Microeconomics Protective Belt and Hawthorne Effect Quiz

Prospect Theory

• The reflection effect is explained by Prospect Theory (PT) due to diminishing sensitivity during the editing phase.
• The full version of PT is unable to account for Event-Splitting Effects (ESEs) because the two events with a common payoff would be combined under the Combination operation during the editing phase.

Stripped-Down Prospect Theory

• Dropping the assumption of Combination in PT explains the reflection effect.
• Moving more probability onto a better outcome has two effects on expected utility: a direct effect (always positive) and an indirect effect (can be positive or negative).

Risk Attitudes

• A 'fair' prospect has an expected value of zero.
• A risk-averse person will always refuse a fair prospect, a risk-neutral person is indifferent, and a risk-loving person will always accept a fair prospect.

Stochastic Dominance

• The stochastic dominance axiom states that if A stochastically dominates B, then A is always preferred to B.

Event-Splitting Effect (ESE)

• EUT treats two lottery pairs as identical, but PT can explain the ESE.

The Protective Belt

• Microeconomists impose additional assumptions on the utility function, leading to testable predictions, but protecting the 'hard core' from scrutiny.

Disadvantages of Experiments

• The Hawthorne effect is a form of reactivity where subjects modify their behavior in response to being studied.

Editing Phase in Prospect Theory

• Coding: outcomes are perceived as gains and losses relative to a reference level (usually the current asset position).
• Combination: prospects are simplified by combining the probabilities associated with identical outcomes.
• Segregation: some prospects contain a riskless component that is segregated from the risky component.
• Cancellation: discarding components shared by offered prospects.

Value Function

• Loss aversion: losses loom larger than gains, and the value function is steeper for losses.
• Diminishing sensitivity: marginal utility falls as we move further away from the reference point.
• Reference point: V(0) = 0, and outcomes are judged in terms of gains and losses.

Weighting Function

• w(0) = 0 and w(1) = 1.
• The weighting function is increasing in p.
• It is not defined close to zero and one.
• Overweighting occurs for small probabilities.
• Subcertainty: decision weights sum to less than one.
• Subproportionality: log(w(p)) is a convex function of log(p).