Abstract
With rare exceptions, new ideas necessarily emerge in the minds of individuals through the recombination of existing ideas, but the epistemic repertoire for this recombination is supplied largely by ideas the individual has acquired from external sources, including interaction with peers. When agents hear new ideas and integrate them into their minds, they also implicitly create potential new ideas which can then become explicit as new ideas through later introspection. In this research, we use a multi-agent model to study such implicit learning in a social network and its relationship with the number of unique novel ideas actually expressed by agents in the network. We focus on the impact of two crucial factors: (1) The structure of the social network; and (2) The selectivity of agents in accepting ideas from their peers. We look at both latent ideas, i.e., those that are still implicit in the minds of individual agents, and novel expressed ideas, i.e., those that are expressed for the first time in the network. The results show that both network structure and the selectivity of influence have significant impact on the outcomes – especially in a system with misinformation.
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Acknowledgment
This work was supported in part by National Science Foundation INSPIRE grant BCS-1247971 to Ali Minai.
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Shekfeh, M., Minai, A.A. (2018). Implicit Learning and Creativity in Human Networks: A Computational Model. In: Morales, A., Gershenson, C., Braha, D., Minai, A., Bar-Yam, Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-96661-8_15
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