We propose a model of decision-making based on Markovian exploration of a choice set. It is inspired by experimental evidence that decision makers search sequentially by making stochastic pairwise comparisons. The model allows us to analyze the impact of a variety of interventions on final choices and to infer unobservable characteristics of the agent. We examine how choice can be influenced by the first fixation, comparability restrictions, time pressure, and by adding a dominated alternative. We show that the long-run choices of an agent are not susceptible to manipulation if the Markovian model is reversible and that such choices are consistent with the well-known Luce model. Further, we identify conditions on the long-run choice data that reveal the agent's consideration set and comparability restrictions. Finally, we show how one can isolate the effect of utility from salience on choice probability.

This paper studies fictitious play in networks of noncooperative two-person games. We show that continuous-time fictitious play converges to the set of Nash equilibria if the overall n-person game is zero-sum. Moreover, the rate of convergence is 1/t, regardless of the size of the network. In contrast, arbitrary n-person zero-sum games with bilinear payoff functions do not possess the continuous-time fictitious-play property. As extensions, we consider networks in which each bilateral game is either strategically zero-sum, a weighted potential game, or a two-by-two game. In those cases, convergence requires a condition on bilateral payoffs or, alternatively, that the network is acyclic. Our results hold also for the discrete-time variant of fictitious play, which implies, in particular, a generalization of Robinson's theorem to arbitrary zero-sum networks. Applications include security games, conflict networks, and decentralized wireless channel selection.