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Learning Objectives

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Learning Objectives

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Now · 1. Expected Value & Decision Trees

Learning Objectives

Apply expected value and decision trees to risky decisions
Distinguish risk preferences (risk-averse, risk-neutral, risk-seeking)
Understand asymmetric information: adverse selection and moral hazard
Week 6: Game theory basics
Probability fundamentals

1. Expected Value & Decision Trees

Expected Value: EV = sum(probability x payoff) for each outcome. Decision Tree: Visual tool for sequential decisions under uncertainty.

2. Risk Preferences

Risk-Averse: Prefer certain outcome over risky with same expected value (diminishing marginal utility of wealth). Risk-Neutral: Indifferent between certain and risky with same EV. Risk-Seeking: Prefer risky over certain with same EV.

3. Asymmetric Information

Adverse Selection: Hidden information before transaction (buying a used car - lemon problem). Moral Hazard: Hidden action after transaction (insured driver drives recklessly). Solutions: Screening (by uninformed party), Signaling (by informed party), Warranties, Deductibles.

Concept	Formula/Description
Expected Value	EV = sum(Pi x Xi)
Risk Premium	Certainty equivalent - EV
Insurance	Pooling risk across individuals

Q1: A project has 60% chance of $100k profit, 40% chance of$ 50k loss. EV?

EV = 0.6 x 100 + 0.4 x (-50) = 60 - 20 = $40k Q2: What is adverse selection in insurance markets?

High-risk individuals are more likely to buy insurance, raising premiums and driving out low-risk individuals. Q3: How does a deductible reduce moral hazard?

By making the insured bear some cost, they have incentive to avoid risky behavior. Join Discord PreviousGame Theory & Strategic DecisionsNextFactor Markets & Labor Economics