主题：【皇家赌场】Decision rule approach to preference modelling
主讲人：波兰波兹南工业大学 Roman Słowiński教授
主办单位：工商管理学院 国际交流与合作处 科研处
Roman Słowiński is a Professor and Founding Chair of the Laboratory of Intelligent Decision Support Systems at Poznań University of Technology, and a Professor in the Systems Research Institute of the Polish Academy of Sciences. As an ordinary member of the Polish Academy of Sciences he is its Vice President, elected for the term 2019-2022. He is a member of Academia Europaea and Fellow of IEEE, IRSS, INFORMS and IFIP. In his research, he combines Operational Research and Artificial Intelligence for Decision Aiding. Recipient of the EURO Gold Medal by the European Association of Operational Research Societies (1991), and Doctor HC of Polytechnic Faculty of Mons (Belgium, 2000), University Paris Dauphine (France, 2001), and Technical University of Crete (Greece, 2008). In 2005 he received the Annual Prize of the Foundation for Polish Science - the highest scientific honor awarded in Poland, and in 2020 - the Scientific Award of the Prime Minister of Poland. Since 1999, he is the principal editor of the European Journal of Operational Research (Elsevier), a premier journal in Operational Research.
The aim of scientific decision aiding is to give the decision maker(s) a recommendation concerning a set of potential actions evaluated from multiple points of view considered relevant for the decision problem at hand. These multiple points of view can be: (i) multiple voters, or (ii) multiple evaluation criteria, or (iii) multiple states of the world. They are the cornerstones of the three big sub-disciplines of decision science: (i) group decision, (ii) multiple-criteria decision making, and (iii) decision under risk and uncertainty. Their common feature is the fact that the only objective information stemming from the formulation of the corresponding decision problems is the dominance relation in the set of actions. As dominance relation is a partial weak order, it makes many actions non-comparable. To rank or classify the actions one needs to aggregate the multiple points of view, taking into account preferences of the decision maker(s) (DMs). The aggregation, which is equivalent to construction of the DMs’ preference model, is a great challenge of scientific decision aiding. In this talk, I will show how this challenge has been solved with a set of dominance-based “if..., then...” decision rules discovered from the data by inductive learning. The data are training examples showing DMs’ past decisions on a set of actions. As these examples can be inconsistent, it is convenient to structure the data prior to induction of rules using the Dominance-based Rough Set Approach (DRSA). DRSA is a methodology for reasoning about ordinal data, which extends the classical rough set approach by handling ordinal evaluations of actions and monotonic relationships between their evaluations. Since its conception, DRSA has been adapted to a large variety of decision problems. We present DRSA to preference discovery in case of multi-attribute classification, choice and ranking, in the case of evolutionary multiobjective optimization, and in the case of decision under uncertainty. The set of dominance-based decision rules has severaladvantages overits competitor preference models (utility functions, and outranking relations): (a) each rule is a readable scenario of a causal relationship between evaluations on a subset of attributes and a comprehensive judgment, (b) decision rules exploit ordinal information only and do not convert ordinal evaluation scales into numeric ones, (c) decision rules are non-compensatory aggregation operators, and (d) they are able to represent more complex interactions than Choquet and Sugeno integrals.