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The rough set theory, based on the original definition of the indiscernibility relation, is not useful for analysing incomplete information tables where some values of attributes are unknown. In this paper we distinguish two different semantics for incomplete information: the "missing value" semantics and the "absent value" semantics. The already known approaches, e.g. based on the tolerance relations, deal with the missing value case. We introduce two generalisations of the rough sets theory to handle these situations. The first generalisation introduces the use of a non symmetric similarity relation in order to formalise the idea of absent value semantics. The second proposal is based on the use of valued tolerance relations. A logical analysis and the computational experiments show that for the valued tolerance approach it is possible to obtain more informative approximations and decision rules than using the approach based on the simple tolerance relation.

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The paper presents the concept of decision aiding process as an extension of the decision process. The aim of the paper is to analyse the type of activities occurring between a "client" and an "analyst" both engaged in a decision process. The decision aiding process is analysed both under a cognitive point of view and an operational point of view: i.e. considering the "products", or cognitive artifacts the process will deliver at the end. Finally the decision aiding process is considered as a reasoning process for which the update and revision problems hold.

The paper presents the author's partial and personal historical reconstruction of how decision theory evolved to decision aiding methodology. The presentation shows mainly how "alternative" approaches to classic decision theory evolved. In the paper is claimed that all such decision "theories" share a common methodological feature which is the use of formal and abstract languages as well as of a model of rationality. Different decision aiding approaches can thus be defined, depending on the origin of the model of rationality used in the decision aiding process. The concept of decision aiding process is then introduced and analysed. The paper ultimate claim is that all such approaches can be seen as part of a decision aiding methodology.

Recommender systems are software applications that attempt to reduce information overload. Their goal is to recommend items of interest to the end users based on their preferences. To achieve that, most Recommender Systems exploit the Collaborative Filtering approach. In parallel, Multiple Criteria Decision Analysis (MCDA) is a well established field of Decision Science that aims at analyzing and modeling decision maker's value system, in order to support him/her in the decision making process. In this work, a hybrid framework that incorporates techniques from the field of MCDA, together with the Collaborative Filtering approach, is analyzed. The proposed methodology improves the performance of simple Multi-rating Recommender Systems as a result of two main causes; the creation of groups of user profiles prior to the application of Collaborative Filtering algorithm and the fact that these profiles are the result of a user modeling process, which is based on individual user's value system and exploits Multiple Criteria Decision Analysis techniques.Experiments in real user data prove the aforementioned statement.

The growing impact of the ''analytics'' perspective in recent years, which integrates advanced data-mining and learning methods, is often associated with increasing access to large databases and with decision support systems. Since its origin, the field of analytics has been strongly business-oriented, with a typical focus on data-driven decision processes. In public decisions, however, issues such as individual and social values, culture and public engagement are more important and, to a large extent, characterise the policy cycle of design, testing, implementation, evaluation and review of public policies. Therefore public policy making seems to be a much more socially complex process than has hitherto been considered by most analytics methods and applications. In this paper, we thus suggest a framework for the use of analytics in supporting the policy cycle-and conceptualise it as ''Policy Analytics''.Keywords Analytics Á Policy analysis Á Decision analysis Á Policy cycle Á Decision support

The paper aims at addressing the problem of what makes specific aiding to decide within public policy making problem situations. Under such a perspective it analyses some basic concepts such as "public policy", "deliberation", "legitimation", "accountability" and shows the necessity to expand the concept of rationality which is expected to be behind the acceptability of a public policy. We then analyse the more recent tentative to construct a rational support to policy making, that is the "evidence-based policy making" approach. Despite the innovation introduced with this approach, we show that it basically fails to address the deep reasons for which supporting the design, implementation and assessment of public policies is a hard problem. We finally show that we need to move one step ahead, specialising decision aiding methodology to meet the policy cycle requirements: a demand for policy analytics.