Sequential Causal Normal Form Games: Theory, Computation, and Strategic Signaling
Dennis Thumm

TL;DR
This paper extends classical game theory to sequential causal games incorporating AI causal reasoning, but finds that causal distinctions do not improve strategic outcomes under rational play, highlighting limitations of current models.
Contribution
It introduces Sequential Causal Normal Form Games, proves their computational complexity, and empirically shows causal reasoning offers no strategic advantage under rational equilibrium assumptions.
Findings
Causal distinctions do not improve welfare over classical equilibria.
Backward induction with rational responses eliminates causal advantages.
Theoretical conditions where benefits could emerge are not realized in practice.
Abstract
Can classical game-theoretic frameworks be extended to capture the bounded rationality and causal reasoning of AI agents? We investigate this question by extending Causal Normal Form Games (CNFGs) to sequential settings, introducing Sequential Causal Multi-Agent Systems (S-CMAS) that incorporate Pearl's Causal Hierarchy across leader-follower interactions. While theoretically elegant -- we prove PSPACE-completeness, develop equilibrium refinements, and establish connections to signaling theory -- our comprehensive empirical investigation reveals a critical limitation: S-CNE provides zero welfare improvement over classical Stackelberg equilibrium across all tested scenarios. Through 50+ Monte Carlo simulations and hand-crafted synthetic examples, we demonstrate that backward induction with rational best-response eliminates any strategic advantage from causal layer distinctions. We…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsGame Theory and Applications · Auction Theory and Applications · Evolutionary Game Theory and Cooperation
