Podcasts about Stochastic

In this lecture, we start by further reviewing confidence intervals (where they come from and what they mean) and prediction intervals and then use them to motivate a simpler way to determine how many replications are needed in a simulation study (focusing first on transient simulations of terminating systems). We then shift our attention to steady-state simulations of non-terminating systems and the issue of initialization bias. We discuss different methods of "warming up" a steady-state simulation to reduce initialization bias and then merge that discussion with the prior discussion on how to choose the number of replications. In the next lecture, we'll finish up with a discussion of the method of "batch means" in steady-state simulations.

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In this lecture, we review estimating absolute performance from simulation, with focus on choosing the number of necessary replications of transient simulations of terminating systems. The lecture starts by overviewing point estimation, bias, and different types of point estimators. This includes an overview of quantile estimation and how to use quantile estimation to use simulations as null-hypothesis-prediction generators. We the introduce interval estimation with confidence intervals and prediction intervals. Confidence intervals, which are visualizations of t-tests, provide an alternative way to choose the number of required replications without doing a formal power analysis.

In this lecture, we introduce the estimation of absolute performance measures in simulation – effectively shifting our focus from validating input models to validating and making inferences about simulation outputs. Most of this lecture is a review of statistics and reasons for the assumptions for various parametric and non-exact non-parametric methods. We also introduce a few more advanced statistical topics, such as non-parametric methods and special high-power tests for normality. We then switch to focusing on simulations and their outputs, starting with the definition of terminating and non-terminating systems as well as the related transient and steady-state simulations. We will pick up next time with discussing details related to performance measures (and methods) for transient simulations next time and steady-state simulations after that. Our goal was to discuss the difference between point estimation and interval estimation for simulation, but we will hold off to discuss that topic in the next lecture.

BONUS: Organizations as Ecosystems — Understanding Complexity, Innovation, and the Three-Body Problem at Work In this fascinating conversation about complex adaptive systems, Simon Holzapfel helps us understand why traditional planning and control methods fail in knowledge work — and what we can do instead. Understanding Ecosystems vs. Systems "Complex adaptive systems are complex in nature and adaptive in that they evolve over time. That's different from a static system." — Simon Holzapfel   Simon introduces the crucial distinction between mechanical systems and ecosystems. While mechanical systems are predictable and static, ecosystems — like teams and organizations — are complex, adaptive, and constantly evolving. The key difference lies in the interactions among team members, which create emergent properties that cannot be predicted by analyzing individuals separately. Managers often fall into the trap of focusing on individuals rather than the interactions between them, missing where the real magic happens. This is why understanding your organization as an ecosystem, not a machine, fundamentally changes how you lead. In this segment, we refer to the Stella systems modeling application. The Journey from Planning to Emergence "I used to come into class with a lesson plan — doop, doop, doop, minute by minute agenda. And then what I realized is that I would just completely squash those questions that would often emerge from the class." — Simon Holzapfel   Simon shares his transformation from rigid classroom planning to embracing emergence. As a history and economics teacher for 10 years, he learned that over-planning kills the spontaneous insights that make learning powerful. The same principle applies to leadership: planning is essential, but over-planning wastes time and prevents novelty from emerging. The key is separating strategic planning (the "where" and "why") from tactical execution (the "how"), letting teams make local decisions while leaders focus on alignment with the bigger picture. "Innovation Arrives Stochastically" "Simply by noticing the locations where you've had your best ideas, we notice the stochasticness of arrival. Might be the shower, might be on a bike ride, might be sitting in traffic, might be at your desk — but often not." — Simon Holzapfel   Simon unpacks the concept of stochastic emergence — the idea that innovation cannot be scheduled or predicted in advance. Stochastic means something is predictable over large datasets but not in any given moment. You know you'll have ideas if you give yourself time and space, but you can't predict when or where they'll arrive. This has profound implications for managers who try to control when and how innovation happens. Knowledge work is about creating things that haven't existed before, so emergence is what we rely on. Try to squash it with too much control, and it simply won't happen. In this segment, we refer to the Systems Innovation YouTube channel. The Three-Body Problem: A Metaphor for Teams "When you have three nonlinear functions working at the same time within a system, you have almost no ability to predict its future state beyond just some of the shortest time series data." — Simon Holzapfel   Simon uses the three-body problem from physics as a powerful metaphor for organizational complexity. In physics, when you have three bodies (like planets) influencing each other, prediction becomes nearly impossible. The same is true in business — think of R&D, manufacturing, and sales as three interacting forces. The lesson: don't think you can master this complexity. Work with it. Understand it's a system. Most variability comes from the system itself, not from any individual person. This allows us to depersonalize problems — people aren't good or bad, systems can be improved. When teams understand this, they can relax and stop treating every unpredictable moment as an emergency. Coaching Leaders to Embrace Uncertainty "I'll start by trying to read their comfort level. I'll ask about their favorite teachers, their most hated teachers, and I'll really try to bring them back to moments in time that were pivotal in their own development." — Simon Holzapfel   How do you help analytical, control-oriented leaders embrace complexity and emergence? Simon's approach is to build rapport first, then gently introduce concepts based on each leader's background. For technical people who prefer math, he'll discuss narrow tail distributions and fat tails. For humanities-oriented leaders, he uses narrative and storytelling. The goal is to get leaders to open up to possibilities without feeling diminished. He might suggest small experiments: "Hold your tongue once in a meeting" or "Ask questions instead of making statements." These incremental changes help managers realize they don't have to be superhuman problem-solvers who control everything. Giving the Board a Number: The Paradox of Prediction "Managers say we want scientific management, but they don't actually want that. They want predictive management." — Simon Holzapfel   Simon addresses one of the biggest tensions in agile adoption: leaders who say "I just need to give the board a number" while also wanting innovation and adaptability. The paradox is clear — you cannot simultaneously be open to innovation and emergent possibilities while executing a predetermined plan with perfect accuracy. This is an artifact of management literature that promoted the "philosopher king" manager who knows everything. But markets are too movable, consumer tastes vary too much, and knowledge work is too complex for any single person to control. The burnout we see in leaders often comes from trying to achieve an impossible standard. In this segment, we refer to the episodes with David Marquet.  Resources for Understanding Complexity "Eric Beinhocker's book called 'The Origin of Wealth' is wonderful. It's a very approachable and well-researched piece that shows where we've been and where we're going in this area." — Simon Holzapfel   Simon recommends two key resources for anyone wanting to understand complexity and ecosystems. First, Eric Beinhocker's "The Origin of Wealth" explains how we developed flawed economic assumptions based on 19th-century Newtonian physics, and why we need to evolve our understanding. Second, the Systems Innovation YouTube channel offers brilliant short videos perfect for curious, open-minded managers. Simon suggests a practical approach: have someone on your team watch a video and share what they learned. This creates shared language around complexity and makes the concepts less personal and less threatening. The Path Forward: Systems Over Individuals "As a manager, our goal is to constantly evaluate the performance of the system, not the people. We can always put better systems in place. We can always improve existing systems. But you can't tell people what to do — it's not possible." — Simon Holzapfel   The conversation concludes with a powerful insight from Deming's work: about 95% of a system's productivity is linked to the system itself, not individual performance. This reframes the manager's role entirely. Instead of trying to control people, focus on improving systems. Instead of treating burnout as individual failure, see it as information that something in the system isn't working. Organizations are ever-changing ecosystems with dynamic properties that can only be observed, never fully predicted. This requires a completely different way of thinking about management — one that embraces uncertainty, values emergence, and trusts teams to figure things out within clear strategic boundaries. Recommended Resources As recommended resources for further reading, Simon suggests:  The Origin of Wealth, by Eric Beinhocker The Systems Innovation YouTube channel   About Simon Holzapfel   Simon Holzapfel is an educator, coach, and learning innovator who helps teams work with greater clarity, speed, and purpose. He specializes in separating strategy from tactics, enabling short-cycle decision-making and higher-value workflows. Simon has spent his career coaching individuals and teams to achieve performance with deeper meaning and joy. Simon is also the author of the Equonomist newsletter on Substack, where he explores the intersection of economics, equality, and equanimity in the workplace.   You can link with Simon Holzapfel on LinkedIn.

