Implicit Learning and Creativity in Human Networks: A Computational Model
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.
This work was supported in part by National Science Foundation INSPIRE grant BCS-1247971 to Ali Minai.
- 4.Delgado, J., Pujol, J.M., Sangüesa, R.: Emergence of coordination in scale-free networks. Web Intell. Agent Syst. 1, 131–138 (2003)Google Scholar
- 5.Fauconnier, G., Turner, M.: The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. Basic Books, New York (2003)Google Scholar
- 9.Minai, A.A., Iyer, L.R., Padur, D., Doboli, S.: A dynamic connectionist model of idea generation. In: Proceedings of the IJCNN 2009, pp. 2109–2116 (2009)Google Scholar
- 12.Sen, O.: Effects of social network topology and options on norm emergence. In: Coordination, Organizations, Institutions and Norms in Agent Systems. LNCS, vol. 6069, pp. 211–222 (2010)Google Scholar
- 13.Shekfeh, M.: MANILA: a multi-agent framework for emergent associative learning and creativity in social networks. Ph.D. thesis, Department of Electrical Engineering and Computer Science, University of Cincinnati (2017)Google Scholar
- 15.Villatoro, D., Sen, S., Sabater-Mir, J.: Topology and memory effect on convention emergence. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technologies, pp. 233–240 (2009)Google Scholar