Semiparametric estimation of (constrained) ultrametric trees

This paper introduces a general, formal treatment of dynamic constraints, i.e., constraints on the state changes that are allowed in a given state space. Such dynamic constraints can be seen as representations of "real world" constraints in a managerial context. The notions of transition, reversible and irreversible transition, and transition relation will be introduced. The link with Kripke models (for modal logics) is also made explicit. Several (subtle) examples of dynamic constraints will be given. Some important classes of dynamic constraints in a database context will be identified, e.g. various forms of cumulativity, non-decreasing values, constraints on initial and final values, life cycles, changing life cycles, and transition and constant dependencies. Several properties of these dependencies will be treated. For instance, it turns out that functional dependencies can be considered as "degenerated" transition dependencies. Also, the distinction between primary keys and alternate keys is reexamined, from a dynamic point of view.

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

A Rapid Reaction to O\u27Bannon: The Need for Analytics in Applying the Sherman Act to Overly Restrictive Joint Venture Schemes

This Article reviews the recent and highly publicized district court decision holding that NCAA rules, which bar student-athletes from any compensation for image rights, violated the Sherman Act, and that big-time athletic programs could lawfully agree among themselves to limit compensation to $5,000 annually in trust for each athlete upon leaving school. This Article briefly discusses why the decision correctly found the current rule to be illegal, but also details why, under settled antitrust law, the critical question of how much compensation would significantly harm consumer appeal for college football and basketball is a question better left to marketing science experts. This Article then explains why neither the flawed survey offered in evidence by the NCAA, nor the anecdotal testimony of NCAA officials, should have been credited. Rather, this Article proposes, as a superior alternative, the use of conjoint analysis, a well-recognized technique of marketing science analytics employed to answer the critical legal question that the antitrust doctrine asks in cases like this

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.

Selecting Competitive Tactics: Try a Strategy Map

When developing strategy, a manager considers how various tactics will affect short-term performance and broad strategic direction. The skilled manager keeps those factors in mind and, simultaneously, gauges what the competition is up to. The authors describe a mapping technique that will help managers to do just that. Not only does the technique provide an accessible measure of relative competitive standing, but it also allows managers to simulate tactical changes and analyze their probably impact on business performance

A spatial interaction model for deriving joint space maps of bundle compositions and market segments from pick-any/J data: An application to new product options

We propose an approach for deriving joint space maps of bundle compositions and market segments from three-way (e.g., consumers x product options/benefits/features x usage situations/scenarios/time periods) pick-any/J data. The proposed latent structure multidimensional scaling procedure simultaneously extracts market segment and product option positions in a joint space map such that the closer a product option is to a particlar segment, the higher the likelihood of its being chosen by that segment. A segment-level threshold parameter is estimated that spatially delineates the bundle of product options that are predicted to be chosen by each segment. Estimates of the probability of each consumer belonging to the derived segments are simultaneously obtained. Explicit treatment of product and consumer characteristics are allowed via optional model reparameterizations of the product option locations and segment memberships. We illustrate the use of the proposed approach using an actual commercial application involving pick-any/J data gathered by a major hi-tech firm for some 23 advanced technological options for new automobiles.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47207/1/11002_2004_Article_BF00434905.pd