John's monologue this time talks about the DOJ placing two federal prosecutors on leave after they wrote in a court filing that on Jan. 6, 2021, “thousands of people comprising a mob of rioters attacked the U.S. Capitol”. The 2 D.C. federal prosecutors, Carlos Valdivia and Samuel White, were locked out of their devices and placed on leave just hours after filing a sentencing memo in the case against Taylor Taranto, a former Jan. 6 defendant who went to President Obama's house after Trump revealed Obama's home address on social media. Then, podcast star is back once again to speak with John about the excuses for the never ending government shutdown and Trump's completely routine MRI. Next, It's the God Squad with theologian Dillon Naber Cruz and Pastor Desimber Rose. John talks with them about the fake cease-fire in Gaza and the fake Christians in the MAGA movement. Then wrapping it up, legal analyst Dr. Tracy Pearson joins the crew to chat with listeners about the many court rulings hampering Trump's malice.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Sign up for Alex's first live cohort, about Hierarchical Model building!Get 25% off "Building AI Applications for Data Scientists and Software Engineers"Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Why GPs still matter: Gaussian Processes remain a go-to for function estimation, active learning, and experimental design – especially when calibrated uncertainty is non-negotiable.Scaling GP inference: Variational methods with inducing points (as in GPflow) make GPs practical on larger datasets without throwing away principled Bayes.MCMC in practice: Clever parameterizations and gradient-based samplers tighten mixing and efficiency; use MCMC when you need gold-standard posteriors.Bayesian deep learning, pragmatically: Stochastic-gradient training and approximate posteriors bring Bayesian ideas to neural networks at scale.Uncertainty that ships: Monte Carlo dropout and related tricks provide fast, usable uncertainty – even if they're approximations.Model complexity ≠ model quality: Understanding capacity, priors, and inductive bias is key to getting trustworthy predictions.Deep Gaussian Processes: Layered GPs offer flexibility for complex functions, with clear trade-offs in interpretability and compute.Generative models through a Bayesian lens: GANs and friends benefit from explicit priors and uncertainty – useful for safety and downstream decisions.Tooling that matters: Frameworks like GPflow lower the friction from idea to implementation, encouraging reproducible, well-tested modeling.Where we're headed: The future of ML is uncertainty-aware by default – integrating UQ tightly into optimization, design, and deployment.Chapters:08:44 Function Estimation and Bayesian Deep Learning10:41 Understanding Deep Gaussian Processes25:17 Choosing Between Deep GPs and Neural Networks32:01 Interpretability and Practical Tools for GPs43:52 Variational Methods in Gaussian Processes54:44 Deep Neural Networks and Bayesian Inference01:06:13 The Future of Bayesian Deep Learning01:12:28 Advice for Aspiring Researchers

In this lecture, we review statistical fundamentals – such as the origins of the t-test, the meaning of type-I and type-II error (and alternative terminology for both, such as false positive rate and false negative rate) and the connection to statistical power (sensitivity). We review the Receiver Operating Characteristic (ROC) curve and give a qualitative description of where it gets its shape in a hypothesis test. We close with a validation example (from Lecture H) where we use a power analysis on a one-sample t-test to help justify whether we have gathered enough data to trust that a simulation model is a good match for reality when it has a similar mean output performance to the real system. Peppered throughout the lecture are also comments about why normality is required for t-tests, why there is a minimum expected count for chi-squared tests, and how to avoid statistical inference issues when making multiple comparisons.

At the start of this lecture, we review statistical topics and fitting techniques from Unit G (particularly Lecture G3, on goodness of fit). In particular, we review hypothesis testing fundamentals (type-I error, type-II error, statistical power, sensitivity, false positive rate, true negative rate, receiver operating characteristic, ROC, alpha, beta) and then go into examples of using Chi-squared and Kolmogorov–Smirnov tests for goodness of fit for arbitrary distributions. We also introduce Anderson–Darling (for flexibility and higher power) and Shapiro–Wilk (for high-powered normality testing). We then pivot to formally defining simulation verification, validation, and calibration and then introducing techniques that incorporate rigorous statistical tools into the validation and calibration process. We focus specifically on the use of the t-test (for confirming that populations of simulation data are consistent with the mean behaviors from the real systems they are meant to represent) and the power analysis (for understanding the conditions when a failure to detect a difference between simulation and real system allows for inferring that the simulation is sufficiently close to the real system).

In this episode of Fishing Without Bait, Jim Ellermeyer and producer Mike Sorg wade into some deep—and dangerous—waters. Together, they explore how stochastic rhetoric and obfuscation have become the most powerful tools in modern political communication. What do those words mean? In short, they describe how leaders and media use vague, coded, or confusing language to manipulate emotions, create division, and dodge accountability. From “dog whistles” to “plausible deniability,” Jim explains how these tactics quietly shape behavior and beliefs—without ever saying things outright. But this episode isn't just about politics; it's about mindfulness in the face of manipulation. Jim and Mike offer practical ways to recognize these tricks, question what we're told, and bring compassion back into our conversations. “If you have a choice between being right and being kind, choose kind—and you're going to be right every time.” In a time of noise and division, Fishing Without Bait invites listeners to slow down, think critically, and reconnect with what truly matters: kindness, awareness, and authenticity.

In this episode of Fishing Without Bait, Jim Ellermeyer and producer Mike Sorg wade into some deep—and dangerous—waters. Together, they explore how stochastic rhetoric and obfuscation have become the most powerful tools in modern political communication. What do those words mean? In short, they describe how leaders and media use vague, coded, or confusing language to manipulate emotions, create division, and dodge accountability. From “dog whistles” to “plausible deniability,” Jim explains how these tactics quietly shape behavior and beliefs—without ever saying things outright. But this episode isn't just about politics; it's about mindfulness in the face of manipulation. Jim and Mike offer practical ways to recognize these tricks, question what we're told, and bring compassion back into our conversations. “If you have a choice between being right and being kind, choose kind—and you're going to be right every time.” In a time of noise and division, Fishing Without Bait invites listeners to slow down, think critically, and reconnect with what truly matters: kindness, awareness, and authenticity.

In this lecture, we (nearly) finish our coverage of Input Modeling, where the focus of this lecture is on parameter estimation and assessing goodness of fit. We review input modeling in general and then briefly review fundamentals of hypothesis testing. We discuss type-I error, p-values, type-II error, effect sizes, and statistical power. We discuss the dangers of using p-values at very large sample sizes (where small p-values are not meaningful) and at very small sample sizes (where large p-values are not meaningful). We give some examples of this applied to best-of-7 sports tournaments and voting. We then discuss different shape parameters (including location, scale, and rate), and then introduce summary statistics (sample mean and sample variance) and maximum likelihood estimation (MLE), with an example for a point estimate of the rate of an exponential. We introduce the chi-squared (lower power) and Kolmogorov–Smirnov (KS, high power) tests for goodness of fit, but we will go into them in more detail at the start of the next lecture.

In this lecture, we continue discussing the choice of input models in stochastic simulation. Here, we pivot from talking about data collection to selection of the broad family of probabilistic distributions that may be a good fit for data. We start with an example where a histogram leads us to introduce additional input models into a flow chart. The rest of the lecture is about choosing models based on physical intuition and the shape of the sampled data (e.g., the shape of histograms). We close with a discussion of probability plots – Q-Q plots and P-P plots, as are used with "fat-pencil tests" – as a good tool for justifying the choice of a family for a certain data set. The next lecture will go over the actual estimation of the parameters for the chosen families and how to quantitatively assess goodness of fit.

In this lecture, we introduce the detailed process of input modeling. Input models are probabilistic models that introduce variation in simulation models of systems. Those input models must be chosen to match statistical distributions in data. Over this unit, we cover collection of data for this process, choice of probabilistic families to fit to these data, and then optimized parameter choice within those families and evaluation of fit with goodness of fit. In this lecture, we discuss issues related to data collection.

Earlier this year the the anomalous mathematical patterns sci-art competition attracted some jaw-dropping entries. The competition was held in connection to the Stochastic systems for anomalous diffusion research programme which took place at the Isaac Newton Institute for Mathematical Sciences (INI) in Cambridge last year.  In this episode of Maths on the Move we talk to Codina Cotar, who co-organised the INI programme and helped put on the competition. Codina explains the maths which served as inspiration and discusses some of the winning entries. From coffee to quantum mechanics and from dance to diffusion, find out how mathematics, nature and art are inextricably linked. Note that the in-person exhibition at the INI is now scheduled for March 2026. The entries discusses in this podcast are shown below. To find out more about some of the mathematical topics mentioned in this podcast see: The Sci-art competition - This article explores some of the mathematics behind the competition, including randomness, diffusion, and many particle systems. The Fields Medals 2022: Maryna Viazovska - This article looks at the mathematics of sphere packings, which won a Fields Medal for the mathematician Maryna Viazovska. Maths in a Minute: Fluid dynamics - A very bried introduction to the mathematics of liquids and gases. A ridiculously short introduction to some very basic quantum mechanics - This article does what the title suggests. A brief history of quantum field theory - A deeper look at the theory that arose from quantum mechanics. Dye Diffusion in Water by Henrique Biasi. Find out more here.   A microcosm of milk by Christian Casaljay. Find out more here.   Work by Lilia Bakanova, which which won the category for textile, sculpture and other medium. Find out more here.    

