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Coordination Mechanisms in Multi Objective Setups: Results of an Agent-Based Simulation

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Coordination, Organizations, Institutions, and Norms in Agent Systems X (COIN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9372))

Abstract

In this paper, we analyze how different modes of coordination and different approaches of of multi objective decision making interfere with organizational performance and speed at which performance improves. The investigation is based on an agent-based simulation of a stylized hierarchical business organization. In particular, we employ a model based on the idea of NK-fitness landscapes, where we map multi objective decision making as adaptive walk on multiple performance landscapes. In our model, each landscape represents one objective. We find that the effect of coordination mode on performance and speed of performance improvement is critically shaped by the choice of multi objective decision making approach. In certain setups, more complex approaches of multi objective decision making turn out to be less sensitive to the choice of coordination mode.

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Notes

  1. 1.

    In order to investigate the research question, we apply a simulation approach. In particular, simulation appears to be a powerful research method that allows mapping hierarchical organizations, different modes of coordination, interacting agents and different methods of multi objective decision making. Due to the potential complexity and unpredictability of repeated simple patterns, formal modeling would lead to intractable dimensions [2]. Controlling the multitude of issues and disentangling effects of variables under research from other effects would find the boundaries of empirical research [33]. Simulation, on the contrary, appears to be a powerful method to face the complexity of the outlined research problem (cf. also [15–19]).

  2. 2.

    A more extensive review of models of agent organizations, autonomous agents in organizations, and approaches to build agent organizations can be found at [3, 4, 14, 35, 37].

  3. 3.

    Please note that superscript \(i\) indicates the single decision \(n^{i,t}\), which is directly related to performance contribution \(p^{i,t}_{g}\). This performance contribution might be affected by decisions other than the one indexed by \(i\), for the other decisions, we utilize superscript \(j\).

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Authors and Affiliations

  1. Faculty of Management and Economics, Alpen-Adria Universität Klagenfurt, Universitätsstraße 65-67, 9020, Klagenfurt, Austria

    Stephan Leitner & Friederike Wall

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  1. Stephan Leitner
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  2. Friederike Wall
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Correspondence to Stephan Leitner .

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Editors and Affiliations

  1. School of Computer Science &, University of Wollongong, Wollongong, New South Wales, Australia

    Aditya Ghose

  2. Department of Computing Science, University of Aberdeen, Aberdeen, United Kingdom

    Nir Oren

  3. Cisco Systems Inc., Durham, North Carolina, USA

    Pankaj Telang

  4. Comp. Science and Information Technol., RMIT University, Melbourne, Victoria, Australia

    John Thangarajah

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Leitner, S., Wall, F. (2015). Coordination Mechanisms in Multi Objective Setups: Results of an Agent-Based Simulation. In: Ghose, A., Oren, N., Telang, P., Thangarajah, J. (eds) Coordination, Organizations, Institutions, and Norms in Agent Systems X. COIN 2014. Lecture Notes in Computer Science(), vol 9372. Springer, Cham. https://doi.org/10.1007/978-3-319-25420-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-25420-3_9

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