christian_2026_resolving

bandit psych-201

One preregistered online experiment (N=2520) on Feynman's restaurant problem, an optimal-stopping task, with one sequence of nights per participant (horizon 7, 14, or 28). On each night a participant chose to explore a new restaurant or exploit the best one seen so far, with hidden restaurant quality values drawn from one of four distributions (uniform, exponential, power law, triangular) varied between subjects. Responses are discrete choices logged per night (with best-known value and reward), and the paper tests whether humans use stopping thresholds that decline linearly with nights remaining, fitting linear-threshold and hierarchical Bayesian models plus reinforcement-learning baselines.

Responses
41,160
1,624 held out
CORE effects
3 / 3
CORE test NLL (avg)
0.367
rank 1 of 7 models
Best model
CORE
test NLL (avg) 0.367

Likelihood of human responses

modelPNLLtest NLL (avg)test responses train NLL (avg)train responses
CORE best596.60.36731,6240.366139,536
CORE (no prediction errors)597.90.36811,6240.367839,536
CORE (no phonemes)599.80.36941,6240.356439,536
CORE (no forgetting)611.20.37641,6240.376339,536
CORE (no hierarchy)871.50.53661,6240.557339,536
Transformer646.30.39801,6240.407639,536
Centaur701.20.43181,624––

Experiments

exptask classparticipantsresponsesconditionssimulatedeffects
exp0RestaurantProblem2,52041,1604yesexploit_increases_as_nights_remaining_decreases, exploit_timing_across_horizons, runtime_balance

Behavioral effects

effectexperimentpaperCORECORE (no prediction errors)CORE (no phonemes)CORE (no forgetting)CORE (no hierarchy)
exploit_increases_as_nights_remaining_decreasesexp0-0.073✓ yes
-0.072
✓ yes
-0.074
✓ yes
-0.064
✓ yes
-0.071
✓ yes
-0.040
exploit_timing_across_horizonsexp0-0.073✓ yes
-0.340
✓ yes
-0.395
✓ yes
-0.362
✓ yes
-0.379
✓ yes
-0.191
runtime_balanceexp0+0.000✓ yes
+0.050
✓ yes
+0.036
✓ yes
+0.050
✓ yes
+0.025
✓ yes
+0.007

Transcript

You are about to spend 7 nights in a new city. Restaurants in the city vary in quality: you cannot know a restaurant's quality until you visit it, and once you visit it its quality never changes. Quality scores are distributed randomly with a mean of 50. Before your trip you saw 84 sample scores from this city to get a sense of how scores are distributed. Your goal is to maximize the total score of the restaurants you visit across all your nights, and you will earn a bonus proportional to your total score.
On each of the 7 nights you choose a restaurant. On every night respond with a single letter: press A to visit a NEW restaurant you have not been to before, press C to return to the BEST restaurant you have found so far, or press B to return to some other restaurant you have already visited.
Night 1 of 7, 7 night(s) remaining. You have not visited any restaurant yet. You press A. You visit a new restaurant; its score is 21.
Night 2 of 7, 6 night(s) remaining. Your best score so far is 21. You press C. You return to your best restaurant; its score is 21.
Night 3 of 7, 5 night(s) remaining. Your best score so far is 21. You press A. You visit a new restaurant; its score is 42.
Night 4 of 7, 4 night(s) remaining. Your best score so far is 42. You press A. You visit a new restaurant; its score is 55.
Night 5 of 7, 3 night(s) remaining. Your best score so far is 55. You press A. You visit a new restaurant; its score is 60.
Night 6 of 7, 2 night(s) remaining. Your best score so far is 60. You press A. You visit a new restaurant; its score is 49.
Night 7 of 7, 1 night(s) remaining. Your best score so far is 60. You press C. You return to your best restaurant; its score is 60.