Send us a textEarlier this year the the anomalous mathematical patterns sci-art competition attracted some jaw-dropping entries. The competition was held in connection to the Stochastic systems for anomalous diffusion research programme which took place at the Isaac Newton Institute for Mathematical Sciences (INI) in Cambridge last year. In this episode of Living Proof, we talk to Codina Cotar, who co-organised the INI programme and helped put on the competition. Codina explains the maths which served as inspiration and discusses some of the winning entries. From coffee to quantum mechanics and from dance to diffusion, find out how mathematics, nature and art are inextricably linked.Note that the in-person exhibition at the INI is now scheduled for March 2026.The entries discusses in this podcast are shown below. To find out more about some of the mathematical topics mentioned in this podcast see:The Sci-art competition - This article explores some of the mathematics behind the competition, including randomness, diffusion, and many particle systems.The Fields Medals 2022: Maryna Viazovska - This article looks at the mathematics of sphere packings, which won a Fields Medal for the mathematician Maryna Viazovska.Maths in a Minute: Fluid dynamics - A very bried introduction to the mathematics of liquids and gases.A ridiculously short introduction to some very basic quantum mechanics - This article does what the title suggests.A brief history of quantum field theory - A deeper look at the theory that arose from quantum mechanics.

Economy! Te Pāti Māori! Freedom of Expression vs Stochastic incitement! Imperialism! If you like listening to podcasts about things of this nature you are in the right place!Open letter to UniSaver Board members 2025This episode's co-hostsPhilip, Simone, Josephine, PmaxTimestamps0:00 Opening 2:28 OCR Interest Rates12:24 Selling Chorus18:40 Media vs Te Pāti Māori25:26 Winston Peters44:19 Ceasefire Deal47:00 Op Shop Find54:25 ClosingsIntro/Outro by The Prophet MotiveSupport us here: https://www.patreon.com/1of200

During this lecture, we review the topics covered up to this point in the course as preparation for the upcoming midterm exam. Students are encouraged to bring their own questions to class so that we can focus on the topics that students feel like they need the most help with.

In this lecture, we review pseudo-random number generation and then introduce random-variate generation by way of inverse-transform sampling. In particular, we start with a review of the two most important properties of a pseudo-random number generator (PRNG), uniformity and independence, and discuss statistically rigorous methods for testing for these two properties. For uniformity, we focus on a Chi-square/Chi-squared test for larger numbers of samples and a Kolmogorov–Smirnov (KS) test for smaller numbers of samples. For independence, we discuss autocorrelation tests and runs test, and then we demonstrate a runs above-and-below-the-mean test. We then shift to discussing inverse-transform sampling for continuous random variates and discrete random variates and how the resulting random-variate generators might be implemented in a tool like Rockwell Automation's Arena.

Episode 214- Fasten your Seatbelts, Things Here IN the US are Going To Get Bumpy. Arrests are Happening, And The Dems Are Using Stochastic Terrorism.   Join Alpha Sierra 288, Bravo Golf 592, and Kilo Romeo for discussions regarding the #HardTurth about today's stories the lamestream won't talk about.    (X): https://x.com/Signal50Podcast   GETTR: Follow Bravo Golf 592 below: https://gettr.com/user/bravogolf592   Truth Social: Follow Bravo Golf 592: https://truthsocial.com/@BravoGolf592   Truth Social: Follow Alpha Sierra 288: https://truthsocial.com/@AlphaSierra288   GETTR: Follow Alpha Sierra 288 below: https://gettr.com/user/alphasierra288   Parler Link to Signal50 Page: https://parler.com/#/user/Signal50podcast   Message Us on Telegram: https://t.me/Signal50podcast   Join our Telegram Group Channel: https://t.me/joinchat/HjXf6ZPLfWl9REdi     Watch us on RUMBLE: https://rumble.com/c/Signal50Podcast   Apple Podcast: Audio Only  https://podcasts.apple.com/us/podcast/signal-50-podcast/id1533557486     Google Podcast: Audio Only https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkLnBvZGJlYW4uY29tL3NpZ25hbDUwcG9kY2FzdC9mZWVkLnhtbA%3D%3D     Podbean: Audio Only https://signal50podcast.podbean.com     Spotify: Audio Only https://open.spotify.com/show/6uO9fsmbEbhwfKaNYYAFYR   Signal 50 on the web:  www.signal50.com   Email Alpha Sierra 288 and Bravo Golf 592 with your comments: Sig50podcast@protonmail.com

Wanna hear the FULL Episode? Sign up for the Grad Program today! Shooting Training and Optics Discussion The hosts announced upcoming training events in Texas and Nevada, promoting classes in pistol and rifle fundamentals. They discussed the benefits of using optics like the EOTech for better visibility and accuracy, especially for those with aging eyes or vision issues. The conversation touched on personal experiences with shooting and the advantages of using modern sights compared to traditional iron sights. EOTech Optics Vision Issues The discussion centered around the use of EOTech optics, particularly a story about a military language specialist with macular degeneration who qualified with an M4 rifle using an EOTech HWS, demonstrating how the optic improved her vision enough to pass qualification.  Brand Loyalty and Wildlife Safety The discussion focused on the decline of brand loyalty in the shooting sports industry due to corporate consolidations, with dad expressing concern about companies being bought up and then discarded. The conversation then shifted to a story about Cornell University students in New York who killed and brought a bear into their dorm for processing, which led to a discussion about proper wildlife handling and safety protocols. Crime and Law Enforcement Challenges Recent incidents of violent crime in New York and Chicago, highlighting how repeat offenders continue to commit crimes despite numerous arrests. They emphasized that progressive policies, such as no-bail laws and criticism of the police, may be contributing to increased violence, while lawful citizens are increasingly forced to defend themselves. TOPICS COVERED THIS EPISODE Huge thanks to our Partners: EOTech | Spike's Tactical TEXAS and Nevada Classes  P201 Martial Application of the Pistol in Texas P301 Advanced Martial Application of the Pistol in Texas R201 Martial Application of the Rifle [0:12:35] EOTech Talk - EOTechInc.com TOPIC: EOTech and Aging Eyes [0:37:02] Warrior of the Week - James Yeager's “Virtues of a Warrior” TOPIC: Bear Hunters www.wate.com [0:46:55] SOTG Homeroom - SOTG University TOPIC: Citizen does Job of Chicago Justice System www.shootingnewsweekly.com

Wanna hear the FULL Episode? Sign up for the Grad Program today! Shooting Training and Optics Discussion The hosts announced upcoming training events in Texas and Nevada, promoting classes in pistol and rifle fundamentals. They discussed the benefits of using optics like the EOTech for better visibility and accuracy, especially for those with aging eyes or vision issues. The conversation touched on personal experiences with shooting and the advantages of using modern sights compared to traditional iron sights. EOTech Optics Vision Issues The discussion centered around the use of EOTech optics, particularly a story about a military language specialist with macular degeneration who qualified with an M4 rifle using an EOTech HWS, demonstrating how the optic improved her vision enough to pass qualification.  Brand Loyalty and Wildlife Safety The discussion focused on the decline of brand loyalty in the shooting sports industry due to corporate consolidations, with dad expressing concern about companies being bought up and then discarded. The conversation then shifted to a story about Cornell University students in New York who killed and brought a bear into their dorm for processing, which led to a discussion about proper wildlife handling and safety protocols. Crime and Law Enforcement Challenges Recent incidents of violent crime in New York and Chicago, highlighting how repeat offenders continue to commit crimes despite numerous arrests. They emphasized that progressive policies, such as no-bail laws and criticism of the police, may be contributing to increased violence, while lawful citizens are increasingly forced to defend themselves.

This episode is presented by Create A Video – After years of urgent warnings about how right-wingers were driven to commit violence due to political rhetoric, the media and Democrats (but I repeat myself) are suddenly silence in the face of an increase in political violence from the left. Help Pete’s Walk to End Alzheimer’s! Subscribe to the podcast at: https://ThePetePod.com/ All the links to Pete's Prep are free: https://patreon.com/petekalinershow Media Bias Check: GroundNews promo code! Advertising and Booking inquiries: Pete@ThePeteKalinerShow.com Get exclusive content here!: https://thepetekalinershow.com/See omnystudio.com/listener for privacy information.

