**Summary:** Collective decision systems such as teams, markets, polls, and votes are central to the way society organizes and allocates resources. The use of these and related modern mechanisms is on the rise, finding applications in areas as diverse as problem-solving, technological and economic forecasting, crowdsourcing, product rating, public policy design, and mapping natural disasters. Hence, providing a sound understanding of and useful design guidelines for these systems is of paramount importance. Although scientists have generated a tremendous number of studies on this topic, they have been much less successful at reconciling some of the inconsistencies and contradictions amongst them. For instance, studies have shown that social interaction (i.e., via communication networks) can either promote the “wisdom of the crowd” or, conversely, lead to the “madness of the mob” by inducing social bias, herding, and group-think. Different theories have disagreed on which communication structural attributes are most relevant, and empirical studies offered an overwhelming lack of consistent evidence. However, many of the studies on this topic only consider, explicitly or implicitly, static communication network structures and stable environments, offering at best a partial view of human collective decision making. Here we address this question using a series of human experiments and supporting simulations. Our results show that the optimal communication structure depends on the “environment,” and that groups provided with high-quality feedback and connected by dynamic communication structures, as opposed to static ones, can adapt to biased and non-stationary information environments, significantly improving both individual and collective judgments. Furthermore, we find that the collective performance of these groups can substantially surpass that of their best individual and that even the best individual’s judgment is substantially benefited from group engagement. Our findings help reconcile some previously conflicting claims from the collective intelligence literature and motivate a future research program to more systematically identify stable principles of collective performance.
Thursday, 4/11/2019
11:00am-12:20pm
Kent 120
A light lunch will be provided by Noodles, Etc.
**Abdullah Almaatouq** is Research Assistant at the Human Dynamics group and pursuing a PhD in Computational Science & Engineering at the Massachusetts Institute of Technology (MIT). He did dual masters’ at MIT in Computation for Design & Optimization (CDO) and Media, Arts & Sciences (MAS), and before that he received his bachelor degree from the School of Electronics and Computer Science at Southampton University, UK. Abdullah’s current research interests lie in the area where computational and social sciences meet. In particular, his work includes conducting theoretical and empirical research on human behavior using innovative approaches and tools ranging from complex systems theory and agent-based modeling, to network analysis, econometric techniques, and behavioral and experimental methods.
The 2018-2019 Computational Social Science Workshop meets Thursdays from 11 a.m. to 12:20 p.m. in Kent 120. All interested faculty and graduate students are welcome.
Students in the Masters of Computational Social Science program are expected to attend and join the discussion by posting a comment on the issues page of the workshop's public repository on GitHub. Further instructions are documented in the Computational Social Science Workshop's README on Github.