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Network Formation by Contagion Averse Agents: Modeling Bounded Rationality with Logit Learning

Published

Author(s)

Vladimir V. Marbukh

Abstract

Economic and convenience benefits of interconnectivity drive current explosive emergence and growth of networked systems. However, as recent catastrophic contagious failures in numerous large scale networked infrastructures demonstrated, these benefits of interconnectivity are inherently associated with various risks, including risk of undesirable contagion. Current research on network formation by contagion risk averse agents, which analyzes Nash or some other game-theoretic equilibrium notion of the corresponding game, suffers from interrelated problems of intractability and oversimplification. We argue that these problems can be alleviated with dynamic view, which assumes logit responses by strategic agents with utilities quantifying multiple competing incentives. While this approach naturally incorporates practically critical assumption of bounded rationality, it also allows for leveraging a vast body of results on network formation, e.g., preferential attachment in growing networks.
Conference Dates
August 28-31, 2018
Conference Location
Barcelona
Conference Title
The 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
(ASONAM 2018) The 4th International Workshop on Dynamics on and of Networks (DYNO 2018)

Keywords

Network formation, contagion risk averseness, bounded rationality, preferential attachment, SIS contagion.

Citation

Marbukh, V. (2018), Network Formation by Contagion Averse Agents: Modeling Bounded Rationality with Logit Learning, The 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018) The 4th International Workshop on Dynamics on and of Networks (DYNO 2018), Barcelona, -1, [online], https://doi.org/10.1109/ASONAM.2018.8508546 (Accessed July 23, 2024)

Issues

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Created August 31, 2018, Updated May 14, 2020