scholarly journals The Promise and Perils of Myopia in Dynamic Pricing With Censored Information

2018 ◽  
Author(s):  
Meenal Chhabra ◽  
Sanmay Das ◽  
Ilya Ryzhov
Keyword(s):  
Dynamic Pricing ◽  
Real Data ◽  
Demand Model ◽  
Keyword Auctions ◽  
Linear Demand ◽  
Myopic Strategy ◽  
User Ratings

A seller with unlimited inventory of a digital good interacts with potential buyers with i.i.d. valuations. The seller can adaptively quote prices to each buyer to maximize long-term profits, but does not know the valuation distribution exactly. Under a linear demand model, we consider two information settings: partially censored, where agents who buy reveal their true valuations after the purchase is completed, and completely censored, where agents never reveal their valuations. In the partially censored case, we prove that myopic pricing with a Pareto prior is Bayes optimal and has finite regret. In both settings, we evaluate the myopic strategy against more sophisticated look-aheads using three valuation distributions generated from real data on auctions of physical goods, keyword auctions, and user ratings, where the linear demand assumption is clearly violated. For some datasets, complete censoring actually helps, because the restricted data acts as a "regularizer" on the posterior, preventing it from being affected too much by outliers.

scholarly journals Dynamic Pricing Research for Container Terminal Handling Charges Based on Demand Forecast

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenxiu Wang ◽  
Yi Ding
Keyword(s):  
Dynamic Pricing ◽  
Container Terminal ◽  
Likelihood Estimation ◽  
Real Data ◽  
Demand Model ◽  
Demand Forecast ◽  
Unknown Parameters ◽  
Pricing Policy ◽  
Container Handling ◽  
Pricing Research

A dynamic pricing model was established based on forecasting the demand for container handling of a specific shipping company to maximize terminal profits to solve terminal handling charges under the changing market environment. It assumes that container handling demand depends on the price and the unknown parameters in the demand model. The maximum quasi-likelihood estimation(MQLE) method is used to estimate the unknown parameters. Then an adaptive dynamic pricing policy algorithm is proposed. At the beginning of each period, through dynamic pricing, determining the optimal price relative to the estimation value of the current parameter and attach a constraint of differential price decision. Meanwhile, the accuracy of demand estimation and the optimality of price decisions are balanced. Finally, a case study is given based on the real data of Shanghai port. The results show that this pricing policy can make the handling price converge to the stable price and significantly increase this shipping company's handling profit compared with the original "contractual pricing" mechanism.

scholarly journals Dynamic Pricing in Cloud Manufacturing Systems under Combined Effects of Consumer Structure, Negotiation, and Demand

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Wei Peng ◽  
Wei Guo ◽  
Lei Wang ◽  
Ruo-Yu Liang
Keyword(s):  
Dynamic Pricing ◽  
Manufacturing Systems ◽  
Wholesale Price ◽  
Cloud Manufacturing ◽  
High Ratio ◽  
Reservation Price ◽  
Combined Effects ◽  
Constant Elasticity ◽  
Price Increases ◽  
Linear Demand

In this study, we proposed a game-theory based framework to model the dynamic pricing process in the cloud manufacturing (CMfg) system. We considered a service provider (SP), a broker agent (BA), and a dynamic service demander (SD) population that is composed of price takers and bargainers in this study. The pricing processes under linear demand and constant elasticity demand were modeled, respectively. The combined effects of SD population structure, negotiation, and demand forms on the SP’s and the BA’s equilibrium prices and expected revenues were examined. We found that the SP’s optimal wholesale price, the BA’s optimal reservation price, and posted price all increase with the proportion of price takers under linear demand but decrease with it under constant elasticity demand. We also found that the BA’s optimal reservation price increases with bargainers’ power no matter under what kind of demand. Through analyzing the participants’ revenues, we showed that a dynamic SD population with a high ratio of price takers would benefit the SP and the BA.

scholarly journals Network Bandwidth Utilization Prediction Based on Observed SNMP Data

2018 ◽  
Vol 13 (1) ◽  
pp. 160-168
Author(s):  
Nandalal Rana ◽  
Krishna P Bhandari ◽  
Surendra Shrestha
Keyword(s):  
Linear Time ◽  
Arima Model ◽  
Real Data ◽  
Service Planning ◽  
Bandwidth Utilization ◽  
Network Nodes ◽  
Network Bandwidth ◽  
Bandwidth Requirement ◽  
Logit Transformation

