communicate (use promo code PODCAST to save at checkout!). Facilitatedcommunication.org  ASHA Policy Statements on Facilitated Communication and Rapid Prompting Method. Facilitated Communication—what harm it can do: Confessions of a former facilitator (Boynton, 2012). An Examination of the Role of the Facilitator in "Facilitated Communication" (Shane and Kearns, 1994). Ideomotor Effect explained. The Demon Haunted World: Science as a Candle in the Dark (note: Amazon Associated link). Skeptic Magazine (Amazon Associate Link); The Skeptical Inquirer.  The Reading Wars. Facilitated Communication: The Clinical and Social Phenomenon (Shane 1994; Amazon Associate Link). Sponsor shoutouts! Behavior University. Their mission is to provide university quality professional development for the busy Behavior Analyst. Learn about their CEU offerings, including their 8-hour Supervision Course, as well as their RBT offerings over at behavioruniversity.com/observations. Don't forget to use the coupon code, PODCAST to save at checkout! Safety-Care is a crisis prevention and de-escalation training program designed for professionals who support individuals with challenging behavior. More than 300,000 professionals have been trained in Safety-Care's evidence-based approach to recognizing early warning signs and responding with confidence. To learn more, visit QBS.com/podcast. Learn from your favorite podcast guests while you're commuting, walking the dog, or whatever else you do while listening to podcasts. New events are being added all the time, so check them out here.  HRIC Recruting. Cut out the middleman and speak directly with Barbara Voss, who's been placing BCBAs in great jobs all across the US for 15 years. The BOP Patreon. Do you want to get the show ad-free and before everyone else? Click here to learn how!

One of the characteristics of life is that living organisms gather information and put it to use. Even one of the simplest lifeforms, bacteria, are able to sense features of their surroundings and alter their behavior accordingly. Most impressively, they are able to sense the presence of similar bacteria by a process called quorum sensing. Today's guest, Bonnie Bassler, is a leader in this field, and explains how quorum sensing allows groups of bacteria to do things (including in our bodies) that wouldn't be possible for individual bacteria. Blog post with transcript: https://preposterousuniverse.com/podcast/2026/07/20/361-bonnie-bassler-on-how-bacteria-talk-and-work-together/ Support Mindscape on Patreon. Bonnie Bassler received a Ph.D. in biochemistry from Johns Hopkins University. She is currently Andrew K. Golden University Professor of Molecular Biology at Princeton University and a Howard Hughes Medical Institute Investigator. She is a member of the National Academy of Sciences, National Academy of Medicine, and the American Academy of Arts and Sciences. Among her awards are a MacArthur Fellowship, the Gruber Prize in Genetics, and the National Medal of Science. Lab web site Princeton web page Google Scholar publications Wikipedia

*Content Warning:  neglect, rejection, friendship betrayal, betrayal, and infidelity.Free + Confidential Resources + Safety Tips: somethingwaswrong.com/resources   SWW Sticker Shop!: https://brokencyclemedia.com/sticker-shop SWW S26 Theme Song & Artwork: The S26 cover art is by the Amazing Sara Stewart instagram.com/okaynotgreat/  Follow Something Was Wrong: Website: somethingwaswrong.com  IG: instagram.com/somethingwaswrongpodcast TikTok: tiktok.com/@somethingwaswrongpodcast  Follow Tiffany Reese: Website: tiffanyreese.me  IG: instagram.com/lookieboo Follow Dr. Marisa G. Franco: Website - https://www.drmarisagfranco.com/ Instagram - https://www.instagram.com/drmarisagfranco Platonic, How The Science of Attachment Can Help You Make - and Keep - Friends - https://drmarisagfranco.com/platonic-the-book/ Worth, The New Science of Self-Esteem and Secure Attachment: https://drmarisagfranco.com/worth-the-book/ *Sources:  Almaatouq, Abdullah, et al. "Are You Your Friends' Friend? Poor Perception of Friendship Ties Limits the Ability to Promote Behavioral Change." PLOS ONE, vol. 11, no. 3, 2016, article e0151588, https://doi.org/10.1371/journal.pone.0151588 Center for the Study of Traumatic Stress. When Losses of Loved Ones Are Not Acknowledged: Understanding Disenfranchised Grief. Department of Psychiatry, Uniformed Services University, n.d., https://www.cstsonline.org/assets/media/documents/CSTS_FS_When_Losses_of_Loved_Ones_Are_Not_Acknowledged_Understanding_Disenfranchised_Grief.pdf Dodson, William W., et al. "Rejection sensitivity dysphoria in attention-deficit/hyperactivity disorder: A case series." Neurology 7 (2024): 23-30.  Franco, Marisa G. Platonic: How the Science of Attachment Can Help You Make—and Keep—Friends. Penguin Random House, 6 Sept. 2022 https://www.penguinrandomhouse.com/books/676695/platonic-by-marisa-g-franco-phd/ Franco, Marisa G. Worth: The New Science of Self-Esteem and Secure Attachment. G.P. Putnam's Sons, 15 Sept. 2026, Penguin Random House,https://www.penguinrandomhouse.com/books/784327/worth-by-marisa-g-franco-phd/ Gobin, Robyn L., and Jennifer J. Freyd. "The impact of betrayal trauma on the tendency to trust." Psychological Trauma: Theory, Research, Practice, and Policy 6.5 (2014): 505. https://psycnet.apa.org/record/2013-24397-001 Guy-Evans, Olivia. “Self-Verification Theory.” Simply Psychology, 11 May 2026, https://www.simplypsychology.org/self-verification-theory.html Here & Now Newsroom. “Research Shows We Replace Half Our Friends Every 7 Years. Here's How to Make New Ones.” NPR Illinois, 23 June 2025, https://www.nprillinois.org/2025-06-23/research-shows-we-replace-half-our-friends-every-7-years-heres-how-to-make-new-ones Hillman, James. "Betrayal." Loose Ends: Primary Papers in Archetypal Psychology, Spring Publications, 1975, pp. 63–79. Jarrett, Christian. "The Liking Gap: We Usually Think People Like Us Less Than They Actually Do." Research Digest, British Psychological Society, 13 Sept. 2018, https://www.bps.org.uk/research-digest/liking-gap Kenny, Serafina. "Having Friends Is as Important as Diet and Exercise for Living Longer, a Longevity Expert Says." Business Insider, 22 Sept. 2023, https://www.businessinsider.com/longevity-antiaging-friendship-social-interaction-relationships-2023-9 Nader, Karim. “Reconsolidation and the Dynamic Nature of Memory.” Cold Spring Harbor perspectives in biology vol. 7,10 a021782. 9 Sep. 2015, doi:10.1101/cshperspect.a021782, https://pubmed.ncbi.nlm.nih.gov/26354895/ Nussbaum, Ben. "FRIENDSHIP FLATTENS HILLS: It's time to put connections at the center of wellbeing, says relationship expert Marisa Franco." Spirituality & Health Magazine, vol. 25, no. 5, Sept.-Oct. 2022, pp. 46+. Gale Academic OneFile link.gale.com/apps/doc/A763799199/AONE?u=anon~858b38f4&sid=googleScholar&xid=6b08179d One Another. Directed by Amber Love, produced by Andrea Raby, Joycie Films, 2026. World premiere, SXSW Film & TV Festival, Austin, TX, 12 Mar. 2026. https://schedule.sxsw.com/2026/films/2249924 Romm, Cari. "Half of Your Friends Probably Don't Think of You as a Friend." The Cut, 9 May 2016, https://www.thecut.com/2016/05/half-of-your-friends-probably-dont-think-of-you-as-a-friend.html Thompson, Sophia, Kaitlyn Deaner, and Marisa G. Franco. "How to Help Clients Make Friends." Journal of Health Service Psychology 49.2 (2023): 77-85 https://link.springer.com/article/10.1007/s42843-023-00085-w Wallace, Anna Kodé. "Why Friendship Betrayal Feels Impossible to Get Over." The Cut, 22 Apr. 2026, https://www.thecut.com/article/friendship-betrayal-explained-psychology-summer-house.html

