Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.12035 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914326015639552 |
|---|---|
| author | Calvano, Emilio Possnig, Clemens Tolvanen, Juha |
| author_facet | Calvano, Emilio Possnig, Clemens Tolvanen, Juha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12035 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Algorithmic Advantage: How Reinforcement Learning Generates Rich Communication Calvano, Emilio Possnig, Clemens Tolvanen, Juha Theoretical Economics 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. |
| title | The Algorithmic Advantage: How Reinforcement Learning Generates Rich Communication |
| topic | Theoretical Economics |
| url | https://arxiv.org/abs/2602.12035 |