A Social Interpolation Model of Group Problem‐Solving

• Published in: Cognitive science Vol. 45; no. 12; pp. e13066 - n/a
• Main Authors: Sloman, Sabina J., Goldstone, Robert L., Gonzalez, Cleotilde
• Format: Journal Article
• Language: English
• Published: United States Wiley 01.12.2021; Wiley Subscription Services, Inc
• Subjects: Cognitive ability; Cognitive Processes; Computational modeling; Computer Simulation; Cooperative Learning; Discovery Learning; Expectation; Exploration–exploitation trade‐off; Exploratory Behavior; Generalization; Generalization, Psychological; Group Dynamics; Humans; Information Seeking; Information Utilization; Learning; Prior Learning; Problem Solving; Social discrimination learning; Social learning; Social networks; Social Problems; Social networks; Social learning; Group dynamics; Computational modeling; Exploration-exploitation trade-off
• ISSN: 0364-0213; 1551-6709; 1551-6709
• DOI: 10.1111/cogs.13066

How do people use information from others to solve complex problems? Prior work has addressed this question by placing people in social learning situations where the problems they were asked to solve required varying degrees of exploration. This past work uncovered important interactions between groups' connectivity and the problem's complexity: the advantage of less connected networks over more connected networks increased as exploration was increasingly required for optimally solving the problem at hand. We propose the Social Interpolation Model (SIM), an agent‐based model to explore the cognitive mechanisms that can underlie exploratory behavior in groups. Through results from simulation experiments, we conclude that “exploration” may not be a single cognitive property, but rather the emergent result of three distinct behavioral and cognitive mechanisms, namely, (a) breadth of generalization, (b) quality of prior expectation, and (c) relative valuation of self‐obtained information. We formalize these mechanisms in the SIM, and explore their effects on group dynamics and success at solving different kinds of problems. Our main finding is that broad generalization and high quality of prior expectation facilitate successful search in environments where exploration is important, and hinder successful search in environments where exploitation alone is sufficient.