Dan Friedman* and Bernardo A. Huberman**
*Economics Dept. UC Santa Cruz
**Information Dynamics Laboratory, HP Labs
Abstract
Human players and automated players (bots) interact in
real time in a congested network. A player’s revenue is
proportional to the number of successful “downloads” and his
cost is proportional to his total waiting time. Congestion
arises because waiting time is an increasing random function
of the number of uncompleted download attempts by all
players. Surprisingly, some human players earn considerably
higher profits than bots. Bots are better able to exploit
periods of excess capacity, but they create endogenous
trends in congestion that human players are better able to
exploit. Nash equilibrium does a good job of predicting the
impact of network capacity and noise amplitude. Overall
efficiency is quite low, however, and players overdissipate
potential rents, i.e., earn lower profits than in Nash
equilibrium.
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