The influence of random interactions and decision heuristics on norm evolution in social networks |
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Authors: | Declan Mungovan Enda Howley Jim Duggan |
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Institution: | (1) Information Systems Department, University of Lausanne, Lausanne, Switzerland;(2) Department of Computer Science, University of Essex, Colchester, UK |
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Abstract: | In this paper we explore the effect that random social interactions have on the emergence and evolution of social norms in
a simulated population of agents. In our model agents observe the behaviour of others and update their norms based on these
observations. An agent’s norm is influenced by both their own fixed social network plus a second random network that is composed
of a subset of the remaining population. Random interactions are based on a weighted selection algorithm that uses an individual’s
path distance on the network to determine their chance of meeting a stranger. This means that friends-of-friends are more
likely to randomly interact with one another than agents with a higher degree of separation. We then contrast the cases where
agents make highest utility based rational decisions about which norm to adopt versus using a Markov Decision process that
associates a weight with the best choice. Finally we examine the effect that these random interactions have on the evolution
of a more complex social norm as it propagates throughout the population. We discover that increasing the frequency and weighting
of random interactions results in higher levels of norm convergence and in a quicker time when agents have the choice between
two competing alternatives. This can be attributed to more information passing through the population thereby allowing for
quicker convergence. When the norm is allowed to evolve we observe both global consensus formation and group splintering depending
on the cognitive agent model used. |
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