Abstract
In complex situations involving communication, agents might attempt to mask their intentions, exploiting Shannon’s theory of information as a theory of misinformation. Here, we introduce and analyze a simple multiagent reinforcement learning task where a buyer sends signals to a seller via its actions, and in which both agents are endowed with a recursive theory of mind. We show that this theory of mind, coupled with pure reward-maximization, gives rise to agents that selectively distort messages and become skeptical towards one another. Using information theory to analyze these interactions, we show how savvy buyers reduce mutual information between their preferences and actions, and how suspicious sellers learn to reinterpret or discard buyers’ signals in a strategic manner.
| Original language | American English |
|---|---|
| Pages (from-to) | 608-624 |
| Number of pages | 17 |
| Journal | Open Mind |
| Volume | 7 |
| DOIs | |
| State | Published - 20 Aug 2023 |
Bibliographical note
Funding Information:We would like to thank Rahul Bhui and Stefan Bucher for helpful comments. This research was been partly funded by Israel Science Foundation grant #1340/18 (NA; JSR), by the Max Planck Society (NA, LS, PD) and the Humboldt Foundation (PD). PD is a member of the Machine Learning Cluster of Excellence, EXC number 2064/1 – Project number 39072764.
Publisher Copyright:
© 2023 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Keywords
- communication
- deception
- disinformation
- IPOMDP
- multi-agent-RL
- skepticism
- theory of mind