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Decision Making under Synchronous Time Stress among Bilateral Decision Makers

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Next Generation Data Technologies for Collective Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 352))

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Abstract

In this chapter, we discuss a theory on process for decision making under time stress which is common or synchronous constraint among two or bilateral decision makers. Its typical application is found in a system that analyzes the pricing mechanism of group-or-collaborative behaviors among multi-party using multi-agents. The problem on time stress is to evaluate cost of time or value of the entire duration of a certain process for decision making.We propose a formula to compute cost of time by introduction of opportunity cost to its evaluation. We also propose a formula on strategic points for decision making to minimize cost of time for a certain process for decision making under time stress as synchronous constraint. A strategic point is always located at the one-third of the entire or remaining duration for decision procedures, instead of a heuristic point of its half time. We have conducted a feasibility check on the proposed formulas on cost of time and strategic points in their applications to a case study.

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Author information

Authors and Affiliations

  1. Computer Science, Department of Information Science and Engineering, Ritsumeikan University, 6-4-10 Wakakusa, Kusatsu, Shiga, 525-0045, Japan

    Hideyasu Sasaki

  2. Keio Research Institute at SFC, Keio University, 5322 Endo, Fujisawa, Kanagawa, 252-0882, Japan

    Hideyasu Sasaki

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  1. Hideyasu Sasaki
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Editor information

Editors and Affiliations

  1. School of Computing & Maths, University of Derby, DE22 1GB, Derby, United Kingdom (UK)

    Nik Bessis

  2. Dept de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Barcelona, Spain

    Fatos Xhafa

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Sasaki, H. (2011). Decision Making under Synchronous Time Stress among Bilateral Decision Makers. In: Bessis, N., Xhafa, F. (eds) Next Generation Data Technologies for Collective Computational Intelligence. Studies in Computational Intelligence, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20344-2_10

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  • DOI: https://doi.org/10.1007/978-3-642-20344-2_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20343-5

  • Online ISBN: 978-3-642-20344-2

  • eBook Packages: EngineeringEngineering (R0)

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