The psychology of relative state, desperation and violence: a commentary on de Courson <i>et al.</i> (2023)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,011 | 0,030 |
| Communication savante | 0,010 | 0,014 |
| Science ouverte | 0,009 | 0,006 |
| Intégrité de la recherche | 0,071 | 0,082 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».