How can agents strategically cause effects?
Causes and Strategies in Multiagent Systems
This paper connects the concepts of causality and strategy in multi-agent systems. It introduces a method to translate a causal model (describing cause-and-effect relationships between variables) into a concurrent game structure (CGS), where agents can make strategic decisions. The paper demonstrates that a group of agents causing an outcome in the causal model is equivalent to them having a joint strategy in the CGS to prevent that outcome, assuming other agents behave as dictated by the causal model.
For LLM-based multi-agent systems, this research offers a bridge between causal reasoning (a strength of LLMs) and strategic decision-making. It suggests a way to represent the environment and agent interactions as a causal model, which can then be transformed into a CGS to analyze and plan agent strategies. This framework could enable LLMs to reason about the causal impact of their actions within a multi-agent environment and choose strategies that achieve desired outcomes by influencing or preventing specific events. The "causal strategy profile" introduced in the paper could also be valuable for aligning LLM agents to predefined behavioral norms derived from the causal model, offering a mechanism for controlling and guiding their actions within the system.