In decision analysis, difficulties of obtaining complete information about model parameters make it advisable to seek robust solutions that perform reasonably well across the full range of feasible parameter values. In this paper, we develop the Robust Portfolio Modeling (RPM) methodology which extends Preference Programming methods into portfolio problems where a subset of project proposals ar...

This paper begins to operationalize a previously published conceptual model of the individual decision to telecommute. Using survey data from 628 employees of the City of San Diego, hypothesized drives to telecommute and constraints on / facilitators of telecommuting are measured. A binary logit model of the preference to telecommute from home is estimated, having a p 2 of 0.68. The explanatory...

One of the new trends in genetic fuzzy systems (GFS) is the use of evolutionary multiobjective optimization (EMO) algorithms. This is because EMO algorithms can easily handle two conflicting objectives (i.e., accuracy maximization and complexity minimization) when we design accurate and compact fuzzy rule-based systems from numerical data. Since the main advantage of fuzzy rule-based systems co...

We establish the conditions that must be satisfied for the mathematical operations of linear algebra and calculus to be applicable. The mathematical foundations of decision theory as well as other theories depend on these conditions which have not been correctly identified in the classical literature.

In the last decades the use of regression-like preference models has found widespread application in marketing research. The Conjoint Analysis models have even more been used to analyze consumer preferences and simulate product positioning. The typical data structure of this kind of models can be enriched by the presence of supplementary information observed on respondents. We suppose that rela...

As proposed in various places, a set of propositional formulas, each associated with a numerical weight, can be used to model the preferences of an agent in combinatorial domains. If the range of possible choices can be represented by the set of possible assignments of propositional symbols to truth values, then the utility of an assignment is given by the sum of the weights of the formulas it ...

Understanding user preference is essential to the optimization of recommender systems. As a feedback of user’s taste, rating scores can directly reflect the preference of a given user to a given product. Uncovering the latent components of user ratings is thus of significant importance for learning user interests. In this paper, a new recommendation approach, called LCR, was proposed by investi...

Customers often show different preferences relative to the same products, such as function, shape, color, costs, etc. They will surely affect product market activities further. In this paper, a model of preference elicitation from customers is proposed to reduce the gap between the systematic representation of preferences and customers’ actual preferences. Firstly, the attribute of customer pre...

Abstract Most of reasoning for decision making in daily life is based on preferences. As other kinds of reasoning processes, there are many formalisms trying to capture preferences, however none of them is able to capture all the subtleties of the human reasoning. In this paper we analise how to formalize the preferences expressed by humans and how to reason with them to produce rankings. Parti...

Abstract Most of reasoning for decision making in daily life is based on preferences. As other kinds of reasoning processes, there are many formalisms trying to capture preferences, however none of them is able to capture all the subtleties of the human reasoning. In this paper we analise how to formalize the preferences expressed by humans and how to reason with them to produce rankings. Parti...