A Conceptual Review of Decision Making in Social Dilemmas: Applying a Logic of Appropriateness
Bibliographic record
Abstract
Despite decades of experimental social dilemma research, "theoretical integration has proven elusive" (Smithson & Foddy, 1999, p. 14). To advance a theory of decision making in social dilemmas, this article provides a conceptual review of the literature that applies a "logic of appropriateness" (March, 1994) framework. The appropriateness framework suggests that people making decisions ask themselves (explicitly or implicitly), "What does a person like me do in a situation like this? " This question identifies 3 significant factors: recognition and classification of the kind of situation encountered, the identity of the individual making the decision, and the application of rules or heuristics in guiding behavioral choice. In contrast with dominant rational choice models, the appropriateness framework proposed accommodates the inherently social nature of social dilemmas, and the role of rule and heuristic based processing. Implications for the interpretation of past findings and the direction of future research are discussed.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".