In this lecture, we first cover some discrete distributions (and the Poisson process) that we ran out of time for during the previous lecture. We then launch into a discussion of how to generate pseudo-random numbers distributed uniformly between 0 and 1 (which are necessary for us to easily generate random variates of any distribution). We talk about the two most important properties of a pseudo-random number generator (PRNG), uniformity and independence. We then talk about desirable properties. Some examples are given of some early PRNG's, and then we introduce the linear congruential generator (LCG) and its variants (like the Combined Linear Congruential Generator, CLCG), which represent a much more modern PRNG that has a number of good properties. We close with a discussion of tests of uniformity. We will continue this discussion and add on tests for independence during next lecture (which will primarily cover random-VARIATE generation).

In this lecture, we review basic probability fundamentals (measure spaces, probability measures, random variables, probability density functions, probability mass functions, cumulative distribution functions, moments, mean/expected value/center of mass, standard deviation, variance), and then we start to build a vocabulary of different probabilistic models that are used in different modeling contexts. These include uniform, triangular, normal, exponential, Erlang-k, Weibull, and Poisson variables. We will finish the discussing next time with the Bernoulli-based discrete variables and Poisson processes.

En este episodio de Indie vs Unicornio nos metemos en los secretos que mueven al mundo emprendedor y a la inteligencia artificial. Contamos la historia de cómo ChatGPT pasó de side project a producto récord con 100 millones de usuarios, y debatimos el concepto de “stochastic parrots” y los riesgos de entrenar IA que repite sin entender.Si sos founder o trabajás en startups, este capítulo es oro: desarmamos las métricas que realmente importan para tu negocio (ARR, MRR, burn rate, revenue por empleado, churn, leads de calidad) y te mostramos los errores más comunes al hacer updates a inversores: métricas de vanidad, pivots sin rumbo, rondas puente, silencio de radio o narrativa sin números.También exploramos el futuro de los navegadores con IA embebida (el caso ARK y su compra por Atlassian) y charlamos sobre cómo Cristóbal está usando Kickstarter para lanzar su libro, incluyendo historias internas sobre Meta, Mark Zuckerberg, Sheryl Sandberg y Javier Oliván y cómo se construye poder dentro de una big tech.Un episodio cargado de ejemplos, aprendizajes y tendencias para founders, emprendedores, developers e inversores que quieren entender cómo se construyen productos masivos y compañías sanas en 2025.Links del episodio:AI Anthropic: https://www.axios.com/2025/09/17/ai-anthropic-amodei-claudeMeasure what matters: https://www.goodreads.com/book/show/39286958-measure-what-matters?ac=1&from_search=true&qid=Tvo04ob34h&rank=1EOS: https://www.eosworldwide.com/Traction, Get a Grip on Your Business: https://www.goodreads.com/book/show/18886376-tractionGet a Grip: https://www.goodreads.com/book/show/13586952-get-a-gripAtlassian aquires The Browser Company: https://www.cnbc.com/2025/09/04/atlassian-the-browser-company-deal.htmlPredicciones de

In this lecture, we introduce the measure-theoretic concept of a random variable (which is neither random nor a variable) and related terms, such as outcomes, events, probability measures, moments, means, etc. Throughout the lecture, we use the metaphor of probability as mass (and thus probability density as mass density, and a mean as a center of mass). This allows us to discuss the "statistical leverage" of outliers in a distribution (i.e., although they happen infrequently, they still have the ability to shift the mean significantly, as in physical leverage). This sets us up to talk about random processes and particular random variables in the next lecture.

This lecture provides some historical background and motivation for System Dynamics Modeling (SDM) and Agent-Based Modeling (ABM), two other simulation modeling approaches that contrast with Discrete Event System (DES) simulation.In particular, in this lecture, we briefly introduce System Dynamics Modeling (SDM) and Agent-Based/Individual-Based Modeling (ABM/IBM) as the two ends of the simulation modeling spectrum (from low resolution to high resolution). The introduction of ABM describes applications in life sciences, social sciences, and engineering (Multi-Agent Systems, MAS)/operations research. NetLogo is introduced, and it is used to present examples of running ABM's as well as the code behind them. This lecture is also be coupled with notes discussing the Lab 3 (Monte Carlo simulation) results and general experience. These comments focus on interval estimation (which is right 95% of the time, as opposed to point estimation that is right 0% of the time) and the role of non-trivial distributions of random variables (as opposed to just their means).

Ignite the legacy, patriots—@intheMatrixxx and @shadygrooove, the relentless truth sentinels, unleash Season 7, Episode 175, “Stochastic Terrorism and End of Democrat Party: The Charlie Kirk Act?,” rallying around VP JD Vance stepping in to host The Charlie Kirk Show as a heartfelt tribute, capturing the falling action crystal clear where Charlie embodies all of us now and forever, while Dan Scavino and Kash Patel deliver jaw-dropping updates and razor-sharp insights on the FBI's relentless manhunt turned arrest for the rooftop sniper who gunned down our brother in broad daylight at Utah Valley University. With unfiltered breakdowns of the conspiracy narratives swirling from elite psyops to media smears justifying the hit, Erika Kirk's unbreakable voice rising strong against the darkness, VP Vance's soaring praise for Charlie's warrior spirit, the lead attorney's stark revelations on the fallout ripping through our nation, and President Trump's no-holds-barred Q&A slicing through the lies—they arm us to question every establishment echo chamber fueling this stochastic plague, probing if the Charlie Kirk Act will finally restore Smith-Mundt to torch domestic propaganda and wield the Constitution as our weapon to dismantle the Democrat machine of division once and for all. The truth is learned, never told—tune in at noon-0-five Eastern LIVE to stand with Trump! Charlie Kirk Act, stochastic terrorism, JD Vance tribute, Erika Kirk, Kash Patel updates, Dan Scavino, Democrat propaganda, Smith-Mundt repeal, political assassination, Trump Q&A, MG Show, @intheMatrixxx, @shadygrooove, Turning Point USA mgshow_s7e175_stochastic_terrorism_end_democrat_party_charlie_kirk_act Tune in weekdays at 12pm ET / 9am PST, hosted by @InTheMatrixxx and @Shadygrooove. Catch up on-demand on https://rumble.com/mgshow or via your favorite podcast platform. Where to Watch & Listen Live on https://rumble.com/mgshow https://mgshow.link/redstate X: https://x.com/inthematrixxx Backup: https://kick.com/mgshow PODCASTS: Available on PodBean, Apple, Pandora, and Amazon Music. Search for "MG Show" to listen. Engage with Us Join the conversation on https://t.me/mgshowchannel and participate in live voice chats at https://t.me/MGShow. Social & Support Follow us on X: @intheMatrixxx https://x.com/inthematrixxx @ShadyGrooove https://x.com/shadygrooove Support the show: Fundraiser: https://givesendgo.com/helpmgshow Donate: https://mg.show/support Merch: https://merch.mg.show MyPillow Special: Use code MGSHOW at https://mypillow.com/mgshow for savings! Wanna send crypto? Bitcoin: bc1qtl2mftxzv8cxnzenmpav6t72a95yudtkq9dsuf Ethereum: 0xA11f0d2A68193cC57FAF9787F6Db1d3c98cf0b4D ADA: addr1q9z3urhje7jp2g85m3d4avfegrxapdhp726qpcf7czekeuayrlwx4lrzcfxzvupnlqqjjfl0rw08z0fmgzdk7z4zzgnqujqzsf XLM: GAWJ55N3QFYPFA2IC6HBEQ3OTGJGDG6OMY6RHP4ZIDFJLQPEUS5RAMO7 LTC: ltc1qapwe55ljayyav8hgg2f9dx2y0dxy73u0tya0pu All Links Find everything on https://linktr.ee/mgshow

This lecture covers content related to implementing simulations with spreadsheets and the motivations for the use of special-purpose Discrete Event System Simulation tools. In particular, we discuss different approaches to implementing Discrete Event System (DES) simulations (DESS) with simple spreadsheets (e.g., Microsoft Excel, Google Sheets, Apple Numbers, etc.). We cover inventory management problems (such as the newsvendor model) as well as Monte Carlo sampling and stochastic activity networks (SAN's). Although we show that spreadsheets can be very powerful for this kind of work, we highlight that this approach is cumbersome for systems with increasing complexity. So this motivates why we would use more sophisticated tools specifically built for simulation (but perhaps not so great for data analysis by themselves), like Arena, FlexSim, Simio, and NetLogo.

In this lecture, we close out our review of DES fundamentals and hand simulation. After going through a hand-simulation example one last time, we show how to implement a Discrete Event System (DES) simulation using a spreadsheet tool like Microsoft Excel without any "macros" (VBA, etc.). This involves defining relationships ACROSS TIME that allow the spreadsheet to (in a declarative fashion) reconstruct the trajectory that is the output of the simulation.At the end of the lecture, we pivot to discussing the previous "Lab 2 (Muffin Oven Simulation)", which lets us introduce common random numbers (CRNs), statistical blocking, requirements of 2-sample and paired t-tests, and more sophisticated statistical methods that better characterize PRACTICAL significance (and take into account the multiple comparisons problem). Thus, the post-lab2 reflections are largely a preview of future topics in the course.

