Multiple myeloma is an incurable cancer of bone marrow plasma cells with a median overall survival of 5 years. With newly approved drugs to treat this disease over the last decade, physicians are afforded more opportunities to tailor treatment to individual patients and thereby improve survival outcomes and quality of life. However, since the optimal sequence of therapy is unknown, selecting a treatment that will result in the most effective outcome for each individual patient is challenging. To understand patients’ treatment responses, we develop an econometric model – the Hidden Markov model, to systematically identify changes in patients’ risk levels. Based on a fine-grained clinical dataset from Seattle Cancer Care Alliance (Seattle, WA) that includes patient-level cytogenetic information, we find that, other than the manifestation of cytogenetic features, previous exposure to certain drugs also affect patients’ underlying risk levels. The effectiveness of different treatments varies significantly among patients, which calls for personalized treatment recommendations. We then formulate the treatment recommendation problem as a Bayesian contextual bandit, which sequentially selects treatments based on contextual information about patients and therapies, with the goal of maximizing overall survival outcomes. Facing the difficulty of evaluating the performance of the policy without field experiments in medical practice, we integrate the structural econometric model into bandit optimization and generate counterfactuals to support the theoretical exploration/exploitation framework with empirical evidence. Compared with clinical practices and benchmark strategies, our method suggests a rise in overall survival outcomes, with higher improvement for aging or high-risk patients with more complications.
{"title":"How Do Tumor Cytogenetics Inform Cancer Treatments? Dynamic Risk Stratification and Precision Medicine Using Multi-armed Bandits","authors":"Zhijin Zhou, Yingfei Wang, H. Mamani, D. Coffey","doi":"10.2139/ssrn.3405082","DOIUrl":"https://doi.org/10.2139/ssrn.3405082","url":null,"abstract":"Multiple myeloma is an incurable cancer of bone marrow plasma cells with a median overall survival of 5 years. With newly approved drugs to treat this disease over the last decade, physicians are afforded more opportunities to tailor treatment to individual patients and thereby improve survival outcomes and quality of life. However, since the optimal sequence of therapy is unknown, selecting a treatment that will result in the most effective outcome for each individual patient is challenging. To understand patients’ treatment responses, we develop an econometric model – the Hidden Markov model, to systematically identify changes in patients’ risk levels. Based on a fine-grained clinical dataset from Seattle Cancer Care Alliance (Seattle, WA) that includes patient-level cytogenetic information, we find that, other than the manifestation of cytogenetic features, previous exposure to certain drugs also affect patients’ underlying risk levels. The effectiveness of different treatments varies significantly among patients, which calls for personalized treatment recommendations. \u0000 \u0000We then formulate the treatment recommendation problem as a Bayesian contextual bandit, which sequentially selects treatments based on contextual information about patients and therapies, with the goal of maximizing overall survival outcomes. Facing the difficulty of evaluating the performance of the policy without field experiments in medical practice, we integrate the structural econometric model into bandit optimization and generate counterfactuals to support the theoretical exploration/exploitation framework with empirical evidence. Compared with clinical practices and benchmark strategies, our method suggests a rise in overall survival outcomes, with higher improvement for aging or high-risk patients with more complications.","PeriodicalId":376757,"journal":{"name":"Decision-Making in Operations Research eJournal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121597932","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}
Jian Chen, Yong Liang, Hao Shen, Z. Shen, Mengying Xue
Problem definition: Observing the retail industry inevitably evolving into omnichannel, we study an offline-channel planning problem that helps an omnichannel retailer make store location and location-dependent assortment decisions in its offline channel to maximize profit across both online and offline channels, given that customers’ purchase decisions depend on not only their preferences across products but also their valuation discrepancies across channels, as well as the hassle costs incurred. Academic/practical relevance: The proposed model and the solution approach extend the literature on retail channel management, omnichannel assortment planning, and the broader field of smart retailing/cities. Methodology: We derive parameterized models to capture customers’ channel choice and product choice behaviors, and customize a corresponding parameter estimation approach employing the expectation-maximization method. To solve the NP-hard optimization model, we develop a tractable mixed-integer second-order conic programming (MISOCP) reformulation and explore the structural properties of the reformulation to derive strengthening cuts in closed-form. Results: We numerically validate the efficacy of the proposed solution approach and demonstrate the parameter estimation approach. We further draw managerial insights from the numerical studies using real data sets. Managerial implications: We verify that omnichannel retailers should provide location-dependent offline assortments. In addition, our benchmark studies reveal the necessity and significance of jointly determining offline store locations and assortments, as well as of incorporating the online channel while making offline-channel planning decisions.
