Evolutionary Dynamics of an Other-Regarding Preferences-Based Involution Game in Networked and Well-Mixed Populations
Bibliographic record
Abstract
Involution, characterized by intensified competition for limited social resources without generating additional societal value, has become a critical issue in modern social systems. This study aims to investigate effective mechanisms to mitigate involution by incorporating other-regarding preferences (ORP) into evolutionary game dynamics on complex networks and in well-mixed populations. Through numerical simulations conducted on square lattices and small-world networks, we systematically analyze the combined effects of crucial parameters including total social resources, the cost of more effort, fairness in resource distribution, and the intensity of ORP on the evolutionary dynamics of involution. Our findings reveal that increasing social resources generally exacerbates involution, whereas greater fairness in resource distribution alleviates it. Notably, the influence of more effort costs on involution is nonlinear and context-dependent, modulated by the interaction between resource abundance and ORP intensity. Critically, ORP emerges as a decisive factor: stronger ORP substantially suppresses involution, promotes the emergence and spatial stability of cooperative clusters, and reduces the equilibrium proportion of defectors. Theoretical analysis based on replicator dynamics validate our simulation results, providing robust theoretical benchmarks for the observed phenomena. This study not only advances the theoretical understanding of involution dynamics but also offers practical insights for addressing involution in real-world social systems. It highlights the importance of cultivating altruistic social norms and ensuring fairness in resource allocation to effectively mitigate involution.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".