The psychology of negative-sum competition in strategic interactions.
Bibliographic record
Abstract
Many real-life examples-from interpersonal rivalries to international conflicts-suggest that people actively engage in competitive behavior even when it is negative sum (benefiting the self at a greater cost to others). This often leads to loss spirals where everyone-including the winner-ends up losing. Our research seeks to understand the psychology of such negative-sum competition in a controlled setting. To do so, we introduce an experimental paradigm in which paired participants have the option to repeatedly perform a behavior that causes a relatively small gain for the self and a larger loss to the other. Although they have the freedom not to engage in the behavior, most participants actively do so and incur substantial losses. We propose that an important reason behind the phenomena is shallow thinking-focusing on the immediate benefit to the self while overlooking the downstream consequences of how the behavior will influence their counterparts' actions. In support of the proposition, we find that participants are less likely to engage in negative-sum behavior, if they are advised to consider the downstream consequences of their actions, or if they are put in a less frenzied decision environment, which facilitates deeper thinking (acting in discrete vs. continuous time). We discuss how our results differ from prior findings and the implications of our research for mitigating negative-sum competition and loss spirals in real life. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".