ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)最新文献

Assortment Optimization and Pricing Under the Threshold-Based Choice Models 阈值选择模型下的分类优化与定价

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2020-09-17 DOI: 10.2139/ssrn.3694222

Xu Tian, Anran Li, R. Steinberg

{"title":"Assortment Optimization and Pricing Under the Threshold-Based Choice Models","authors":"Xu Tian, Anran Li, R. Steinberg","doi":"10.2139/ssrn.3694222","DOIUrl":"https://doi.org/10.2139/ssrn.3694222","url":null,"abstract":"In this paper, we study revenue maximization assortment and pricing problems under the threshold-based choice model, in which, a product is placed into a consumer's consideration set if its utility to the consumer exceeds the utility of a specified threshold. We consider two cases: when the random shock is logistically distributed or Gumbelly distributed. For both these two cases, the revenue-maximizing assortment problem is NP-hard. Although in the two cases, the best revenue-ordered assortment and the same-price policy can not achieve the optimal profit for the assortment problem and the pricing problem, respectively, we show that, they can guarantee a good bound on the optimal revenue. Finally, we show that when the random shock is logistically distributed, our policies can be asymptotically optimal if the search cost of consumers is sufficiently small. When the random shock is Gumbelly distributed, the best revenue-ordered assortment can asymptotically admit a 0.77 approximation of the optimal revenue for the assortment problem; the same-price policy can be asymptotically optimal for the pricing problem. These suggest that our policies share some robustness to achieve a good performance guarantee for the optimal revenue.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130354480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Techniques to Estimate Parameters of Linear Models 线性模型参数估计的鲁棒技术

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2020-07-26 DOI: 10.2139/ssrn.3694906

Neel Pandey

引用次数: 0

Identification of Random Coefficient Latent Utility Models 随机系数潜在效用模型的识别

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2020-02-29 DOI: 10.2139/ssrn.3545696

R. Allen, John Rehbeck

引用次数: 3

An Algorithm for Assortment Optimization Under Parametric Discrete Choice Models 参数离散选择模型下的分类优化算法

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2019-04-12 DOI: 10.2139/ssrn.3370776

Tien Mai, Andrea Lodi

{"title":"An Algorithm for Assortment Optimization Under Parametric Discrete Choice Models","authors":"Tien Mai, Andrea Lodi","doi":"10.2139/ssrn.3370776","DOIUrl":"https://doi.org/10.2139/ssrn.3370776","url":null,"abstract":"This work concerns the assortment optimization problem that refers to selecting a subset of items that maximizes the expected revenue in the presence of the substitution behavior of consumers specified by a parametric choice model. The key challenge lies in the computational difficulty of finding the best subset solution, which often requires exhaustive search. The literature on constrained assortment optimization lacks a practically efficient method which that is general to deal with different types of parametric choice models (e.g., the multinomial logit, mixed logit or general multivariate extreme value models). In this paper, we propose a new approach that allows to address this issue. The idea is that, under a general parametric choice model, we formulate the problem into a binary nonlinear programming model, and use an iterative algorithm to find a binary solution. At each iteration, we propose a way to approximate the objective (expected revenue) by a linear function, and a polynomial-time algorithm to find a candidate solution using this approximate function. We also develop a greedy local search algorithm to further improve the solutions. We test our algorithm on instances of different sizes under various parametric choice model structures and show that our algorithm dominates existing exact and heuristic approaches in the literature, in terms of solution quality and computing cost.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115699036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Equivalent Choice Functions and Stable Mechanisms 等效选择函数与稳定机制

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2018-12-18 DOI: 10.2139/ssrn.3306009

Jan Christoph Schlegel

引用次数: 3

Do Greater Sanctions Deter Youth Crime? Evidence from a Regression Discontinuity Design 更严厉的制裁能阻止青少年犯罪吗?来自回归不连续设计的证据

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2018-10-25 DOI: 10.2139/ssrn.3116414

N. Lovett, Yuhan Xue

引用次数: 6

BLP-LASSO for Aggregate Discrete Choice Models with Rich Covariates 富协变量聚合离散选择模型的BLP-LASSO

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2018-10-09 DOI: 10.2139/ssrn.2700775

B. Gillen, Sergio Montero, H. Moon, M. Shum

引用次数: 2

Customer Choice Models versus Machine Learning: Finding Optimal Product Displays on Alibaba 客户选择模型与机器学习:在阿里巴巴上寻找最佳产品展示

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2018-08-15 DOI: 10.2139/ssrn.3232059

