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Collective intelligence under a task-dependent framework




15: Management

Faculty Supervisor:

Abdullah Almaatouq

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Project Description

A large body of work has shown that a group of individuals can often achieve higher levels of intelligence than the group members working alone. Despite these expectations of group advantage, many examples of collective failure have been documented -- from market crashes to the spread of false and harmful rumors. To reconcile these results, a significant effort in the study of collective decision making has been focused on understanding the role of group composition and communication patterns in promoting the "wisdom of the crowd" or, conversely, leading to the "madness of the mob." In the past decades, much of this effort has been devoted to inferring the importance of a particular attribute, in isolation, by its capacity to explain the accuracy of collective judgments. In this project, we argue that such a perspective can lead to inconsistent conclusions: an 'incoherency problem.' We assert that the importance of an individual-level or structural attribute may depend on the type and complexity of the task at hand. Hence, we propose a research agenda to investigate the relative importance of the group composition and the structure of interaction networks under a task-dependent framework.


[Research can be carried out **remotely**] The UROP will aid in developing and administering tasks on Amazon Mechanical Turk (MTurk). This includes UI/UX design and implementation of dynamic surveys as well as the participation in the production of research products (e.g., conference papers, talks). Skills you SHOULD have already: Strong UI/UX design concepts, React.js/Javascript/HTML/CSS. Skills you will learn/hone: UI/UX design, experimental design, data analysis, theories of collective intelligence. Pluses: HCI experience, amazon mechanical turk (MTurk)