94 research outputs found
Personalized Pricing When Consumers Can Purchase Multiple Items*
Lu Q., Matsushima N.. Personalized Pricing When Consumers Can Purchase Multiple Items*. Journal of Industrial Economics , (2024); https://doi.org/10.1111/joie.12400.We study the impact of competitive personalized pricing in a Hotelling duopoly model where consumers can purchase from both firms. We show that the impact crucially depends on the magnitude of the additional utility from consuming the second product. Compared with uniform pricing, personalized pricing benefits both consumers and firms when the additional utility is moderate; but it harms consumers while benefiting firms when the additional utility is large. These results contrast with the existing research on competitive personalized pricing, which assumes that consumers purchase one product only
Content Licensing with Endogenous Homing
This paper examines the licensing strategy of a monopoly content provider that supplies horizontally differentiated content through downstream distributors to consumers who can potentially purchase from both distributors. When consumers' additional gain from the second purchase is high, the mismatch cost is low, and the quality of the extra content is high, some consumers purchase from both firms, which is called multi-homing. Apart from that, all consumers purchase from either distributor. When some consumers multi-home, the content provider always licenses to only one distributor. When all consumers single-home, the content provider either licenses to one distributor or shares the licensing
Welfare Implications of Personalized Pricing in Competitive Platform Markets: The Role of Network Effects
This study explores the welfare impact of personalized pricing for consumers in a duopolistic twosided market, with consumers single-homing and developers affiliating with a platform according to their outside option. Personalized pricing, which is private in nature, cannot influence expectations regarding the network sizes, inducing the platforms to offer lower participation fees for developers. Those lower fees increase network benefits for consumers, allowing the platforms to exploit these benefits through personalized pricing. Personalized prices are higher when the network value for developers is high, benefiting competing platforms at the expense of consumers. These findings offer policy insights on personalized pricing
Life cycle economic viability analysis of battery storage in electricity market
Battery storage is essential to enhance the flexibility and reliability of
electric power systems by providing auxiliary services and load shifting.
Storage owners typically gains incentives from quick responses to auxiliary
service prices, but frequent charging and discharging also reduce its lifetime.
Therefore, this paper embeds the battery degradation cost into the operation
simulation to avoid overestimated profits caused by an aggressive bidding
strategy. Based on an operation simulation model, this paper conducts the
economic viability analysis of whole life cycle using the internal rate of
return(IRR). A clustering method and a typical day method are developed to
reduce the huge computational burdens in the life-cycle simulation of battery
storage. Our models and algorithms are validated by the case study of two
mainstream technology routes currently: lithium nickel cobalt manganese oxide
(NCM) batteries and lithium iron phosphate (LFP) batteries. Then a sensitivity
analysis is presented to identify the critical factors that boost battery
storage in the future. We evaluate the IRR results of different types of
battery storage to provide guidance for investment portfolio.Comment: 17 pages, accepted by JP
Experimental measurement of the quantum geometric tensor using coupled qubits in diamond
Geometry and topology are fundamental concepts, which underlie a wide range
of fascinating physical phenomena such as topological states of matter and
topological defects. In quantum mechanics, the geometry of quantum states is
fully captured by the quantum geometric tensor. Using a qubit formed by an NV
center in diamond, we perform the first experimental measurement of the
complete quantum geometric tensor. Our approach builds on a strong connection
between coherent Rabi oscillations upon parametric modulations and the quantum
geometry of the underlying states. We then apply our method to a system of two
interacting qubits, by exploiting the coupling between the NV center spin and a
neighboring C nuclear spin. Our results establish coherent dynamical
responses as a versatile probe for quantum geometry, and they pave the way for
the detection of novel topological phenomena in solid state
Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data
Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe
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