Response times are a simple low-cost indicator of the process of reasoning in strategic games (Rubinstein, 2007; Rubinstein, 2016). We leverage the dynamic nature of response-time data from repeated strategic interactions to measure the strategic complexity of a situation by how long people think on...

Response times are a simple low-cost indicator of the process of reasoning in strategic games (Rubinstein, 2007; Rubinstein, 2016). We leverage the dynamic nature of response-time data from repeated strategic interactions to measure the strategic complexity of a situation by how long people think on...

A central question in game theory and artificial intelligence is how a rational agent should behave in a complex environment, given that it cannot perform unbounded computations. We study strategic aspects of this question by formulating a simple model of a game with additional costs...

It is common to model costs of carrying out strategies in games in relation to the complexity, in some sense, of the strategies. We show a particularly general definition of complexity for this purpose, one that subsumes many alternatives as special cases. We explore how this definition can be...

Evidence supports the contention that humans find it costly to carry out some sorts of strategies. Such costs are unlikely to be observed directly, but various models have been proposed to represent them. We discuss a method for testing the empirical worth of these models.

Gill and Prowse (2023) study response times using a repeated p-beauty contest (p = 0.7). Looking at between-subject variation in response times, they found that subjects who think for longer, on average, win more rounds and choose lower numbers. When comparing average response times and level-k...