Zero-Sum Beliefs Across Age and Generations
Bibliographic record
Abstract
Zero-sum thinking is pervasive and affects how people perceive the world around them. The belief that for one person to gain another must lose not only is associated with negative affect, greed, and lower life satisfaction, but it also has societal consequences that impact how people interact with each other. Endorsing zero-sum beliefs undermines judgment across contexts such as interpersonal relations, labor relations, public policies, international relations, and economic transactions. Here I investigate how zero-sum beliefs change as people age and why it happens. In Chapters 1-2, I discover and replicate that older people hold fewer zero-sum beliefs than younger individuals. By measuring executive functioning, I show that differences in zero-sum beliefs cannot be explained by the cognitive decline that comes with age. In Chapters 3-4, I show that a decrease in zero-sum beliefs is partly because older people adopt more positive thinking than younger people and because they perceive resources as less scarce. Importantly, I find that the zero-sum beliefs of older individuals are significantly lower than younger individuals when it comes to zero-sum, but not with beliefs that are irrelevant to zero-sum thinking. Finally, in Chapter 5, I examine if my experimental findings are applicable beyond the lab. Using World Values Survey data from a quarter of a million people around the world across fifteen years, I find that the reduction in zero-sum beliefs is a function of both age and generation. People who are older today have been less zero-sum as a generation than the current generation of young individuals regardless of their age, and becoming older reduced their zero-sum beliefs. Broadly, when people perceive situations that are not zero-sum as if they were zero-sum, it undermines the potential to gain mutual benefits. The reduction of this bias with age could provide older people with opportunities they missed out on when they were younger. This could be important for considerations of policy and politics. Reducing zero-sum thinking could lead to creative policies as well as more fruitful political negotiation. Having older people on the team could very well bring about these benefits of seeing the world as non-zero-sum.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".