A Note on Kuhn's Theorem with Ambiguity Averse Players
arXiv:1408.10221.214 citationsh-index: 10
Originality Synthesis-oriented
AI Analysis
For game theorists studying ambiguity aversion, this provides a counterexample to a foundational theorem, highlighting the need for alternative analysis methods.
This note demonstrates that Kuhn's Theorem fails for ambiguity-averse players using maxmin decision rules and full Bayesian updating, showing that mixed and behavioral strategies are not equivalent in such settings.
Kuhn's Theorem shows that extensive games with perfect recall can equivalently be analyzed using mixed or behavioral strategies, as long as players are expected utility maximizers. This note constructs an example that illustrate the limits of Kuhn's Theorem in an environment with ambiguity averse players who use maxmin decision rule and full Bayesian updating.