Most leaders crave certainty—but the world doesn't work that way. In this episode, Shane unpacks the idea of a stochastic mindset—a way of thinking that embraces randomness, probabilities, and adaptability. Discover why letting go of guarantees reduces stress, sharpens decisions, and unlocks freedom in a fast-changing world. Show Notes: Get a free listen here to part of the first audio in my Audio Club monthly series:  The Hidden Driver Of Elite Performance.   Get Inspired Every Monday Morning & Join The Community:  For free delivery of my weekly email join my mailing list at www.shanecradock.com My bestselling book The Inner CEO: The Inner CEO is available to buy in ebook, paperback, hardback and audio formats. All details are here: www.theinnerceo.com Connect With Me: Have you been inspired from something you've heard on my podcast or do you have a question? I'd love to hear from you. Email me at support@shanecradock.com Follow: Follow me on Instagram, Facebook, LinkedIn and X   

In this lecture, we review fundamentals of Discrete Event System (DES) simulation (e.g., entities, resources, activities, processes, delays, attributes) and we run through a number of DES modeling examples. These examples show how different research/operations questions can lead to different choices of entities/resources/etc. We close with a hand-simulation example of a single-channel, single-server queue with provided interarrival times and service times.

In this lecture, we cover fundamentals of discrete-event system (DES) simulation (DESS). This involves reviewing basic simulation concepts (entities, resources, attributes, events, activities, delays) and introducing the event-scheduling world view, which provides a causality framework on which an automatic simulation of a DES system can be built. We also discuss briefly how the stochastic modeling inherent to DESS means that outputs will be variable and thus will require rigorous statistics to make sense of.

Stochastic conversations is a new series from Deutsche Bank's QIS Research Team, featuring team members' journeys from their background to their current roles. The series will also discuss recent market drivers and the performance of "Portfolio 365". In the first episode, Caio Natividade, Global Head of QIS Research introduces Vivek Anand, Head of Portfolio Construction Research from the QIS team.

In this lecture, we introduce the three different simulation methodologies (agent-based modeling, system dynamics modeling, and discrete event system simulation) and then focus on how stochastic modeling is used within discrete-event system simulation. In particular, we define terms such as system, dynamic system, state, state variable, activity, delay, resource, entity, and the notion of "input modeling."

UK & Ireland Director of Intelligence Enterprise at GlobalLogic, Tim Hatton, explores how principles of control theory, exemplified by SpaceX's Starship, apply to the design of effective enterprise agentic AI systems. Reaching for the stars has always been the pinnacle of human ingenuity. The relentless desire to push beyond known boundaries is what drives innovation and advancement all around the globe. The recent example of SpaceX's latest Starship spacecraft soaring into the skies and returning with precision isn't just a milestone in aerospace engineering - it's a vivid illustration of what's possible when our boundless creativity fuels cutting-edge technologies. SpaceX's success demonstrates that autonomous software can effectively control a sophisticated system and steer it toward defined goals. This seamless blend of autonomy, awareness, intelligent adaptability, and results-driven decision-making offers a compelling analogy for enterprises. It's a beacon for a future where agentic AI systems revolutionise workflows, drive innovation, and transform industries. Control theory: A proven framework Control theory underpins self-regulating systems that balance performance and adaptability. It dates from the 19th century when Scottish physicist and mathematician James Clerk Maxwell first described the operation of centrifugal 'governors'. Its core principles - feedback loops, stability, controllability, and predictability - brought humanity into the industrial age. Starting with stabilising windmill velocity, up to today's spaceflights, nuclear stations and nation-spanning electricity grids. We see control theory in action when landing a rocket, for example. The manoeuvre relies on sensors to measure actual parameters, controllers to adjust based on feedback, and the system to execute corrections. Comparing real-time data to desired outcomes minimises errors, ensuring precision and safety. It's a framework that extends to enterprise workflows. Employees function as systems, supervisors as controllers, and tasks as objectives. A seasoned worker might self-correct without managerial input, paralleling autonomous systems' ability to adapt dynamically. Challenges in agentic AI Agentic AI systems combine traditional control frameworks' precision with advanced AI models' generative power. However, while rockets rely on the time-tested principles of control theory, AI-driven systems are powered by large language models (LLMs). This introduces new layers of complexity that make designing resilient AI agents that deliver precision, adaptability, and trustworthiness uniquely challenging. Computational irreducibility: LLMs like GPT-4 defy simplified modelling. They are so complex and their internal workings so intricate that we cannot predict their exact outputs without actually running them. Predicting outputs requires executing each computational step, complicating reliability and optimisation. A single prompt tweak can disrupt workflows, making iterative testing essential, yet time-consuming. Nonlinearity and high dimensionality: Operating in high-dimensional vector spaces, with millions of input elements, LLMs process data in nonlinear ways. This means outputs are sensitive to minor changes. Testing and optimising the performance of single components of complex workflows, like text-to-SQL queries, under these parameters, becomes a monumental task. Blurring code and data: Traditional systems separate code and data. In contrast, LLMs embed instructions within prompts, mixing the two. This variability introduces a host of testing, reliability, and security issues. This blurring of ever-growing data sets with the prompts introduces variability that is difficult to model and predict, which also compounds the dimensionality problem described above. Stochastic behaviour: LLMs may produce different outputs for the same input due to factors like sampling methods during generation. This means they introduce randomness - an asset for creati...

The search for a foundational theory of quantum mechanics that all physicists can agree on remains active. Over the last century a number of contenders have emerged, including Many-Worlds, pilot-wave theories, and others, but all of them have aspects that many people object to. Jacob Barandes has taken up the challenge, proposing a new formulation of quantum theory in which there is no wave function, only real degrees of freedom with fundamentally stochastic dynamics. We talk about this new theory and the challenges facing it.Blog post with transcript: https://www.preposterousuniverse.com/podcast/2025/07/28/323-jacob-barandes-on-indivisible-stochastic-quantum-mechanics/Support Mindscape on Patreon.Jacob Barandes received his Ph.D. in physics from Harvard University. He is currently Senior Preceptor in Physics and Associated Faculty in Philosophy at Harvard. He teaches both physics and philosophy courses at Harvard, where he has been the recipient of several teaching awards.Web siteHarvard web pagePhilPeople profileGoogle scholar publicationsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Interview with Anil Dash egirl Grok x Claude Zuckerberg announces Meta's new AI data centers for superintelligence Meta Hires Two More OpenAI Researchers Reflections on OpenAI RSS is (not) dead (yet) (NED #3) – audra mcnamee Perplexity AI browser Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads Stewart Holbrook: Portland Mythmaker How I Learned to Stop Worrying and Have Fun With A.I. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Anil Dash Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/twit agntcy.org Melissa.com/twit

Interview with Anil Dash egirl Grok x Claude Zuckerberg announces Meta's new AI data centers for superintelligence Meta Hires Two More OpenAI Researchers Reflections on OpenAI RSS is (not) dead (yet) (NED #3) – audra mcnamee Perplexity AI browser Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads Stewart Holbrook: Portland Mythmaker How I Learned to Stop Worrying and Have Fun With A.I. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Anil Dash Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/twit agntcy.org Melissa.com/twit

Interview with Anil Dash egirl Grok x Claude Zuckerberg announces Meta's new AI data centers for superintelligence Meta Hires Two More OpenAI Researchers Reflections on OpenAI RSS is (not) dead (yet) (NED #3) – audra mcnamee Perplexity AI browser Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads Stewart Holbrook: Portland Mythmaker How I Learned to Stop Worrying and Have Fun With A.I. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Anil Dash Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/twit agntcy.org Melissa.com/twit

Interview with Anil Dash egirl Grok x Claude Zuckerberg announces Meta's new AI data centers for superintelligence Meta Hires Two More OpenAI Researchers Reflections on OpenAI RSS is (not) dead (yet) (NED #3) – audra mcnamee Perplexity AI browser Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads Stewart Holbrook: Portland Mythmaker How I Learned to Stop Worrying and Have Fun With A.I. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Anil Dash Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/twit agntcy.org Melissa.com/twit

Interview with Anil Dash egirl Grok x Claude Zuckerberg announces Meta's new AI data centers for superintelligence Meta Hires Two More OpenAI Researchers Reflections on OpenAI RSS is (not) dead (yet) (NED #3) – audra mcnamee Perplexity AI browser Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads Stewart Holbrook: Portland Mythmaker How I Learned to Stop Worrying and Have Fun With A.I. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Anil Dash Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/twit agntcy.org Melissa.com/twit

