Lec6 - Evidence on dynamic choice

Ways to test if people plan & how it relates to DC

Ask for plan and force them to follow it

• incentivised but doesnt test dynamic consistency as plan turned into pre-commitment decision

Ask for plan and allow departure from it for a cost

• incentive to stick to plan distorts test of Dynamic Consistency

Randomly select whether initial plan or final decision implemented for payoffs

• Changes decision problem by adding a new chance node / randomisation

ALL FLAWED

Bone et al. (2009) vs Cubitt et al. (1998)

Bone

• Test if people plan at all - Not what the specific plan is

• Set up a decision problem with certain behaviours that no sensible plan could rationalise.

  • Then if we see those behaviours, we'll know there was no plan.

Cubitt

• Reconceptualise theoretical framework so DC not a ‘within’ problem but now an ‘across’ problem

• Across problem restriction used instead (Timing Independence used instead of DC)

Timing Independence vs Dynamic Consistency

DC - Requires final plan / action to match initial plan

TI - Requires final action to match what subject would do in a different problem where they precommit before 1st chance node to both possibilities

Within-subject design flaws

Demand effect - Can lead to subject figuring out what relationship is and then doing results that obey that consistency

Incentive structure design flaws

Income effect - Paying out after every task can lead to income effects as subjects richer than at start (need randomisation of payoff round)

Randomisation of payoff - Subject could see whole experiment as 1 big dynamic choice problem with a chance node at the end (that determines payoff)

• Not Testing same dynamic choice principles that want to test

Decision tree design flaws

Can’t be sure all subjects understand decision-trees

• have to test frame independence as its not true by design

CCS approach - use words not trees to present decision problems

• phrase same thing in 2 different ways but have same tree to test

Between subject design for DC testing (CCS approach for within subject flaws)

Each subject faces 1 task with real money outcomes

Different groups of subjects face each task - compare groups rather than individuals

• compare proportions of subjects making same choice in a group

Random assignment of subjects to groups - no systematic differences in risk attitudes between groups (unless task causes it which we’re testing)

CRE example - CSS 1998 OVERVIEW

q - probability

r - Common ratio

When problem scaled down preference flips from certainty to riskier option

different versions of the same problem presented to subjects to test each different rational dynamic decision making assumptions

• EUT says scaled UP vs Scaled down identical BUT CRE argues there is a difference

CRE example - CSS 1998 test of rational dynamic decision making

Test of separability - Scaled UP vs Prior lottery problem (subsequent option)

Test of Timing Independence - Prior lottery vs pre-commitment problem - same problem but make choice before lottery (timing shouldn’t affect decision)

Test of Frame independence - Pre-commit vs 2 stage problem - same problem but with 1st stage inside vs outside options (decision tree identical)

Test of Reduction of compound lotteries - 2 stage vs Scaled DOWN - same problem but compound lotteries reduced to simple ones

CRE example - CSS 1998 RESULTS table

CRE example - CSS 1998 RESULTS explained + implication

Classic test of CRE - P1 vs P5 → Difference has direction of CRE but not sig at 5%

Dynamic CRE - P1 vs P4 → Difference has direction of CRE + stat sig

• EUT violated

Pooled test - (P1 + P2) vs (P3 + P4 + P5) → Difference has direction of CRE + stat sig

People are taking the risky option more frequently when they can pre-commit to it, than when they can't - Violates TI principle

CRE & CCE as violations of EUT

Violates EUTs property of linearity in probabilities

Need a model that is non-linear (e.g. prospect theory)

• max something else