{"title":"Offline-Channel Planning in Smart Omnichannel Retailing","authors":"Jian Chen, Yong Liang, Hao Shen, Z. Shen, Mengying Xue","doi":"10.2139/ssrn.3748903","DOIUrl":"https://doi.org/10.2139/ssrn.3748903","url":null,"abstract":"Problem definition: Observing the retail industry inevitably evolving into omnichannel, we study an offline-channel planning problem that helps an omnichannel retailer make store location and location-dependent assortment decisions in its offline channel to maximize profit across both online and offline channels, given that customers’ purchase decisions depend on not only their preferences across products but also their valuation discrepancies across channels, as well as the hassle costs incurred. Academic/practical relevance: The proposed model and the solution approach extend the literature on retail channel management, omnichannel assortment planning, and the broader field of smart retailing/cities. Methodology: We derive parameterized models to capture customers’ channel choice and product choice behaviors, and customize a corresponding parameter estimation approach employing the expectation-maximization method. To solve the NP-hard optimization model, we develop a tractable mixed-integer second-order conic programming (MISOCP) reformulation and explore the structural properties of the reformulation to derive strengthening cuts in closed-form. Results: We numerically validate the efficacy of the proposed solution approach and demonstrate the parameter estimation approach. We further draw managerial insights from the numerical studies using real data sets. Managerial implications: We verify that omnichannel retailers should provide location-dependent offline assortments. In addition, our benchmark studies reveal the necessity and significance of jointly determining offline store locations and assortments, as well as of incorporating the online channel while making offline-channel planning decisions.","PeriodicalId":376757,"journal":{"name":"Decision-Making in Operations Research eJournal","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125671470","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}
Abstract The matrix that transforms the response variable in a regression to its predicted value is commonly referred to as the hat matrix. The trace of the hat matrix is a standard metric for calculating degrees of freedom. The two prominent theoretical frameworks for studying hat matrices to calculate degrees of freedom in local polynomial regressions – ANOVA and non-ANOVA – abstract from both mixed data and the potential presence of irrelevant covariates, both of which dominate empirical applications. In the multivariate local polynomial setup with a mix of continuous and discrete covariates, which include some irrelevant covariates, we formulate asymptotic expressions for the trace of both the non-ANOVA and ANOVA-based hat matrices from the estimator of the unknown conditional mean. The asymptotic expression of the trace of the non-ANOVA hat matrix associated with the conditional mean estimator is equal up to a linear combination of kernel-dependent constants to that of the ANOVA-based hat matrix. Additionally, we document that the trace of the ANOVA-based hat matrix converges to 0 in any setting where the bandwidths diverge. This attrition outcome can occur in the presence of irrelevant continuous covariates or it can arise when the underlying data generating process is in fact of polynomial order.
{"title":"Calculating Degrees of Freedom in Multivariate Local Polynomial Regression","authors":"N. McCloud, Christopher F. Parmeter","doi":"10.2139/ssrn.3812825","DOIUrl":"https://doi.org/10.2139/ssrn.3812825","url":null,"abstract":"Abstract The matrix that transforms the response variable in a regression to its predicted value is commonly referred to as the hat matrix. The trace of the hat matrix is a standard metric for calculating degrees of freedom. The two prominent theoretical frameworks for studying hat matrices to calculate degrees of freedom in local polynomial regressions – ANOVA and non-ANOVA – abstract from both mixed data and the potential presence of irrelevant covariates, both of which dominate empirical applications. In the multivariate local polynomial setup with a mix of continuous and discrete covariates, which include some irrelevant covariates, we formulate asymptotic expressions for the trace of both the non-ANOVA and ANOVA-based hat matrices from the estimator of the unknown conditional mean. The asymptotic expression of the trace of the non-ANOVA hat matrix associated with the conditional mean estimator is equal up to a linear combination of kernel-dependent constants to that of the ANOVA-based hat matrix. Additionally, we document that the trace of the ANOVA-based hat matrix converges to 0 in any setting where the bandwidths diverge. This attrition outcome can occur in the presence of irrelevant continuous covariates or it can arise when the underlying data generating process is in fact of polynomial order.","PeriodicalId":376757,"journal":{"name":"Decision-Making in Operations Research eJournal","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123037884","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}
Shaik Mastan, U. Balakrishnan, G. Sankar Sekhar Raju
The Optimization of a large-scales travelling salesman Problem (TSP) mostly in telecommunication networks that may be a well-known NP-hard downside in combinatorial improvement, may be a long downside. During this paper, the planned heuristic algorithmic program is intended for quick parameter, accuracy and computation time. planned algorithmic program has been compared with brute force associated hymenopterous insect colony improvement that referred to as an algorithmic program that's accustomed confirm the shortest path and best price at minimum iterations attainable for a random knowledge attack the premise of Euclidean space formula. Planned algorithmic program takes solely 0.0075 seconds to supply shortest path answer that sixty nodes combination. The planned algorithmic program has 6 June 1944 less accuracy from brute force and provides 5.59% higher answer for forty-four nodes through sixty nodes.
