Quantitative organizational modeling and design for multi-agent systems

As the scale and scope of distributed and multi-agent systems grow, it becomes increasingly important to design and manage the participants\u27 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 particular and different roles, responsibilities and peers. These additional constraints can allow agents to operate effectively within a large-scale system, with little or no sacrifice in utility. Different designs applied to the same problem will have different performance characteristics, therefore it is important to understand and model the behavior of candidate designs. 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