Speaker: Keith Hipel, University Professor, Department of Systems Design Engineering University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
Place: Forum Chriesbach, FC-D24

The Graph Model for Conflict Resolution (GMCR) is applied to the explosive dispute that arose over the discovery of a carcinogen in the aquifer supplying water to the town of Elmira, located in Southern Ontario, Canada, to demonstrate how one can realistically investigate conflict in order to obtain strategic insights for supporting informed decision making. This highly divisive groundwater contamination conflict is utilized to explain a rich range of inherent capabilities of GMCR, as well as worthwhile avenues for extensions, which make GMCR a truly powerful decision technology for addressing challenging conflict situations. Moreover, the crucial importance of scientifically taking into account both the societal and physical systems aspects of this complex problem is emphasized within an interdisciplinary system of systems thinking perspective. A flexible preference elicitation method, called option prioritization, is employed to obtain the relative preferences of each decision maker (DM) in the dispute over the states or scenarios which can occur, based upon naturally expressed preference statements regarding the options or courses of actions available to the DMs. Solution concepts, reflecting how a chess player thinks in terms of moves and counter-moves, are utilized to mirror the ways humans may behave under conflict, varying from short to long term thinking and from risk-averse to risk-seeking outlooks. After ascertaining the best outcome that a DM can achieve on his or her own in a conflict, coalition analysis is used to check if a DM can fare even better by cooperating with others. For the Elmira dispute, potential equilibria or compromise resolutions are predicted and the reasons for the decision of two of the disputants to form a coalition and bring about a dramatic resolution to the conflict are explained. The ability of GMCR to capture emotions, strength of preference, attitudes, misunderstandings (referred to as hypergames), and uncertain preferences (unknown, fuzzy, grey and probabilistic), greatly broadens its scope of applicability. Techniques for tracing how a conflict can evolve over time from a status quo state to a final specified outcome, as well as how to handle hierarchical structures, such as when a central government interacts with its provinces or states, further enforce the comprehensive nature of GMCR. In fact, an Artificial Intelligence algorithm is available to determine how DMs in a conflict must think in terms of preference in order to reach a desirable outcome in what is called the inverse engineering problem. Learning how DMs may think allows creativity in purposefully directing a dispute towards a win/win resolution.