{"title":"A Quick Heuristic Algorithm for Travelling Salesman Problem","authors":"Shaik Mastan, U. Balakrishnan, G. Sankar Sekhar Raju","doi":"10.2139/ssrn.3497489","DOIUrl":"https://doi.org/10.2139/ssrn.3497489","url":null,"abstract":"The Optimization of a large-scales travelling salesman Problem (TSP) mostly in telecommunication networks that may be a well-known NP-hard downside in combinatorial improvement, may be a long downside. During this paper, the planned heuristic algorithmic program is intended for quick parameter, accuracy and computation time. planned algorithmic program has been compared with brute force associated hymenopterous insect colony improvement that referred to as an algorithmic program that's accustomed confirm the shortest path and best price at minimum iterations attainable for a random knowledge attack the premise of Euclidean space formula. Planned algorithmic program takes solely 0.0075 seconds to supply shortest path answer that sixty nodes combination. The planned algorithmic program has 6 June 1944 less accuracy from brute force and provides 5.59% higher answer for forty-four nodes through sixty nodes.","PeriodicalId":376757,"journal":{"name":"Decision-Making in Operations Research eJournal","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134472659","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}
Almost every vendor faces uncertain and time-varying demand. Inventory level and price optimization while catering to stochastic demand are conventionally formulated as variants of newsvendor problem. Despite its ubiquity in potential applications, the time-dependent (multi-period) newsvendor problem in its general form has received limited attention in the literature due to its complexity and the highly nested structure of its ensuing optimization problems. The complexity level rises even more when there are more than one decision maker in a supply channel, trying to reach an equilibrium. The purpose of this paper is to construct an explicit and e cient solution procedure for multi-period price-setting newsvendor problems in a Stackelberg framework. In particular, we show that our recursive solution algorithm can be applied to standard contracts such as buy back contracts, revenue sharing contracts, and their generalizations.
{"title":"Markets With Memory: Dynamic Channel Optimization Models With Price-Dependent Stochastic Demand","authors":"Reza Azad Azad Gholami, L. Sandal, J. Ubøe","doi":"10.2139/ssrn.3450493","DOIUrl":"https://doi.org/10.2139/ssrn.3450493","url":null,"abstract":"Almost every vendor faces uncertain and time-varying demand. Inventory level and price optimization while catering to stochastic demand are conventionally formulated as variants of newsvendor problem. Despite its ubiquity in potential applications, the time-dependent (multi-period) newsvendor problem in its general form has received limited attention in the literature due to its complexity and the highly nested structure of its ensuing optimization problems. The complexity level rises even more when there are more than one decision maker in a supply channel, trying to reach an equilibrium. The purpose of this paper is to construct an explicit and e cient solution procedure for multi-period price-setting newsvendor problems in a Stackelberg framework. In particular, we show that our recursive solution algorithm can be applied to standard contracts such as buy back contracts, revenue sharing contracts, and their generalizations.","PeriodicalId":376757,"journal":{"name":"Decision-Making in Operations Research eJournal","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130072762","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}
A Bayesian decision maker is choosing among two alternatives with uncertain payoffs and an outside option with known payoff. Before deciding which alternative to adopt, the decision maker can purchase sequentially multiple informative signals on each of the two alternatives. To maximize the expected payoff, the decision maker solves the problem of optimal dynamic allocation of learning efforts as well as optimal stopping of the learning process. We show that the decision maker considers an alternative for learning or adoption if and only if the expected payoff of the alternative is above a threshold. Given both alternatives in the decision maker's consideration set, we find that if the outside option is weak and the decision maker's beliefs about both alternatives are relatively low, it is optimal for the decision maker to learn information from the alternative that has a lower expected payoff and less uncertainty, given all other characteristics of the two alternatives being the same. If the decision maker subsequently receives enough positive informative signals, the decision maker will switch to learning the better alternative; otherwise the decision maker will rule out this alternative from consideration and adopt the currently more preferred alternative. We find that this strategy works because it minimizes the decision maker's learning efforts. We also characterize the optimal learning policy when the outside option is relatively high, and discuss several extensions.
