A basic problem, at the present stage of the Information society, is how to manage the cognitive process while taking into account its intrinsic features of uncertainty, including imprecision and vagueness. This has both theoretical and practical implications in Technology, Economics, Bio-medicine, etc. Traditional Statistics has developed tools and procedures for coping with this problem, assuming that uncertainty is basically due to random mechanisms appropriately handled by means of Probability. The theory of Fuzzy Sets and its generalization to what may be called "Fuzzy thinking" has widened the scope of Statistics enabling us to deal with more general sources of uncertainty such as vagueness and imprecision as referred to both empirical data and/or models for data analysis.

On the other hand, the need for statistical methods capable of managing complex problems exploiting heterogeneous information (diversity of definitions and measurements, different degrees of imprecision and confidence with reference to both data and assumptions) has been greatly increased by the availability of large data bases and information systems in almost every field of activity. In recent years, many of the ideas and techniques developed by the "Fuzzy Community" have been translated into the statistical language, yielding new methodological proposals, especially (but not exclusively) in the areas of Regression and Cluster Analysis. At the same time, several suggestions have put forward, concerning the use of fuzzy random variables and of the traditional probabilistic machinery for extending the scope of classical (crisp) inferential methods (e.g. the general linear model) to cover fuzzy data. In spite of the impact of this growing literature, there is relevant room for further developments in several directions, including foundations, methodology and applications of the Fuzzy approach to Statistics.

Focus:

This track is meant to stimulate and present contributions and all of above directions. Foundational issues may include:

- Use of Possibility Theory in Statistics
- Construction and utilization of Fuzzy Probabilistic models in statistical inference
- Relationship between Conditional Probability and Fuzzy Information
- Comparison between Fuzzy and Traditional (crisp) Statistical Methods
- Fuzzy approach vs Interval Analysis approach Methodological

Contributors may encompass such field as:
- Regression (possibilistic, least squares, or mixed approaches)
- Classification (cluster and discriminant analyses)
- Latent structure analysis and scaling techniques
- Inferential procedures for the analysis of dependence and association.

Applications of Fuzzy Statistical methods may cover various domains such as:
- Biomedicine
- Finance and Economics
- Social sciences and Psychology
- Environmental sciences
- Technology

The track will offer a forum for discussing and comparing different viewpoints and proposals on a critical junction of the new Information Science, located at the crossroads of so far separated pathways of knowledge and research: cognitive sciences, artificial intelligence, engineering, informatics, mathematics, philosophy of science, statistics, and others.

Co-Chairs:

Renato Coppi Department of Statistics University of Rome P.le Aldo Moro, 5 00185 Rome Italy E-mail: renato.coppi@uniroma1.it	Maria Angeles Gil Department of Statistics University of Oviedo C/Calvo Sotelo,s/n 33007 Oviedo Spain E-mail: angeles@pinon.ccu.uniovi.es