CaltechTHESIS
  A Caltech Library Service

Online Platforms in Networked Markets: Transparency, Anticipation and Demand Management

Citation

Pang, John Zhen Fu (2019) Online Platforms in Networked Markets: Transparency, Anticipation and Demand Management. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/XY8M-8D94. http://resolver.caltech.edu/CaltechTHESIS:03132019-143428796

Abstract

The global economy has been transformed by the introduction of online platforms in the past two decades. These companies, such as Uber and Amazon, have benefited and undergone massive growth, and are a critical part of the world economy today. Understanding these online platforms, their designs and how participation change with anticipation and uncertainty can help us identify the necessary ingredients for successful implementation of online platforms in the future, especially for those with underlying network constraints, e.g., the electricity grid.

This thesis makes three main contributions. First, we identify and compare common access and allocation control designs for online platforms, and highlight their trade-offs between transparency and control. We make these comparisons under a networked Cournot competition model and consider three popular designs: (i) open access, (ii) discriminatory access, and (iii) controlled allocation. Our findings reveal that designs that control over access are more efficient than designs that control over allocations, but open access designs are susceptible to substantial search costs. Next, we study the impact of demand management in a networked Stackelberg model considering network constraints and producer anticipation. We provide insights on limiting manipulation under these constrained networked marketplaces with nodal prices, and show that demand management mechanisms that traditionally aid system stability also help plays a vital role economically. In particular, we show that demand management empower consumers and give them "market power" to counter that of producers, limiting the impact of their anticipation and their potential for manipulation. Lastly, we study how participants (e.g., drivers on Uber) make competitive real-time production (driving) decisions. To that end, we design a novel pursuit algorithm for making online optimization under limited inventory constraints. Our analysis yields an algorithm that is competitive and applicable to achieve optimal results in the well known one-way trading problem, and new variants of the original problem.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Online Platforms, Online Optimization, Demand Management
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Computing and Mathematical Sciences
Awards:Amori Doctoral Prize in CMS, 2019
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Wierman, Adam C.
Group:Caltech Rigorous Systems Research Group (RSRG)
Thesis Committee:
  • Chandrasekaran, Venkat (chair)
  • Doval, Laura
  • Low, Steven H.
  • Wierman, Adam C.
Defense Date:9 May 2019
Non-Caltech Author Email:johnpzf (AT) gmail.com
Funders:
Funding AgencyGrant Number
Agency for Science, Technology and Research (A*STAR)UNSPECIFIED
Record Number:CaltechTHESIS:03132019-143428796
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:03132019-143428796
DOI:10.7907/XY8M-8D94
Related URLs:
URLURL TypeDescription
https://arxiv.org/abs/1903.04574arXivArticle adapted for Ch. 3
https://arxiv.org/abs/1901.09161arXivArticle adapted for Ch. 5
ORCID:
AuthorORCID
Pang, John Zhen Fu0000-0002-6485-7922
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:11425
Collection:CaltechTHESIS
Deposited By: John Pang
Deposited On:28 May 2019 19:12
Last Modified:04 Jun 2019 17:48

Thesis Files

[img]
Preview
PDF - Final Version
See Usage Policy.

923Kb

Repository Staff Only: item control page