# Social Diversity Reduces the Complexity and Cost of Fostering Fairness

arXiv: https://arxiv.org/abs/2211.10517 · HTML: https://mlsanity.com/paper/2211.10517/

## 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 (most cited first, 9 of 9)

- [Axioms for Centrality](https://mlsanity.com/paper/1308.2140/) (2013)
- [Centrality Measures in Networks](https://mlsanity.com/paper/1608.05845/) (2016)
- [Cost efficiency of institutional incentives in finite populations](https://mlsanity.com/paper/2103.01131/) (2021)
- [Exploring optimal institutional incentives for public cooperation](https://mlsanity.com/paper/1907.10989/) (2019)
- [The evolution of trust and trustworthiness](https://mlsanity.com/paper/2008.12948/) (2020)
- [Institutional Incentives for the Evolution of Committed Cooperation: Ensuring Participation is as Important as Enhancing Compliance](https://mlsanity.com/paper/2110.13307/) (2021)
- [Exogenous Rewards for Promoting Cooperation in Scale-Free Networks](https://mlsanity.com/paper/1905.04964/) (2019)
- [Optimal distribution of incentives for public cooperation in heterogeneous interaction environments](https://mlsanity.com/paper/1409.1560/) (2014)
- [A Novel Methodology for designing Policies in Mobile Crowdsensing Systems](https://mlsanity.com/paper/2001.06437/) (2020)

## Built on by (most cited first, 7 of 7)

- [Evolutionary mechanisms that promote cooperation may not promote social welfare](https://mlsanity.com/paper/2408.05373/) (2024)
- [Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software](https://mlsanity.com/paper/2412.08167/) (2024)
- [Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety](https://mlsanity.com/paper/2303.03174/) (2023)
- [Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluations](https://mlsanity.com/paper/2412.15433/) (2024)
- [Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure](https://mlsanity.com/paper/2506.17925/) (2025)
- [The evolutionary advantage of guilt: co-evolution of social and non-social guilt in structured populations](https://mlsanity.com/paper/2302.09859/) (2023)
- [Mitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decoding](https://mlsanity.com/paper/2608.21415/) (2026)

## 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.