Adaptive Multidimensional Scaling: The Spatial Representation of Brand Consideration and Dissimilarity Judgments

We propose Adaptive Multidimensional Scaling (AMDS) for simultaneously deriving a brand map and market segments using consumer data on cognitive decision sets and brand dissimilarities.In AMDS, the judgment task is adapted to the individual respondent: dissimilarity judgments are collected only for those brands within a consumers' awareness set.Thus, respondent fatigue and subjects' unfamiliarity with any subset of the brands are circumvented; thereby improving the validity of the dissimilarity data obtained, as well as the multidimensional spatial structure derived.Estimation of the AMDS model results in a spatial map in which the brands and derived segments of consumers are jointly represented as points.The closer a brand is positioned to a segment's ideal brand, the higher the probability that the brand is considered and chosen.An assumption underlying this model representation is that brands within a consumers' consideration set are relatively similar.In an experiment with 200 subjects and 4 product categories, this assumption is validated.We illustrate adaptive multidimensional scaling on commercial data for 20 midsize car brands evaluated by 212 members of a consumer panel.Potential applications of the method and future research opportunities are discussed.scaling;brands;market segmentation

Modal Similarity

Just as Boolean rules define Boolean categories, the Boolean operators define higher-order Boolean categories referred to as modal categories. We examine the similarity order between these categories and the standard category of logical identity (i.e. the modal category defined by the biconditional or equivalence operator). Our goal is 4-fold: first, to introduce a similarity measure for determining this similarity order; second, to show that such a measure is a good predictor of the similarity assessment behaviour observed in our experiment involving key modal categories; third, to argue that as far as the modal categories are concerned, configural similarity assessment may be componential or analytical in nature; and lastly, to draw attention to the intimate interplay that may exist between deductive judgments, similarity assessment and categorisation

Kernel functions based on triplet comparisons

Given only information in the form of similarity triplets "Object A is more similar to object B than to object C" about a data set, we propose two ways of defining a kernel function on the data set. While previous approaches construct a low-dimensional Euclidean embedding of the data set that reflects the given similarity triplets, we aim at defining kernel functions that correspond to high-dimensional embeddings. These kernel functions can subsequently be used to apply any kernel method to the data set

On a Consensus Measure in a Group Multi-Criteria Decision Making Problem.

A method for consensus measuring in a group decision problem is presented for the multiple criteria case. The decision process is supposed to be carried out according to Saaty's Analytic Hierarchy Process, and hence using pairwise comparison among the alternatives. Using a suitable distance between the experts' judgements, a scale transformation is proposed which allows a fuzzy interpretation of the problem and the definition of a consensus measure by means of fuzzy tools as linguistic quantifiers. Sufficient conditions on the expert's judgements are finally presented, which guarantee any a priori fixed consensus level to be reached.group decision making; multiple criteria; degree of consensus; fuzzy preferences

When We Don\u27t See Eye to Eye: Discrepancies Between Supervisors and Subordinates in Absence Disciplinary Decisions

This study provided a within-subjects assessment of the factors associated with absence disciplinary decisions for both supervisors and subordinates. In addition, this study examined discrepancies in disciplinary decisions between a supervisor and his or her subordinates based on differences in psychological and demographic attributes. A sample of non-academic employees from 19 intact triads (one supervisor; two subordinates) at a large Midwest university responded to hypothetical scenarios describing factors that might contribute to absence disciplinary decisions. The results demonstrated that both supervisors and subordinates consider the same set of factors as relevant to disciplinary decisions. Furthermore, with few exceptions, psychological and demographic differences between supervisors and subordinates related positively to discrepancies in disciplinary decisions. The implications of these findings for managing disciplinary programs in organizations are discussed

Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks

Generative adversarial networks (GANs) are increasingly attracting attention in the computer vision, natural language processing, speech synthesis and similar domains. Arguably the most striking results have been in the area of image synthesis. However, evaluating the performance of GANs is still an open and challenging problem. Existing evaluation metrics primarily measure the dissimilarity between real and generated images using automated statistical methods. They often require large sample sizes for evaluation and do not directly reflect human perception of image quality. In this work, we describe an evaluation metric we call Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals. Our results show that Neuroscore has superior performance to the current evaluation metrics in that: (1) It is more consistent with human judgment; (2) The evaluation process needs much smaller numbers of samples; and (3) It is able to rank the quality of images on a per GAN basis. A convolutional neural network (CNN) based neuro-AI interface is proposed to predict Neuroscore from GAN-generated images directly without the need for neural responses. Importantly, we show that including neural responses during the training phase of the network can significantly improve the prediction capability of the proposed model. Materials related to this work are provided at https://github.com/villawang/Neuro-AI-Interface

Supervised Classification: Quite a Brief Overview

The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements---also called features or inputs---to the so-called class label---or output. The fields of pattern recognition and machine learning study ways of constructing such classifiers. The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class? This chapter provides a basic introduction to the underlying ideas of how to come to a supervised classification problem. In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner