Bibliographic record
Abstract
In this article, we ask how much, if anything, of Robert Frank’s (1988, 2004) theory of emotions as evolved strategic commitment devices can survive rejection of its underlying game-theoretic model. Frank’s thesis is that emotions serve to prevent people from reneging on threats and promises with enough reliability to support cooperative equilibria in prisoner’s dilemmas and similar games with inefficient dominant equilibria. We begin by showing that Frank, especially in light of recent revisions to the theory, must be interpreted as endorsing a version of so-called ‘constrained maximization’ as proposed by Gauthier (1986). This concept has been subjected to devastating criticism by Binmore (1994), which we endorse: no consistent mathematical sense can be made of games in which constrained maximization is allowed. However, this leaves open the question of whether Frank has identified a genuine empirical phenomena by means of his confused theoretical model. We argue that he in fact has; but that seeing this depends on our rejecting a muddled folk-psychological model of emotions, which Frank himself follows, according to which emotions are inner states of people. Instead, following Dennett (1987, 1991) and other so-called ‘externalist’ philosophers of cognitive science, we argue that emotions, properly speaking, are social signals coded in culturally evolved intentional conventions that find their identity conditions outside of individuals, in the social environment. As such, their evolutionary proper functions lie in their capacity to enable individuals to solve what we call ‘game determination’ problems - that is, coordination on multiple-equilibrium meta-games over which base-games to play. This allows emotions to indeed serve as commitment devices in assurance games (though not in prisoner’s dilemmas). Thus the empirical core of Frank’s thesis is recovered, though only by way of drastic revisions to both the game theory and the psychology incorporated in his model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".