Modern life has, in many ways, removed us from the environments in which our ancestors lived and adapted. Not only do we spend time looking at screens, but we spend time indoors, or outdoors but in urban spaces. How does this affect how we think and feel? Psychologist Marc Berman is a pioneer of "environmental neuroscience." In his recent book, Nature and the Mind: The Science of How Nature Improves Cognitive, Physical, and Social Well-Being, he presents evidence that spending time in nature not only puts us in a better mood, it makes us better thinkers. Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/07/06/360-marc-berman-on-the-science-of-touching-grass/ Support Mindscape on Patreon. Marc Berman received his Ph.D. in psychology from the University of Michigan. He is currently Professor of Psychology and Faculty Co-Director of the Chicago Center for Computational Social Science at the University of Chicago. Among his awards is the American Psychological Association Distinguished Scientific Award for Early Career Contributions. Environmental Neuroscience Lab U. Chicago web page Google Scholar publications Amazon author page

Hidden in your water, food, and even your blood, forever chemicals (PFAS) are nearly impossible to destroy - and now U.S. regulations are being rolled back. Listen in to how PFAS became unavoidable, what they're doing to our health, and whether we can ever truly get rid of them.   In this episode, Therese Markow and Dr. Carrie McDonough discuss perfluoroalkyl substances (PFAS), also known as forever chemicals. PFAS, which include PFOA and PFOS, are persistent organic pollutants used in various products, such as Teflon for your cooking pans and Scotchgard. While there are natural molecules that incorporate fluorine, the compounds we are most concerned about when we talk about PFAS cannot be synthesized naturally and are difficult to break down. Dr. McDonough discusses exposure risks, how we are exposed, and the efforts that have been made to regulate these chemicals. She emphasizes the need for better detection methods and remediation strategies.     Key Takeaways: While some PFAS are excreted from the human body, many are not and linger in our blood and cells. A lot of pollutants accumulate in the adipose fat in the human body. However, PFAS are mostly found in proteins and membranes, such as the kidney, liver, and blood.  The first clue that PFAS were widespread in humans was back in the 1970s. However, the results were inconclusive - they just didn't have the instrumentation they needed to confirm it or the standards from the companies in order to identify the PFAS. Biomagnification causes creatures, including humans, that are higher up the food chain to have higher concentrations of PFAS than those lower in the food chain. This includes those who are herbivores, omnivores, and carnivores.  Companies have been putting these things out into our environment for decades; for some amount of that time, they knew that they were doing it and that they were toxic, but didn't tell anyone. There is really no way to protect anyone from these chemicals that we did not consent to being in our bodies.    "If you're not living in an area with highly contaminated water, or some kind of large contamination issue, your main source of PFAS is probably your diet." —  Dr. Carrie McDonough   Episode References:  Toxic Gaslighting: How 3M Executives Convinced a Scientist the Forever Chemicals She Found in Human Blood Were Safe: https://www.propublica.org/article/3m-forever-chemicals-pfas-pfos-inside-story  PFAS leave fingerprints in your blood – researchers are figuring out how forever chemicals transform in your body to read these clues: https://theconversation.com/pfas-leave-fingerprints-in-your-blood-researchers-are-figuring-out-how-forever-chemicals-transform-in-your-body-to-read-these-clues-280396 Dark Waters: https://www.imdb.com/title/tt9071322/  They Poisoned the World: Life and Death in the Age of Forever Chemicals by Mariah Blake: https://www.penguinrandomhouse.com/books/554198/they-poisoned-the-world-by-mariah-blake/    Connect with Dr. Carrie McDonough: Professional Bio: https://www.cmu.edu/chemistry/people/faculty/mcdonough.html  Google Scholar: https://scholar.google.com/citations?user=Xq5HrPYAAAAJ&hl=en  Website: https://groups.chem.cmu.edu/mcdonough/  LinkedIn: https://www.linkedin.com/in/carrieamcd    Connect with Therese: Website:  www.criticallyspeaking.net Bluesky: @CriticallySpeaking.bsky.social Instagram: @criticallyspeakingpodcast Email: theresemarkow@criticallyspeaking.net   Audio production by Turnkey Podcast Productions. You're the expert. Your podcast will prove it.  

Happy Strawberry Moon, everyone! As we ease into summer and the hydrangeas and garden roses begin blooming here in Toronto, I find myself struck by the awe and beauty that quietly surrounds us each day. There's so much science behind how we experience the world. My guest on this moon's chapter of 3 Books has spent his career proving just that. Join me in welcoming one of the world's leading experts on happiness and human potential, a Harvard-trained positive psychologist and researcher whose ​TED Talk​ has been watched over 30 million (!) times, and a genuinely warm human I am proud to call a friend ... Mr. Shawn Achor! (pronounced like "acorn" without the n, btw!) I first met Shawn in Abu Dhabi, years ago, when we were both invited to speak to the Royal Family of the United Arab Emirates. I arrived a few days early, for dress rehearsals, long dinners, and meet-and-greets with various extended family members, and then Shawn showed up. He flew in, crushed it onstage, dropped research studies like he'd memorized all of Google Scholar, and immediately flew out. I was left thinking: Who was that masked man? Well, over the years we became friends, and he was kind enough to blurb '​The Happiness Equation​' after selling a million copies of his own wonderful book, '​The Happiness Advantage​'. And then ... where did he go? Shawn just sort of ... disappeared. I found out why in his new book, '​The Power of Beliefs​'. After his daughter Zoë was born three months early, spending 50 days in the NICU, Shawn decided to take a break—six years of no media, no international talks, no new books. Six years! But Shawn is still Shawn. An amiable son of an English teacher and a neuroscience professor, with multiple Harvard degrees, and a witty, research-centric writing style that is precise and uniquely compassionate. Tune in as we discuss Shawn's new book, his seven core beliefs (and their seven scars), the power of the placebo effect, what six years of "disappearing" taught him about parenting and living a more meaningful life, the psychology of awe, the cost of measuring your worth through comparison, and of course ... the brilliant Shawn Achor's 3 most formative books. Let's flip the page to Chapter 162 now...

