More than 200 years ago, Thomas Bayes came up with a brilliant idea that has helped shape the world today, called Bayes Theorem. This…
Prior Modeling
In Bayesian inference the prior model provides a valuable opportunity to incorporate domain expertise into our inferences. Unfortunately this opportunity often becomes a contentious issue in many fields, and this potential value is lost in the debate. In this case study I will discuss the challenges of building prior models that capture meaningful domain expertise and some practical strategies for ameliorating those challenges as much as possible.
Bayesian Inference of Social Norms as Shared Constraints on Behavior
People act upon their desires, but often, also act in adherence to implicit social norms. How do people infer these unstated social norms from others' behavior, especially in novel social contexts? We propose that laypeople have intuitive theories of social norms as behavioral constraints shared across different agents in the same social context. We formalize inference of norms using a Bayesian Theory of Mind approach, and show that this computational approach provides excellent predictions of how people infer norms in two scenarios. Our results suggest that people separate the influence of norms and individual desires on others' actions, and have implications for modelling generalizations of hidden causes of behavior.
Causality
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.
Useful Mental Model: Normal Distribution - Scott H Young
Some ideas are so powerful once you know them, you start to see them everywhere. Normal distributions are one of these ideas. Let's understand it!
The Periodic Table of Cognition
I’ve been studying the early history of electricity’s discovery as a map for our current discovery of artificial intelligence. The smartest people alive back then, including Isaac Newton, who may have been the smartest person who ever lived, had confident … Continue reading →
The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning
Abstract Determining the knowledge that guides human judgments is fundamental to understanding how people reason, make decisions, and form predictions. We use an experimental procedure called ‘‘iterated learning,’’ in which the responses that people give on one trial are used to generate the data they see on the next, to pinpoint the knowledge that informs people's predictions about everyday events (e.g., predicting the total box office gross of a movie from its current take). In particular, we use this method to discriminate between two models of human judgments: a simple Bayesian model ( Griffiths & Tenenbaum, 2006 ) and a recently proposed alternative model that assumes people store only a few instances of each type of event in memory (Min K ; Mozer, Pashler, & Homaei, 2008 ). Although testing these models using standard experimental procedures is difficult due to differences in the number of free parameters and the need to make assumptions about the knowledge of individual learners, we show that the two models make very different predictions about the outcome of iterated learning. The results of an experiment using this methodology provide a rich picture of how much people know about the distributions of everyday quantities, and they are inconsistent with the predictions of the Min K model. The results suggest that accurate predictions about everyday events reflect relatively sophisticated knowledge on the part of individuals.
The Past and Present of Computer-Augmented Hypothesis Generation
Don Swanson’s career started on a path familiar to many who read this blog.
The Buffett Formula: Going to Bed Smarter Than When You Woke Up
Learn how Warren Buffett and Charlie Munger turn curiosity into insight. Discover how interest-driven learning builds understanding and reveals hidden opportunities.
Unifying Principles of Generalization: Past, Present, and Future
Generalization, defined as applying limited experiences to novel situations, represents a cornerstone of human intelligence. Our review traces the evolution and continuity of psychological theories of generalization, from its origins in concept learning (categorizing stimuli) and function learning (learning continuous input-output relationships) to domains such as reinforcement learning and latent structure learning. Historically, there have been fierce debates between approaches based on rule-based mechanisms, which rely on explicit hypotheses about environmental structure, and approaches based on similarity-based mechanisms, which leverage comparisons to prior instances. Each approach has unique advantages: Rules support rapid knowledge transfer, while similarity is computationally simple and flexible. Today, these debates have culminated in the development of hybrid models grounded in Bayesian principles, effectively marrying the precision of rules with the flexibility of similarity. The ongoing success of hybrid models not only bridges past dichotomies but also underscores the importance of integrating both rules and similarity for a comprehensive understanding of human generalization.
AI That Evolves in the Wild | Edge.org
I’m interested not in domesticated AI—the stuff that people are trying to sell. I'm interested in wild AI—AI that evolves in the wild. I’m a naturalist, so that’s the interesting thing to me. Thirty-four years ago there was a meeting just like this in which Stanislaw Ulam said to everybody in the room—they’re all mathematicians—"What makes you so sure that mathematical logic corresponds to the way we think?" It’s a higher-level symptom. It’s not how the brain works. All those guys knew fully well that the brain was not fundamentally logical.
Terence Tao – Kepler, Newton, and the true nature of mathematical discovery
“And what those stories teach us about how AI will revolutionize math”
The Irrational Decision
How the computer revolution shaped our conception of rationality—and why human problems require solutions rooted in human intuition, morality, and judgment
A philosopher’s 350-year-old trick to get people to change their minds is now backed up by psychologists
The 17th century philosopher Blaise Pascal is perhaps best known for Pascal’s Wager which, in the first formal use of decision theory, argued that believing in God is the most pragmatic decision. But it seems the French thinker also had a knack for psychology. As Brain Pickings points out, Pascal set out the most effective way to get someone to change their mind, centuries before experimental psychologists began to formally study persuasion:
A Bayesian Truth Serum for Subjective Data
Subjective judgments, an essential information source for science and policy, are problematic because there are no public criteria for assessing judgmental truthfulness. I present a scoring method for eliciting truthful subjective data in situations ...
Mental Models: The Best Way to Make Intelligent Decisions (~100 Models Explained)
The smartest people in the world use mental models to make intelligent decisions, avoid stupidity, and increase productivity. Let's take a look at how ...
