The influence of career histories on team perfomance in new product development: a study of the video game industry

The purpose of this research is to contribute to the literature on organizational demography and new product development by investigating how diverse individual career histories impact team performance. Moreover we highlighted the importance of considering also the institutional context and the specific labour market arrangements in which a team is embedded, in order to interpret correctly the effect of career-related diversity measures on performance. The empirical setting of the study is the videogame industry, and the teams in charge of the development of new game titles. Video games development teams are the ideal setting to investigate the influence of career histories on team performance, since the development of videogames is performed by multidisciplinary teams composed by specialists with a wide variety of technical and artistic backgrounds, who execute a significant amounts of creative thinking. We investigate our research question both with quantitative methods and with a case study on the Japanese videogame industry: one of the most innovative in this sector. Our results show how career histories in terms of occupational diversity, prior functional diversity and prior product diversity, usually have a positive influence on team performance. However, when the moderating effect of the institutional setting is taken in to account, career diversity has different or even opposite effect on team performance, according to the specific national context in which a team operates.

Distributed optimization and games over networks: a system theoretical perspective

Several decision and control tasks involve networks of cyber-physical systems that need to be coordinated and controlled according to a fully-distributed paradigm involving only local communications without any central unit. This thesis focuses on distributed optimization and games over networks from a system theoretical perspective. In the addressed frameworks, we consider agents communicating only with neighbors and running distributed algorithms with optimization-oriented goals. The distinctive feature of this thesis is to interpret these algorithms as dynamical systems and, thus, to resort to powerful system theoretical tools for both their analysis and design. We first address the so-called consensus optimization setup. In this context, we provide an original system theoretical analysis of the well-known Gradient Tracking algorithm in the general case of nonconvex objective functions. Then, inspired by this method, we provide and study a series of extensions to improve the performance and to deal with more challenging settings like, e.g., the derivative-free framework or the online one. Subsequently, we tackle the recently emerged framework named distributed aggregative optimization. For this setup, we develop and analyze novel schemes to handle (i) online instances of the problem, (ii) ``personalized'' optimization frameworks, and (iii) feedback optimization settings. Finally, we adopt a system theoretical approach to address aggregative games over networks both in the presence or absence of linear coupling constraints among the decision variables of the players. In this context, we design and inspect novel fully-distributed algorithms, based on tracking mechanisms, that outperform state-of-the-art methods in finding the Nash equilibrium of the game.