Generalizing GAMETH: Inference rule procedure..

In this paper we present a generalisation of GAMETH framework, that play an important role in identifying crucial knowledge. Thus, we have developed a method based on three phases. In the first phase, we have used GAMETH to identify the set of “reference knowledge”. During the second phase, decision rules are inferred, through rough sets theory, from decision assignments provided by the decision maker(s). In the third phase, a multicriteria classification of “potential crucial knowledge” is performed on the basis of the decision rules that have been collectively identified by the decision maker(s).Knowledge Management; Knowledge Capitalizing; Managing knowledge; crucial knowledge;

Dynamic stable set

We study a dynamic vNM stable set in a compact metric space under the assumption of complete and continuous dominance relation. Internal and external stability are defined with respect to farsighted dominance. Stability of an outcome is conditioned on the history via which it is reached. A dynamic stable set always exists. Any covering set by Dutta (1988) coincides with the set of outcomes that are implementable via a dynamic stable set. The maximal implementable outcome set is a version of the ultimate uncovered set.vNM stable set, dynamic, history

Rough set and rule-based multicriteria decision aiding

The aim of multicriteria decision aiding is to give the decision maker a recommendation concerning a set of objects evaluated from multiple points of view called criteria. Since a rational decision maker acts with respect to his/her value system, in order to recommend the most-preferred decision, one must identify decision maker's preferences. In this paper, we focus on preference discovery from data concerning some past decisions of the decision maker. We consider the preference model in the form of a set of "if..., then..." decision rules discovered from the data by inductive learning. To structure the data prior to induction of rules, we use the Dominance-based Rough Set Approach (DRSA). DRSA is a methodology for reasoning about data, which handles ordinal evaluations of objects on considered criteria and monotonic relationships between these evaluations and the decision. We review applications of DRSA to a large variety of multicriteria decision problems

Dominance-based Rough Set Approach, basic ideas and main trends

Dominance-based Rough Approach (DRSA) has been proposed as a machine learning and knowledge discovery methodology to handle Multiple Criteria Decision Aiding (MCDA). Due to its capacity of asking the decision maker (DM) for simple preference information and supplying easily understandable and explainable recommendations, DRSA gained much interest during the years and it is now one of the most appreciated MCDA approaches. In fact, it has been applied also beyond MCDA domain, as a general knowledge discovery and data mining methodology for the analysis of monotonic (and also non-monotonic) data. In this contribution, we recall the basic principles and the main concepts of DRSA, with a general overview of its developments and software. We present also a historical reconstruction of the genesis of the methodology, with a specific focus on the contribution of Roman S{\l}owi\'nski.Comment: This research was partially supported by TAILOR, a project funded by European Union (EU) Horizon 2020 research and innovation programme under GA No 952215. This submission is a preprint of a book chapter accepted by Springer, with very few minor differences of just technical natur

Common reasoning in games: a Lewisian analysis of common knowledge of rationality

The game-theoretic assumption of ‘common knowledge of rationality’ leads to paradoxes when rationality is represented in a Bayesian framework as cautious expected utility maximisation with independent beliefs (ICEU). We diagnose and resolve these paradoxes by presenting a new class of formal models of players’ reasoning, inspired by David Lewis’s account of common knowledge, in which the analogue of common knowledge is derivability in common reason. We show that such models can consistently incorporate any of a wide range of standards of decision-theoretic practical rationality. We investigate the implications arising when the standard of decision-theoretic rationality so assumed is ICEU.Common reasoning; common knowledge; common knowledge of rationality; David Lewis; Bayesian models of games