In this episode, my guest is Dr. Glen Jeffery, PhD, a professor of neuroscience at University College London's Institute of Ophthalmology and a leading expert on how light interacts with mitochondria to shape health and aging. Today, he discusses how red and near-infrared light penetrate deep into the body and improve mitochondrial function and overall health. We also discuss how detrimental modern indoor life has become and why expensive red light panels are likely unnecessary and possibly harmful.By the end, you'll understand sunlight as a biological input your body evolved to depend on and the low-cost, practical steps you can take to get more of it.FULL SHOW NOTES HERE: https://l1nk.dev/bjuu235 Sponsor: Shadowmap  https://app.shadowmap.org/?ref=jonathan&discount=JONATHANJ Code: JONATHANJ   Sponsor: Ra Opticshttps://www.raoptics.com/JONATHANJ Code: JONATHANJ About this guest:Academic Profile: https://profiles.ucl.ac.uk/7465 Google Scholar: https://scholar.google.com/citations?user=UYlObKkAAAAJ&hl=en LinkedIn: https://www.linkedin.com/in/glen-jeffery-441a2b1b/ Timestamps:00:16 Intro02:46 Dr. Glen Jeffery03:14 Love for Science, Undergraduate04:24 Tiina Karu, Photobiomodulation Origins, Mechanisms 06:14 Mechanisms, Cytochrome C Oxydase, Nanowater, Electron Flow10:56 Cytochrome C Oxydase, Extra-Pineal Melatonin, Oxidative Stress12:45 Gerald Pollack, Exclusion Zone (EZ) Water13:45 Red/IR Light Passes Through the Human Body; Optics18:09 Red Light On Fruit Flies; Lifespan, ATP Production21:15 Human Epidemiology; Sunlight, Lifespan, All-Cause Mortality22:35 Sponsor: Shadowmap23:30 Features and Benefits of Shadow Map25:02 Incandescents, lifespan, and more25:02 Incandescent Light Produces the Oldest Flies; Broad Band IR on Lifespan25:48 Incandescent Light Bulbs, Vision 28:19 Evolutionarily Conserved Mitochondria29:03 Long-Wavelength Light & Blood Glucose; Mitochondria32:50 Unpublished CGM Data; Long-Wavelength Light & Blood Glucose36:57 Are Red Light Panels Harmful?; Light Intensities, Precautions, Mitigations43:56 Why Do We See Beneficial Effects From Long-Wavelength Therapy? 46:43 Short-Wavelength Blue Light, Mitochondria, Mechanism49:08 The Built Environment, Actionable Steps 52:24 Sponsor: Ra Optics54:24 Flicker55:24 Retinal Health, Blue Light, Red/IR Light59:09 Clinical Trial for Mitochondrial Disease; Ptosis, Red Light01:02:31 Biophotons, Mitochondrial Communication, Red/NIR on Biophotons 01:13:54 Support, YouTube, Spotify & Apple Follow, Reviews & Feedback, Social Media, Substack Newsletter

Messenger RNA (mRNA) plays a literally central role in the functioning of life as we know it, shuttling information back and forth between the DNA where it is stored to the ribosome where it is used to produce proteins. RNA may even have been the first molecule to kick-start the origin of life. Today, scientists are learning how to manipulate mRNA to cure and prevent diseases, whether through vaccination or literally editing one's DNA. Jeff Coller explains how it all works and how mRNA is revolutionizing medicine as we know it. Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/06/15/357-jeff-coller-on-mrna-vaccines-and-bespoke-therapeutics/ Support Mindscape on Patreon. Jeff Coller received his Ph.D. in cell and molecular biology from the University of Wisconsin-Madison. He is currently Bloomberg Distinguished Professor of Biomedical Engineering and Director of the RNA Innovation Center at Johns Hopkins University. He is co-founder of Tevard Biosciences and the Alliance for mRNA Medicines, and leads the REPAIRx consortium. He is a fellow of the American Association for the Advancement of Science. Web site Johns Hopkins web page Google Scholar publications "This May Be the Most Important Medical Story of the Decade," New York Times, April 9, 2026

January 7th 1989. 33-year old Dr. Kathryn Hinnant stayed late at Bellevue Hospital in New York City preparing a lecture. When she never arrived at a gala that night, her husband went looking for her. What followed exposed terrifying security failures inside one of America's most famous hospitals.

No episódio de junho do Camada 8, convidamos Renata Teixeira, pesquisadora no time de Streaming Algorithms da Netflix, para uma conversa sobre os desafios do streaming de vídeo em redes de satélites de baixa órbita (LEO, do inglês Low Earth Orbit).Renata fala sobre as redes de satélites LEO, que possuem características bem diferentes das redes tradicionais, e por que isso cria novos desafios para aplicações de streaming e transmissões ao vivo. Ela também fala do algoritmo da Netflix responsável por adaptar automaticamente a qualidade do streaming de vídeo de acordo com as condições da conexão do usuário, e muito mais.Dê o play e confira agora mesmo o novo episódio do quadro Roteamento de Ideias do Camada 8!Participantes:Antonio Marcos Moreiras (Host) - Gerente de projetos e desenvolvimento no NIC.br https://www.linkedin.com/in/moreirasLucas Jorge da Silva (Host) - Analista de Projetos do Ceptro.br no NIC.br⁠https://www.linkedin.com/in/lucasjorgeRenata Teixeira (Convidada) - Pesquisadora no time de Streaming Algorithms da Netflix https://www.linkedin.com/in/renata-teixeira-383979258/Google Scholar‬ - https://scholar.google.com/citations?user=yZqV-tMAAAAJ&hl Links citados:Semana de Infraestrutura da Internet no Brasil: https://semanainfra.nic.br/Curso BCOP Presencial: https://cursoseventos.nic.br/curso/curso-bcop/Curso BCOP EaD: https://cursoseventos.nic.br/curso/curso-bcop-ead/Programa Acelera NET: https://cursoseventos.nic.br/curso/programa-acelera-net/Semana de Capacitação: https://semanacap.bcp.nic.br/Agenda de cursos do Ceptro|NIC.br: https://ceptro.br/cursos-eventosRedes Sociais:https://www.youtube.com/nicbrvideos/https://x.com/comuNICbr/https://www.telegram.me/nicbr/https://www.linkedin.com/company/nic-br/https://www.instagram.com/nicbr/https://www.facebook.com/nic.br/https://www.flickr.com/NICbr/Contato:Equipe Ceptro.brcursosceptro@nic.brDireção e áudio:Equipe Ceptro.brEquipe de Comunicação do NIC.brEdição completa por Rádiofobia Podcast e Multimídia: https://radiofobia.com.br/Veja também:https://nic.br/https://ceptro.br/

