MPRA Munich Personal RePEc Archive Intentions, Insincerity, and Prosocial Behavior
Bibliographic record
Abstract
Dalhousie, Minnesota (School of Law) and Ryerson universities for very helpful comments and encouragement on earlier version of this paper. I thank Roland Benabou for drawing my attention to related work and SSHRC for financial support. Consider a world with two people, 1 and 2, where person 1 (the proposer) may offer to help person 2 (the responder). The proposer may be altruistic towards the responder either out of a genuine desire to make her happy or out of guilt. The responder derives disutility from apparent acts of altruism motivated by guilt because she considers them to be insincere. She rejects some offers, depending on her beliefs about the proposer’s type. I model this social interaction as a game with interdependent preference types under incomplete information where the responder cares about the intentions behind the proposer’s prosocial behavior. I consider two recent formulations of endogenous guilt: simple guilt and guilt from blame. These formulations make the social interaction a psychological game. I find that the beliefs held by the players can lead to an equilibrium in which all offers are sincere and so no mutually beneficial trades are rejected, although the responder has incomplete information about the proposer’s type. Equilibria with insincere offers are possible under simple guilt but are impossible under guilt from blame. I discuss intrinsic and instrumental motivations for sincerity. I also discuss the implications of insincerity aversion for co-operation, altruism, political correctness, choice of identity, and trust.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.690 | 0.391 |
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".