Fairness-aware design of nudging policies under stochasticity and prejudices

TL;DR

This paper introduces a fairness-aware model for innovation diffusion that accounts for social inequalities and designs incentive policies to promote equitable adoption, preventing the reinforcement of prejudices.

Contribution

It extends the Generalized Linear Threshold model with stochastic thresholds and develops a fair Model Predictive Control scheme for equitable incentive allocation.

Findings

Injustice reduces overall adoption rates.

Fair policies smooth incentive distribution.

Equity-focused incentives reduce disparities.

Abstract

We present an injustice-aware innovation-diffusion model extending the Generalized Linear Threshold framework by assigning agents activation thresholds drawn from a Beta distribution to capture the stochastic nature of adoption shaped by inequalities. Because incentive policies themselves can inadvertently amplify these inequalities, building on this model, we design a fair Model Predictive Control (MPC) scheme that incorporates equality and equity objectives for allocating incentives. Simulations using real mobility-habit data show that injustice reduces overall adoption, while equality smooths incentive distribution and equity reduces disparities in the final outcomes. Thus, incorporating fairness ensures effective diffusion without exacerbating existing social inequalities.