Interview with Anil Dash egirl Grok x Claude Zuckerberg announces Meta's new AI data centers for superintelligence Meta Hires Two More OpenAI Researchers Reflections on OpenAI RSS is (not) dead (yet) (NED #3) – audra mcnamee Perplexity AI browser Perplexity CEO says its browser will track everything users do online to sell 'hyper personalized' ads Stewart Holbrook: Portland Mythmaker How I Learned to Stop Worrying and Have Fun With A.I. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Anil Dash Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/twit agntcy.org Melissa.com/twit

Political violence doesn't emerge in a vacuum — MAGA has built a system of terror out of manufactured rage and conspiracy. The assassinations and attempted assassinations in Minnesota over the weekend are the logical result of this effort. Fox News, Facebook, and Twitter are the hypodermic needles injecting the MAGA base with vile hatred, allowing them to see anyone who disagrees with them not as adversaries, but as existential threats—pedophiles, communists, invaders, subhumans. These stories aren't just fringe; they're algorithmically amplified, monetized, and echoed by politicians and law enforcement alike. When this kind of rhetoric becomes normalized, stochastic terrorism becomes inevitable. The country is reaching a flashpoint — not because individuals snap, but because entire platforms profit from pushing them to the edge. Learn more about your ad choices. Visit megaphone.fm/adchoices

This episode is presented by Create A Video – A maniac shot four people in Minnesota - two of them Democrat state lawmakers - and we are now being subjected to accusations that Republican politicians are somehow to blame. These accusations come from the very people who excuse political violence when committed by their Leftist allies. Subscribe to the podcast at: https://ThePetePod.com/ All the links to Pete's Prep are free: https://patreon.com/petekalinershow Media Bias Check: If you choose to subscribe, get 15% off here! Advertising and Booking inquiries: Pete@ThePeteKalinerShow.comGet exclusive content here!: https://thepetekalinershow.com/See omnystudio.com/listener for privacy information.

Outline00:00 - Intro01:07 - Early steps02:47 - Why control?05:20 - The move to the US07:40 - The first journal paper13:30 - What is backstepping?17:08 - Grad school25:10 - Stochastic stabilization29:53 - The interest in PDEs43:24 - Navier-Stokes equations52:12 - Hyperbolic PDEs and traffic models57:51 - Predictors for long delays1:08:14 - Extremum seeking1:27:14 - Safe control1:36:30 - Interplay between machine learning and control1:42:28 - Back to the roots: robust adaptive control1:50:50 - On service1:55:54 - AdviceLinksMiroslav's site: https://flyingv.ucsd.edu/Tuning functions paper: https://tinyurl.com/yznv6r9rP. Kokotović: https://tinyurl.com/mwmbm9yhSeparation and swapping:  https://tinyurl.com/y4fre6t8Adaptive nonlinear stabilizers: https://tinyurl.com/4a9wmmvxKKK book: https://tinyurl.com/2kw2b4k6Stochastic nonlinear stabilization: https://tinyurl.com/4td3537aFollow-up with unknown covariance: https://tinyurl.com/4c4n7fd7Boundary state feedbacks for PIDEs: https://tinyurl.com/4e9y4tdrBoundary Control of PDEs: https://tinyurl.com/d8x38bmjStabilization of Navier–Stokes systems: https://tinyurl.com/4a8cbjemTraffic congestion control: https://tinyurl.com/525jphs5Delay compensation: https://tinyurl.com/5yz6uj9pNonlinear predictors for long delays: https://tinyurl.com/7wvce6vyStability of extremum seeking: https://tinyurl.com/mr5cvzd3Nash equilibrium seeking: https://tinyurl.com/yeywrysnInverse optimal safety filters: https://tinyurl.com/9dkrpvkkNeural operators for PDE control: https://tinyurl.com/5yynsp7vBode lecture: https://tinyurl.com/mp92cs9uCSM article: Support the showPodcast infoPodcast website: https://www.incontrolpodcast.com/Apple Podcasts: https://tinyurl.com/5n84j85jSpotify: https://tinyurl.com/4rwztj3cRSS: https://tinyurl.com/yc2fcv4yYoutube: https://tinyurl.com/bdbvhsj6Facebook: https://tinyurl.com/3z24yr43Twitter: https://twitter.com/IncontrolPInstagram: https://tinyurl.com/35cu4kr4Acknowledgments and sponsorsThis episode was supported by the National Centre of Competence in Research on «Dependable, ubiquitous automation» and the IFAC Activity fund. The podcast benefits from the help of an incredibly talented and passionate team. Special thanks to L. Seward, E. Cahard, F. Banis, F. Dörfler, J. Lygeros, ETH studio and mirrorlake . Music was composed by A New Element.

******Support the channel****** Patreon: https://www.patreon.com/thedissenter PayPal: paypal.me/thedissenter PayPal Subscription 1 Dollar: https://tinyurl.com/yb3acuuy PayPal Subscription 3 Dollars: https://tinyurl.com/ybn6bg9l PayPal Subscription 5 Dollars: https://tinyurl.com/ycmr9gpz PayPal Subscription 10 Dollars: https://tinyurl.com/y9r3fc9m PayPal Subscription 20 Dollars: https://tinyurl.com/y95uvkao   ******Follow me on****** Website: https://www.thedissenter.net/ The Dissenter Goodreads list: https://shorturl.at/7BMoB Facebook: https://www.facebook.com/thedissenteryt/ Twitter: https://x.com/TheDissenterYT   This show is sponsored by Enlites, Learning & Development done differently. Check the website here: http://enlites.com/   Dr. David Wolpert is a Professor at the Santa Fe Institute, external professor at the Complexity Science Hub in Vienna, adjunct professor at ASU, and research associate at the ICTP in Trieste. He has over 30,000 citations, with most of his papers in thermodynamics of computation, foundations of physics, dynamics of social organizations, machine learning, game theory, and distributed optimization / control.   In this episode, we focus on his paper, “The Past as a Stochastic Process”. We first talk about what a stochastic process is, how to study history, a stochastic process framework, and history itself as a stochastic process. We also discuss the jumps in the sociopolitical complexity of polities, narrative approaches in history, and predicting the future. -- A HUGE THANK YOU TO MY PATRONS/SUPPORTERS: PER HELGE LARSEN, JERRY MULLER, BERNARDO SEIXAS, ADAM KESSEL, MATTHEW WHITINGBIRD, ARNAUD WOLFF, TIM HOLLOSY, HENRIK AHLENIUS, FILIP FORS CONNOLLY, DAN DEMETRIOU, ROBERT WINDHAGER, RUI INACIO, ZOOP, MARCO NEVES, COLIN HOLBROOK, PHIL KAVANAGH, SAMUEL ANDREEFF, FRANCIS FORDE, TIAGO NUNES, FERGAL CUSSEN, HAL HERZOG, NUNO MACHADO, JONATHAN LEIBRANT, JOÃO LINHARES, STANTON T, SAMUEL CORREA, ERIK HAINES, MARK SMITH, JOÃO EIRA, TOM HUMMEL, SARDUS FRANCE, DAVID SLOAN WILSON, YACILA DEZA-ARAUJO, ROMAIN ROCH, DIEGO LONDOÑO CORREA, YANICK PUNTER, CHARLOTTE BLEASE, NICOLE BARBARO, ADAM HUNT, PAWEL OSTASZEWSKI, NELLEKE BAK, GUY MADISON, GARY G HELLMANN, SAIMA AFZAL, ADRIAN JAEGGI, PAULO TOLENTINO, JOÃO BARBOSA, JULIAN PRICE, EDWARD HALL, HEDIN BRØNNER, DOUGLAS FRY, FRANCA BORTOLOTTI, GABRIEL PONS CORTÈS, URSULA LITZCKE, SCOTT, ZACHARY FISH, TIM DUFFY, SUNNY SMITH, JON WISMAN, WILLIAM BUCKNER, PAUL-GEORGE ARNAUD, LUKE GLOWACKI, GEORGIOS THEOPHANOUS, CHRIS WILLIAMSON, PETER WOLOSZYN, DAVID WILLIAMS, DIOGO COSTA, ALEX CHAU, AMAURI MARTÍNEZ, CORALIE CHEVALLIER, BANGALORE ATHEISTS, LARRY D. LEE JR., OLD HERRINGBONE, MICHAEL BAILEY, DAN SPERBER, ROBERT GRESSIS, IGOR N, JEFF MCMAHAN, JAKE ZUEHL, BARNABAS RADICS, MARK CAMPBELL, TOMAS DAUBNER, LUKE NISSEN, KIMBERLY JOHNSON, JESSICA NOWICKI, LINDA BRANDIN, NIKLAS CARLSSON, GEORGE CHORIATIS, VALENTIN STEINMANN, PER KRAULIS, ALEXANDER HUBBARD, BR, MASOUD ALIMOHAMMADI, JONAS HERTNER, URSULA GOODENOUGH, DAVID PINSOF, SEAN NELSON, MIKE LAVIGNE, JOS KNECHT, ERIK ENGMAN, LUCY, MANVIR SINGH, PETRA WEIMANN, CAROLA FEEST, STARRY, MAURO JÚNIOR, 航 豊川, TONY BARRETT, BENJAMIN GELBART, NIKOLAI VISHNEVSKY, AND STEVEN GANGESTAD! A SPECIAL THANKS TO MY PRODUCERS, YZAR WEHBE, JIM FRANK, ŁUKASZ STAFINIAK, TOM VANEGDOM, BERNARD HUGUENEY, CURTIS DIXON, BENEDIKT MUELLER, THOMAS TRUMBLE, KATHRINE AND PATRICK TOBIN, JONCARLO MONTENEGRO, AL NICK ORTIZ, NICK GOLDEN, AND CHRISTINE GLASS! AND TO MY EXECUTIVE PRODUCERS, MATTHEW LAVENDER, SERGIU CODREANU, BOGDAN KANIVETS, ROSEY, AND GREGORY HASTINGS!