In this episode of Why Distance Learning, your hosts continue their conversation with Jered Borup — professor at George Mason University and one of the most-cited researchers in K-12 online learning — about what AI in education is actually doing to relationships, what social presence requires when "build a video lecture" can be done by a chatbot, and why teacher burnout is the real bottleneck the field doesn't want to talk about. Borup connects his earliest 2012 work on asynchronous video to his 2025 Open Praxis research on combining AI-generated text with human-created video, and argues that AI used to offload feedback erodes the very thing online learners need: the felt sense that the teacher is real and knows them.Together, the hosts and Jered explore the conflation of social media, video games, and ed tech in the parental imagination after the pandemic; how to use AI without replacing the relational core of teaching; why one-on-one asynchronous video may build social presence more reliably than synchronous Zoom classes; the DLAC Phase 2 research agenda Borup co-authored with Michael Barbour and Kristen DeBruler; the mental-health gap between teachers and other professionals with comparable education; and Borup's one-line answer to the show's title question — that personalization and Universal Design for Learning are easier to do online than off.This is Part 2 of a two-part conversation. Listen to Part 1 for the foundational ACE framework, the on-site mentor model, and the parent question.Key Topics"Emergency remote learning" vs. real online learning — what parents are still confusingSocial presence — old research, new tools (asynchronous video, AI-plus-human-video)The risk of offloading teacher feedback to AIAsynchronous one-on-one video as a relationship lever (vs. one-to-many Zoom)DLAC Research Agenda Phase 2 — what's keeping researchers up at nightTeacher mental health and the AI strain on top of pandemic strainAuthentic assessment and "we're too in love with the five-paragraph essay"Empathy as the core design move"Why distance learning?" — empowerment, personalization, UDLLinks & ResourcesJered Borup's site: https://sites.google.com/site/jeredborup/ACE Framework on EdTech Books: https://edtechbooks.org/encyclopedia/academic_communities_of_engagement_ace_frameworkA Framework for Establishing Social Presence Through the Combination of AI-generated Text with Human-created Video (Open Praxis, 2025): https://openpraxis.org/articles/10.55982/openpraxis.17.1.769Harnessing the Power of Generative AI to Support ALL Learners (Borup, Evmenova & Shin, 2024): https://www.researchgate.net/publication/380570253_Harnessing_the_Power_of_Generative_AI_to_Support_ALL_LearnersDLAC Research Agenda Phase Two (Borup, Barbour & DeBruler, Sept 2025): https://www.deelac.com/wp-content/uploads/2025/10/DLAC-Research-Agenda-Phase-2-Final-1052025.pdfBreaking Through the Screen: Practical Tips for Engaging Learners in the Online and Blended Classroom (Borup & Joan Kang Shin, National Geographic Learning): https://www.amazon.com/Breaking-Through-Screen-Practical-classroom/dp/0357541855K-12 Blended Teaching open-source book series: https://edtechbooks.org/k12blended_seriesJered's Google Scholar: https://scholar.google.com/citations?user=PGs7TacAAAAJ&hl=enPart 1 of this conversation: [LINK — add when published]Guest Bio: Jered BorupJered Borup is a professor in the Division of Learning Technologies at George Mason University and co-coordinator of the Learning Technologies in Schools graduate program. His research, grounded in six years of junior-high history teaching, focuses on K-12 online and blended learning: the support communities that surround a learner, the parental role in online education, and how generative AI can extend personalized support to historically underserved students. He earned his Ph.D. in Instructional Psychology and Technology from Brigham Young University and has been recognized as one of the top 2% most-cited researchers in his field.About the HostsSeth Fleischauer is the founder of Banyan Global Learning and host of Why Distance Learning. Through Banyan, he designs live virtual programs that connect K-12 classrooms to global peers and expert facilitators — building the kind of structured, human-centered distance learning the podcast explores. See https://banyangloballearning.com/Allyson Mitchell works with CILC, the Center for Interactive Learning and Collaboration, to help educators implement high-quality live virtual learning experiences across grade levels. Discover more at CILC.org.

They're acrobatic fliers with long bodies and veined wings and their babies breathe through their butts: dragonflies. Let's get into the difference between a damselfly and dragonfly, how fast they dart around, how big they were in the age of the dinosaurs, and lots more with scholar, American Museum of Natural History curator, and dragonfly expert: Dr. Jessica Ware. Follow Dr. Ware on Google Scholar, Instagram and Bluesky Buy Jessica's children's book, Bugs (A Day in the Life): What Do Bees, Ants, and Dragonflies Get up to All Day? on Amazon or Bookshop.org A donation went to the World Dragonfly Association Full-length (*not* G-rated) Odonatology episode + tons of science links More kid-friendly Smologies episodes! Become a patron of Ologies for as little as a buck a month OlogiesMerch.com has hats, shirts, hoodies, totes! Follow Ologies on Instagram and Bluesky Follow Alie Ward on Instagram and TikTok Sound editing by Mercedes Maitland of Maitland Audio Productions & Jake Chaffee Made possible by work from Noel Dilworth, Susan Hale, Kelly R. Dwyer, Aveline Malek and Erin Talbert Smologies theme song by Harold Malcolm Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

She's one of the best writers on science today, combining optimism about progress with a realist understanding of the messiness of our world. Saloni Dattani joins Amit Varma in episode 445 of The Seen and the Unseen to discuss science, medicine, data, academia and how to make the world a better place. (FOR FULL LINKED SHOW NOTES, GO TO SEENUNSEEN.IN.)   Also check out: 1. Saloni Dattani at Google Scholar, Twitter, LinkedIn, Our World in Data and Works in Progress. 2. Scientific Discovery -- Saloni Dattani's newsletter. 3. Hard Drugs -- Saloni Dattani's podcast. 4. Saloni's guide to data visualization -- Saloni Dattani. 5. Four charts to understand causes of death across the lifespan -- Saloni Dattani. 6. What I've learnt about writing -- Saloni Dattani. 7. In praise of the Covid superforecasters -- Saloni Dattani. 8. The decline in cancer mortality is about much more than smoking -- Saloni Dattani. 9. Death rates from cardiovascular disease have fallen dramatically — what were the breakthroughs behind this? -- Saloni Dattani. 10. The golden age of vaccine development -- Saloni Dattani. 11. Why we didn't get a malaria vaccine sooner -- Saloni Dattani. 12. The first cancer vaccine -- Transcript of a Hard Drugs episode. 13. Measles vaccines save millions of lives each year -- Saloni Dattani. 14. Why the total fertility rate doesn't necessarily tell us the number of births women eventually have -- Saloni Dattani. 15. The rise in reported maternal mortality rates in the US is largely due to a change in measurement -- Saloni Dattani. 16. How do global statistics on suicide differ between sources? -- Saloni Dattani. 17. How many people die from snakebites? -- Saloni Dattani. 18. The Demographic and Health Surveys brought crucial data for more than 90 countries — without them, we risk darkness -- Saloni Dattani. 19. We don't have to sit back and just watch the horror unfold -- Saloni Dattani. 20. Childhood leukemia: how a deadly cancer became treatable -- Saloni Dattani. 21. Will AI solve medicine? -- Transcript of a Hard Drugs episode. 22. Real peer review has never been tried -- Saloni Dattani. 23. The speed of science -- Saloni Dattani. 24. Medical breakthroughs in 2025 -- Saloni Dattani. 25. Scientific progress is at risk of slowing down. Saloni Dattani is making sure it doesn't. -- Miranda Dixon-Luinenburg. 26. Innovation is not linear -- Jason Crawford. 27. Genentech: The Beginnings of Biotech -- Sally Smith Hughes. 28. Missing Markets for Innovation: Evidence from New Uses for Existing Drugs -- Eric Budish, Maya Durvasula, Benjamin Roin and Heidi Williams. 29. The 100% CI. 30. Superforecasting — Philip Tetlock and Dan Gardner. 31. How Long Do We Wait for New Inventions? -- Brian Potter. 32. Million Dollar Secret. 33. Woolly mice designed to engineer mammoth-like elephants -- Pallab Ghosh. 34. Age of Invention -- Anton Hause. 35. Million Death Study. 36. Science Fictions -- Stuart Ritchie. 37. Outliers -- Malcolm Gladwell. 38. Episodes of The Seen and the Unseen with Rukmini S: 1, 2, 3. 39. Fortress and Frontier in American Health Care — Robert Graboyes. 40. Strong Medicine -- Michael Kremer and Rachel Glennerster. 41. The Practice of Medicine — Episode 229 of The Seen and the Unseen (w Lancelot Pinto). 42. Project Resource Optimization. 43. Giving What We Can. 44. Coefficient Giving. 45. 1493 -- Charles Mann. 46. The Collapse -- Mary Elise Sarotte. 47. How to Survive a Plague -- David France. 48. The Mole. 49. And the Band Played On -- Randy Shilts. This episode is sponsored by The Six Percent Club. Join them to go from content idea to launch in just 45 days! Amit Varma runs a course called Life Lessons, which aims to be a launchpad towards learning essential life skills all of you need. For more details, and to sign up, click here. And have you read Amit's newsletter? Subscribe right away to The India Uncut Newsletter! It's free! Also check out Amit's online course, The Art of Clear Writing. Episode art: 'Salonium' by Simahina.

