Achievement and Fragility of Long-term Equitability

TL;DR

This paper develops a dynamic, feedback-driven approach to allocate resources among local communities to maximize long-term equitability, revealing its fragility under competing priorities like equality.

Contribution

It introduces a novel data-driven online optimization framework for long-term resource allocation that adapts to community feedback and evolution.

Findings

Long-term equitability is fragile and can be lost with competing priorities.

Naive compromises may increase inequality despite seeming fair.

The methodology is demonstrated with healthcare and education examples in Sub-Saharan Africa.

Abstract

Equipping current decision-making tools with notions of fairness, equitability, or other ethically motivated outcomes, is one of the top priorities in recent research efforts in machine learning, AI, and optimization. In this paper, we investigate how to allocate limited resources to {locally interacting} communities in a way to maximize a pertinent notion of equitability. In particular, we look at the dynamic setting where the allocation is repeated across multiple periods (e.g., yearly), the local communities evolve in the meantime (driven by the provided allocation), and the allocations are modulated by feedback coming from the communities themselves. We employ recent mathematical tools stemming from data-driven feedback online optimization, by which communities can learn their (possibly unknown) evolution, satisfaction, as well as they can share information with the deciding bodies. We design dynamic policies that converge to an allocation that maximize equitability in the long term. We further demonstrate our model and methodology with realistic examples of healthcare and education subsidies design in Sub-Saharian countries. One of the key empirical takeaways from our setting is that long-term equitability is fragile, in the sense that it can be easily lost when deciding bodies weigh in other factors (e.g., equality in allocation) in the allocation strategy. Moreover, a naive compromise, while not providing significant advantage to the communities, can promote inequality in social outcomes.