{"title":"Optimal Learning Before Choice","authors":"T. Ke, J. M. Villas-Boas","doi":"10.2139/ssrn.2844417","DOIUrl":"https://doi.org/10.2139/ssrn.2844417","url":null,"abstract":"A Bayesian decision maker is choosing among two alternatives with uncertain payoffs and an outside option with known payoff. Before deciding which alternative to adopt, the decision maker can purchase sequentially multiple informative signals on each of the two alternatives. To maximize the expected payoff, the decision maker solves the problem of optimal dynamic allocation of learning efforts as well as optimal stopping of the learning process. We show that the decision maker considers an alternative for learning or adoption if and only if the expected payoff of the alternative is above a threshold. Given both alternatives in the decision maker's consideration set, we find that if the outside option is weak and the decision maker's beliefs about both alternatives are relatively low, it is optimal for the decision maker to learn information from the alternative that has a lower expected payoff and less uncertainty, given all other characteristics of the two alternatives being the same. If the decision maker subsequently receives enough positive informative signals, the decision maker will switch to learning the better alternative; otherwise the decision maker will rule out this alternative from consideration and adopt the currently more preferred alternative. We find that this strategy works because it minimizes the decision maker's learning efforts. We also characterize the optimal learning policy when the outside option is relatively high, and discuss several extensions.","PeriodicalId":376757,"journal":{"name":"Decision-Making in Operations Research eJournal","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114789589","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}
Least squares Monte Carlo (LSM) is an approximate dynamic programming (ADP) technique commonly used for the valuation of high dimensional financial and real options, but has broader applicability. It is known that the regress-later version of this method is an approximate linear programming (ALP) relaxation that implicitly provides a potential solution to a familiar ALP deficiency. Focusing on a generic finite horizon Markov decision process, we provide both theoretical and numerical backing for the usefulness of this solution, respectively using a worst-case error bound analysis and a numerical study dealing with merchant ethanol production, an energy real option application, based on an ALP heuristic that we propose. When both methodologies are applicable, our research supports the use of regress-later LSM rather than this ALP technique to approximately solve intractable Markov decision processes. Our numerical findings motivate additional research to obtain even better methods than the regress-later version of LSM.
{"title":"Least Squares Monte Carlo and Approximate Linear Programming: Error Bounds and Energy Real Option Application","authors":"Selvaprabu Nadarajah, N. Secomandi","doi":"10.2139/ssrn.3232687","DOIUrl":"https://doi.org/10.2139/ssrn.3232687","url":null,"abstract":"Least squares Monte Carlo (LSM) is an approximate dynamic programming (ADP) technique commonly used for the valuation of high dimensional financial and real options, but has broader applicability. It is known that the regress-later version of this method is an approximate linear programming (ALP) relaxation that implicitly provides a potential solution to a familiar ALP deficiency. Focusing on a generic finite horizon Markov decision process, we provide both theoretical and numerical backing for the usefulness of this solution, respectively using a worst-case error bound analysis and a numerical study dealing with merchant ethanol production, an energy real option application, based on an ALP heuristic that we propose. When both methodologies are applicable, our research supports the use of regress-later LSM rather than this ALP technique to approximately solve intractable Markov decision processes. Our numerical findings motivate additional research to obtain even better methods than the regress-later version of LSM.","PeriodicalId":376757,"journal":{"name":"Decision-Making in Operations Research eJournal","volume":"517 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116232977","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}
Heeyun Kim, M. Oster, Natsumi Ueda, Stephen L. Desjardins
In this study, we explore what factors predict student decisions to enroll at law schools and how the probability of enrollment varies across students with various profiles and conditions. To find the predictors of enrollment and differences in the probability of enrollment across groups, we employ a logistic regression model using the institutional data obtained from one of the top-ranked law schools in the nation. After estimating the logistic regression model, the probabilities of enrollment are calculated for students with specific profiles and conditions based on the coefficients generated by the logistic regression analysis. The findings reveal many factors that are associated with the probability of enrollment at this law school. Particularly, students with higher academic qualifications, underrepresented minority status, the most selective undergraduate school, STEM background, and previous applicant status have a lower probability of enrollment compared to their respective counterparts. Simulation analysis findings show that the increase in financial aid does not increase the probability of enrollment for URM students and that out-of-state and international students are more sensitive to financial aid increases than in-state students. Admissions and enrollment management offices at individual institutions could apply this exercise with their own data to understand who is more or less likely to enroll and how their students with various profiles respond differently to various financial aid offers and recruitment efforts. It is our hope that this article is used as an example to other law schools to leverage their institutional data to create enrollment models that will help make more effective admission decision making.
