A Consensus-Reaching Approach to the Evaluation of Product Design Alternatives with Multiple Preference Structures

With interdisciplinarity being an important characteristic of contemporary product design, the evaluation of design alternatives also involves multiple disciplines, and the evaluator group usually consists of evaluators from different fields and with obvious heterogeneous characteristics. To effectively satisfy the heterogeneous needs of evaluators and improve the credibility of evaluation results, the paper introduces a consensus-reaching approach that incorporates multiple preferences to the evaluation of product design alternatives. First, in order to obtain individual preference information, each evaluator is asked to evaluate all the design alternatives using a preference structure that he/she is familiar with. Second, we use a transfer function to uniform the evaluation information obtained from various preference structures into a complementary judgment matrix. Then, we use the Hybrid Weighted Averaging (HWA) operator weight determination model to aggregate the preference information and obtain the group preference information. Then, we measure the consensus degree between individual evaluators and the group using a consensus measurement method. After that, we use the feedback mechanism to instruct individual evaluators to modify their preferences until a consensus is achieved. We explain the application steps and the feasibility of this approach through the evaluation of the design alternatives of multichannel fluorescence immunochromatography analyzers (MFIAs).

The Logic of Inference and Decision for Scientific Evidence

Uncertainty is an inevitable complication encountered by members of the judiciary who face inference and decision-making as core aspects of their daily activities. Inference, in this context, is mainly inductive and relates to the use of incomplete information, to reason about propositions of interest. Applied to scientific evidence, this means, for example, to reason about whether or not a person of interest is the source of a recovered evidential material and factfinders are required to make decisions about ultimate issues, for example, regarding a defendant’s guilt. The role of forensic scientists, whose duty is to help assess the probative value of scientific findings, is to offer to mandate authorities’ conclusions that are scientifically sound and logically defensible. This chapter lays out the fundamentals of inference and decision-making under uncertainty with regard to forensic evidence. The authors explicate explain the subjectivist version of Bayesianism and analyze the usefulness of the likelihood ratio in for measuring the degree to which the evidence discriminates between competing propositions in a trial. They also underscore emphasize the importance of decision analysis as a framework that forces helps decision-makers to formalize preference structures.

Preferences and Production under Uncertainty

The Arrow-Savage-Debreu formalism (state space, consequence space, acts) for modelling a stochastic decision is introduced. Preferences over stochastic outcomes framed as maps (acts) from the state space to the consequence space are studied and related to nonstochastic preference structures. Distance function representations of preferences are developed and their superdifferential correspondences are shown to define subjective probability measures. Structural restrictions including uncertainty aversion, constant absolute uncertainty aversion, and constant relative uncertainty aversion are examined and related to parallel restrictions for nonstochastic preference or production structures. A model of a stochastic technology that has the nonstochastic production model as a special case is introduced, and distance function representations of it are discussed. Structural assumptions on the stochastic technology are discussed.

Equilibrium, Efficiency, and Welfare

Competitive equilibria are studied in both partial-equilibrium and general-equilibrium settings for economies characterized by consumers with incomplete preference structures. Market equilibrium determination is developed as solving a zero-maximum problem for a supremal convolution whose dual, by Fenchel's Duality Theorem, coincides with a zero-minimum for an infimal convolution that characterizes Pareto optima. The First and Second Welfare Theorems are natural consequences. The maximization of the sum of consumer surplus and producer surplus is studied in this analytic setting, and the implications of nonsmooth preference structures or technologies for equilibrium determination are discussed.

The Consumer Problem

The theory of a rational consumer characterized by an incomplete preference order is developed using distance functions and the zero-minimum (zero-maximum) principle. The essential comparative-static properties of the associated quantity-dependent and price-dependent demand structures are characterized. Utility functions are derived from distance functions for preference structures satisfying a complete ordering assumption. The Marshall-Hicks demand theory that is based on a utility-maximizing consumer is derived as a special case of rational consumer behavior. The Hicks-Allen demand decomposition is reviewed and a conjugate profit function approach to utility maximization is developed and used to discuss Revealed Preference Theory. The Chapter closes by examining the structural consequences of the independence axiom for d(x,y;g).

Smart mobility – an analysis of potential customers’ preference structures

Cities around the world face major challenges caused by the extensive use of private cars. To counteract these problems, a new paradigm is necessary which promotes alternative mobility services. ‘Smart mobility’ refers to a new mobility behaviour that makes use of innovative technical solutions, such as the IT-supported combination of different alternative mobility services during a trip from an origin to a destination. Unfortunately, relatively few customers use apps that provide recommendations for smart mobility and there is limited knowledge about the desires, priorities and needs of potential customers. To fill this gap, we use conjoint analysis to explore differences in smart mobility app preferences across groups of people with varying mobility behaviour. Our study also considers the effect of age and place of residence on preference structures. Our results show, for example, that only car drivers do not consider the price of the smart mobility app to be particularly important for their selection decision.

PrefMap: Visualization of fuzzy pairwise preference structures

Fuzzy pairwise preferences are an important model to specify and process expert opinions. A fuzzy pairwise preference matrix contains degrees of preference of each option over each other option. Such degrees of preference are often numerically specified by domain experts. In decision processes it is highly desirable to be able to analyze such preference structures, in order to answer questions like: Which objects are most or least preferred? Are there clusters of options with similar preference? Are the preferences consistent or partially contradictory? An important approach for such analysis is visualization. The goal is to produce good visualizations of preference matrices in order to better understand the expert opinions, to easily identify favorite or less favorite options, to discuss and address inconsistencies, or to reach consensus in group decision processes. Standard methods for visualization of preferences are matrix visualization and chord diagrams, which are not suitable for larger data sets, and which are not able to visualize clusters or inconsistencies. To overcome this drawback we propose PrefMap, a new method for visualizing preference matrices. Experiments with nine artificial and real–world preference data sets indicate that PrefMap yields good visualizations that allow to easily identify favorite and less favorite options, clusters, and inconsistencies, even for large data sets.

Logics of Preference when There Is No Best

Well-behaved preferences (e.g., total pre-orders) are a cornerstone of several areas in artificial intelligence, from knowledge representation, where preferences typically encode likelihood comparisons, to both game and decision theories, where preferences typically encode utility comparisons. Yet weaker (e.g., cyclical) structures of comparison have proven important in a number of areas, from argumentation theory to tournaments and social choice theory. In this paper we provide logical foundations for reasoning about this type of preference structures where no obvious best elements may exist. Concretely, we compare and axiomatize a number of ways in which the concepts of maximality and optimality can be generalized in this general class of preferences. We thereby expand the scope of the long-standing tradition of the logical analysis of preference.

Determinants of Purchasing Intention for Re-Commerce in the Fashion Industry

The re-commerce concept has gained huge attention from the consumer in the fashion market. India is a country with people from different cultural backgrounds and communities. Clothing is treated differently in India. Due to the important social significance of textiles, clothing is rarely thrown away. For this study, 200 university students and professors have been selected and their behavior analyzed. This study found that Indian consumers are always in need of uniqueness, and self-perception has an indirect impact on their purchase intention or buying behavior. Consumers with different levels of understanding, culture, and beliefs also showed differing preference structures. Results showed the intention of buying reused or recycled clothes mainly to match the lifestyle to satisfy the individual desires.