Strategic Learning with Asymmetric Rationality

2025-10-27

AI Summary

This paper studies dynamic communication and decision-making under information asymmetry—where the sender is fully rational and privately informed, while the receiver is boundedly rational, uninformed, and subject to memory constraints. Methodologically, it employs game-theoretic modeling, represents the receiver’s information processing via a finite-state machine, and integrates Bayesian learning with incentive compatibility analysis to construct a dynamic mechanism design framework. Theoretical results show that information avoidance, opinion polarization, and decision hesitation are not irrational biases but equilibrium outcomes arising from rational strategic interaction under asymmetry. The key contribution is the first endogenous incorporation of memory constraints as a finite-state machine structure, enabling precise characterization of the optimal trade-off between information transmission and learning while preserving incentive compatibility. This yields a computationally tractable and empirically falsifiable theoretical benchmark for strategic communication in cognitively constrained environments.

Technology Category

• Game Theory and Economic Paradigms: Imperfect Information
• Multiagent Systems: Mechanism Design
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

Abstract

This paper analyzes the dynamic interaction between a fully rational, privately informed sender and a boundedly rational, uninformed receiver with memory constraints. The sender controls the flow of information, while the receiver designs a decision-making protocol, modeled as a finite-state machine, that governs how information is interpreted, how internal memory states evolve, and when and what decisions are made. The receiver must use the limited set of states optimally, both to learn and to create incentives for the sender to provide information. We show that behavior patterns such as information avoidance, opinion polarization, and indecision arise as equilibrium responses to asymmetric rationality. The model offers an expressive framework for strategic learning and decision-making in environments with cognitive and informational asymmetries, with applications to regulatory review and media distrust.

Problem

• Analyzes strategic interaction between rational sender and bounded-rational receiver
• Examines how memory constraints affect information interpretation and decision-making
• Explains behavioral patterns like information avoidance and opinion polarization

Innovation

• Finite-state machine models receiver's decision protocol
• Sender controls information flow with private knowledge
• Optimal state use balances learning and incentive creation