Modeling networked systems using the topologically distributed bounded rationality framework. (7th May 2016)

• Record Type: Journal Article
• Authors: Kasthurirathna, Dharshana; Piraveenan, Mahendra; Uddin, Shahadat
• Is Part Of: Complexity. Volume 21(2016)Supplement 2
• Page Start: 123
• Page End: 137
• Publication Date: 2016-05-07
• Subjects: networked systems -- peer‐to‐peer networks -- network routing -- computer security -- game theory -- TDBR model; Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1002/cplx.21789
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : In networked systems research, game theory is increasingly used to model a number of scenarios where distributed decision making takes place in a competitive environment. These scenarios include peer‐to‐peer network formation and routing, computer security level allocation, and TCP congestion control. It has been shown, however, that such modeling has met with limited success in capturing the real‐world behavior of computing systems. One of the main reasons for this drawback is that, whereas classical game theory assumes perfect rationality of players, real world entities in such settings have limited information, and cognitive ability which hinders their decision making. Meanwhile, new bounded rationality models have been proposed in networked game theory which take into account the topology of the network. In this article, we demonstrate that game‐theoretic modeling of computing systems would be much more accurate if a topologically distributed bounded rationality model is used. In particular, we consider (a) link formation on peer‐to‐peer overlay networks (b) assigning security levels to computers in computer networks (c) routing in peer‐to‐peer overlay networks, and show that in each of these scenarios, the accuracy of the modeling improves very significantly when topological models of bounded rationality are applied in the modeling process. Our results indicate that it is possible to use game theory to model competitive scenarios in networked systems in a way that closely reflects real world behavior, topology, and dynamics of such systems. © 2016 Wiley Periodicals, Inc. Complexity 21: 123–137, 2016