Method to uncover hidden altruism and costs in large populations
Inverse Learning of the Altruism and Cost Level in Mixed-Individual Mean Field Games
Machine Learning
Summary
It can be hard to know how much people care about others versus themselves or what their personal costs are when making decisions in big groups. This paper introduces a way to figure out these hidden traits by looking at noisy data about people's actions over time. The authors blend ideas from both selfish and fully cooperative behaviors to better model real groups. Their method shows it is possible to estimate these unseen preferences accurately, which can help design better policies and incentives.
What this means in practice
- •For policy analysts: Estimate hidden altruism and cost parameters to tailor better incentive programs for large populations.
- •For behavioral data scientists: Infer underlying preference structures from noisy behavioral data in social or economic systems.
Authors
Haoyang Cao, Gökçe Dayanıklı, Xiaofei Shi
Abstract
Understanding how humans respond to incentives, both at the individual and collective levels, is crucial to the design of effective policies. Within the continuous-time stochastic framework for large interacting populations, mean field games (MFGs) model populations of non-cooperative agents, whereas mean field control (MFC) describes the fully cooperative benchmark, interpreted in our setting as fully altruistic behavior. Mixed-individual MFGs interpolate between these two extremes through a parameter governing the degree of altruism. A central challenge for regulators and policymakers, however, is that intrinsic altruism levels and other private structural parameters, such as individual labor costs, are typically unobservable. To address this challenge, we develop an inverse learning framework for mixed-individual MFGs. Our approach enables the recovery of (latent) altruism and labor cost levels from noisy observations, with experiments demonstrating the feasibility and accuracy of our method. These findings underscore the promise of inverse MFG methodologies for uncovering latent preference structures in large populations, with important implications for incentive design, empirical behavioral modeling, and data-driven policy analysis.