How do we best organize agents to solve difficult problems? Should they compete or collaborate? If they collaborate, who should collaborate with whom? While organizing problem-solving work as a competition can provide strong incentives to exert high levels of effort and allows exploring multiple solutions in parallel, collaboration can allow learning from others and leverage synergies. The nature of the problem to be solved, information sharing structures among agents attempting to solve the problem, and characteristics and knowledge of agents can all affect which mode of organizing agents performs best.
This project blends theories and methods from the social sciences with computational methods from computer science and mathematical modeling. Specifically, we study the tradeoffs between competition and collaboration through experiments and agent-based modelling. We implement a series of experiments and simulations in which agents/individuals attempt to selforganize and solve complex tasks. Agents/individuals are exposed to a variety of treatments modifying (a) the amount and type of information shared, (b) the structure of the underlying communication network (which agent can communicate with which other agents; i.e., network topology), and (c) the heterogeneity of agents. This research identifies social network processes within and across groups of competing and collaborating individuals and determines their impact on practices and performance of individuals, teams, and organizations. This research makes important contributions to the literature on collaborative work and collective decision-making. This research supports the changing nature of work by providing insights into individual and organizational factors that drive collective decision-making and collaborative networks applied to solve complex and dynamic problems. This research contributes to a better understanding of the changing nature of work in which large groups of agents operate as self-organized systems.
Christoph Riedl, Faculty, Network Science