Podcast appearances and mentions of Sam L Savage

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: [Cross-post] Is the Fermi Paradox due to the Flaw of Averages?, published by Aryeh Englander on January 18, 2023 on LessWrong. [This article is copy-pasted from the Lumina blog, very lightly edited for LessWrong.] Where is everybody?— Enrico Fermi The omnipresence of uncertainty is part of why making predictions and decisions is so hard. We at Lumina advocate treating uncertainty explicitly in our models using probability distributions. Sadly this is not yet as common as it should be. A recent paper “Dissolving the Fermi Paradox” (2018) is a powerful illustration of how including uncertainty can transform conclusions on the fascinating question of whether our Earth is the only place in the Universe harboring intelligent life. The authors, Anders Sandberg, Eric Drexler and Toby Ord (whom we shall refer to as SDO), show elegantly that the apparent paradox is simply the result of the mistake of ignoring uncertainty, what Sam L. Savage calls the Flaw of Averages. In this article, we review their article and link to an Analytica version of their model that you can explore. The Fermi Paradox Enrico Fermi. From Wikimedia commons. One day in 1950, Enrico Fermi, the Nobel prize-winning builder of the first nuclear reactor, was having lunch with a few friends in Los Alamos. They were looking at a New Yorker cartoon of cheerful aliens emerging from a flying saucer. Fermi famously asked “Where is everybody?”. Given the vast number of stars in the Milky Way Galaxy and the likely development of life and extraterrestrial intelligence, how come no ETs have come to visit or at least been detected? This question came to be called the “Fermi Paradox”. Ever since, it has bothered those interested in the existence of extraterrestrial intelligence and whether we are alone in the Universe. The Flaw of Averages on Steroids Dr. Sam Savage who coined the term “Flaw of Averages” Sam L. Savage, in his book, The Flaw of Averages, shows how ignoring uncertainty and just working with a single mean or “most likely” value for each uncertain quantity can lead to misleading results. To illustrate how dramatically this approach can distort your conclusions, SDO offer a toy example. Suppose there are nine factors that multiplied together give the probability of extraterrestrial intelligence (ETI) arising on any given star. If you use a point estimate of 0.1 for each factors, you could infer that there is a 10−9probability of any given star harboring ETI. There are about 1011 stars in the Milky Way, so the probability that no star other than our own has a planet harboring intelligent life would be extremely small, (1−10−9)100B≈3.7×10−44. On the other hand, suppose that, based on what we know, each factor could be anywhere between 0 and 0.2, and assign a uniform uncertainty over this interval, using the probability distribution, Uniform(0, 0.2). If you combine these distributions probabilistically, using Monte Carlo simulation for example, the mean of the result is 0.21 – over 5,000,000,000,000,000,000,000,000,000,000,000,000,000,000 times more likely! The Drake Equation Frank Drake, a radio astronomer who worked on the search for extraterrestrial intelligence (SETI), tried to formalize Fermi's estimate of the number of ETIs. He suggested that we can estimate N, the number of detectable, intelligent civilizations in the Milky Way galaxy from what is now called the “Drake equation”. It is sometimes referred to as the “second most-famous equation in science (after E= mc2)”: Frank Drake (1930-2022). N=R∗×fp×ne×fl×fi×fc×L Where R∗ is the average rate of formation of stars in our galaxy,fp is the fraction of stars with planets.ne is the average number of those planets that could potentially support life.fl is the fraction of those on which life had actually developed;fi is the fraction of those with life that ...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: [Cross-post] Is the Fermi Paradox due to the Flaw of Averages?, published by Aryeh Englander on January 18, 2023 on LessWrong. [This article is copy-pasted from the Lumina blog, very lightly edited for LessWrong.] Where is everybody?— Enrico Fermi The omnipresence of uncertainty is part of why making predictions and decisions is so hard. We at Lumina advocate treating uncertainty explicitly in our models using probability distributions. Sadly this is not yet as common as it should be. A recent paper “Dissolving the Fermi Paradox” (2018) is a powerful illustration of how including uncertainty can transform conclusions on the fascinating question of whether our Earth is the only place in the Universe harboring intelligent life. The authors, Anders Sandberg, Eric Drexler and Toby Ord (whom we shall refer to as SDO), show elegantly that the apparent paradox is simply the result of the mistake of ignoring uncertainty, what Sam L. Savage calls the Flaw of Averages. In this article, we review their article and link to an Analytica version of their model that you can explore. The Fermi Paradox Enrico Fermi. From Wikimedia commons. One day in 1950, Enrico Fermi, the Nobel prize-winning builder of the first nuclear reactor, was having lunch with a few friends in Los Alamos. They were looking at a New Yorker cartoon of cheerful aliens emerging from a flying saucer. Fermi famously asked “Where is everybody?”. Given the vast number of stars in the Milky Way Galaxy and the likely development of life and extraterrestrial intelligence, how come no ETs have come to visit or at least been detected? This question came to be called the “Fermi Paradox”. Ever since, it has bothered those interested in the existence of extraterrestrial intelligence and whether we are alone in the Universe. The Flaw of Averages on Steroids Dr. Sam Savage who coined the term “Flaw of Averages” Sam L. Savage, in his book, The Flaw of Averages, shows how ignoring uncertainty and just working with a single mean or “most likely” value for each uncertain quantity can lead to misleading results. To illustrate how dramatically this approach can distort your conclusions, SDO offer a toy example. Suppose there are nine factors that multiplied together give the probability of extraterrestrial intelligence (ETI) arising on any given star. If you use a point estimate of 0.1 for each factors, you could infer that there is a 10−9probability of any given star harboring ETI. There are about 1011 stars in the Milky Way, so the probability that no star other than our own has a planet harboring intelligent life would be extremely small, (1−10−9)100B≈3.7×10−44. On the other hand, suppose that, based on what we know, each factor could be anywhere between 0 and 0.2, and assign a uniform uncertainty over this interval, using the probability distribution, Uniform(0, 0.2). If you combine these distributions probabilistically, using Monte Carlo simulation for example, the mean of the result is 0.21 – over 5,000,000,000,000,000,000,000,000,000,000,000,000,000,000 times more likely! The Drake Equation Frank Drake, a radio astronomer who worked on the search for extraterrestrial intelligence (SETI), tried to formalize Fermi's estimate of the number of ETIs. He suggested that we can estimate N, the number of detectable, intelligent civilizations in the Milky Way galaxy from what is now called the “Drake equation”. It is sometimes referred to as the “second most-famous equation in science (after E= mc2)”: Frank Drake (1930-2022). N=R∗×fp×ne×fl×fi×fc×L Where R∗ is the average rate of formation of stars in our galaxy,fp is the fraction of stars with planets.ne is the average number of those planets that could potentially support life.fl is the fraction of those on which life had actually developed;fi is the fraction of those with life that ...

