Диссертации, представленные на защиту и подготовленные в НИУ ВШЭ

The subject of this dissertation is learning in experimental games from both theoretical and empirical perspectives. The main objective of this work is to develop a generalized concept of strategy-based learning and to test its application, including empirical identification, in a class of simple experimental games. First, we discuss why there are so many learning models, what properties in a dynamic context are crucial, and what are the criteria for the "goodness" of these models. Classification of models of learners based on their crucial properties is presented.  Second, we explicitly test whether the models in the set are distinguishable given a set of models and the sampling constraint. Finally, the experiment supports the viability of the methodological study and an improvement in explanatory power with a new class of models. In a repeated game framework, we explore the response of human subjects to the uncertain behavior of the strategically sophisticated opponent. We build a strategic extension of the classical learning models and calibrate its practical application to our experimental data.