2015/10/27 by Mihaela van der Schaar, van der Schaar, Mihaela, Simpson Zhang +1
Decision Sciences · Physics and Astronomy · Social Sciences · #Evolutionary Game Theory and Cooperation #FOS: Computer and information sciences #FOS: Economics and business #FOS: Physical sciences #Game Theory and Applications #General Economics (econ.GN) #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1510.08103
openalex publication_date 2015/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
This paper considers the evolution of a network in a discrete time, stochastic setting in which agents learn about each other through repeated interactions and maintain/break links on the basis of what they learn from these interactions. Agents have homophilous preferences and limited capacity, so they maintain links with others who are learned to be similar to themselves and cut links to others who are learned to be dissimilar to themselves. Thus learning influences the evolution of the network, but learning is imperfect so the evolution is stochastic. Homophily matters. Higher levels of homophily decrease the (average) number of links that agents form. However, the effect of homophily is anomalous: mutually beneficial links may be dropped before learning is completed, thereby resulting in sparser networks and less clustering than under complete information. There may be big differences between the networks that emerge under complete and incomplete information. Homophily matters here as well: initially, greater levels of homophily increase the difference between the complete and incomplete information networks, but sufficiently high levels of homophily eventually decrease the difference. Complete and incomplete information networks differ the most when the degree of homophily is intermediate. With multiple stages of life, the effects of incomplete information are large initially but fade somewhat over time.