In today's era—of inflation, a pandemic, war, and more—it can sometimes feel like market risk is at an all-time high. But the truth is, how we evaluate and navigate risk hasn't changed mathematically in hundreds of years. And our guest this week is an expert in risk management (no matter the external circumstances).    Alex Sidorenko is an experienced executive across strategic, investment, and operational risks and insurance working within multibillion-dollar corporations in Australia, GCC, and Europe. He's successfully implemented changes to quantitative risk analysis, risk-based decision-making, and neuroscience.    Tune into the full episode for more on risk management, the “predictable irrationality” of human beings, the role of global conflict in risk estimation, and Alex's tips for small businesses to become more resilient in the face of constantly changing global markets.   Here's a Glimpse of What You'll Learn:    How Alex used “kindergarten math” within the risk management industry, and how it's that simple math has saved companies significant amounts of money yearly  Why geography plays a role in how much risk is overestimated or underestimated  What it means for humans to be “predictably irrational” and how that relates back to Alex's work How global conflict affects (and doesn't affect) risk exposure The inherent problems with single-point KPIs Alex's 3 steps to make your business more resilient More about the concept of “expected losses”   Why Alex decided to move from a major city to a small village in Spain More about Alex's book about risk management for small businesses   Resources Mentioned in This Episode: Alex's Risk Academy blog Risk Academy on YouTube Alex Sidorenko on LinkedIn Alex's free risk management book: “Guide to Effective Risk Management 3.0” Risk Awareness Week 2022 “Thinking, Fast and Slow” by Daniel Kahneman “Predictably Irrational: The Hidden Forces that Shape Our Decisions” by Dan Ariely “The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty” by Sam L. Savage 40 Strategy Contact 40 Strategy Carl J. Cox on LinkedIn

