Social Diversity Reduces the Complexity and Cost of Fostering Fairness
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Abstract (arXiv)
Institutions and investors are constantly faced with the challenge of appropriately distributing endowments. No budget is limitless and optimising overall spending without sacrificing positive outcomes has been approached and resolved using several heuristics. To date, prior works have failed to consider how to encourage fairness in a population where social diversity is ubiquitous, and in which investors can only partially observe the population. Herein, by incorporating social diversity in the Ultimatum game through heterogeneous graphs, we investigate the effects of several interference mechanisms which assume incomplete information and flexible standards of fairness. We quantify the role of diversity and show how it reduces the need for information gathering, allowing us to relax a strict, costly interference process. Furthermore, we find that the influence of certain individuals, expressed by different network centrality measures, can be exploited to further reduce spending if minimal fairness requirements are lowered. Our results indicate that diversity changes and opens up novel mechanisms available to institutions wishing to promote fairness. Overall, our analysis provides novel insights to guide institutional policies in socially diverse complex systems.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 9 of its 54 references that are in the map. First posted on arXiv 18 Nov 2022.
Builds on
9 of 54 references.
References · 9
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Built on by
Citing papers per year: 2023: 2; 2024: 3; 2025: 1; 2026: 1.
7 papers.
- 2024Evolutionary mechanisms that promote cooperation may not promote social welfarecited by 3
- 2024Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Softwarecited by 3
- 2023Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safetycited by 2
- 2024Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluationscited by 1
- 2025Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structurecited by 1
- 2023The evolutionary advantage of guilt: co-evolution of social and non-social guilt in structured populationscited by 0
- 2026Mitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decodingcited by 0
- 2026Mitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decoding12 Aug 2026
- 2025Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure22 Jun 2025
- 2024Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluations19 Dec 2024
- 2024Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software11 Dec 2024
- 2024Evolutionary mechanisms that promote cooperation may not promote social welfare9 Aug 2024
- 2023Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety6 Mar 2023
- 2023The evolutionary advantage of guilt: co-evolution of social and non-social guilt in structured populations20 Feb 2023