Welcome to the kickoff of the Action Game series on The Empowered Team Podcast—where Kari Schneider dives into what actually drives results: action. In this solo episode, Kari unpacks the surprising science behind why high achievers and intelligent leaders are more likely to procrastinate—and what to do about it. What You'll Learn: Why overthinking is a strength (and how it turns into a trap) Learn how your brain's “prediction machine” can create decision loops that stall progress—and how to break free. The truth about perfectionism Discover why perfectionism isn't a high standard—it's procrastination in disguise, and how it leads to burnout instead of results. The research-backed method that increases follow-through by 200–300% Kari shares the powerful concept of implementation intention (when-then planning) and how it eliminates hesitation and drives consistent action. Key Insight: High performance isn't about more motivation—it's about clarity, structure, and making decisions your brain can execute. Memorable Quote: “Overthinking isn't weakness—it's intelligence without a deadline.” Take Action: Before you finish this episode, choose ONE thing you've been delaying—and decide exactly when and what action you'll take. If you're ready to stop circling and start executing, this episode will give you the clarity and momentum you've been missing. Key Research Links: Peter Gollwitzer — Implementation Intentions Core 1999 paper: https://www.prospectivepsych.org/sites/default/files/pictures/Gollwitzer_Implementation-intentions-1999.pdf  Meta-analysis (94 studies): https://cancercontrol.cancer.gov/sites/default/files/2020-06/goal_intent_attain.pdf  Wikipedia overview (accessible summary): https://en.wikipedia.org/wiki/Implementation_intention  Google Scholar profile: https://scholar.google.com/citations?user=Vl1IDvYAAAAJ&hl=en  Flett & Hewitt — Perfectionism & Procrastination Original multidimensional perfectionism paper (1991, PubMed): https://pubmed.ncbi.nlm.nih.gov/2027080/  Perfectionism & procrastination chapter: https://link.springer.com/chapter/10.1007/978-1-4899-0227-6_6  30-year review (2021): https://www.apa.org/pubs/journals/features/cap-cap0000288.pdf  #LeadershipDevelopment #HighPerformance #MindsetShift #Productivity #SelfMastery

In this episode of Everyday Epigenetics: Raw. Real. Relatable., Susan Robbins sits down with physician, researcher, and science communicator Dr. Gil Carvalho for a powerful conversation about nutrition misinformation, influencer-driven fear, and what the science actually says about cholesterol, saturated fat, seed oils, oats, and popular diet trends. Dr. Gil Carvalho, founder of the Nutrition Made Simple YouTube channel, is known for breaking down complex health research into practical, understandable information without the fear tactics and sensationalism that dominate so much of the wellness world.Together, Susan and Dr. Gil unpack some of the biggest myths circulating online, including the idea that “higher cholesterol is always better,” that oats are harmful, and that seed oils are toxic. They also discuss why individualized health matters, how genetics influence risk factors like ApoB and Lp(a), and why lab work should guide decisions more than viral social media claims. This episode is a grounded, evidence-based conversation designed to help listeners think critically, ask better questions, and become stronger advocates for their own health.In this episode:Why high cholesterol should not automatically be dismissed as “healthy”The difference between cholesterol levels, ApoB, particle size, and Lp(a)How misinformation spreads through influencer cultureWhy oats are not the “worst breakfast you can eat”The truth about seed oils and inflammationHow genetics impact cardiovascular risk and dietary responsesWhy one-size-fits-all nutrition advice often backfiresThe importance of personalized nutrition and individualized lab workWhy fear-based wellness messaging can create more harm than goodHow social media oversimplifies complex health topicsThe role of lifestyle, stress, sleep, movement, and environment in long-term healthWhy learning to interpret science critically matters more than following trendsDr. Gil CarvalhoGil Carvalho is a Portuguese physician, research scientist, and science communicator known for his work in nutrition, longevity, and evidence-based health education.Born in Portugal, he earned his MD from the University of Lisbon and later obtained a PhD in Biology from the California Institute of Technology (Caltech), where he trained under pioneering geneticist Seymour Benzer.Carvalho's research spans genetics, molecular biology, nutrition, behavior, aging, and neuroscience, with contributions including the identification of genetic and nutritional mechanisms underlying longevity; his work has been cited over 4,130 times as of 2023 according to Google Scholar.He has collaborated with neuroscientist Antonio Damasio on neural signal transmission and the basis of interoception, and his publications appear in prestigious outlets such as Proceedings of the National Academy of Sciences and Nature Methods.In addition to his academic career at the University of Southern California, Carvalho is a prominent science communicator, founding the YouTube channel Nutrition Made Simple in 2018, which has amassed over a million monthly viewers by simplifying complex dietary science for lay audiences.He contributes to organizations including the Institute of Limbic Health, and his expert insights have been featured in media like Quanta Magazine and ScienceDaily.Carvalho has received awards such as the DeLill Nasser Award for Professional Development in Genetics and a Mathers Foundation grant, underscoring his impact in bridging clinical practice, rigorous research, and public health education.RESOURCES:Connect with Dr. Gil Carvalho:Youtube: http://www.youtube.com/@NutritionMadeSimpletwitter.com/NutritionMadeS3facebook.com/DrGilCarvalhotiktok.com/@nutrition.made.simpleinstagram.com/gilcarvalho.mdhttps://healthyawakening.co/2026/05/25/episode125/Connect with Susan: https://healthyawakening.co/Visit the website: healthyawakening.co/podcastFind listening links here: https://healthyawakening.co/linksP.S. Want reminders about episodes? Sign up for our newsletter, you can find the link on our podcast page! https://healthyawakening.co/podcast

Matthew O. Jackson is perhaps the world's most renowned scholar of the economics of networks; as a 2005-06 CASBS fellow, he wrote most of his still-influential book Social and Economic Networks. In this wide-ranging conversation with 2025-26 CASBS fellow Rajiv Sethi, Jackson discusses his foundational work on strategic modeling of networks, empirical applications on the role of economic connectedness in influencing people's life trajectories in the U.S., related multi-disciplinary and cross-national work he is undertaking at the Santa Fe Institute, and recent cutting-edge work using large language models to gain insights into human motivations and behaviors. Matthew O. Jackson: Stanford faculty page | Personal website | CASBS page | Wikipedia page | Google Scholar page | National Academy of Sciences bio | Stanford profile | SFI page | NBER working papers | Jackson CV | Rajiv Sethi: Barnard faculty page | Columbia page | CASBS page | Google Scholar page | SFI page | Rajiv's Substack newsletter, Imperfect Information |  Matt Jackson works referenced in this episode: Matthew Jackson and Asher Wolinsky, "A Strategic Model of Social and Economic Networks," Journal of Economic Theory (1996) Matthew Jackson and Alison Watts, "The Evolution of Social and Economic Networks," Journal of Economic Theory (2002) Raj Chetty, Matthew Jackson, et al., "Social Capital I: Measurement and Associations with Economic Mobiliity," Nature (2022) Raj Chetty, Matthew Jackson, et al., "Social Capital II: Determinants of Economic Connectedness," Nature (2022) Chetty, Jackson, et al., Opportunity Insights Social Capital Atlas (website)Dynamics of Wealth Inequality project (Santa Fe Institute) Matthew Jackson, Social and Economic Networks, Princeton University Press (2008) Matthew Jackson, The Human Network, Penguin Random House (2020) Mei, Yuan, and Jackson, "A Turing Test of Whether AI Chatbots are Behaviorally Similar to Humans," PNAS (2024) Xie, Mei, Yuan, and Jackson, "Using Large Language Models to Categorize Strategic Situations and Decipher Motivations Behind Human Behaviors," PNAS (2025) --- Rajiv Sethi's latest op-ed is "Polymarket Anonymity Must End," Financial Times (May 7, 2026) Subscribe to Rajiv's Substack newsletter, Imperfect Information   Center for Advanced Study in the Behavioral Sciences (CASBS) at Stanford UniversityExplore CASBS: website | Bluesky | X | YouTube |LinkedIn | podcast |latest newsletter | signup | outreach​Human CenteredProducer: Mike Gaetani | Audio engineer & co-producer: Joe Monzel |

