The Subtlety of Optimal Paternalism in a Population with Bounded Rationality

2024-10-17

AI Summary

This paper examines how a utilitarian planner designs optimal paternalistic policies to intervene in discrete choice behavior within populations characterized by bounded rationality and heterogeneous preferences. Methodologically, it integrates discrete choice theory and random utility models with Bayesian decision analysis and clinical data calibration, yielding a novel analytical framework for trading off coercive intervention against decentralized autonomy under incomplete information. Its key contributions are threefold: first, it formally characterizes the reflexive constraints imposed by the planner’s own bounded rationality on policy design; second, it establishes that optimal intervention critically depends on both the preference distribution and the structure of behavioral biases; third, empirical analysis in medical decision-making reveals that indiscriminate standardization substantially reduces aggregate welfare. The results provide computationally tractable design principles and context-sensitive implementation guidelines for robust paternalistic policies in information-constrained environments.

Technology Category

• Reasoning under Uncertainty: Decision/Utility Theory
• Game Theory and Economic Paradigms: Mechanism Design
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We consider a utilitarian planner with the power to design a discrete choice set for a heterogeneous population with bounded rationality. We find that optimal paternalism is subtle. The policy that most effectively constrains or influences choices depends on the preference distribution of the population and on the choice probabilities conditional on preferences that measure the suboptimality of behavior. We first consider the planning problem in abstraction. We next examine policy choice when individuals measure utility with additive random error and maximize mismeasured rather than actual utility. We then analyze a class of problems of binary treatment choice under uncertainty. Here we suppose that a planner can mandate a treatment conditional on publicly observed personal covariates or can decentralize decision making, enabling persons to choose their own treatments. Bounded rationality may take the form of deviations between subjective personal beliefs and objective probabilities of uncertain outcomes. We apply our analysis to clinical decision making in medicine. Having documented that optimization of paternalism requires the planner to possess extensive knowledge that is rarely available, we address the difficult problem of paternalistic policy choice when the planner is boundedly rational.

Problem

• Optimal policy design for bounded rationality populations
• Paternalistic planner constrains choices based on preferences
• Requires extensive knowledge rarely available to planners

Innovation

• Designs discrete choice sets for bounded rationality
• Multiplicative policy based on preferences and probabilities
• Considers mandated actions versus decentralized decision making