This week we talk about Luigi Mangione, VAW attacks, and mass shootings.We also discuss stochastic violence, terrorism, and Cybertrucks.Recommended Book: Some Desperate Glory by Emily TeshTranscriptThe terms “Lone Wolf,” “Lone Wolf Actor,” and “Lone Wolf Killer” are interchangeably used in many countries—though most commonly and prominently in the United States—to describe someone who commits a mass-killing or other mass-casualty event, but who is not part of an organization like a terrorist group or other criminal network like a gang.The term is hotly contested in the scholarly world, as it's applied loosely and inconsistently, and the definition varies somewhat by location, government, law enforcement entity investigating said killings, and the press reporting upon it. But in general, to be defined as a mass-casualty event or mass-killing, a collection of murders must occur in public—so it can't be a person killing their family at home, for instance—it must involve at least four victims—so someone killing or injuring three strangers in a public place will typically not be categorized in this way—and it must not occur as part of another crime, like a robbery gone wrong, or as part of a larger conflict between two rival gangs.Within this context of mass-killings and mass-casualty attacks, a lone wolf is someone who acts solo, the term originating with the concept of a wolf that has been separated from, or perhaps outcast from its pack.Someone who kills a bunch of people at the instruction of a terrorist organization like ISIS, then, would not be considered a lone wolf, even if they committed the act without any direct aid from that group; though this definition is wobbly even in that regard, as someone who takes inspiration from a group like ISIS, committing a mass-killing to support that group's cause, but not directly connected to the group, might be labeled a lone wolf, or not. And there's no hard-set rule as to which definition is correct.This was a somewhat common issue back in the late-20th century, when many so-called lone wolf terrorists were committing acts of violence in support of anarchist ends, but the anarchist groups from which they derived their inspiration, and in some cases with which they collaborated, were leaderless by nature—so it couldn't really be said that they were instructed to carry out these acts, they were just inspired by these fellow ideological travelers, and that made determining whether they acted on their own behest or not a tricky and perhaps impossible undertaking; a lot of it is semantics.Also confounding the simple categorization of such killers and attacks is the concept of stochastic terrorism, which is a type of violence that is almost always political or ideological in nature, as opposed to being revenge-driven or otherwise personal, and it's generally incited by someone with a public persona—a politician or other leader—who creates an environment in which violence is more likely to occur, that violence seemingly random, but on average directed in a specific direction.So a politician who says something like “Man, people from the opposing party really believe some horrible stuff, I wouldn't be surprised if something happened to them, considering how evil they are,” while at the same time stoking the flames of potential violence throughout the population by increasing animosity between political parties and maybe even religious groups, might be aiming to spark stochastic terror that would benefit them and their ambitions.By riling up their base in this way, by sowing the seeds for potential attacks against their perceived enemies, violence in their favor, aimed at those enemies, is more likely to happen, but in a way that's deniable for them—just a random act of ideological murder that they can denounce, despite arguably having asymmetrically instigated it.Is stochastic terror an example of planting seeds for violence that makes the resultant killings something more like directed attacks, and therefore not lone wolf in nature, then? Or are all lone wolves arguably inspired by something they've learned or experienced or been told, and thus arguably stochastic in nature—no direct guidance or instruction, but still inspired by someone or something, somewhere along the way?What I'd like to talk about today are three instances of recent supposedly lone wolf attacks, and why some experts are predicting we'll see more such attacks, especially but not exclusively in the US, in the coming years.—There were nearly 500 officially recognized mass-shootings in the US in 2024—and again, that means 4 or more people injured or killed in public, and not as part of another crime being committed.That's down from previous years, the preceding four of which have each had more than 600 mass shootings, and on average a little less than 10 people are killed in these shootings—though that figure is nudged upward by the largest of these mass killings, like one in Las Vegas in 2017 that saw 60 people killed and more than 800 wounded, many in the resulting stampede, by a 64-year-old seemingly lone wolf gunman who fired on an open-air music festival from the 32nd floor window of a nearby hotel.Gun homicides in the US are rampant beyond mass-killings: there were about 21,000 murders committed with guns in the country in 2021, alone—and notably, self-inflicted gun deaths, suicides using these weapons, eclipse that number, tallying more than 26,000 that same year.That means more than 50 people are killed by guns in the US every single day, and about 4 out of every 5 murders are committed using guns in the country; which makes sense, as guns are very effective at what they're meant to do, which is killing something, and there are a lot of guns in the US: about 120 of them per 100 people, as of 2018.And to be clear, that doesn't mean everyone owns a gun: that average is driven sky-high by the gun-enthusiasts who tend to buy a lot of the things, though gun ownership has continued to increase in scope in recent years, as political and economic uncertainty, especially in areas where perception of crime levels, if not always actual elevated crime levels, increases, tends to drive more widespread gun sales.Given all of that, it's maybe not a huge surprise that many apparent lone wolf attacks in the United States are committed using firearms; sometimes assault rifles, sometimes guns that have been augmented using bump-stocks or similar add-ons to make a normal gun into basically an assault rifle, and sometimes just using a pistol, which can be easily pocketed and carried around pretty much everywhere in this country.On December 4, 2024, the CEO of UnitedHealthcare, which is part of the largest health insurance company in the United States, UnitedHealth Group, Brian Thompson, was gunned down in front of the Midtown Manhattan Hilton Hotel.The alleged killer, who was later identified by law enforcement officials as Luigi Mangione, was captured on nearby CCTV cameras, was wearing a hoodie and an expensive backpack while shooting Thompson, and used a pistol with a suppressor—a silencer—to shoot him multiple times, the bullet casings left behind inscribed with the words Delay, Deny, and Depose; terms that have been associated with the US health insurance industry for legal tactics they lean on in order to pocket more money, allegedly at the expense of their customers who have their claims denied or long-term delayed, in some cases leaving them without the care they require, and in some cases leaving them in crippling debt following a necessary medical procedure that the insurance company says they won't pay for.The response to killings of any kind, even in a gun-happy country like the US, tends to be fairly grim and sad; the endless mutterings of “thoughts and prayers” by politicians and other public figures has become so common and toothless as to be near-satire at this point, but generally the tone is antagonistic toward whomever committed the killing, before then swinging toward calls for more security and policing if you're on the political right, and more gun regulation if you're on the political left. And that's generally where we leave things until the next headlines-capturing shooting; and we typically, unfortunately, don't have long to wait.Thompson's murder, though, was almost immediately met with celebration across the political spectrum; working class folks, Democrats and Republicans and everyone in between and on the furthest political extremes basically muttering about how it serves him right, before realizing everyone else was muttering the same thing, and that led to outright enthusiasm, especially online, and even calls for more of the same across the social media landscape—many normal people doing the politician and ideologue thing by basically posting their hopes that someone will knock off other CEOs as well, seemingly aiming to spark more stochastic violence in their favored direction.The wealthy and especially the CEO class were horrified at this response, perhaps understandably, and there was pushback from mainstream journalistic and political entities across the board, with lots of tut-tutting and finger-wagging at anyone who dared celebrate what looked to be the cold-blooded murder of another human being.But the nature of American healthcare and especially health insurance being what it is—massively imperfect at least, and by some assessments borderline abusive or even outright evil—this was seen by many as just desserts for someone who himself had committed millions of dollars worth of fraud and gotten away with it, and who was running UnitedHealthcare in such a way that it denies more claims than any of its peers, which in turn has allowed itself to massively enrich itself and its shareholders at the expense of its customers.There were many cries of “serves him right,” then, alongside some requests that other CEOs be next; many of these requests couched in memes and jokes, but also seemingly earnest.The nature of the alleged killer, who was eventually shown to be a good-looking young man of privilege who had maybe suffered under the auspices of the American healthcare system, due to chronic