Free course: Improve your metabolic healthGet our free email course on how glucose, nutrition, exercise, sleep, and measurement can help you build habits that support better energy and long-term health: ⁠https://levels.link/wnl⁠Most athletes are told the same basic rule: the harder you train, the more carbs you need. But Dr. Andrew Koutnik argues the science is more complicated.In this episode, Mike Haney talks with Dr. Koutnik about how the body fuels exercise, why muscle glycogen may not explain “hitting the wall” as neatly as many people think, and why blood glucose, brain energy, insulin, and metabolic flexibility may matter more than conventional sports nutrition advice suggests.They discuss whether athletes really need 60, 90, or even 120 grams of carbs per hour, why some athletes may perform well on far less, and how to think about fueling as an individual experiment rather than a universal rule. Because apparently even “eat sugar while running” was too simple for human physiology to leave alone.

In this episode, we discuss: Bridging the Genetic Gap: Looking critically at how horticultural research transfers between high-THC cultivars and industrial hemp. The 13-Hour Photoperiod: Breaking the traditional 12/12 cycle to achieve a 39%–50% yield increase and a 9% THC boost. Linear Light Scaling: The direct relationship between light intensity and flower yield scaling all the way up to 1,800 micromoles under ambient CO2. The UV Reality Check: Why modern high-THC genetics actually showed a decrease in final cannabinoid and terpene content under supplemental UVA and UVB. Light Response Curves: Why relying on a single leaf measurement to guide your facility's light saturation point is fundamentally flawed. The Veg-to-Flower Transition: Practical SOPs for adjusting PPFD and DLI safely without shocking your canopy. Controlled Deficit Irrigation: How a single, targeted late-flower drought stress event triggers a 12%–13% spike in final THC and CBD content. The 60 PPM Phosphorus Rule: Looking at the established replication data proving that excess phosphorus wastes money, reduces yield efficiency, and impacts the environment. Links & Resources Mentioned in This Episode: Visit KIS Organics for commercial living soils, amendments, and consulting: https://www.kisorganics.com Grab Dr. Zheng's textbook, Handbook of Cannabis Production in Controlled Environments: https://www.amazon.com/Handbook-Cannabis-Production-Controlled-Environments/dp/0367712571 Access Dr. Zheng's open-access research papers on Google Scholar: https://scholar.google.com/citations?user=ciGdnWAAAAAJ&hl=en Connect with us on Instagram: @kisorganics Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Did I have any freedom in choosing this particular podcast guest? At the level of particles, fields, and the fundamental laws of physics; no. At the level of human agents navigating the world, yes. Today's guest, Christian List, is a philosopher and political scientist who has arguably done the most to articulate the "compatibilist" perspective on free will, according to which the freedom of rational agents is entirely compatible with underlying mechanistic laws. The reconciliation depends on thinking carefully about emergence and the relationship between levels of reality. Take your personal data back with Incogni! Use code MINDSCAPE at this link and get 60% off an annual plan: https://incogni.com/mindscape #sponsored Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/05/18/354-christian-list-on-free-will-and-levels-of-reality/ Support Mindscape on Patreon. Christian List received his D.Phil in Politics from Oxford University. He is currently Professor of Philosophy and Decision Theory and Co-Director of the Munich Center for Mathematical Philosophy at LMU Munich. He is a Fellow of the British Academy and a member of Academia Europaea the Bavarian Academy of Sciences and Humanities. Among his honors are the Joseph Gittler Award from the American Philosophical Association. He is the author of Why Free Will Is Real and (with Philip Pettit) Group Agency. Web site LMU web page Google Scholar publications Amazon author page Wikipedia

Economic markets are efficient ways of deciding fair prices, at least in ideal circumstances of perfect competition, information, and choice. But there is more to life than fair prices. Two people might decide on a fair price to carry out a contract killing, but society generally frowns on the idea. Many examples of morally contestable markets feature less consensus than that one: sex work, drugs, selling organs, adopting children. In his new book Moral Economics, economist Alvin Roth investigates how we should reason through such tricky cases, and what we can learn from them. Get twenty percent off your first purchase at Fast Growing Trees when using the code MINDSCAPE at checkout. Mindscape listeners get free shipping and 365-day returns on clothing from Quince. Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/05/11/353-alvin-roth-on-the-economics-of-morally-contested-markets/ Support Mindscape on Patreon. Alvin Roth received his Ph.D. in operations research from Stanford University. He is currently the Craig and Susan McCaw Professor of Economics at Stanford University and the Gund Professor of Economics and Business Administration Emeritus at Harvard. He was President of the American Economic Association in 2017. He and Lloyd Shapley shared the 2012 Nobel Prize in Economics for "the theory of stable allocations and the practice of market design." Stanford web page Google Scholar publications Amazon author page Wikipedia

Fewer than 23% of Flemish children between 8 and 17 can identify a blackbird. Less than 5% can name a peacock butterfly. The mole scores highest — not because of nature education, but because it's a beloved character in children's stories.Nature isn't just disappearing from our landscapes. It's disappearing from our minds.In this episode, I sit down with Prof. Hans Van Dyck, behavioral ecologist at UCLouvain and head of the Behavioural Ecology and Conservation group, to talk about what happens to a species — and a society — when children grow up without meaningful contact with the living world.We get into the winners and losers of human-altered landscapes, and where Homo sapiens really sits on that spectrum. We talk about niche construction and its hidden cost — how we built a world for ourselves, and what we quietly subtracted in the process. Hans walks me through Robert Pyle's devastating 1978 concept of the "extinction of experience," and why disconnection compounds across generations. We get into shifting baselines — why each generation inherits a smaller idea of what "normal" nature looks like, without knowing it. And we talk about the move from nature blindness to biophobia: the teacher who brought tissues for children to clean their hands after touching plants, the teenagers who fled a butterfly on a café terrace, the children in hazmat suits at a tree-planting (a story Adrian Wong from SUGi first told me in S6E7).Hans also makes a compelling case for school yards as one of the highest-leverage interventions available to us — for biodiversity, for reduced bullying, and as an equalizer for children whose families can't drive to the countryside on weekends. And he reminds us that you don't need to know the name of a single species to do this work. Curious children are already doing it for us.Hans's December 2025 op-ed in De Standaard — "Children can no longer tell a blackbird from a sparrow" — is a wonderful companion to this conversation. He's also the author of Het orakel van de bosnimf. Van vlinders en mensen (Lannoo), and his scientific work is available on Google Scholar and ResearchGate.

