Bounded Rationality in Strategic Decisions: Undershooting in a Resource Pool-Choice Dilemma
Bibliographic record
Abstract
This research studies a resource pool-choice dilemma, in which a group of resource seekers independently choose between a larger pool containing more resources and a smaller pool containing fewer resources, knowing that the resources in each pool will be divided equally among its choosers, so that the more (fewer) people choose a certain pool, the fewer (more) resources each of them will get. This setting corresponds to many real-world situations, ranging from students choosing majors as a function of job opportunities to entrepreneurs choosing markets as a function of customer bases. Ten studies reveal a systematic undershooting bias: fewer people choose the larger pool relative to both the normative equilibrium benchmark and chance (random choice), thus advantaging those who choose the larger pool and disadvantaging those who choose the smaller pool. We present evidence showing that the undershooting bias is driven by bounded rationality in strategic thinking and discuss the relationship between our paradigm and other coordination games. This paper was accepted by Yuval Rottenstreich, decision analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".