{"title":"Predicting Law School Enrollment: The Strategic Use of Financial Aid to Craft a Class","authors":"Heeyun Kim, M. Oster, Natsumi Ueda, Stephen L. Desjardins","doi":"10.2139/ssrn.3208882","DOIUrl":"https://doi.org/10.2139/ssrn.3208882","url":null,"abstract":"In this study, we explore what factors predict student decisions to enroll at law schools and how the probability of enrollment varies across students with various profiles and conditions. To find the predictors of enrollment and differences in the probability of enrollment across groups, we employ a logistic regression model using the institutional data obtained from one of the top-ranked law schools in the nation. After estimating the logistic regression model, the probabilities of enrollment are calculated for students with specific profiles and conditions based on the coefficients generated by the logistic regression analysis. The findings reveal many factors that are associated with the probability of enrollment at this law school. Particularly, students with higher academic qualifications, underrepresented minority status, the most selective undergraduate school, STEM background, and previous applicant status have a lower probability of enrollment compared to their respective counterparts. Simulation analysis findings show that the increase in financial aid does not increase the probability of enrollment for URM students and that out-of-state and international students are more sensitive to financial aid increases than in-state students. Admissions and enrollment management offices at individual institutions could apply this exercise with their own data to understand who is more or less likely to enroll and how their students with various profiles respond differently to various financial aid offers and recruitment efforts. It is our hope that this article is used as an example to other law schools to leverage their institutional data to create enrollment models that will help make more effective admission decision making.","PeriodicalId":376757,"journal":{"name":"Decision-Making in Operations Research eJournal","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114843302","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}
This paper explores the link between bounded rationality, and complexity of decision problems in a newsvendor setting. We compare behaviors of newsvendors who manage one versus two stores, and find that individuals making two simultaneous newsvendor decisions, in both same-margin and mixed-margin scenarios, exhibit worse performances than making a single newsvendor decision, driven by lower levels of learning, and stronger demand chasing behavior. Furthermore, while in our setting, the two newsvendor decisions are independent to each other, order decisions are impacted by both exogenous demand signals and endogenous order decisions of the other store. We call it the “cross store influence.” We develop a behavioral model, based on linear adjustment dynamics, to explain the findings, and provide an theoretical analysis of the long term behavior of this model. These results highlight the importance of assigning the right amount of decision responsibilities to managers, and keeping them not distracted from unrelated information.
{"title":"The Behavioral Traps in Making Multiple, Simultaneous, Newsvendor Decisions","authors":"Kay-Yut Chen, Shan Li","doi":"10.2139/ssrn.2817126","DOIUrl":"https://doi.org/10.2139/ssrn.2817126","url":null,"abstract":"This paper explores the link between bounded rationality, and complexity of decision problems in a newsvendor setting. We compare behaviors of newsvendors who manage one versus two stores, and find that individuals making two simultaneous newsvendor decisions, in both same-margin and mixed-margin scenarios, exhibit worse performances than making a single newsvendor decision, driven by lower levels of learning, and stronger demand chasing behavior. Furthermore, while in our setting, the two newsvendor decisions are independent to each other, order decisions are impacted by both exogenous demand signals and endogenous order decisions of the other store. We call it the “cross store influence.” We develop a behavioral model, based on linear adjustment dynamics, to explain the findings, and provide an theoretical analysis of the long term behavior of this model. These results highlight the importance of assigning the right amount of decision responsibilities to managers, and keeping them not distracted from unrelated information.","PeriodicalId":376757,"journal":{"name":"Decision-Making in Operations Research eJournal","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114894981","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}
Pub Date : 2012-07-09DOI: 10.1007/978-3-642-31724-8_8
Maria Letizia Guerra, C. Magni, Luciano Stefanini
{"title":"Average Rate of Return With Uncertainty","authors":"Maria Letizia Guerra, C. Magni, Luciano Stefanini","doi":"10.1007/978-3-642-31724-8_8","DOIUrl":"https://doi.org/10.1007/978-3-642-31724-8_8","url":null,"abstract":"","PeriodicalId":376757,"journal":{"name":"Decision-Making in Operations Research eJournal","volume":"27 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123277482","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}
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