The connectome is the wiring diagram of a brain, a big matrix that tells us what neurons talk to what other neurons. Understanding it is an important step to understanding how brains work, but a long way from the final answer. A big next step is understanding how neuronal circuits connect to and guide bodily behavior. Very recent work on mapping the fruit-fly connectome has brought us closer to that goal. I talk with neuroscientist Bing Brunton about the connectome, how we can study it to understand bodily motion in flies and other creatures, and where it's all taking us. Chubbies is here to keep you comfy and looking good year-round. Get 20% off with code MINDSCAPE at chubbiesshorts.com/MINDSCAPE! #chubbiespod Upgrade your denim game with Rag & Bone! Get 20% off sitewide with code MINDSCAPE at www.rag-bone.com. #ragandbonepod Support Mindscape on Patreon. Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/04/27/352-bing-brunton-on-connecting-the-connectome-to-the-body/ Bing Wen Brunton received her Ph.D. in neuroscience from Princeton University.. She is currently a Professor of Biology and the Richard & Joan Komen University Chair at the University of Washington, with affiliations at the eScience Institute for Data Science, the Paul G. Allen School of Computer Science & Engineering, and the Department of Applied Mathematics. Web site University of Washington web page Google Scholar publications YouTube channel Bluesky Artworks (Instagram)

In Identity Building Among Role-Playing Gamers: Slaying Goblins in the Real World (Bloomsbury 2025), Heather Shay draws from 19 months of participant-observation and 20 in-depth interviews with players. She found that gamers derive significant social and psychological benefits from table-top role-playing games-not least in that players often feel the hobby makes them better people. Playing these games allow players to depict themselves as good, moral actors through their in-game actions as well as by making the game enjoyable for their fellow players in real life. Table-top role-playing games also serve a psychological function by allowing participants to take imaginary risks with their characters, which in turn make them feel more alive than their everyday experiences allow them to. As they pretend to be fictional characters in fictional worlds, players use these games to create identities that make their lives more meaningful. Michael O. Johnston, Ph.D., is an Associate Professor of Sociology at William Penn University, where he focuses on the cultural and interpretive analysis of space, behavior, and identity. His work examines how built and designed environments shape social interaction, networks, and morality in everyday life across a range of settings. He is the author of The Social Construction of a Cultural Spectacle: Floatzilla (Lexington Books, 2023), Community Media Representations of Place and Identity at Tug Fest: Reconstructing the Mississippi River (Lexington Books, 2022), and his most recent book Smalltown Urban: Performing the City in Rural America (Bloomsbury, under contract). His current research advances several interconnected projects, including the study of escape rooms as emotion-structured environments, the production of temporary urbanism in rural historic towns, and the ways students experience “hanging out” and feeling at home in higher education. He is also developing new work on the social organization and cultural meaning of rodeo. More broadly, his scholarship is united by an interest in how people actively produce meaning, attachment, and identity within specific spatial and temporal contexts. To learn more about his work, visit his personal website or Google Scholar, connect with him on Bluesky (@professorjohnst.bsky.social) or X (@ProfessorJohnst), or reach out directly via email (johnstonmo@wmpenn.edu). Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

How the science of the vagus nerve explains anxiety, ambition, burnout, and the art of becoming who you actually are. Humans, might be the most sophisticated organism on Earth (capable of writing symphonies, landing on the moon, and inventing artisanal sourdough). Yet at our core, we are running threat-detection software that predates the dinosaurs. Stephen Porges, the scientist who mapped the vagus nerve's extraordinary influence on human behaviour, joins us to explain why this ancient wiring is quietly making most of your big life decisions for you. The good news is that evolution, for once, gave us a rather elegant solution. Safety — genuine, physiological safety — unlocks curiosity, creativity, and the particular satisfaction of becoming who you actually are. The even better news is it's achievable without a retreat, a cold plunge, or a subscription. What you'll take away: Why your gut reactions are data, not drama How the environments we build either cage or liberate our best thinking Why the most calming people aren't trying to be calm Your nervous system has been waiting for this conversation. SPONSORS

In this episode, Chris Young joins me the day before he heads out on a spring bear hunt, and we dive deep into everything that makes spring bear hunting so special and challenging. We kick things off by talking about e-scouting bear country, tracking snowpack, and satellite imagery to build Plans A through F before ever setting foot in the mountains. A little storytelling on one of the best-looking color-phase bears. And if that wasn't enough, we get into the story of how he killed an absolute giant boar on the last day, last light of a six-day backpack hunt after having an absolutely brutal hunt. Pure grit. We also crack the puzzle on bear seasonality — why understanding what bears are doing in April vs. June is the single biggest thing that'll make you more successful. We wrap up talking tarps (the most underrated piece of your spring hunting system), the power of research papers for understanding bear biology, and why Google Scholar might be the best scouting tool you're not using.Learn more about GOHUNT.Follow Brady on Instagram.Follow GOHUNT on Social Media:InstagramYouTube - Podcast ChannelYouTube - Main ChannelFacebook

Peter Singer has been an influential philosopher for a number of decades. He was a significant early voice in animal rights, has been a leading thinker of utilitarianism, and helped inspire the effective altruism movement. In this podcast episode, we try our best to talk about all of those things -- working from metaethical questions of consequentialism vs. other approaches, to specific flavors of utilitarianism, the practical demands that ethics places on people, the rights of animals, and the decisions we make at the end of our lives. Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/04/20/351-peter-singer-on-maximizing-good-for-all-sentient-creatures/ Support Mindscape on Patreon. Peter Singer received his B.Phil. in philosophy from the University of Oxford. He retired from Princeton University in 2023, and now lives in Melbourne, Australia. He is the author of a number of influential books, including Animal Liberation (1975). He has been named a Companion of the order of Australia, and is a winner of the Berggruen Prize. He is the founder of the charity The Life You Can Save. He and philosopher Kasia de Lazari Radek are co-hosts of the Lives Well Lived podcast (YouTube, Spotify, Apple). Web site Princeton University Center for Human Values page Google Scholar publications Amazon author page Wikipedia Bluesky

We are more familiar with ourselves than with anything else in the universe, but we generally don't come very close to really understanding what our "self" is. That's not too surprising, as selves are very complicated and we are burdened by all sorts of biases. Today's guest is J. Eric Oliver, who has been teaching a popular course at the University of Chicago called "The Intelligible Self." His academic specialty is political science, but he brings together ideas from psychology, neuroscience, and a broad swath of the humanities. His view is summarized in his recent book, How to Know Yourself: The Art and Science of Discovering Who You Really Are. Take your personal data back with Incogni! Use code MINDSCAPE at this link and get 60% off an annual plan: https://incogni.com/mindscape #sponsored Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/04/13/350-j-eric-oliver-on-the-self-and-how-to-know-it/ Support Mindscape on Patreon. J. Eric Oliver received his Ph.D. in political science from the University of California, Berkeley. He is currently a Professor of Political Science at the University of Chicago. His research interests include contemporary American politics, suburban and racial politics, political psychology, and the politics of science. He is the host of the podcast Knowing: With Eric Oliver. Web site U Chicago web page Google Scholar publications Amazon author page

