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Dynamic Pricing in the Presence of Participation-Dependent Social Learning

Published: 26 June 2018 Publication History

Abstract

For Internet-based services, users' quality of service (QoS) depends on not only the available resource (capacity) but also the number of users who use the resource simultaneously (e.g., congestion effect). When a new Internet-based service provider first enters the market, there can be uncertainties regarding both the capacity and congestion, and hence the uncertainty of QoS. In this paper, we consider a participation-dependent social learning over the QoS through users' online reviews, where the QoS changes with the number of review participants. We study how such a learning process affects the provider's dynamic pricing strategy. With a simple two-period model, we analyze the strategic interactions between the provider and the users, and characterize the provider's optimal two-period dynamic pricing policy. Our results show that when the capacity is small or the users' prior QoS belief is high, the provider will choose a higher introductory price in the first period (than the price in the second period). This is in sharp contrast with the common practice of setting a lower introductory price to attract users (when congestion is not an issue). Furthermore, the learning process is beneficial to the provider with a large capacity.

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  • (2024)Competitive Pricing in Participation-Dependent Social-Learning Markets2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS59351.2024.10427120(798-806)Online publication date: 3-Jan-2024
  • (2023)Optimal Subscription Policies for Participation-Dependent Social-Learning MarketsIEEE Transactions on Network and Service Management10.1109/TNSM.2023.326743820:4(4141-4157)Online publication date: Dec-2023
  • (2021)Monopoly Pricing with Participation‐Dependent Social Learning About Quality of ServiceProduction and Operations Management10.1111/poms.1349730:11(4004-4022)Online publication date: 1-Nov-2021
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      cover image ACM Conferences
      Mobihoc '18: Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing
      June 2018
      329 pages
      ISBN:9781450357708
      DOI:10.1145/3209582
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 26 June 2018

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      Author Tags

      1. Social learning
      2. dynamic pricing
      3. participation
      4. stochastic game

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      • Refereed limited

      Funding Sources

      • US Army Research Laboratory (ARL) Cooperative Agreement
      • General Research Funds
      • US Army Research Office (ARO) Grant

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      View all
      • (2024)Competitive Pricing in Participation-Dependent Social-Learning Markets2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS59351.2024.10427120(798-806)Online publication date: 3-Jan-2024
      • (2023)Optimal Subscription Policies for Participation-Dependent Social-Learning MarketsIEEE Transactions on Network and Service Management10.1109/TNSM.2023.326743820:4(4141-4157)Online publication date: Dec-2023
      • (2021)Monopoly Pricing with Participation‐Dependent Social Learning About Quality of ServiceProduction and Operations Management10.1111/poms.1349730:11(4004-4022)Online publication date: 1-Nov-2021
      • (2021)Learning End-User Behavior for Optimized Bidding and User/Network AssociationIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2020.30344427:3(845-855)Online publication date: Sep-2021

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