More than 8,192 Ways to Skin a Cat: Modeling Behavior in Multidimensional Strategy Spaces

How can we model behavior on complex, real-world tasks with a large range of possible strategies that may vary along multiple dimensions? In this paper, we show show how an emerging approach to cognitive modeling, cognitively bounded rational analysis, can be applied to efficiently specify large, multidimensional strategy spaces, and to predict which strategies within the space are followed. The approach also supports a novel way of analyzing error control strategies, by directly modeling error recovery procedures and factoring these into strategy prediction. We apply this approach in a model of a typing task exemplifying three dimensions of strategic variability: decomposition of tasks into subtasks, parallel vs. serial processing of subtasks, and control of errors. We present empirical results showing the strategies people adopted on the task. The model successfully predicts the strategies used, by optimizing over the strategy space for a utility function defined as the performance-based payoff used in the experiment.