Department of Computer Science and Technology

Technical reports

Cooperation and deviation in market-based resource allocation

Jörg H. Lepler

March 2005, 173 pages

This technical report is based on a dissertation submitted November 2004 by the author for the degree of Doctor of Philosophy to the University of Cambridge, St John’s College.

DOI: 10.48456/tr-622


This thesis investigates how business transactions are enhanced through competing strategies for economically motivated cooperation. To this end, it focuses on the setting of a distributed, bilateral allocation protocol for electronic services and resources. Cooperative efforts like these are often threatened by transaction parties who aim to exploit their competitors by deviating from so-called cooperative goals. We analyse this conflict between cooperation and deviation by presenting the case of two novel market systems which use economic incentives to solve the complications that arise from cooperation.

The first of the two systems is a pricing model which is designed to address the problematic resource market situation, where supply exceeds demand and perfect competition can make prices collapse to level zero. This pricing model uses supply functions to determine the optimal Nash-Equilibrium price. Moreover, in this model the providers’ market estimations are updated with information about each of their own transactions. Here, we implement the protocol in a discrete event simulation, to show that the equilibrium prices are above competitive levels, and to demonstrate that deviations from the pricing model are not profitable.

The second of the two systems is a reputation aggregation model, which seeks the subgroup of raters that (1) contains the largest degree of overall agreement and (2) derives the resulting reputation scores from their comments. In order to seek agreement, this model assumes that not all raters in the system are equally able to foster an agreement. Based on the variances of the raters’ comments, the system derives a notion of the reputation for each rater, which is in turn fed back into the model’s recursive scoring algorithm. We demonstrate the convergence of this algorithm, and show the effectiveness of the model’s ability to discriminate between poor and strong raters. Then with a series of threat models, we show how resilient this model is in terms of finding agreement, despite large collectives of malicious raters. Finally, in a practical example, we apply the model to the academic peer review process in order to show its versatility at establishing a ranking of rated objects.

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BibTeX record

  author =	 {Lepler, J{\"o}rg H.},
  title = 	 {{Cooperation and deviation in market-based resource
  year = 	 2005,
  month = 	 mar,
  url = 	 {},
  institution =  {University of Cambridge, Computer Laboratory},
  doi = 	 {10.48456/tr-622},
  number = 	 {UCAM-CL-TR-622}