Computational Models of Thought and Behavior
Fall 2020
Course site for MGT 451A, with a list of class meetings and assignments.
In this course, we review computational models of thought and behavior from across the behavioral and social sciences, including economics, psychology, evolutionary biology, network science, information systems, and sociology. The course begins by studying individual thought and behavior, from predicting the future to taking risks. The course proceeds to study group thought and behavior, from social influence to the design of voting mechanisms. Each class covers a new topic in the study of thought and behavior and introduces the range of empirical and theoretical computational approaches to studying that topic.
Class meetings
A whirlwind tour of thought and behavior, together and apart.
Monday, 8/26
Topics:
- Overview of class
- Small-group discussion to spark interest
- Review this syllabus
Readings:
- This syllabus
- “The Standard Equipment” by Steve Pinker
- NCM Chapter 1
Part I: Computational models of individual thought and behavior.
On predicting the future.
Day, Date
Topics:
- The Anthropic Principle, in
- Basics of Bayesian inference
- “Optimal predictions in everyday life” by Griffiths & Tenenbaum.
- The end-of-history illusion
- The recency illusion
- Retrofuturism
Readings:
- The Anthropic Principle by J. Richad Gott
- Retrofuturism subreddit (https://www.reddit.com/r/RetroFuturism/top/?t=all)
Assignment #1: In this assignment, you will replicate the experiment detailed in the “Optimal predictions…” reading.
On reasoning about what might have been.
Day, Date
Topics:
- Causal graphical models
- Measures of simplicity in causal explanation
- The survivorship bias
On seeking and avoiding risk.
Day, Date
Topics:
- Kahneman and Tversky and the history of behavioral economics
- Risk aversion
- The certainty effect
- Expected Value Theory
- Expected Utility Theory
Assignment #2: In this assignment, you will implementing Expected Value Theory and Expected Utility Theory using the Python language and demonstrate a choice anomaly.
On gaining and losing.
Day, Date
Topics:
- Prospect theory
- “When less is more” and counterfactual reasoning
- Anchoring and adjustment
On choosing between now and later.
Day, Date
Topics:
- Intertemporal choice
- Hyperbolic vs. exponential discounting
- Cooperating with the future
Assignment #3: In this assignment, you will measuring your own temporal discounting function and determine whether it is better described as a hyperbolic or exponential function.
On being rational.
Day, Date
Topics:
- Marr’s three levels and definitions of rationality
- The Allais paradox
- Ellsberg paradox
- The Law of Small Numbers
- Gambler’s Fallacy
On exploring and exploiting.
Day, Date
Topics:
- Multiarmed bandit problems
- The Gittins index
- The Upper Confidence Bound algorithm
- Epsilon-greedy strategies for solving multiarmed bandit problems
- Thompson sampling
- From multiarmed bandits to contextual bandits
On deciding under incomplete information.
Day, Date
Topics:
- Markov Processes
- Markov Decision Processes
- Partially Observable Markov Decision Processes
Assignment #4: In this assignment, you will implement a Partially Observable Markov Decision process using the Python programming language.
On thinking, fast and slow.
Day, Date
Topics:
- Drift diffusion models of choice
- System I and System II thinking
- The idea of time as a resource
On summarizing one’s experience.
Day, Date
Topics:
- Peak-end rule
- Extension neglect
- Duration neglect
- Representativeness heuristic
On having limits.
Day, Date
Topics:
- Bounded rationality
- The concept of attentional limits
- Availability heuristics
- Base-rate neglect
- Conjunction fallacy
- Resource rationality
Assignment #5: In this assignment, you will test some of your own perceptual and attentional limits and consider various ways to quantify them.
On knowing thyself.
Day, Date
Topics:
- Metacognition
- Dunning-Kruger effect
- Curse of knowledge
On having preferences.
Day, Date
Topics:
- Preferences
- Implicit Associations
- Multi-objective scalarization
- Multi-objective optimization
Assignment #6: In this assignment, you will take an online version of the Implicit Association Test.
On wishing it were so.
Day, Date
Topics:
- Motivated reasoning
- Survivorship bias
- Selection biases
“But I like it / Because it is bitter / And because it is my heart.”
Day, Date
Topics:
- The Endowment Effect
- The Ikea Effect
- Not Invented Here disorder
- The Illusion of Control
On the troubled history of intelligence.
Day, Date
Topics:
- What is intelligence?
- The troubled history of studying intelligence
- The foundations of eugenics
- Measuring and modeling “general” intelligence (G)
- The concept of multiple intelligences
- The Intelligence Quotient (IQ)
Midterm exam.
Part II: Computational models of collective thought and behavior.
On evolving over time.
Day, Date
Topics:
- Is evolution actually survival of the fittest?
- The Iterated Prisoner’s Dilemma (IPD)
- IPD on structured networks
Readings:
- NCM Chapter 6
Assignment #7: In this assignment, you will write an evolutionary IPD tournament in Python.
On the wisdom of the crowd.
Day, Date
Topics:
- Francis Galton and the voice of the people
- The Delphi Method
- The Bayesian Truth Serum
Readings:
- Vox Populi
- Bayesian Truth Serum paper
Assignment #8: In this assignment, you will replicating the wisdom of the crowds effect from the Galton reading.
On designing good mechanisms.
Day, Date
Topics:
- Algorithmic Game Theory
- Game theory vs. mechanism design
- Algorithmic mechanism design
- The Price of Anarchy
- Mechanisms for Voting
- Mechanisms for Auctions
On collective intelligence.
Day, Date
Topics:
- Schooling in fish
- Social learning
- Collective intelligence
- Crowdsourced markets
On recursive social reasoning.
Day, Date
Topics:
- Theory of mind
- Schelling coordination games
- Keynesian beauty contests
- El Farol Bar Problem
- The minority game
- Limits to recursive reasoning
On social influence.
Day, Date
Topics:
- Conformity bias
- Prestige bias
- Herding in crowds
- Information cascades
- Ingroup bias
- Fundamental attribution error
On being fair.
Day, Date
Topics:
- Algorithmic fairness
- Cake cutting
- Fair division
Assignment #9: In this assignment, you will studying the fairness of an AI system.
On being altruistic.
Day, Date
Topics:
- The Dictator Game
- The Ultimatum Game
On remembering together.
Day, Date
Topics:
- Collective memory
- Transactive memory
- Interactive cueing
- Forgetting on networks
Assignment #10: In this assignment, you will replicate the effect of interactive cueing with a friend.