.png){kind=icon}
返回
Greedoid-based noncompensatory inference
.png){kind=icon}
PRE
AI
DOI：10.1287/mksc.1060.0213.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Greedoid languages provide a basis to infer best-fitting noncompensatory decision rules from full-rank conjoint data or partial-rank joint 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 considerthen-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. LBXs 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.
Keyword:
lexicography
noncompensatory decision rules
choice heuristics
optimization methods in marketing
conjoint analysis
product development
consideration sets
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.1
论文数:
3.4K
被引数:
2.2W
机构
暂无机构信息
引用论文
Age-Specific Gene Expression Signatures for Breast Tumors and Cross-Species Conserved Potential Cancer Progression Markers in Young Women.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏Reasoning the fast and frugal way: Models of bounded rationality快速而节俭的推理方式: 有限理性的模型PSYCHOLOGICAL REVIEWOAAI
PSYCHOLOGICAL REVIEWIF5.8
OAAI分享收藏
查看更多内容