 Bandwidth requirement prediction is an important part of network design and service planning. The natural way of predicting bandwidth requirement for existing network is to analyze the past trends and apply appropriate mathematical model to predict for the future. For this research, the historical usage data of FWDR network nodes of Nepal Telecom is subject to univariate linear time series ARIMA model after logit transformation to predict future bandwidth requirement. The predicted data is compared to the real data obtained from the same network and the predicted data has been found to be within 10% MAPE. This model reduces the MAPE by 11.71% and 15.42% respectively as compared to the non-logit transformed ARIMA model at 99% CI. The results imply that the logit transformed ARIMA model has better performance compared to non-logit-transformed ARIMA model. For more accurate and longer term predictions, larger dataset can be taken along with season adjustments and consideration of long term variations.Journal of the Institute of Engineering, 2017, 13(1): 160-168

A Data-Driven Exploratory Approach for Level Curve Estimation With Autonomous Underwater Agents

2017 ◽  
Author(s):  
Hsien-Chung Lin ◽  
Eugen Solowjow ◽  
Masayoshi Tomizuka ◽  
Edwin Kreuzer
Keyword(s):  
Concentration Field ◽  
Real Data ◽  
Support Vector ◽  
Level Curve ◽  
Data Set ◽  
Curve Estimation ◽  
Numerical Studies ◽  
Exploratory Approach ◽  
Vector Machines ◽  
Myopic Strategy

This contribution presents a method to estimate environmental boundaries with mobile agents. The agents sample a concentration field of interest at their respective positions and infer a level curve of the unknown field. The presented method is based on support vector machines (SVMs), whereby the concentration level of interest serves as the decision boundary. The field itself does not have to be estimated in order to obtain the level curve which makes the method computationally very appealing. A myopic strategy is developed to pick locations that yield most informative concentration measurements. Cooperative operations of multiple agents are demonstrated by dividing the domain in Voronoi tessellations. Numerical studies demonstrate the feasibility of the method on a real data set of the California coastal area. The exploration strategy is benchmarked against random walk which it clearly outperforms.

scholarly journals Self-Assisted First-Fix Method for A-BDS Receivers with Medium- and Long-Term Ephemeris Extension

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Zilong Shen ◽  
Jing Peng ◽  
Wenxiang Liu ◽  
Feixue Wang ◽  
Shibing Zhu ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Rapid Development ◽  
Real Data ◽  
Satellite System ◽  
Time Interval ◽  
Signal Acquisition ◽  
Positioning Accuracy ◽  
Broadcast Ephemeris ◽  
Intelligent Logistics

As a sensor for standalone position and velocity determination, the BeiDou Navigation Satellite System (BDS) receiver is becoming an important part of the intelligent logistics systems under rapid development in China. The applications in the mass market urgently require the BDS receivers to improve the performance of such functions, that is, shorter Time to First Fix (TTFF) and faster navigation signal acquisition speed with Ephemeris Extension (EE) in standalone mode. As a practical way to improve such functions of the Assisted BDS (A-BDS) receivers without the need for specialized hardware support, a Self-Assisted First-Fix (SAFF) method with medium- and long-term EE is proposed in this paper. In this SAFF method, the dynamic Medium- and Long-Term Orbit Prediction (MLTOP) method, which uses the historical broadcast ephemeris data with the optimal configuration of the dynamic models and orbit fitting time interval, is utilized to generate the extended ephemeris. To demonstrate the performance of the MLTOP method used in the SAFF method, a suit of tests, which were based on the real data of broadcast ephemeris and precise ephemeris, were carried out. In terms of the positioning accuracy, the overall performance of the SAFF method is illustrated. Based on the characteristics of the medium- and long-term EE, the simulation tests for the SAFF method were conducted. Results show that, for the SAFF method with medium- and long-term EE of the BeiDou MEO/IGSO satellites, the horizontal positioning accuracy is about 12 meters, and the overall positioning accuracy is about 25 meters. The results also indicate that, for the BeiDou satellites with different orbit types, the optimal configurations of the MLTOP method are different.

A Mixture Price Trend Model for Long-Term Risk Management

2010 ◽  
pp. 157-173
Author(s):  
Eric S. Fung ◽  
Wai-Ki Ching ◽  
Tak-Kuen Siu
Keyword(s):  
Risk Management ◽  
Financial Risk ◽  
Estimation Method ◽  
Real Data ◽  
Price Trend ◽  
Trend Model ◽  
Proposed Model ◽  
Price Trends ◽  
Long Term Forecasting

In financial forecasting, a long-standing challenging issue is to develop an appropriate model for forecasting long-term risk management of enterprises. In this chapter, using financial markets as an example, we introduce a mixture price trend model for long-term forecasts of financial asset prices with a view to applying it for long-term financial risk management. The key idea of the mixture price trend model is to provide a general and flexible way to incorporate various price trend behaviors and to extract information from price trends for long-term forecasting. Indeed, the mixture price trend model can incorporate model uncertainty in the price trend model, which is a key element for risk management and is overlooked in some of the current literatures. The mixture price trend model also allows the incorporation of users’ subjective views on long-term price trends. An efficient estimation method is introduced. Statistical analysis of the proposed model based on real data will be conducted to illustrate the performance of the model.

