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.