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Using quantitative models to search for appropriate organizational designs

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Abstract

As the scale and scope of distributed and multi-agent systems grow, it becomes increasingly important to design and manage the participants’ interactions. The potential for bottlenecks, intractably large sets of coordination partners, and shared bounded resources can make individual and high-level goals difficult to achieve. To address these problems, many large systems employ an additional layer of structuring, known as an organizational design, that assigns agents different roles, responsibilities and peers. These additional constraints can allow agents to operate more efficiently within the system by limiting the options they must consider. Different designs applied to the same problem will have different performance characteristics, therefore it is important to understand the behavior of competing candidate designs. In this article, we describe a new representation for capturing such designs, and in particular we show how quantitative information can form the basis of a flexible, predictive organizational model. The representation is capable of capturing a wide range of multi-agent characteristics in a single, succinct model. We demonstrate the language’s capabilities and efficacy by comparing a range of metrics predicted by detailed models of a distributed sensor network and information retrieval system to empirical results. These same models also describe the space of possible organizations in those domains and several search techniques are described that can be used to explore this space, using those quantitative predictions and context-specific definitions of utility to evaluate alternatives. The results of such a search process can be used to select the organizational design most appropriate for a given situation.

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  1. Bryan Horling

    Present address: Google, Inc., Mountain View, CA, USA

Authors and Affiliations

  1. Department of Computer Science, University of Massachusetts, Amherst, MA, USA

    Bryan Horling & Victor Lesser

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  1. Bryan Horling
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  2. Victor Lesser
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Correspondence to Bryan Horling.

Additional information

This material is based upon work supported in part by the National Science Foundation Engineering Research Centers Program under NSF Award No. EEC-0313747. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Effort also sponsored in part by the Defense Advanced Research Projects Agency (DARPA) and Air Force Research Laboratory Air Force Materiel Command, USAF, under agreement number F30602-99-2-0525. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Defense Advanced Research Projects Agency (DARPA), Air Force Research Laboratory or the US Government.

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Horling, B., Lesser, V. Using quantitative models to search for appropriate organizational designs. Auton Agent Multi-Agent Syst 16, 95–149 (2008). https://doi.org/10.1007/s10458-007-9020-y

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