The psychology of relative state, desperation and violence: a commentary on de Courson <i>et al.</i> (2023)
Bibliographic record
Abstract
Why does violence persist when the apparent incentives for violence decline?A recent paper by de Courson et al. [1] offers answers based on agent-based fitness modelling, making two integrative claims about incentives to violence.First, factors increasing desperation will lead to more violence; second, communities can become trapped in violence long after the desperation subsides, as agents must demonstrate they are not easily victimized.The model's compelling insights may be particularly useful for guiding research into the relevant psychology of relative state, which describes dis/advantage calibrated within a competitive landscape.Here, we summarize the model, review the evidence linking the psychology of relative state to ecologically derived deprivation, and speculate on the policy implications of the model.De Courson et al.'s model has numerous agents interacting in a population.Each agent has some resources which convert into fitness, but each is subject to a 'desperation threshold'-agents who fall below that threshold suffer greatly in fitness terms.Agents choose one of three social strategies: (i) take resources from others through crime (exploit); (ii) do not take but react violently to perceived exploitation (violent); or (iii) passively let others take from oneself (submit).The model addresses when each of these strategies predominates.The model makes two important assumptions about incentives to violence.First, violence is a risky way to extract resources from others: successful violence provides easy resources, but unsuccessful violence incurs great costs.If an individual is desperate-below a minimal acceptable threshold with no safe way of meeting that threshold-the model shows they will take risks, like engaging in violence, as a desperate ploy to meet that threshold, consistent with risk-sensitivity theory (reviewed in [2,3]).The more desperate agents there are, the more violence there will be.This claim is consistent with empirical evidence indicating that such conditions as high economic inequality, poverty and a preponderance of single males are associated with elevated violence (e.g.[4-6]).Second, violence deters exploitation: others are less likely to exploit someone with a violent reputation, for fear of suffering the costs of that violence [5].This observation is consistent with traditional models of partner choice (e.g.[7]), except that agents are choosing whom to exploit rather than ally with.De Courson et al.[1] combine these two explanations in a single model that explains both the high variation observed in violence rates and the persistence of violence even after conditions improve.Importantly, de Courson and colleagues show that both poverty and inequality lead to exploitative crime and violence, through three key mechanisms.First, as poverty or inequality rises, more agents will fall below the desperation threshold and thus become exploitative as a desperate attempt to get above the threshold.Second, when more individuals are desperate, there are more potential exploiters, so violence is worth the risk because it deters exploitation (i.e.exploiters will avoid targeting violent individuals).Third, communities can become 'trapped' in high violence: once violence is common, all individuals must cultivate a violent reputation, lest they become the sole target for all exploiters.Thus, the violence of a community depends not just on current conditions, but also its history of violence or peacefulness.De Courson's predictions match several established findings about violence.Poverty and inequality explain the rates of many crimes [4][5][6].Exploitative
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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 | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.071 | 0.082 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".