The evolution of zero-sum and positive-sum worldviews
Bibliographic record
Abstract
People and cultures differ in the extent to which they view the world as a zero-sum environment (where one person's gain is another's loss) or a positive-sum environment (where certain actions can benefit everyone). These beliefs shape individuals' willingness to work, invest, collaborate, or show hostility toward out-groups, and accept or reject various social policies. We model dyadic interactions in a heterogeneous population where individuals biased toward a zero-sum worldview are more likely to invest in competition, while those biased toward a positive-sum worldview are more likely to invest in cooperation. The environment alternates stochastically between cooperative and competitive states. Without social influence, the more accurate worldview yields higher utilities and spreads throughout the population. However, assortative matching by bias can favor the positive-sum worldview even if a positive-sum environment is somewhat less likely. With peer conformity, inaccurate worldviews can persist after a structural change in the environment, leading to cultural evolutionary mismatch. In the presence of cultural authorities who can alter beliefs, either both worldviews can coexist or one excludes the other. Moreover, when assortative matching and conformity interact, authorities may profit by amplifying individuals' biases, creating enclaves of similarly biased people who can pay the authorities enough to make investment in persuasive technology economically viable. Cultural evolutionary mismatch is more likely in cultures marked by strong peer conformity and high responsiveness to authority when the authority promotes a suboptimal worldview. This study demonstrates how real-world conditions, peer influence, and authority interventions can perpetuate or shift zero-sum and positive-sum worldviews-at times leading to inaccurate beliefs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".