Jan W. Owsiński

Consensus reaching via a GDSS with fuzzy majority and clustering of preference profiles

An interactive DSS for consensus reaching is presented. Experts provide their testimonies as fuzzy preference relations. The consensus reaching process is supervised by a moderator (“super-expert”). A degree of consensus, based on the concept of a fuzzy majority given as a linguistic quantifier is employed. Algorithms of cluster analysis are used to find groups of experts having similar preferences.

Using Poset Theory to Compare Fuzzy Multidimensional Material Deprivation Across Regions

In this paper, a new approach to the fuzzy analysis of multidimensional material deprivation data is provided, based on partial order theory. The main feature of the methodology is that the information needed for the deprivation assessment is extracted directly from the relational structure of the dataset, avoiding any kind of scaling and aggregation procedure, so as to respect the ordinal nature of the data. An example based on real data is worked out, pertaining to material deprivation in Italy for the year 2004.

Analysis of Water Use and Needs in Agriculture Through a Fuzzy Programming Model

A regional agricultural system is represented and optimized via a two-level linear programming model of significant dimensions. One of its major purposes is to assess the role of water resources in the system with special emphasis on the feasibility of irrigation. Some of the model’s data are assumed fuzzy because of the specifics of the problem, i.e. lack of precise knowledge and an appropriate statistical basis, with a simultaneous clear interest in attaining or not exceeding certain predefined levels. The paper presents the model and how fuzziness is represented in it. Results of several runs are shown and commented, related to the use of water, to its significance for the system’s overall performance, as well as to the interrelations of water with other crucial resources such as, e.g., capital. The conclusions refer mainly to the features of the system and to the technical and interpretational aspects of the ways fuzziness is represented and manipulated.

A New Heuristic Possibilistic Clustering Algorithm for Feature Selection

The paper deals with the problem of selection of the most informative features. A new effective and efficient heuristic possibilistic clustering algorithm for feature selection is proposed. First, a brief description of basic concepts of the heuristic approach to possibilistic clustering is provided. A technique of initial data pre- processing is described and a fuzzy correlation measure is considered. The new algorithm is described and then illustrated on the well-known Iris data set benchmark and the results obtained are compared with those by us- ing the conventional, well-known and widely employed method of principal component analysis (PCA). Conclu- sions and suggestions for future research are given.

Agriculture, population, environment and balanced regional development: A modelling project in Poland

Abstract This paper considers a regional agricultural development programme, assuming intensive water systems and irrigation constructions and a build-up of agricultural production. In order to analyse possible future developments and policies, a number of mathematical computerised models were created. These models are briefly characterised, together with their joint functioning as a system of models for multi-aspect regional planning, balancing the various values—economic, social and environmental—involved in any socio-economic development related to agriculture. In particular, a detailed regional agricultural model is presented, along with its mode of operation and an example of the results. The interrelationship of basic development-triggered values and objectives is illustrated by means of these results.

A Simple Software System for Eliciting Structured Sets of Notions From a Group of Experts (Methods and Experiences)

A simple software system is presented, meant primarily for IBM-PC-like equipment, based mainly upon a relatively flexible application of three types of algorithms: aggregation of precedences clustering and simple structural analysis.

On dynamic modeling of car populations. Part I

Abstract A computer model is presented, derived previously for dynamic modeling of biological populations, and applied to numbers of cars classified according to car “age” and broadly conceived “type.” The model allows first of all an easy construction of various hypotheses concerning car market and car characteristics, related to types, survival, etc., and then an easy assessment of consequences of such hypotheses. Besides model structure, the paper indicates its possible uses by showing instances of runs.

Structuring the set of MPs in Polish Parliament: A simple clustering exercise

The paper presents a simple exercise of application of cluster analysis to the set of voting data for the members of Polish Parliament of the previous term, which started in 1993. Each MP is characterized by the vector containing the specification of MPs behaviour during individual votings, distinguishing the following categories: “For”, “Against”, “Abstained”, “Not voted”, and “Absent”. The distances between the “MP descriptions” containing the thus defined categories of behaviour during votings (forming the matrix of dimensions: number of MPs × number of votings) constitute the basis for the analytic procedure based upon clustering. We are looking for the clusters of similar MP descriptions which are, simultaneously, possibly different between clusters.The analysis performed does not account for the membership of the MPs in the political groupings (parties and alliances) within the parliament, and one of the main goals of the analysis is just to identify the degree of agreement between the results of the cluster analysis and the membership in such groupings. This is done after the procedure of clustering has been performed. Another aspect being of interest in the analysis is dependence upon (sensitivity to) the parametric definitions of distances with respect to the categories adopted in the description of voting (e.g., “is absence the same as not voting?”). The results of analysis are meant to show to what extent the actual behaviour of the MPs reflect their political membership and what are the actual relations (in terms of true behaviour) between political groupings represented in the parliament.The objective of the altogether modest study was two-fold: (1) to assess the capacity of the (definite algorithms of) cluster analysis in identification of the group structure of attitudes expressed through voting behaviour, and, given that such a capacity exists, (2) to compare the structure obtained with the formal membership in the political organizations, and also, though only marginally, with the structure of the political scene assumed or perceived in the mass media or through the political pronouncements.

Preferences, agreement, consensus — measuring, aggregation and control

This paper presents a model for analysis and a practical method of management of a session meant at attainment of consensus regarding preferences over a set of multi-aspect options, these preferences being expressed through pairwise comparisons or orderings. The model and the method accept and process valued or fuzzy preferences, while preserving an entirely “crisp” procedure throughout the approach. The aggregation method presented previously in Owsiński and Zadrożny [17, 18] is referred to and expanded for the case of clusterwise preference aggregation and consensus measurement. The framework of the clustering approach of Owsiński [19] is applied, allowing for a natural definition of consensus.

On dynamic modeling of car populations. Part II: A data-based analysis

Abstract The conversational computer model of the dynamics of car populations, presented in Part I of this paper, is shown against the background of actual data. First, contents of the model are outlined, and its basic assumptions are illustrated with the data available. Then, model runs are cited to provide a basis for comparative analysis of actual and theoretical behavior and potential causes of eventual divergences.