Part 1: Networks and organizations I: Core concepts and methods to understand networks in organizations
Instructor: Eric Quintane
The first part of the course introduces social network analysis within organizations. We will start by discussing key ideas and debates in social network analysis, such as the notion of embeddedness, network structure and the role of individual agency. We will then take a deeper dive into the methodological implications of doing research with social network data. We will cover 1) measures used to identify network positions and key network characteristics, 2) more advanced statistical models developed to handle the problem of dependence of observations and 3) concepts and measures regarding network dynamics. This first part will equip you with the knowledge that you need to embark in more advanced topics in network analysis.
Part 2: Networks and organizations II: Further topics in network analysis, including semantic networks,
strategic network formation, and Bonacich centrality
Instructor: Matt Bothner
The second part of the course explores empirical applications of network-analytic methods to a wide array of agents—professional auto racers, gangsters, college fraternity members, and words in semantic networks. In addition to our empirical emphasis, we’ll consider a game-theoretic network formation model designed to better understand the performance-related consequences of peer monitoring within the firm.
Part 3: Organizational learning, behavioral strategy, and luck
Instructor: Chengwei Liu
The third part of the course focuses on organizational learning and its implications for strategy. We will cover canonical ideas/models, such as the exploration and exploitations trade-off, the traps when organizations learn from successes and failures, how randomness complicates learning, and how learning reinforces or changes organizational routines. A framework of strategy as arbitrage will be introduced to connect several ideas covered in this course.
Please see schedule and syllabus attached.
