摘要翻译：
预测未来成功的设计和相应的市场机会是产品设计公司的一个基本目标。因此，数量方法有很长的历史，旨在捕捉不同的消费者偏好，然后将这些偏好转化为市场中相应的“设计差距”。我们通过开发一种深度学习方法来预测市场中的设计差距来扩展这项工作。这些设计差距代表了尚不存在的设计集群，但被预测为（1）消费者高度偏好，以及（2）在工程和制造限制下可行的构建。这种方法是在整个美国汽车市场上使用数百万真实的购买数据进行测试的。我们追溯预测市场上的设计差距，并将预测的设计差距与实际已知的成功设计进行比较。我们的初步结果表明，预测设计差距是可能的，这表明这种方法有希望早期识别市场机会。
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英文标题：
《Predicting "Design Gaps" in the Market: Deep Consumer Choice Models
  under Probabilistic Design Constraints》
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作者：
Alex Burnap, John Hauser
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最新提交年份：
2018
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Predicting future successful designs and corresponding market opportunity is a fundamental goal of product design firms. There is accordingly a long history of quantitative approaches that aim to capture diverse consumer preferences, and then translate those preferences to corresponding "design gaps" in the market. We extend this work by developing a deep learning approach to predict design gaps in the market. These design gaps represent clusters of designs that do not yet exist, but are predicted to be both (1) highly preferred by consumers, and (2) feasible to build under engineering and manufacturing constraints. This approach is tested on the entire U.S. automotive market using of millions of real purchase data. We retroactively predict design gaps in the market, and compare predicted design gaps with actual known successful designs. Our preliminary results give evidence it may be possible to predict design gaps, suggesting this approach has promise for early identification of market opportunity.
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PDF链接：
https://arxiv.org/pdf/1812.11067