The aim is to present rough granular methodology as a promising and a serviceable tool in modelling of both social agents, interacting under uncertain and incomplete information, as well as systems of such agents. Mathematical and logical tools are used. The paper is partly based on the authoress' earlier results (since 1999). Also some key notions of the rough set theory are recalled. The rough-set methodology is particularly useful when approximate solutions to problems are acceptable. A number of illustrative examples are given.The notion of an information granule, fundamental for granular computing, is discussed within the rough-set framework. Various examples of information granules are provided, e.g. rough classifiers, rule complexes, and economic clusters. A social agent, both individual and collective, may be modelled as a rule complex, i.e., an information granule; and similarly for the case of agent systems. Having a model of a system of social agents built of information granules, one can reason about social interaction under incomplete and vague information. Rough granular methodology may be used to build models of a social agent and a system of such agents. However, it will take time and effort to work out all details needed to apply the method to real-life social agent systems.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.