The problem considered in this chapter is how to assess different perceptions of changing socio-technical conflicts. Our approach to the solution to this problem of assessing conflict dynamics is to consider negotiation views within the context of perceptual information systems. Briefly, perceptual information systems (succinctly, perceptual systems) are real-valued, total, deterministic information systems. This particular form of an information system is a variant of the deterministic information system model introduced by Zdzislaw Pawlak during the early 1980s. This leads to a near set approach to evaluating perceptual granules derived from conflict situations considered in the context of perceptual systems. A perceptual granule is a set of perceptual objects originating from observations of objects in the physical world. Conflict situations typically result from different sets of viewpoints (perceptions) about issues under negotiation. Perceptual systems provide frameworks for representing and reasoning about different perceptions of socio-technical conflicts. Reasoning about conflict dynamics is made possible with nearness relations and tolerance perceptual near sets used to define a measure of nearness. Several approaches to the analysis of conflict situations are presented in this paper, namely, conflict graphs, approximation spaces and risk patterns. An illustrative example of a requirements scope negotiation for an automated lighting system is presented. The contribution of this chapter is a new way of representing and reasoning about conflicts in the context of requirements engineering with near set theory.
Top1. Introduction
All knowledge takes its place within the horizons opened up by perception. –Merleau-Ponty, Phenomenology of Perception, 1945.
Conflict analysis and conflict resolution play an important role in negotiation during contract-management situations in many organizations. Conflict situations also result due to different sets of viewpoints about issues under negotiation. In other words, perceptions about issues play an important role in conflict analysis. The main problem considered in this chapter is how to discover perceptual granules useful in conflict analysis. A perceptual granule is a set of perceptual objects originating from observations of objects in the physical world. In this article, there is a shift in the view of information systems, where information extracted from perception of physical objects is contrasted with information in attribute-value tables may or may not originate in the physical world. The other distinguishing feature of the proposed approach is a reliance on probe functions (total, real-valued functions) representing features of perceptual granules rather than the traditional rough set approach, where there is as reliance on partial functions representing attributes defined by information tables. Granulation can be viewed as a human way of achieving data compression and it plays a key role in implementing the divide-and-conquer strategy in human problem-solving. A comprehensive study of granular computing can be found in Bargiela and Pedrycz (2003)Zadeh (1997), Yao, J.T. (2008) and Yao, Y.Y. (2008), Skowron, A., Peters, J. F. (2008). In this chapter, our approach to the solution to the problem of discovery and reasoning about conflict situations comes from near set theory (see, e.g., Peters (2007b, 2007c), Peters and Wasilewski 2009).
The notion of a perceptual system was introduced by Peters (2007a, 2007b), Peters (2008) and elaborated in Peters and Wasilewski (2009). The idea of a perceptual system has its origins in earlier work on granulation by Peters, Skowron, Synak and Ramanna (2003) and in the study of rough set-based ethology by Peters, Henry and Ramanna (2005). Such a system is a new perception-based interpretation of the traditional notion of a deterministic information system by Zdzisław Pawlak(1981a, 1981b) as a real-valued, total, deterministic information system. Deterministic information systems were introduced independently by Zdzisław Pawlak (1981a) and elaborated by Ewa Orłowska (1998). It was also Orłowska (1982) who originally suggested that an approximation space provides a formal basis for perception or observation. This view of approximation spaces captures the kernel of near set theory, where perception is viewed at the level of classes in partitions of perceptual granules rather than at the level of individual objects (see, e.g., Peters (2007a, 2007b, 2007c) Peters, Skowron and Stepaniuk, 2007).