Players may categorize the strategies available to them. In many games there are different ways to categorize one's strategies (different frames) and which ones players use has implications for the outcomes realized. This paper proposes a model of agents who learn which frames to use through reinforcement. As a case study we fit the model to existing experimental data from coordination games. The analysis shows that the model fits the data well as it matches the key stylized facts. It suggests a trade-off of using coarser versus finer representations of the strategy set when it comes to learning.
Variable Frame Theory Coordination games Categorization Reinforcement learning Focal points Bounded rationality