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CRDS 1005 · Strategic Reasoning and Decision Under Uncertainty

Led by Nash Simulacrum

5 modules 5 modules · ~30 hours Interdisciplinary School Updated 2 days ago

Strategic reasoning from decision theory and game theory through bargaining, mechanism design, and practical decision-making under uncertainty.

Decision Theory: Exp…1Game Theory Foundati…2Bargaining, Cooperat…3Mechanism Design: De…4Decision-Making in P…5
  1. Module 1

    Decision Theory: Expected Value, Utility, and the Allais Paradox

    Led by Nash Simulacrum

    The question

    A simple decision under uncertainty: you can take £100 for certain, or flip a coin for £250. The expected value of the coin flip is £125 (0.5 × £250 = £125), which is higher than £100. Expected value says: flip the coin. But most people take the £100 — they are risk-averse. Expected utility theory accommodates this by recognising that the value of money is not linear: the difference between £0 and £100 feels larger than the difference between £100 and £200.

    Outcome

    The student can calculate expected value and explain expected utility theory, describe the Allais Paradox and why it violates EU theory, describe prospect theory's three key features (reference dependence, loss aversion, probability weighting), and explain framing effects. (Decision theory)

    Sub-units

    1. 1.1 Expected Value: The Probability-Weighted Average
    2. 1.2 Expected Utility: When Money Is Not Linear
    3. 1.3 The Allais Paradox: When Certainty Trumps Probability
    4. 1.4 Prospect Theory: How People Actually Decide
    5. 1.5 Framing Effects: How the Question Changes the Answer
  2. Module 2

    Game Theory Foundations: Nash Equilibrium and the Prisoner's Dilemma

    Led by Nash Simulacrum

    The question

    In a decision problem, you choose against nature — the world is indifferent to your choice. In a game, you choose against another mind — and that mind is choosing against you. The game is the interaction of strategic agents: your optimal choice depends on what the other player does, and their optimal choice depends on what you do. The Nash equilibrium is the point where neither player can improve their outcome by unilaterally changing their strategy — the gravitational centre of strategic interaction.

    Outcome

    The student can represent a game in normal form, identify dominant and dominated strategies, find Nash equilibria in 2×2 games, describe the Prisoner's Dilemma and its paradox, and explain mixed-strategy equilibria. (Game theory foundations)

    Sub-units

    1. 2.1 The Normal Form: Players, Strategies, Payoffs
    2. 2.2 Dominant and Dominated Strategies
    3. 2.3 The Nash Equilibrium: Nobody Wants to Move
    4. 2.4 The Prisoner's Dilemma: When Individual Rationality Fails the Group
    5. 2.5 Mixed Strategies: When Predictability Is a Weakness
  3. Module 3

    Bargaining, Cooperation, and the Evolution of Trust

    Led by Nash Simulacrum

    The question

    The Prisoner's Dilemma seems to prove that cooperation is irrational. But cooperation is everywhere — in business, in nature, in everyday life. How? The answer is repetition. In a one-shot Prisoner's Dilemma, defection is rational. In a repeated game — where you interact with the same person again and again — cooperation can emerge, sustained by the threat of retaliation and the promise of future gains. This module explores how cooperation arises in a world of self-interested agents.

    Outcome

    The student can describe why cooperation emerges in repeated games, describe tit-for-tat's four properties, explain the shadow of the future, describe the Nash bargaining solution, and explain two mechanisms for the evolution of trust (assortment, indirect reciprocity). (Cooperation and trust)

    Sub-units

    1. 3.1 The Repeated Game: Why Repetition Changes Everything
    2. 3.2 Tit-for-Tat: The Champion Strategy
    3. 3.3 The Shadow of the Future: When the End Is Near
    4. 3.4 Nash Bargaining: Dividing the Surplus
    5. 3.5 The Evolution of Trust: Cooperation Without a Contract
  4. Module 4

    Mechanism Design: Designing Rules That Produce Good Outcomes

    Led by Nash Simulacrum

    The question

    Game theory asks: given the rules, what will rational agents do? Mechanism design asks the inverse: given the outcome we want, what rules should we create? This is the engineering side of game theory — designing institutions, incentives, and procedures so that self-interested agents, each pursuing their own goals, collectively produce a desirable outcome. Auctions, voting systems, matching markets, and regulatory structures are all mechanism design problems.

    Outcome

    The student can explain the revelation principle, describe the four auction types and revenue equivalence, explain why the Vickrey auction incentivises truth-telling, describe the Gale-Shapley algorithm, and explain the Shapley value. (Mechanism design)

    Sub-units

    1. 4.1 The Revelation Principle: Simplifying the Design Problem
    2. 4.2 Auction Design: Four Types and Revenue Equivalence
    3. 4.3 The Vickrey Auction: Truth-Telling as the Dominant Strategy
    4. 4.4 Matching Markets: The Gale-Shapley Algorithm
    5. 4.5 The Shapley Value: Fair Division of Cooperative Gains
  5. Module 5

    Decision-Making in Practice: Uncertainty, Irreversibility, and Option Value

    Led by Nash Simulacrum

    The question

    Theory is elegant; practice is messy. Real decisions involve uncertainty that cannot be quantified (Knightian uncertainty — you do not know the probabilities), irreversibility (some decisions cannot be undone), and option value (the value of keeping your options open). This module connects the theoretical frameworks of the previous four modules to practical decision-making — in careers, investments, policy, and everyday life.

    Outcome

    The student can distinguish risk from Knightian uncertainty, explain the precautionary principle, describe irreversibility and option value, explain scenario planning, and integrate all five CRDS modules into a complete decision analysis framework. (Decision-making in practice)

    Sub-units

    1. 5.1 Knightian Uncertainty: When You Cannot Calculate the Odds
    2. 5.2 The Precautionary Principle: Caution Under Deep Uncertainty
    3. 5.3 Irreversibility and Option Value: The Value of Waiting
    4. 5.4 Scenario Planning: Robust Decisions Under Uncertainty
    5. 5.5 Integrating the Framework: A Complete Decision Analysis