The Emergence of Social Networks from Interactive Learning
Reviewing the Geography of Innovation literature about Knowledge Spillovers phenomena we find the idea that knowledge externalities spring from an interactive learning process between agents. Learning by personal contacts does not only require a geographical closeness, but also cognitive proximity that makes possible understanding and social proximity that facilitates personal interactions. We explore this issue developing an agent-based model in which the capacity of learning is a function of the knowledge distance. After a successful meeting agents reinforce the corresponding relation, drawing their own map of preferences that will condition next contacts in the future. The social network is defined from a record of successful contacts that actually represents an adjacency matrix of a weighted network.
KeywordsReinforcement Learning Absorptive Capacity Weighted Network Knowledge Externality Choice Rule
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