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Optimising influence in social networks using bounded rationality models

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Abstract

Influence models enable the modelling of the spread of ideas, opinions and behaviours in social networks. Bounded rationality in social networks suggests that players make non-optimum decisions due to the limitations of access to information. Based on the premise that adopting a state or an idea can be regarded as being ‘rational’, we propose an influence model based on the heterogeneous bounded rationality of players in a social network. We employ the quantal response equilibrium model to incorporate the bounded rationality in the context of social influence. We hypothesise that bounded rationality of following a seed or adopting the strategy of a seed is negatively proportional to the distance from that node, and it follows that closeness centrality is the appropriate measure to place influencers in a social network. We argue that this model can be used in scenarios where there are multiple types of influencers and varying pay-offs of adopting a state. We compare different seed placement mechanisms to compare and contrast the optimum method to minimise the existing social influence in a network when there are multiple and conflicting seeds. We ascertain that placing of opposing seeds according to a measure derived from a combination of the betweenness centrality values from the seeds, and the closeness centrality of the network provide the maximum negative influence. Further, we extend this model to a strategic decision-making scenario where each seed operates a strategy in a strategic game.

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Authors and Affiliations

  1. Complex Systems Research Group, Faculty of Engineering and IT, University of Sydney, Sydney, NSW, 2006, Australia

    Dharshana Kasthurirathna, Michael Harrè & Mahendra Piraveenan

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  1. Dharshana Kasthurirathna
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  2. Michael Harrè
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  3. Mahendra Piraveenan
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Correspondence to Dharshana Kasthurirathna.

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Kasthurirathna, D., Harrè, M. & Piraveenan, M. Optimising influence in social networks using bounded rationality models. Soc. Netw. Anal. Min. 6, 54 (2016). https://doi.org/10.1007/s13278-016-0367-4

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  • DOI: https://doi.org/10.1007/s13278-016-0367-4

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