Simulating Eutopia: Revisiting Long-term Fairness with Outcomes, Performativity, and Dynamics
Abstract
As AI-driven Decision Makers (ADMs) influence our socioeconomic reality, their roles in both enhancing efficiency and amplifying the social biases have drawn significant attention. In this paper, we revisit the nuances of long-term fairness achievable by an ADM-- specifically, through the lens of a loan approver inducing a population-level wealth dynamics. The literature generally (a) considers passive environments, i.e. the decisions of an ADM does not change the population's behaviour, and (b) measures bias in terms of disparity in the instantaneous decisions rather than downstream equity. Modern ADMs challenge both the notions. To address these caveats, we first formalise the wealth dynamics induced by a loan approving ADM interacting with a multi-demographic population as a Performative Markov Decision Process. Then, we mitigate the absence of such a performative test-bed by developing Eutopia: a lending-process simulator enabled with a novel performative data generator to learn long-term fair strategies. With Eutopia, we test reinforcement learning algorithms with different fairness-aware utilities dependent on approval decisions and downstream wealth. Results show that performative dynamics-aware learning with fairness-aware utility that incorporates the downstream outcomes induce better long-term equity and inclusivity.
Related
- Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI
- Conformal Prediction at Performative Equilibrium: The Price of Performativity
- Statistical Inference under Performativity: From Policy Feedback to Self-Evolving Systems
- The Role of Uncertainty in Assessing the Fairness of Machine Learning Models
- Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)
BibTeX
@misc{palit2026simulating,
title = {{Simulating Eutopia: Revisiting Long-term Fairness with Outcomes, Performativity, and Dynamics}},
author = {Vedant Palit and Udvas Das and Brahim Driss and Debabrota Basu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.19389},
eprint = {2607.19389},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.19389},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Simulating Eutopia: Revisiting Long-term Fairness with Outcomes, Performativity, and Dynamics
AU - Vedant Palit
AU - Udvas Das
AU - Brahim Driss
AU - Debabrota Basu
PY - 2026
DA - 2026/09/26
PB - arXiv
DO - 10.48550/arxiv.2607.19389
UR - https://arxiv.org/abs/2607.19389
AB - As AI-driven Decision Makers (ADMs) influence our socioeconomic reality, their roles in both enhancing efficiency and amplifying the social biases have drawn significant attention. In this paper, we revisit the nuances of long-term fairness achievable by an ADM-- specifically, through the lens of a loan approver inducing a population-level wealth dynamics. The literature generally (a) considers passive environments, i.e. the decisions of an ADM does not change the population's behaviour, and (b) measures bias in terms of disparity in the instantaneous decisions rather than downstream equity. Modern ADMs challenge both the notions. To address these caveats, we first formalise the wealth dynamics induced by a loan approving ADM interacting with a multi-demographic population as a Performative Markov Decision Process. Then, we mitigate the absence of such a performative test-bed by developing Eutopia: a lending-process simulator enabled with a novel performative data generator to learn long-term fair strategies. With Eutopia, we test reinforcement learning algorithms with different fairness-aware utilities dependent on approval decisions and downstream wealth. Results show that performative dynamics-aware learning with fairness-aware utility that incorporates the downstream outcomes induce better long-term equity and inclusivity.
ER -