ECON7870 Chap.9 Prospect Theory and Reference Dependence
Prospect Theory and Reference Dependence
Code outcomes relative to a benchmark
Prospect theory evaluates gains and losses from a reference point rather than final wealth alone. Its value function is concave for gains, convex for losses and steeper below the reference.
Diminishing sensitivity and loss aversion explain different features of the response.
Transform influence, not belief
Decision weights give small probabilities disproportionate influence and reduce the impact of many moderate probabilities.
The reference point must be justified from expectations, ownership, targets or framing; choosing it after observing behaviour makes the account too flexible.
Separate the mechanisms
Reference dependence decides whether the outcome is a gain or loss. Diminishing sensitivity explains why an equal change matters less farther from zero. Loss aversion compares equal movements across the two sides.
Probability weighting changes how likely outcomes influence choice. Naming the mechanism prevents loss aversion from becoming a label for every behavioural pattern.
Test the frame against final outcomes
Rewrite each option in final wealth. If two frames generate the same distribution but different choices, framing is a plausible explanation.
Then justify the reference from a target, entitlement, current holding or credible expectation. Evidence that people treat the stated benchmark as provisional rather than owned should weaken the loss account.
Make the explanation predict
State what should happen if the reference point moves while final outcomes remain fixed. Compare that prediction with expected utility, which evaluates final wealth.
A behavioural account that can move its reference after every observation is not informative. The useful version identifies the context that fixes the benchmark before the decision is observed.
Design a discriminating test
Hold final outcomes and probabilities constant while changing a credible reference, such as ownership, an announced target or a prior expectation.
Reference dependence predicts that the same outcome can switch between gain and loss coding. Expected utility based only on final wealth does not. Then vary probability without moving the benchmark to isolate decision weighting. A useful behavioural explanation states these changes before seeing the choice.
If the reference is selected afterward to fit each observation, the account loses predictive force and cannot distinguish loss aversion from ordinary differences in wealth or beliefs.
What this chapter covers
- 01
Reference dependence
- 02
Gain and loss coding
- 03
Diminishing sensitivity
- 04
Loss aversion
- 05
Probability weighting
- 06
Certainty effect
- 07
Managerial framing
Worked example · free
Diagnose a bonus frame
- 2State the candidate reference point for each employee.
- 2Code the final amount as a gain or loss.
- 2Name the evidence needed to justify the reference.
Key terms
- Reference Dependence
- Valuation of outcomes as gains or losses relative to a benchmark rather than as final wealth alone.
- Diminishing Sensitivity
- The property that an equal change has less value impact farther from the reference point.
- Loss Aversion
- The steeper value response to losses than to equal-sized gains near the reference point.
- Decision Weight
- The transformed influence a probability receives in prospect valuation, distinct from a stated belief.
- Certainty Effect
- The disproportionate change in decision weight when an outcome moves to or from certainty.
Prospect Theory and Reference Dependence FAQ
How is loss aversion different from risk aversion?
Risk aversion under expected utility comes from concavity over final wealth. Loss aversion compares equal movements on opposite sides of a reference point and makes the loss more influential. A risky choice can display one mechanism without establishing the other.
Why can people buy both lotteries and insurance?
Small probabilities are overweighted. The tiny chance of a large gain can make a lottery attractive, while the tiny chance of a large loss can make insurance attractive. Outcome sign changes the value-function region even as probability weighting amplifies both rare events.
How do I choose the reference point?
Use contextual evidence such as current ownership, an accepted target, a credible expectation, a promised amount or explicit framing. Prospect theory does not mechanically select the benchmark. An answer should explain what observation would weaken its proposed reference rather than fitting it after the fact.
Exam move
Rewrite each choice in final outcomes and then relative outcomes. If final distributions match but choices differ, identify the candidate frame. Keep a table separating reference dependence, curvature and probability weighting so loss aversion does not become a catch-all explanation.
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