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Multi-resource Minority Games: Redefining the Game

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Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 16))

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Abstract

The minority game has been studied by the scientific community in Artificial Intelligence and Multi-Agent Systems as a model for resource allocation. This paper contributes to the accumulating scientific literature in minority games by investigating an area lacking in current research: the availability of multiple resources and the effect of parameters in their utilization. Even though there is a research topic called multi-resource minority game, we argue here that they are more like a “multi-option” minority game. Under a multi-resource scenario we investigate two issues: (i) strategy sharing and (ii) the effect of resource capacity to agent attendance, variance, and winning rate. Furthermore, we introduce a new criteria named resource usage that has not been studied in minority game research and captures how well a resource is used. We find that the use of a single strategy is not as effective as using different strategies when attempting to utilize resources simultaneously.

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Notes

  1. 1.

    Assuming a resource capacity of around 50% of the population.

  2. 2.

    Only one process can occupy, hold or use a CPU (or a core of CPU in a multi-core CPU). So, when we talk about holding a CPU (or a core), we address the ready queue of the system’s processor. Sometimes, it is better for a process not to wait in a ready queue of a specific CPU, because it may take a long time and lead the process into a deadlock situation.

  3. 3.

    The first element of the strategy is taken into account corresponding to the situation where all previous outcomes of the game (or the recent ones in memory) should be zero (i.e. no agent chooses to use or go for the resource). Similarly, the last element represents the situation where all of the previous outcomes of the game is one (i.e. all agents choose to use the resource).

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Acknowledgment

The authors acknowledge support from National Science Foundation (NSF) grant No. 1263011.

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Authors and Affiliations

  1. Department of Computer Science, Weber State University, Ogden, UT, USA

    Daniel Romero

  2. Department of Computer Science and Information Systems, George Fox University, Newberg, OR, USA

    Elissa Shinseki

  3. Department of Computer Sciences, Florida Institute of Technology, Melbourne, FL, USA

    S. M. Mahdi Seyednezhad & Ronaldo Menezes

Authors
  1. Daniel Romero
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  2. Elissa Shinseki
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  3. S. M. Mahdi Seyednezhad
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  4. Ronaldo Menezes
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Corresponding author

Correspondence to S. M. Mahdi Seyednezhad .

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Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Romero, D., Shinseki, E., Seyednezhad, S.M.M., Menezes, R. (2018). Multi-resource Minority Games: Redefining the Game. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-56991-8_15

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  • DOI: https://doi.org/10.1007/978-3-319-56991-8_15

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