讲座主题：Bridging Level-K to Nash Equilibrium
主讲人：张露瑶博士
主持人：姜树广博士
时间：2018年11月28日（周三）14：00-16：00
地点：经济学院（6号楼）210
主办单位：浙江省一流学科“理论经济学”、经济学院
主讲人简介：
张露瑶博士本科毕业于北京大学经济学和数学专业，并将于美国俄亥俄州立大学获得经济学博士学位，在博士期间是俄亥俄州立大学校长讲学金的获得者并获得国家科学基金学位论文资助，并已在American Economic Review发表学术论文。张露瑶博士的研究兴趣涉及微观经济理论，行为和实验经济学，博弈论和机制设计，产业组织等广泛领域，特别热衷于对有限理性建模和相关的应用研究。
Luyao Zhang is a Ph.D. Candidate holding Presidential Fellowship at the Ohio State University and her research is supported by the National Science Foundation Dissertation Grant. She has been striving to be a microeconomist with a broad theoretical and empirical overview, particularly, but not restricted to, decision theory, game theory, mechanism design, industrial organization, and experimental economics. She has an abiding passion for formal modelling of bounded rationality and its applications. She is also keenly interested in transdisciplinary collaborations.
内容摘要：
In this project, we propose a new solution concept NLK, that aims to augment two existing concepts in game theory, Nash Equilibrium (NE) and Level-K model. Of these two, NE is contradicted by mounting and robust evidence for not predicting behaviors well in laboratory experiments. As opposed to NE, Level-K model explicitly allows players to assume their opponents are less sophisticated than themselves. However, it does not allow players to use an important element of strategic thinking, namely “put yourself in the others’ shoes” and believe the opponent can think in the same way they do. Bridging NE and Level-K, NLK allows a player in a game to believe that the opponent may be either less- or as sophisticated as they—a view supported by various studies in psychology. We compare the performance of NLK to that of NE and some versions of Level-K by applying it to data from three experimental papers published in top economics journals and to data from a field study. These studies allow us to test NLK on: (1). A static game of complete information, (2). A static game of incomplete information, (3). A dynamic game of perfect information, and (4). On field data. NLK provides additional insights to those of NE and Level-K. Moreover, a simple version of it explains the experimental data better in many cases. As a new solution concept, NLK shares a similar foundation to NE but is also applicable to games with players of different cognitive or reasoning abilities. As an analytical tool, NLK exists and gives a sharp prediction in general, and therefore it can be applied to empirical analysis in a broad range of settings.
