Congestion allocation for distributed networks: an experimental study

This paper reports an experimental study of two prominent congestion and cost allocation mechanisms for distributed networks. We study the fair queuing (or serial) and the FIFO (or average cost pricing) mechanisms under two different treatments: a complete information treatment and a limited information treatment designed to simulate distributed networks. Experimental results show that the fair queuing mechanism performs significantly better than FIFO in all treatments in terms of efficiency, predictability measured as frequency of equilibrium play, and the speed of convergence to equilibrium.

[1]  Colin Camerer,et al.  Experience‐weighted Attraction Learning in Normal Form Games , 1999 .

[2]  T. Palfrey,et al.  An Experimental Comparison of Collective Choice Procedures for Excludable Public Goods , 2005 .

[3]  Urs Fischbacher,et al.  z-Tree - Zurich toolbox for readymade economic experiments: experimenter's manual , 1999 .

[4]  ShenkerScott.  Making greed work in networks: a game-theoretic analysis of gateway service disciplines , 1990 .

[5]  Eric J. Friedman,et al.  Three Methods to Share Joint Costs or Surplus , 1999 .

[6]  Scott Shenker.  Making greed work in networks: a game-theoretic analysis of gateway service disciplines , 1990, SIGMETRICS '90.

[7]  Amnon Rapoport,et al.  Equilibrium Play in Single-Server Queues with Endogenously Determined Arrival Times , 2004 .

[8]  Frederick W. Rankin,et al.  Selection dynamics and adaptive behavior without much information , 2007 .

[9]  Elena Yanovskaya,et al.  Serial cost sharing , 2006 .

[10]  Scott Shenker,et al.  Analysis and simulation of a fair queueing algorithm , 1989, SIGCOMM '89.

[11]  Yan Chen,et al.  Learning under limited information , 2003, Games Econ. Behav..

[12]  Yan Chen.  An experimental study of serial and average cost pricing mechanisms , 2003 .

[13]  Scott Shenker,et al.  Core-stateless fair queueing: achieving approximately fair bandwidth allocations in high speed networks , 1998, SIGCOMM '98.

[14]  Robert E. Dorsey,et al.  An Experimental Evaluation of the Serial Cost Sharing Rule , 2007 .

[15]  Nicolaas J. Vriend,et al.  Evolving market structure: An ACE model of price dispersion and loyalty , 2001 .

[16]  Andrew McLennan,et al.  Gambit: Software Tools for Game Theory , 2006 .

[17]  Eric J. Friedman,et al.  Learning and Implementation on the Internet , 1997 .

[18]  D. Fudenberg,et al.  The Theory of Learning in Games , 1998 .

[19]  R. Sarin,et al.  Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice , 1999 .

[20]  Paul R. Milgrom,et al.  Rationalizability, Learning, and Equilibrium in Games with Strategic Complementarities , 1990 .

[21]  Yan Chen,et al.  When Does Learning in Games Generate Convergence to Nash Equilibria? The Role of Supermodularity in an Experimental Setting ⁄ , 2004 .

[22]  Scott Shenker,et al.  Learning in Network Contexts: Experimental Results from Simulations , 2001, Games Econ. Behav..

[23]  H. Moulin,et al.  Average Cost Pricing versus Serial Cost Sharing: An Axiomatic Comparison , 1994 .

[24]  David M. Aadland,et al.  Environmental determinants of cost sharing an application to irrigation , 2001 .