Creative Thinking and Modelling for the Decision Support in Water Management

This paper reviews the state of art in knowledge and preferences elicitation techniques. The purpose of the study was to evaluate various cognitive mapping techniques in order to conclude with the identification of the optimal technique for the NetSyMod methodology. Network Analysis – Creative System Modelling (NetSyMod) methodology has been designed for the improvement of decision support systems (DSS) with respect to the environmental problems. In the paper the difference is made between experts and stakeholders knowledge and preference elicitation methods. The suggested technique is very similar to the Nominal Group Techniques (NGT) with the external representation of the analysed problem by means of the Hodgson Hexagons. The evolving methodology is undergoing tests within several EU-funded projects such as: ITAES, IISIM, NostrumDSS.Creative modelling, Cognitive mapping, Preference elicitation techniques, Decision support

Piagetian and Neo-Piagetian variables in science problem solving: directions for practice

Este artigo apresenta uma visão global da investigação sobre variáveis piagetianas e neopiagetianas implicadas na solução de problemas, focando, ainda, como estas va riáveis afetam a performance dos envolvidos na solução daqueles. Algumas das variáveis discutidas dizem respeito à habilidade formal de operacionalização: habilidade de razoamento formal, M-espaço, fator campo (dependência/independência de campo), estilo cognitivo móbil/fixo, capacidade da memória de trabalho e colaboração nas tarefas de resolução de problemas. A partir da discussão efetuada, são sugeridas algumas recomendações para a melhora da instrução na resolução de problemas

Towards Quantum Gravity: A Framework for Probabilistic Theories with Non-Fixed Causal Structure

General relativity is a deterministic theory with non-fixed causal structure. Quantum theory is a probabilistic theory with fixed causal structure. In this paper we build a framework for probabilistic theories with non-fixed causal structure. This combines the radical elements of general relativity and quantum theory. The key idea in the construction is physical compression. A physical theory relates quantities. Thus, if we specify a sufficiently large set of quantities (this is the compressed set), we can calculate all the others. We apply three levels of physical compression. First, we apply it locally to quantities (actually probabilities) that might be measured in a particular region of spacetime. Then we consider composite regions. We find that there is a second level of physical compression for the composite region over and above the first level physical compression for the component regions. Each application of first and second level physical compression is quantified by a matrix. We find that these matrices themselves are related by the physical theory and can therefore be subject to compression. This is the third level of physical compression. This third level of physical compression gives rise to a new mathematical object which we call the causaloid. From the causaloid for a particular physical theory we can calculate verything the physical theory can calculate. This approach allows us to set up a framework for calculating probabilistic correlations in data without imposing a fixed causal structure (such as a background time). We show how to put quantum theory in this framework (thus providing a new formulation of this theory). We indicate how general relativity might be put into this framework and how the framework might be used to construct a theory of quantum gravity.Comment: 23 pages. For special issue of Journal of Physics A entitled "The quantum universe" in honour of Giancarlo Ghirard

Proceedings of the ECCS 2005 satellite workshop: embracing complexity in design - Paris 17 November 2005

Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr). Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr)

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers