In the multi-agent systems community, relatively little attention has been paid to understanding and comparing organizations at a quantitative level. In this thesis, I show that it is possible to develop such an understanding, and in particular I show how quantitative information can form the basis of a predictive, proscriptive organizational model. This can in turn lead to more efficient, robust and context-sensitive systems by increasing the level of detail at which competing organizational designs are evaluated.

To accomplish this, I introduce a new, domain-independent organizational design representation able to model and predict the quantitative performance characteristics of agent organizations. This representation, capable of capturing a wide range of multi-agent characteristics in a single, succinct model, supports the selection of an appropriate design given a particular operational context. I demonstrate the representational capabilities and efficacy of the language by comparing a range of metrics predicted by detailed models of a distributed sensor network and information retrieval system to empirical results. In addition to their predictive ability, these same models also describe the range of possible organizations in those domains. I show how general search techniques can be used to explore this space, using those quantitative predictions to evaluate alternatives and enable automated organizational design.

@phdthesis{Horling-409,
  author    = "Bryan Horling",
  title     = "{Quantitative Organizational Modeling and Design
               for Multi-Agent Systems}",
  school    = "University of Massachusetts at Amherst",
  month     = "February",
  year      = "2006",
  url       = "http://mas.cs.umass.edu/paper/409",
}