A Computational Analysis of Bundle Trading Markets Design for Distributed Resource Allocation

Zhiling Guo (), Gary J. Koehler () and Andrew B. Whinston ()
Additional contact information
Zhiling Guo: Department of Information Systems, College of Business, City University of Hong Kong, Kowloon, Hong Kong
Gary J. Koehler: Department of Information Systems and Operations Management, Warrington College of Business Administration, University of Florida, Gainesville, Florida 32611
Andrew B. Whinston: Department of Information, Risk, and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, Texas 78712

Information Systems Research, 2012, vol. 23, issue 3-part-1, 823-843

Abstract: Online auction markets play increasingly important roles for resource allocations in distributed systems. This paper builds upon a market-based framework presented by Guo et al. (Guo, Z., G. J. Koehler, A. B. Whinston. 2007. A market-based optimization algorithm for distributed systems. Management Sci . 53 (8) 1345--1458), where a distributed system optimization problem is solved by self-interested agents iteratively trading bundled resources in a double auction market run by a dealer. We extend this approach to a dynamic, asynchronous Internet market environment and investigate how various market design factors including dealer inventory policies, market communication patterns, and agent learning strategies affect the computational market efficiency, market liquidity, and implementation. We prove finite convergence to an optimal solution under these various schemes, where individual rational and budget-balanced trading leads to an efficient auction outcome. Empirical investigations further show that the algorithmic implementation is robust to a number of dealer and agent manipulations and scalable to larger sizes and more complicated bundle trading markets. Interestingly, we find that, though both asynchronous communication and asymmetric market information negatively affect the speed of market convergence and lead to more agent welfare loss, agents' ability to predict market prices has a positive effect on both. Contrary to conventional wisdom that a dealer's intertemporal liquidity provisions improve market performance, we find that the dealer's active market intervention may not be desirable in a simple market trading environment where an inherent market liquidity effect dominates, especially when the dealer owns a significant amount of resources. Different from the traditional market insight, our trading data suggest that high trading volume does not correlate to low price volatility and quicker price discovery.

Keywords: electronic markets and auctions; electronic commerce; resource allocation; computational experiment; simulation (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://dx.doi.org/10.1287/isre.1110.0366 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:23:y:2012:i:3-part-1:p:823-843

Access Statistics for this article

More articles in Information Systems Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().