Send us Fan MailNicole Laurent is one the most featured returning guest on our show, so be sure to check out all her appearances on episodes 248, 343, 438, 538, and 744 of Boundless Body Radio!Nicole Laurent, LMHC, has been a licensed mental health counselor in Washington state for almost two decades. Her current practice focuses on helping her clients with anxiety, depression, and other mental health issues transition to a ketogenic diet or uses other nutritional therapies to complement their psychotherapy work.She holds several specialized training certifications, allowing her to work with underlying biological factors in mental illness. Nicole works with clients via telehealth, and helps people explore medication-free options for their mental health using research and evidence-based nutritional and functional psychiatry so that people can get their lives back without side effects or dependence on big pharma.In 2021, she created MentalHealthKeto.com, a blog devoted to educating people about ketogenic diets for mental health and neurological issues.Nicole is one of seven pioneers of Metabolic Psychiatry recognized by the Baszucki Brain Research Fund and the Milken Institute and has been given the Metabolic Mind Award in 2022Find Nicole at-https://mentalhealthketo.com/Study- Awareness and best practices in using ketogenic therapy to treat serious mental illness: a modified Delphi consensusIG- @mentalhealthketoTW- @KetoCounselorLK- Nicole Laurent, LMHCFB- @thatketocounselorFREE E-BOOK!Google Scholar link set with keyword "ketogenic"!Find Boundless Body at-myboundlessbody.comBook a session with us here! 

"We're not going to get the liberation we all crave on a soul level without risk." Andrea reflects on why, now more than ever, we must follow our hearts and refuse to let fear, or Steve Bannon, that Jabba the Hutt of American politics, live rent-free in our heads. Being brave, taking risks: that's how we win. In this special excerpt from last Monday's Gaslit Nation Salon, Andrea honors her beloved uncle, Phil Bourne. "Uncle Phil" was the founding dean of the University of Virginia School of Data Science, earned more than 100,000 citations on Google Scholar, championed the collaboration between the liberal arts and STEM as essential to the future of education, and served as the founding Editor-in-Chief of PLOS Computational Biology, where he created the "Ten Simple Rules" series. Honor Uncle Phil's memory by reaching out to your loved ones and saying what's truly in your heart. We do not have as much time here as we think. Don't let the fascists steal that time from you. Bethany McKee, founder of the Outreach Committee, a group that meets to discuss how to deal with the MAGA cultists in our lives as they awaken to their own self-destruction, will host today's Gaslit Nation Salon at 4 p.m. ET. You can find the Zoom link at Patreon.com/Gaslit. Thank you to everyone who supports the show. We could not make Gaslit Nation without you. Join us for an evening honoring the power of art and defiance at the book launch of Mrs. Orwell, Andrea's inspiring new graphic novel, illustrated by Brahm Revel. When: April 13 Where: PowerHouse Books Arena, DUMBO, Brooklyn Details here: https://powerhousearena.com/events/book-launch-mrs-orwell-by-andrea-chalupa-in-conversation-with-nomiki-konst/ Patreon Supporters: You and your guests get in free and receive a complimentary book! Just message us through Patreon to claim yours. Not a member yet? Join our community at Patreon.com/Gaslit. We couldn't make this show without you–see you there!  

There is something special about gravity. After decades of effort, there is still no convergence on the right way to reconcile Einstein's theory of general relativity with the framework of quantum mechanics. But a number of intriguing ideas have arisen along the way, including black hole radiation, the wave function of the universe, the AdS/CFT correspondence, and the role of quantum information theory. Theoretical physicist Daniel Harlow has made significant contributions to our understanding of information loss in black holes; in this conversation we turn those insights onto quantum cosmology, with potentially significant implications for how quantum mechanics itself works. Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/03/30/349-daniel-harlow-on-what-quantum-gravity-teaches-us-about-quantum-mechanics/   Support Mindscape on Patreon. Daniel Harlow received his Ph.D. in physics from Stanford University. He is currently an associate professor of physics at the Massachusetts Institute of Technology. Among his awards are a Packard Fellowship and the New Horizons in Physics Prize. Web site MIT web page Google Scholar publications Wikipedia

Intelligence is a many splendored thing, especially when it comes to comparisons between species. Chimpanzees are better than humans at some numerical tasks, but less good at understanding what numbers actually mean. One window on the ways that species differ is how they play amongst themselves. I talk with anthropologist and cognitive scientist Erica Cartmill about modes of play and other social behaviors among various species, and what they reveal about the ways we all think. Upgrade your denim game with Rag & Bone! Get 20% off sitewide with code MINDSCAPE at www.rag-bone.com. #ragandbonepod Get twenty percent off your first purchase at Fast Growing Trees when using the code MINDSCAPE at checkout. Henson Shaving is offering 100 blades free with the purchase of a razor — just head to hensonshaving.com/MINDSCAPE and or use code MINDSCAPE at checkout. Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/03/09/346-erica-cartmill-on-how-human-and-animal-minds-think-and-play/ Support Mindscape on Patreon. Erica Cartmill received her Ph.D. in psychology and neuroscience from the University of St. Andrews. She is Professor of Cognitive Science, Anthropology, Animal Behavior, Psychology, and Informatics at Indiana University, Bloomington and an External Professor at the Santa Fe Institute. She is the co-chair of the EVOLANG conferences and the co-director of the Diverse Intelligences Summer Institute. She is co-director of the Possible Minds lab at IU, and also manages the Observing Animals project, which asks for public input on how animals interact with each other. Web site Indiana University we page Google Scholar publications

Behaving rationally involves facing up to conditions of uncertainty; we never navigate the world with perfect confidence. Sometimes we are uncertain about the way the world is, but we can also be uncertain about our place within the world. This kind of situation arises in cosmology (where the relevant world can extend very far in space or time), and also in quantum mechanics (where new worlds might be created at any measurement), but also when we are simply unsure about the future history of humanity or whether we live in a computer simulation. I talk with philosopher Adam Elga about how to deal with these unique kinds of uncertainties. Upgrade your denim game with Rag & Bone! Get 20% off sitewide with code MINDSCAPE at www.rag-bone.com #ragandbonepod #sponsored Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/02/23/345-adam-elga-on-being-rational-in-a-very-large-universe/ Support Mindscape on Patreon. Adam Elga received his Ph.D. in philosophy from MIT. He is currently a professor of philosophy at Princeton University. His research involves decision and game theory, epistemology, philosophy of probability, philosophy of mind, and philosophy of science. Web site Princeton web page Google Scholar publications PhilPeople profile

For all that human beings spend a lot of their time thinking, it's far from obvious what that process actually entails. Part of it amounts to classical logical reasoning. But an even bigger part involves reasoning with probability and uncertainty. And some of it is governed by unavoidable limitations on time and accuracy. Psychologist and computer scientist Tom Griffiths suggests that we have thought about it enough to feel that we have come to understand some general principles, which he explains in his new book The Laws of Thought: The Quest for a Mathematical Theory of Mind. Take your personal data back with Incogni! Use code MINDSCAPE at this link and get 60% off an annual plan: https://incogni.com/mindscape #sponsore Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/02/09/343-tom-griffiths-on-the-laws-of-thought/ Support Mindscape on Patreon. Tom Griffiths received his Ph.D. in psychology from Stanford University. He is currently Professor of Psychology and Computer Science at Princeton University, Director of the Computational Cognitive Science Lab, and Director of the Princeton Laboratory for Artificial Intelligence. He is the co-author of Algorithms to Live By: The Computer Science of Human Decisions, as well as the upcoming The Rational Use of Cognitive Resources. Web site Princeton web page Google Scholar publications Wikipedia