We can study how mechanisms evolve without knowing the rules of chess or the workings of the brain
Bibliographic record
Abstract
We made two main points in our article on the “behavioral gambit” ( Fawcett et al. forthcoming, 2012 ). We argued not that the gambit should be jettisoned forthwith but that behavioral ecologists should recognize when they are using it and why it may sometimes fail, rather than accepting it blindly. Our second point was that by shifting our focus to the psychological mechanisms governing behavioral flexibility, we can gain important insights and generate interesting new predictions about how behavior changes as individuals gain feedback about the consequences of their actions. We are delighted that our article has attracted comment from 3 leading researchers with an active interest in the adaptive mechanisms controlling behavior. Stevens (forthcoming, 2012 ) highlights the seminal work by Herbert Simon and argues that decision rules are limited by an organism’s cognitive capacities, the “building blocks” used to implement the rules. Although this is undoubtedly true, it leaves open the question of how and why those particular building blocks arose. This is where evolution comes in. Despite acknowledging a role for evolution, very little of the work on heuristics and rules of thumb explicitly addresses their evolutionary properties. Behavioral ecologists certainly have much to learn from those studying bounded rationality, but equally, an evolutionary approach is needed to understand how, when, and why rationality is bounded. Somewhere in between these two disciplines lies fertile ground ( Hutchinson and Gigerenzer 2005 ).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".