Calibrated Clustering and Analogy-Based Expectation Equilibrium
Pre-print, Working paper: Families of normal-form two-player games are categorized by players into K analogy classes applying the K-means clustering technique to the data generated by the distributions of opponent's behavior. This results in Calibrated Analogy-Based Expectation Equilibria in which strategies are analogy-based expectation equilibria given the analogy partitions and analogy partitions are derived from the strategies by the K-means clustering algorithm. We discuss various concepts formalizing this, and observe that distributions over analogy partitions are sometimes required to guarantee existence. Applications to games with linear best-responses are discussed highlighting the differences between strategic complements and strategic substitutes.
Keywords JEL
Keywords
- K-mean clustering
- Analogy-based Expectation Equilibrium K-mean clustering
Internal reference
- PSE Working Papers n°2023-21
Pages
- 54 p.
URL of the HAL notice
Version
- 1