ailments and an insurance system that didn't even serve someone like him, who grew up with substantial advantages, further fanned those flames, and as of the day I'm recording this he's in custody, has pleaded not guilty, and is facing eleven state and four federal charges, including first-degree murder and a terrorism charge, the former of which could lead to the death penalty.Just shy of a month later, in the early morning hours of January 1, 2025, a new year's celebration on the well-know and well-traversed, and on that night, incredibly crowded Bourbon Street in New Orleans was attacked by a man in a large pickup truck, who plowed the vehicle into a crowd of revelers, driving at high-speed across three blocks that were partitioned-off for the celebration.The driver was apparently trying to hit as many people as possible, and then, after crashing into a utility vehicle, he stepped out of the truck and started firing a gun into the crowd.Police fired back at him, but he was wearing body armor, and two of them were injured before they managed to kill him, recovering an assault rifle and a semi-automatic pistol from his body. They also found a pair of explosive devices in coolers he had planted around the area before the attack, and further investigation led to the discovery of more bomb-making materials where he was staying in New Orleans.At least 35 people were injured and 14 people were killed in the attack, alongside the killer, who was later identified as 42-year-old Shamsud-Din Jabbar: an American-born Army veteran and Texas resident who had apparently been recently radicalized, possibly by online content posted by ISIS, and who had posted videos pledging his allegiance to the group mere hours before he drove into the crowd, an ISIS flag adorning the vehicle.More guns in this attack, then, but much of the damage was caused by the truck, and similar so-called “vehicle as a weapon,” or VAW attacks have been committed around the world in recent years, raising concerns especially in places where firearms are harder to come by, though also at large, open-air events where vehicles might cause more deaths and injuries in a short period of time than even an assault rifle, as seemed to be the case here.This attacker seemed to be self-radicalized, based on testimony from his friends and family, who were shocked at the change in his personality and expressed beliefs. The FBI has said they're pretty confident he acted alone, though they're looking into recent trips he took to Egypt and Canada, in case he met up with someone from ISIS or a similar group, while traveling.And apparently while he initially planned to kill his family—he's had several divorces that led to financial problems, due to many child support payments that exceeded his means—he didn't believe killing his family would have provoked enough of a response to spark a “war between the believers and the disbelievers.” Jabbar was brought up Muslim but left the faith for years, before apparently adopting a more intense and violent reinterpretation of it just recently, and that seemingly helped him justify and perhaps even inspired these acts.This has been called a lone wolf attack, then, but it was apparently heavily influenced by ISIS ideology, despite Jabbar possibly never having been in contact with anyone from that group.Just a handful of hours later, that same morning, at 8:49 January 1, 2025, a Tesla Cybertruck that was parked outside the front lobby of the Trump International Hotel in Las Vegas exploded—its occupant apparently having died from a self-inflicted gunshot wound to the head right before a bunch of fireworks and gas canisters placed in the trunk were detonated.That occupant was Matthew Alan Livelsberger, who was also American-born, and like Jabbar, had been in the US military, though Livelsberger was an active-duty Special Forces soldier from Colorado who was on leave at the time.The blast didn't kill anyone, and while it hurt a few bystanders, no one was seriously injured. But the intention, according to two letters recovered from his phone by the FBI, was apparently to make a political statement related to alleged clandestine US military operations, and advanced technologies the US and China allegedly secretly possess; though he was also apparently in the midst of a serious mental health crisis, including significant PTSD episodes and what might have been paranoid delusions.The vehicle also contained an assault rifle and two pistols, though none of these weapons were used, as while Livelsberger was seemingly intent on escaping across the Mexican border following the attack, based on what he said in those aforementioned letters, he seemingly decided to kill himself instead—which may support the assertion that this was primarily, if not exclusively, a mental health crisis issue.Livelsberger also apparently had family issues, due in large part to his support of president-elect Trump and his family's opposition to that support, and he was apparently suffering from untreated depression, that lack of treatment possibly the result of stigma toward such things within the military, which sometimes results in people not getting treatment that they might benefit from, because they worry doing so will see them sidelined by their superiors.A manifesto penned by Livelsberger that was sent to a retired Army Intelligence officer claims that he was being monitored by the military because of his knowledge of war crimes and those aforementioned military advanced technologies, and that he didn't intend to self-harm, the divulgence of which has led to some conspiracy theories about this not having been a suicide.That said, this attack is being investigated as potential terrorism, and while it was initially being explored as part of a larger wave of such actions, since that attack in New Orleans happened just hours earlier, and both attackers used the same online car rental service to procure the vehicles they were driving, investigators have since indicated they don't believe these attacks were connected.Interestingly, Livelsberger's letters also criticized income inequality, though with a politically conservative bent, basically saying that the country had become too liberal and effeminate, and that Trump, Elon Musk, and Robert Kennedy Jr needed to take control and make the US more masculine so that it could compete against entities like China, Russia, and Iran.Experts on ideological violence and political fracturing have warned that we may see more lone wolf and lone wolf-esque violence in a more polarized society, in which people are less likely to consider those on the opposite side of the aisle to be people they disagree with, and more likely to think of them as bad or evil or even subhuman, which makes violence more thinkable.That's not ideal, as these sorts of attacks are difficult to prevent, their solo nature meaning there's no network to track and pluck apart, nothing to infiltrate and fewer easily accessible data points to aggregate and in which to recognize a pattern. Lone wolf attackers tend to cause less damage than groups can, then, but they're often almost invisible, to the organizations that hope to stop them, anyway, right up till the moment they start killing and injury people.We're also entering an era in which trust in authority has degraded substantially, new technologies have made the research, hardware procurement, and implementation of such attacks a lot more attainable to more people, which means folks suffering from different sorts of psychological or physical torments, or those who simply have strong opinions and a lot of perceived enemies, are more likely to be able to act on that confusion or those hatreds, in some cases at a moment's notice, and in many cases without anyone beyond their immediate friends and family recognizing that something might be up.We may be entering a period of heightened threat, then, in the US especially, because of the number and wide distribution of highly effective weapons throughout the population, and because of the period of political polarization and animosity we seem to be wading through, but also throughout the rest of the world, to some degree at least, because of those same political and ideological factors, and because of how big and weapon-like vehicles have become, and how relatively easy it is to get one's hands on information that allows for the construction of things like bombs and the technologies required to 3D-print and otherwise manufacture deadly implements of all shapes and sizes.Show Noteshttps://en.wikipedia.org/wiki/Luigi_Mangionehttps://www.vox.com/politics/390438/luigi-mangione-healthcare-shooting-ghost-gunhttps://en.wikipedia.org/wiki/Killing_of_Brian_Thompsonhttps://en.wikipedia.org/wiki/2017_Las_Vegas_shootinghttps://www.bbc.com/news/world-us-canada-41488081https://en.wikipedia.org/wiki/2025_New_Orleans_truck_attackhttps://www.bbc.com/news/articles/c205ek63433ohttps://www.nytimes.com/2025/01/03/us/new-orleans-victims-truck-attack.htmlhttps://en.wikipedia.org/wiki/Trump_International_Hotel_Las_Vegas_Tesla_Cybertruck_explosionhttps://www.nytimes.com/2025/01/04/us/matthew-livelsberger-las-vegas-cybertruck.htmlhttps://www.nytimes.com/2025/01/05/us/new-orleans-attack-travel.htmlhttps://www.nytimes.com/2025/01/04/us/new-orleans-attack-shamsud-din-jabbar-isis.htmlhttps://scholarlycommons.law.northwestern.edu/cgi/viewcontent.cgi?article=1257&context=nulr_online&preview_mode=1&z=1519320539https://en.wikipedia.org/wiki/Stochastic_terrorismhttps://en.wikipedia.org/wiki/Lone_wolf_attackhttps://www.ojp.gov/ncjrs/virtual-library/abstracts/lone-wolf-terrorism-americahttps://journals.sagepub.com/doi/10.1177/1088767917736797 This is a public episode. 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We talk about the Cybertruck psyop with Insurgent Notes, Race Traitor, and Hard Crackers vets John Garvey and Zhana Kurti.In the fun half, available to subscribers at http://patreon.com/theantifada, we read the prophecies of alien intervention and free energy by the Balkan Nostradamus Baba Vanga, and hear Sean's interpretation of Charli XCX's Brat.Song: Kendrick Lamar - wacced out murals