scholarly journals A System Dynamics Model of Online Stores’ Sales: Positive and Negative E-WOM and Promotion Perspective

2019 ◽  
Vol 11 (21) ◽  
pp. 6045 ◽  
Author(s):  
Qiang Yan ◽  
Simin Zhou ◽  
Xiaoyan Zhang ◽  
Ye Li
Keyword(s):  
System Dynamics ◽  
Real Data ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Sustainable Operations ◽  
Main Research ◽  
Interaction Diagram ◽  
Online Stores ◽  
The Impact

In this paper, we build a causal interaction diagram between the factors that may influence the sales and profits of online stores. An online store’s real operation data were used to help determine the causal relationship between variables. Finally, we proposed a system dynamics model and conducted a simulation of the operation of an online store. In this model, we focused on the impact of promotion and positive/negative electronic word of mouth (e-WOM) on the sales and profits of the online stores. The simulation results showed a similar trend to the real data and the main research finding showed that promotion is not a long-term measure for the sustainable development of online stores. Excessive promotion effort may lead to consumers’ dissatisfaction leading the increase of negative e-WOM. The systematic simulation can help us understand better the long-term effect of promotion and e-WOM on the operation of online stores. Finally, we gave some management suggestions for online stores’ sustainable operations.

The Long-term and Spillover Effects of Price Promotions on Retailing Platforms: Evidence from a Large Randomized Experiment on Alibaba

2020 ◽  
Vol 66 (6) ◽  
pp. 2589-2609 ◽  
Author(s):  
Dennis J. Zhang ◽  
Hengchen Dai ◽  
Lingxiu Dong ◽  
Fangfang Qi ◽  
Nannan Zhang ◽  
...  
Keyword(s):  
Dynamic Pricing ◽  
Spillover Effects ◽  
Unintended Consequences ◽  
Customer Behavior ◽  
Customer Engagement ◽  
Control Group ◽  
Price Promotions ◽  
Promotion Program ◽  
Shopping Cart

Dynamic pricing through price promotions has been widely used by online retailers. We study how a promotion strategy, one that offers customers a discount for products in their shopping cart, affects customer behavior in the short and long term on a retailing platform. We conduct a randomized field experiment involving more than 100 million customers and 11,000 retailers with Alibaba Group, one of the world’s largest retailing platform. We randomly assign eligible customers to either receive promotions for products in their shopping cart (treatment group) or not receive promotions (control group). In the short term, our promotion program doubles the sales of promoted products on the day of promotion. In the long term, we causally document unintended consequences of this promotion program during the month after our treatment period. On the positive side, it boosts customer engagement, increasing the daily number of products that customers view and their purchase incidence on the platform. On the negative side, it intensifies strategic customer behavior in the posttreatment period in two ways: (1) by increasing the proportion of products that customers add to their shopping cart conditional on viewing them, possibly because of their intention to get more shopping cart promotions, and (2) by decreasing the price that customers subsequently pay for a product, possibly because of their strategic search for lower prices. Importantly, these long-term effects of price promotions on consumer engagement and strategic behavior spill over to sellers who did not previously offer promotions to customers. Finally, we examine heterogeneous treatment effects across promotion, seller, and consumer characteristics. These findings have important implications for platforms and retailers. This paper was accepted by Vishal Gaur, operations management.

Feasibility study of collective heating and cooling based on foundation pile heat exchangers in Vejle (Denmark)

2020 ◽  
pp. qjegh2020-114
Author(s):  
D. Cerra ◽  
M. Alberdi-Pagola ◽  
T.R. Andersen ◽  
K.W. Tordrup ◽  
S.E. Poulsen
Keyword(s):  
Energy Demand ◽  
District Heating ◽  
Demand Model ◽  
Heating And Cooling ◽  
Energy Piles ◽  
Geophysical Investigations ◽  
Foundation Pile ◽  
Long Term Performance ◽  
Term Performance

We assess the feasibility of a collective district heating and cooling network based on a foundation pile heat exchanger in a new urban area in Vejle, Denmark. A thermogeological model for the area is developed based on geophysical investigations and borehole information. In tandem with a building energy demand model, the subsurface thermal properties serve as the input for a newly developed computational temperature model for collective heating and cooling with energy piles. The purpose of the model is to estimate the long-term performance and maximum liveable area that the energy piles are able to support. We consider two case studies where residential and office buildings dominate the building mass. We find that three to four floors can be supplied with heating and cooling from the energy piles, depending on the use and design of the buildings.

Sign in / Sign up

Export Citation Format

Share Document