Jacob B. Feldman, Dennis J. Zhang, Xiaofei Liu, N. Zhang

{"title":"Customer Choice Models versus Machine Learning: Finding Optimal Product Displays on Alibaba","authors":"Jacob B. Feldman, Dennis J. Zhang, Xiaofei Liu, N. Zhang","doi":"10.2139/ssrn.3232059","DOIUrl":"https://doi.org/10.2139/ssrn.3232059","url":null,"abstract":"We compare the performance of two approaches for finding the optimal set of products to display to customers landing on Alibaba's two online marketplaces, Tmall and Taobao. Both approaches were placed online simultaneously and tested on real customers for one week. The first approach we test is Alibaba's current practice. This procedure embeds thousands of product and customer features within a sophisticated machine learning algorithm that is used to estimate the purchase probabilities of each product for the customer at hand. The products with the largest expected revenue (revenue * predicted purchase probability) are then made available for purchase. The downside of this approach is that it does not incorporate customer substitution patterns; the estimates of the purchase probabilities are independent of the set of products that eventually are displayed. Our second approach uses a featurized multinomial logit (MNL) model to predict purchase probabilities for each arriving customer. In this way we use less sophisticated machinery to estimate purchase probabilities, but we employ a model that was built to capture customer purchasing behavior and, more specifically, substitution patterns. We use historical sales data to fit the MNL model and then, for each arriving customer, we solve the cardinality-constrained assortment optimization problem under the MNL model online to find the optimal set of products to display. Our experiments show that despite the lower prediction power of our MNL-based approach, it generates significantly higher revenue per visit compared to the current machine learning algorithm with the same set of features. We also conduct various heterogeneous-treatment-effect analyses to demonstrate that the current MNL approach performs best for sellers whose customers generally only make a single purchase.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116087960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

The Empirical Content of Binary Choice Models 二元选择模型的经验内容

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2018-08-01 DOI: 10.2139/ssrn.2960282

Debopam Bhattacharya

{"title":"The Empirical Content of Binary Choice Models","authors":"Debopam Bhattacharya","doi":"10.2139/ssrn.2960282","DOIUrl":"https://doi.org/10.2139/ssrn.2960282","url":null,"abstract":"An important goal of empirical demand analysis is choice and welfare prediction on counterfactual budget sets arising from potential policy interventions. Such predictions are more credible when made without arbitrary functional‐form/distributional assumptions, and instead based solely on economic rationality, that is, that choice is consistent with utility maximization by a heterogeneous population. This paper investigates nonparametric economic rationality in the empirically important context of binary choice. We show that under general unobserved heterogeneity, economic rationality is equivalent to a pair of Slutsky‐like shape restrictions on choice‐probability functions. The forms of these restrictions differ from Slutsky inequalities for continuous goods. Unlike McFadden–Richter's stochastic revealed preference, our shape restrictions (a) are global, that is, their forms do not depend on which and how many budget sets are observed, (b) are closed form, hence easy to impose on parametric/semi/nonparametric models in practical applications, and (c) provide computationally simple, theory‐consistent bounds on demand and welfare predictions on counterfactual budge sets.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125502574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

A Comparative Empirical Study of Discrete Choice Models in Retail Operations 零售经营中离散选择模型的比较实证研究

ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic) Pub Date : 2018-03-06 DOI: 10.2139/ssrn.3136816

Gerardo Berbeglia, Agustín Garassino, Gustavo J. Vulcano

{"title":"A Comparative Empirical Study of Discrete Choice Models in Retail Operations","authors":"Gerardo Berbeglia, Agustín Garassino, Gustavo J. Vulcano","doi":"10.2139/ssrn.3136816","DOIUrl":"https://doi.org/10.2139/ssrn.3136816","url":null,"abstract":"Choice-based demand estimation is a fundamental task in retail operations and revenue management, providing necessary input data for inventory control, assortment, and price-optimization models. The task is particularly difficult in operational contexts where product availability varies over time and customers may substitute into the available options. In addition to the classical multinomial logit (MNL) model and extensions (e.g., nested logit, mixed logit, and latent-class MNL), new demand models have been proposed (e.g., the Markov chain model), and others have been recently revisited (e.g., the rank list-based and exponomial models). At the same time, new computational approaches were developed to ease the estimation function (e.g., column-generation and expectation-maximization (EM) algorithms). In this paper, we conduct a systematic, empirical study of different choice-based demand models and estimation algorithms, including both maximum-likelihood and least-squares criteria. Through an exhaustive set of numerical experiments on synthetic, semisynthetic, and real data, we provide comparative statistics of the predictive power and derived revenue performance of an ample collection of choice models and characterize operational environments suitable for different model/estimation implementations. We also provide a survey of all the discrete choice models evaluated and share all our estimation codes and data sets as part of the online appendix. This paper was accepted by Vishal Gaur, operations management.","PeriodicalId":165362,"journal":{"name":"ERN: Discrete Regression & Qualitative Choice Models (Single) (Topic)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127972532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 44