{"title":"Bundling in a Symmetric Bertrand Duopoly","authors":"Araz Khodabakhshian, G. Roels, U. Karmarkar","doi":"10.2139/ssrn.3491164","DOIUrl":"https://doi.org/10.2139/ssrn.3491164","url":null,"abstract":"Competitive bundling may lead to such different outcomes as preempting entry, intensifying price competition, or softening it. These different outcomes have been shown to emerge under different industry structures when firms have restricted ranges of action. But how general are these results? In this paper, we investigate whether they still hold under the most generic model of competition, namely: Two symmetric firms competing on price with regard to two homogeneous zero-cost components, without restrictions on their product offering. We show that all three outcomes emerge in equilibrium, respectively as a full mixed-bundling monopoly, a full mixed-bundling competitive duopoly, and a pure or partial-mixed bundling differentiated duopoly. Furthermore, we establish that there are first-mover advantages to bundling and that, unlike in a monopoly, firms may be better off limiting their product offering. Our stylized approach highlights the importance of market operating rules for equilibrium selection: Bundling is not anticompetitive per se, unless firms attempt to coordinate or preempt entry by fully covering the market.","PeriodicalId":159138,"journal":{"name":"DecisionSciRN: Decision Modeling (Sub-Topic)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121921596","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}
{"title":"Data-Driven Newsvendor with Profit Risk Consideration","authors":"Shao-Bo Lin, Frank Y. Chen, Yanzhi Li, Z. Shen","doi":"10.2139/ssrn.3540448","DOIUrl":"https://doi.org/10.2139/ssrn.3540448","url":null,"abstract":"We study a risk-averse newsvendor problem where demand distribution is unknown. The focal product is new, and only the historical demand information of related products is available. The newsvendor aims to maximize its expected profit subject to a profit risk constraint. We develop a model with a value-at-risk constraint and propose a data-driven approximation to the theoretical risk-averse newsvendor model. Specifically, based on the covariate information, we use machine learning methods to weight the similarity between the new product and the previous ones. The sample-dependent weights are then embedded to approximate the expected profit and the profit risk constraint. Afterward, we show that the data-driven risk-averse newsvendor solution entails a closed-form quantile structure and can be efficiently computed. Finally, we prove that this data-driven solution is asymptotically optimal. Experiments based on real data and synthetic data demonstrate the effectiveness of our approach. We find that under data-driven decision making, contrary to that in the theoretical risk-averse newsvendor model, the average realized profit may benefit from a stronger risk aversion. It further reveals that under data-driven decision making, even a risk-neutral newsvendor can benefit from incorporating a risk constraint, which plays a regularizing role in mitigating issues of data-driven decision making such as sampling error and model misspecification. The above effects however diminish as the size of the training data set increases, as the asymptotic optimality result implies.","PeriodicalId":159138,"journal":{"name":"DecisionSciRN: Decision Modeling (Sub-Topic)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122276784","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}
{"title":"Parallel Search for Information","authors":"T. Ke, Wenpin Tang, J. M. Villas-Boas, Y. Zhang","doi":"10.2139/ssrn.3389057","DOIUrl":"https://doi.org/10.2139/ssrn.3389057","url":null,"abstract":"We consider the problem of a decision-maker searching for information on multiple alternatives when information is learned on all alternatives simultaneously. The decision-maker has a running cost of searching for information, and has to decide when to stop searching for information and choose one alternative. The expected payoff of each alternative evolves as a diffusion process when information is being learned. We present necessary and sufficient conditions for the solution, establishing existence and uniqueness. We show that the optimal boundary where search is stopped (free boundary) is star-shaped, and present an asymptotic characterization of the value function and the free boundary. We show properties of how the distance between the free boundary and the diagonal varies with the number of alternatives, and how the free boundary under parallel search relates to the one under sequential search, with and without economies of scale on the search costs.","PeriodicalId":159138,"journal":{"name":"DecisionSciRN: Decision Modeling (Sub-Topic)","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131688152","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}