Greedoid languages provide a basis to infer best-fitting noncompensatory decision rules from full-rank conjoint data or partial-rank data such as consider-then-rank, consider-only, or choice data. Potential decision rules include elimination by aspects, acceptance by aspects, lexicographic by features, and a mixed-rule lexicographic by aspects (LBA) that nests the other rules. We provide a dynamic program that makes estimation practical for a moderately large numbers of aspects. We test greedoid methods with applications to SmartPhones (339 respondents, both full-rank and consider-then-rank data) and computers (201 respondents from Lenk et al. 1996). We compare LBA to two compensatory benchmarks: hierarchical Bayes ranked logit (HBRL) and LINMAP. For each benchmark, we consider an unconstrained model and a model constrained so that aspects are truly compensatory. For both data sets, LBA predicts (new task) holdouts at least as well as compensatory methods for the majority of the respondents. LBA's relative predictive ability increases (ranks and choices) if the task is full rank rather than consider then rank. LBA's relative predictive ability does not change if (1) we allow respondents to presort profiles, or (2) we increase the number of profiles in a consider-then-rank task from 16 to 32. We examine trade-offs between effort and accuracy for the type of task and the number of profiles.lexicography, noncompensatory decision rules, choice heuristics, optimization methods in marketing, conjoint analysis, product development, consideration sets
We present a prototype system which enables users to explore the global structure for digital imagery archives as well as drill-down into individual pictures.Our search engine builds upon computer vision advances made over the past decade in low-level feature matching, large data handling and object recognition. We demonstrate hierarchical clustering among images semicooperatively shot around MIT, automatic linking of flickr photos and aerial frames from the Grand Canyon, and video segment identification for a TV broadcast. Moreover, our software tools incorporate visible vs infrared band selection, color content quantization and human face detection. Ongoing and future extensions of this image search system are discussed.
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