The Algorithmic Advantage: How Reinforcement Learning Generates Rich Communication
Theoretical Economics
2026-02-13 v1
Abstract
We analyze strategic communication when advice is generated by a reinforcement-learning algorithm rather than by a fully rational sender. Building on the cheap-talk framework of Crawford and Sobel (1982), an advisor adapts its messages based on payoff feedback, while a decision maker best-responds. We provide a theoretical analysis of the long-run communication outcomes induced by such reward-driven adaptation. With aligned preferences, we establish that learning robustly leads to informative communication even from uninformative initial policies. With misaligned preferences, no stable outcome exists; instead, learning generates cycles that sustain highly informative communication and payoffs exceeding those of any static equilibrium.
Cite
@article{arxiv.2602.12035,
title = {The Algorithmic Advantage: How Reinforcement Learning Generates Rich Communication},
author = {Emilio Calvano and Clemens Possnig and Juha Tolvanen},
journal= {arXiv preprint arXiv:2602.12035},
year = {2026}
}