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Decision And Rational Choice·Advanced·5 lessons·~300 min
How rational agents choose when outcomes depend on chance
What you'll learn
Lessons
Introduces the central idea of decision theory: that the quality of a decision is determined by the reasoning used to make it, not by the outcome that happens to occur. Establishes the core vocabulary of options, states, consequences, and preferences.
Teaches the mechanics of computing expected value across outcomes, introduces utility functions that capture risk aversion through diminishing marginal utility, and walks through the St Petersburg paradox as motivation for why utility must sometimes replace money in the calculation.
Teaches students to build decision matrices that lay out options against states of the world, identify dominant and dominated strategies, and apply expected-value reasoning where probabilities are known while recognizing where genuine uncertainty calls for different tools.
Introduces the most common decision errors — sunk-cost thinking, opportunity-cost neglect, loss aversion, framing effects, and anchoring — and positions prospect theory as the descriptive counterpart to normative expected-utility theory. Students learn to detect these errors in their own and others' reasoning.
An integrative lesson that applies the full toolkit of decision theory — expected utility, dominance, opportunity cost, framing correction, and descriptive bias awareness — to complex real-world decisions in career choice, investment, public policy, and ethical tradeoffs. Cases are designed to require multiple concepts working together.
How to study
Each lesson opens with a guided walkthrough — read it before the activity.
Look at why each step follows, not just what the answer is.
Know which rule applies and what would make the response weak before you start.
Optional context for the unit. Each lesson surfaces the concepts and rules it uses — these are here when you want the bigger picture.
The probability-weighted average of an action's possible outcomes, computed as EV = sum over outcomes of probability times payoff.
A numerical measure of how much an outcome is worth to a particular agent, calibrated so that higher numbers always correspond to more preferred outcomes.
Risk refers to situations where the probabilities of outcomes are known or estimable; uncertainty refers to situations where those probabilities themselves are unknown or contested.
Option A dominates option B when A yields an outcome at least as good as B in every possible state of the world, and strictly better in at least one state.
A ranking of alternatives that a decision maker holds, ideally satisfying completeness (every pair is comparable) and transitivity (if A is preferred to B and B to C, then A is preferred to C).
The value of the best alternative you give up when you choose one option over another.
A cost that has already been incurred and cannot be recovered regardless of future action.
Evaluating decisions by asking about the incremental costs and benefits of small changes rather than about total averages.
The property that successive units of a good produce smaller and smaller increases in utility; the tenth dollar gained matters less to you than the first.