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A cognitive computational model of collective search with social information

Sabina J. Sloman
Dr. Robert Goldstone
Indiana University
Prof. Cleotilde (Coty) Gonzalez
Carnegie Mellon University ~ Social and Decision Sciences Department

Many of the decisions we make in day-to-day life are made on the basis of incomplete information. The experiences of members of our social network are often an important source of decision-relevant information. In a 2008 experiment, Mason, Jones, and Goldstone showed that a person’s social network structure can have an impact on their success at identifying the optimal decision given incomplete information: Members of more interconnected networks excelled at easier tasks, while members of more dispersed networks did comparatively well when the task was more difficult. Drawing on these results, we synthesize work from various areas of cognitive science into a computational cognitive model of search in a social context: the Social Interpolation Model (SIM). The SIM incorporates three avenues for individual difference, or free parameters: breadth of generalization, degree of optimism, and degree to which personal experience is weighted more heavily than the experiences of others. We report the results of simulations of interacting agents who are embedded in the same task structure as the one designed by Mason et al. (2008) and whose behavior is determined by the SIM. Based on these simulation results, we discuss qualitative effects of varying each of the SIM’s free parameters in the context of different social network structures. Our work highlights interaction effects between information-processing biases, social context and task structure on agents’ success at identifying the optimal solution.



social networks
exploration-exploitation trade-off
agent-based modeling

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Cite this as:

Sloman, S., Goldstone, R., & Gonzalez, C. (2021, July). A cognitive computational model of collective search with social information. Paper presented at Virtual MathPsych/ICCM 2021. Via