How much to copy from others?

Campbell, C., Izquierdo, E. & Goldstone, R. L. (2020).  How much to copy from others? The role of partial copying in social learning.  Proceedings of the 42nd Annual Conference of the Cognitive Science Society. (pp. 916-922). Toronto, Canada: Cognitive Science Society.

One of the major ways that people engage in adaptive problem solving is by copying the solutions of others. Most of the work on this field has focused on three questions: when to copy, who to copy from, and what to copy. However, how much to copy has been relatively less explored. In the current research, we are interested in the consequences for a group when its members engage in social learning strategies with different tendencies to copy entire or partial solutions and different complexities of search problems. We also consider different network topologies that affect the solutions visible to each member. Using a computational model of collective problem solving, we demonstrate that strategies where social learning involves partial copying outperform strategies where individuals copy entire solutions. We analyze the exploration/exploitation dynamics of these social learning strategies under the different conditions.

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Simple Threshold Rules Solve Explore/Exploit Trade-offs in a Resource Accumulation Search Task

Sang, K., Todd, P. M., Goldstone, R. L., & Hills, T. T. (2020).  Simple threshold rules solve explore/exploit tradeoffs in a resource accumulation search task. Cognitive Science44, e12817.

How, and how well, do people switch between exploration and exploitation to search for and accumulate resources? We study the decision processes underlying such exploration/exploitation trade-offs using a novel card selection task that captures the common situation of searching among multiple resources (e.g., jobs) that can be exploited without depleting. With experience, participants learn to switch appropriately between exploration and exploitation and approach optimal performance. We model participants’ behavior on this task with random, threshold, and sampling strategies, and find that a linear decreasing threshold rule best fits participants’ results. Further evidence that participants use decreasing threshold-based strategies comes from reaction time differences between exploration and exploitation; however, participants themselves report nondecreasing thresholds. Decreasing threshold strategies that “front-load” exploration and switch quickly to exploitation are particularly effective in resource accumulation tasks, in contrast to optimal stopping problems like the Secretary Problem requiring longer exploration.

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The Great Melting Pot: generating diversity by combining solutions across a global population

Izquierdo, E. J., Campbell, C. M., & Goldstone, R. L. (2018). The Great Melting Pot: generating diversity by combining solutions across a global population.  Annual Meeting of Collective Intelligence.  Zurich, Switzerland.

 One of the major ways that people engage in adaptive problem solving is by copying or imitating the solutions of others. Imitation saves an individual time and mitigates potential risks from individual trial-and-error learning. When an individual finds a neighbor with a better solution than theirs, copying their entire solution guarantees an improvement over the individual’s current condition. However, this reduces the diversity of solutions in the group and can lead the group to getting stuck in a local optima. One alternative is to copy the neighbor’s solution only partially, although this comes at a risk for the individual. Mixing two solutions may or may not lead to an improvement over their previous solution, but mixing has the potential to allow the group to explore entirely new areas of solution space. So, although partial copying comes at a cost to the individual, under what conditions does it benefit the group? In the current research, we are interested in the consequences for the group when its members engage in social learning strategies with different tendencies to copy entire or partial solutions, with different network topologies that affect the neighbors’ solutions visible to each member, and with different complexities of search tasks.

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