Asymmetric Empirical Similarity

The paper offers a formal model of analogical legal reasoning and takes the model to data. Under the model, the outcome of a new case is a weighted average of the outcomes of prior cases. The weights capture precedential influence and depend on fact similarity (distance in fact space) and precedential authority (position in the judicial hierarchy). The empirical analysis suggests that the model is a plausible model for the time series of U.S. maritime salvage cases. Moreover, the results evince that prior cases decided by inferior courts have less influence than prior cases decided by superior courts

Construction and refinement of preference ordered decision classes

Preference learning methods are commonly used in multicriteria analysis. The working principle of these methods is similar to classical machine learning techniques. A common issue to both machine learning and preference learning methods is the difficulty of the definition of decision classes and the assignment of objects to these classes, especially for large datasets. This paper proposes two procedures permitting to automatize the construction of decision classes. It also proposes two simple refinement procedures, that rely on the 80-20 principle, permitting to map the output of the construction procedures into a manageable set of decision classes. The proposed construction procedures rely on the most elementary preference relation, namely dominance relation, which avoids the need for additional information or distance/(di)similarity functions, as with most of existing clustering methods. Furthermore, the simplicity of the 80-20 principle on which the refinement procedures are based, make them very adequate to large datasets. Proposed procedures are illustrated and validated using real-world datasets

Intelligent decision support systems for collaboration in industrial plants

Dissertação apresentada para obtenção do Grau de Doutor em Sistemas de Informação Industriais, Engenharia Electrotécnica, pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaThe objective of this thesis is to contribute for a structured and systematic decision-making process for industrial companies, particularly involving several actors, helping them make the best use of their resources. The paradigms of how industrial companies operate have been progressively changing over the last two decades. The flexible and dynamic flow of information and persons over companies has created new challenges and opportunities for industry. It is not possible to dissociate an enterprise from its human resources and the knowledge they create and use. Companies face decisions constantly, involving several actors and situations. With the market pressure and rapid changing environments, decisions are becoming more complex, and involving more people with complementary expertise. The knowledge processes are only efficient if the actors can anchor and relate the information handled to the extended enterprise. Therefore, an enterprise model is a fundamental aspect to support decision-making in industry. This work includes an overview of existing modelling methodologies and standards. Afterwards, it proposes an enterprise model to represent an extended or virtual enterprise, suitable not only for decision-making applications but also for others. This thesis considers methods and systems to support decision and analyses decision types and processes. Afterwards, the thesis presents some considerations on decision-making in industry and a generic decision-making process, including, a review of decision criteria commonly used in industry. Two of the methods widely used in some of the mentioned areas, case-based reasoning and the analytic hierarchy process, have been used in the scope of problem solving and decision-making, respectively. This thesis presents an approach based on a combination of case-based reasoning and analytic hierarchy process to support innovation, particularly product design in industry. The combination overcomes shortcomings of both methods to provide the most adequate decision support for multi-disciplinary teams in innovation processes. Moreover, the work presented proposes an algorithm for automatic adjustment of the weight of the actors in the decision process. This thesis includes case studies, developed in the scope of several research projects, used as practical applications of the work developed. These practical applications include seven test cases (with two manufacturing companies, two assembling companies, two engineering services companies and one software company) where the proposed enterprise model and methods have been applied with the purpose of supporting decisions. This highlights the wide application of the proposed model, describing its possible interpretations and the successful use of the decision support approach in industrial companies.Projects PICK (IST-1999-10442), AIM (IST-2001-52222), FOKSai (COOP-CT-2003-508637), InLife (FP6-2005-NMP2-CT-517018), InAmI (FP6-2004-IST-NMP-2-16788) and K-NET (FP7-ICT-1-215584), all of which were partially funded by the Research Framework Programs of the European Unio

Emotions and cognitive workload in economic decision processes - A NeuroIS Approach

The influence of cognitive and emotions on decision processes have been recently highlighted. Emotions interplay with the process of cognition, and determine decision processes. In this work, the role of external and internal influences on economic decision processes are studied. A NeuroIS method is applied for measuring emotions and cognitive workload. The lack of a suitable experimental platform for performing NeuroIS studies was recognized and the platform Brownie was developed and evaluated

Evaluating Case-based Decision Theory: Predicting Empirical Patterns of Human Classification Learning (Extensions)

We introduce a computer program which calculates an agent’s optimal behavior according to Case-based Decision Theory (Gilboa and Schmeidler, 1995) and use it to test CBDT against a benchmark set of problems from the psychological literature on human classification learning (Shepard et al., 1961). This allows us to evaluate the efficacy of CBDT as an account of human decision-making on this set of problems. We find: (1) The choice behavior of this program (and therefore Case-based Decision Theory) correctly predicts the empirically observed relative difficulty of problems and speed of learning in human data. (2) ‘Similarity’ (how CBDT decision makers extrapolate from memory) is decreasing in vector distance, consistent with evidence in psychology (Shepard, 1987). (3) The best-fitting parameters suggest humans aspire to an 80 − 85% success rate, and humans may increase their aspiration level during the experiment. (4) Average similarity is rejected in favor of additive similarity

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

Classification is one of the critical issues in the operations management of spare parts. The issue of managing spare parts involves multiple criteria to be taken into consideration, and therefore, a number of approaches exists that consider criteria such as criticality, price, demand, lead time, and obsolescence, to name a few. In this paper, we first review proposals to deal with inventory control. We then propose a three-phase multicriteria classification framework for spare parts management using the dominance-based rough set approach (DRSA). In the first phase, a set of ‘if–then’ decision rules is generated from historical data using the DRSA. The generated rules are then validated in the second phase by using both the automated and manual approaches, including cross-validation and feedback assessments by the decision maker. The third and final phase is to classify an unseen set of spare parts in a real setting. The proposed approach has been successfully applied to data collected from a manufacturing company in China. The proposed framework was practically tested on different spare parts and, based on the feedback received from the industry experts, 96% of the spare parts were correctly classified. Furthermore, the cross-validation results show that the proposed approach significantly outperforms other well-known classification methods. The proposed approach has several important characteristics that distinguish it from existing ones: (i) it is a learning-set based analysis approach; (ii) it uses a powerful multicriteria classification method, namely the DRSA; (iii) it validates the generated decision rules with multiple strategies; and (iv) it actively involves the decision maker during all the steps of the decision making process

Case-Based knowledge and Induction

Abstract—Case-Based Decision Theory (CBDT) is a theory of decision making under uncertainty, suggesting that people tend to choose acts that performed well in similar cases they recall. The theory has been developed from a decision-/game-/economic-theoretical point of view, as a potential alternative to expected utility theory (EUT). In this paper, we attempt to reconsider CBDT as a theory of knowledge representation, to contrast it with the rulebased approach, and to study its implications regarding the process of induction. Index Terms—Analogical reasoning, case-based decision theory. I