In Episode 25 trifft Marco Klaus Leopold, den Erfinder von FLIGHT LEVELS und dem Co-Founder der Flight Levels Academy, zum Gespräch. Das Flight Levels Modell dient dazu, nicht nur Agilität in Teams zu bringen, sondern soll die ganze Unternehmung befähigen sich agil auszutauschen. Uns interessiert wie das agile Mindset Flight Levels funktioniert. Gemeinsam nähern sich Klaus und Marco anschließend der Frage, ob und wenn wie, es in Kombination mit dem OKR Framework gedacht werden kann. Dabei werden die unterschiedlichsten Perspektiven interessant beleuchtet, die am Ende zu einem sehr spannenden Ergebnis führen. Linkedin: https://www.linkedin.com/in/klausleopold/?originalSubdomain=at Buchempfehlungen: Donald Miller, “Building a Story Brand” Daniel Kahnemann, “Thinking fast and slow” Sam L. Savage, “The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty” Douglas W. Hubbard, “How to Measure Anything: Finding the Value of Instangibles in Business” Richard Rumelt, “Good Strategy/ Bad Strategy: The Difference And Why It Matters”

Top 3 QuotesRisk Management is not really a profession. It's a competency that should be part of most degrees, if not all the degrees, at universities.Most organizations have been disillusioned with the astrology version of risk management.Sometimes, even a little quantification improves the quality of decision-making significantly.Show Highlights[01:17] Alex shares what the Risk Academy provides[03:02] How Alex got into risk[05:13] Alex's "controversial" blog[08:04] Methodologies, strategies, importance[13:52] What forces Alex to be controversial[16:16] Brilliant idea of dumbing it down[17:42] Approaching risk quantification[20:37] The real question is, how complex can we go?[23:29] How and when organizations should approach quantification[26:00] An unrealistic fairytale based on averages[29:03] Cultural difference in risk management approach[30:00] Alex's predictions in the coming years[34:17] Final nuggets of wisdomResources:RISK-ACADEMYConnect with Alex on LinkedInConnect with Alex on TwitterProspect Theory: An Analysis of Decision Under Risk by Daniel Kahneman and Amos TverskyJudgment under Uncertainty: Heuristics and Biases by Daniel Kahneman and Amos TverskyFoundations of Behavioral and Experimental Economics by Daniel Kahneman and Vernon SmithHow to Measure Anything: Finding the Value of ‘Intangibles’ in BusinessProbability Management ConferenceMonte Carlo SimulationMoneyballThe Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty by Sam L. Savage

The buzz: “As wireless technologies in GM cars evolve, those vehicles will become nodes on the larger Internet, sending vast amounts of information to the cloud where it can be shared with your mechanic, your smartphone, apps and other cars” (fortune.com 03/27/15). The dawn of data flowing from sensors in connected, autonomous vehicles has inevitably sparked a new value proposition: automakers can profit by selling that data to businesses and tech giants like Google and Apple. Daydream or viable? The experts speak. Heather Ashton, IDC: “My sensors indicate you're somewhat disturbed, Michael” (Knight Rider, 1983). Joe Barkai, Analyst: “Information has no value at all unless it has the potential to change a decision” (Sam L. Savage). Larry Stolle, SAP: “Have you noticed the people most likely to be up in arms about governments apparently spying on us tend to be the most non-private people you know?” (Russell Smith). Join us for The Connected Car and Your Data: Who's Cashing In?

The buzz: “As wireless technologies in GM cars evolve, those vehicles will become nodes on the larger Internet, sending vast amounts of information to the cloud where it can be shared with your mechanic, your smartphone, apps and other cars” (fortune.com 03/27/15). The dawn of data flowing from sensors in connected, autonomous vehicles has inevitably sparked a new value proposition: automakers can profit by selling that data to businesses and tech giants like Google and Apple. Daydream or viable? The experts speak. Heather Ashton, IDC: “My sensors indicate you're somewhat disturbed, Michael” (Knight Rider, 1983). Joe Barkai, Analyst: “Information has no value at all unless it has the potential to change a decision” (Sam L. Savage). Larry Stolle, SAP: “Have you noticed the people most likely to be up in arms about governments apparently spying on us tend to be the most non-private people you know?” (Russell Smith). Join us for The Connected Car and Your Data: Who's Cashing In?

Ever seen the cartoon of the statistician who waded in a river whose average depth was three feet and drowned when it dipped to six feet? Averages only tell you so much, and Professor Sam L. Savage of Stanford University has made a cause of his career warning against simplistic mathematical assumptions. The author of "The Flaw of Averages: Why We Underestimate the Face of Uncertainty" offers his perspective in this segment.