MétaCan
Menu
Back to cohort
Record W2072665327 · doi:10.1177/1046878104270471

In the long run: Biological versus economic rationality

2005· article· en· W2072665327 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSimulation & Gaming · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsCarleton University
Fundersnot available
KeywordsRationalityValue (mathematics)EconomicsGame of chanceSubjective expected utilityExpected utility hypothesisPsychologyActuarial scienceMicroeconomicsFinancial economicsComputer science

Abstract

fetched live from OpenAlex

Eight computer simulations examined how long hypothetical gamblers could continue gambling without going broke in different games of chance. Gamblers began with a fixed amount of money and paid a fixed ante to play each game. Games had equal expected value but varied in their probability of winning and amount won. When the expected value was zero or positive, gamblers playing low ante, low-risk games (high chances of small wins) had longer runs than did gamblers playing high ante, high-risk games (low chances of big wins). When the expected value was negative, gamblers playing high-risk games had longer runs than gamblers playing low-risk games. The results extend Slobodkin and Rapoport’s concept of biological rationality and explain why people with limited wealth are wise to avoid risks in winning situations and take risks in losing situations, a central principle of prospect theory.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.337
GPT teacher head0.485
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it