@Article{	  kephart00a,
  author =	 {Jeffrey O. Kephart and James E. Hanson and Amy
                  R. Greenwald},
  title =	 {Dynamic Pricing by Software Agents },
  googleid =	 {_KhRTON1GyMJ:scholar.google.com/},
  journal =	 {Computer Networks},
  year =	 2000,
  volume =	 {32},
  number =	 {6},
  pages =	 {731--752},
  abstract =	 {We envision a future in which the global economy and
                  the Internet will merge and evolve together into an
                  information economy bustling with billions of
                  economically motivated software agents that exchange
                  information goods and services with humans and other
                  agents. Economic software agents will differ in
                  important ways from their human counterparts, and
                  these differences may have significant beneficial or
                  harmful effects upon the global economy. It is
                  therefore important to consider the economic
                  incentives and behaviors of economic software
                  agents, and to use every available means to
                  anticipate their collective interactions. We survey
                  research conducted by the Information Economies
                  group at IBM Research aimed at understanding
                  collective interactions among agents that
                  dynamically price information goods or services. In
                  particular, we study the potential impact of
                  widespread shopbot usage on prices, the price
                  dynamics that may ensue from various mixtures of
                  automated pricing agents (or ``pricebots''), the
                  potential use of machine learning algorithms to
                  improve profits, and more generally the interplay
                  among learning, optimization, and dynamics in
                  agent-based information economies. These studies
                  illustrate both beneficial and harmful collective
                  behaviors that can arise in such systems, suggest
                  possible cures for some of the undesired phenomena,
                  and raise fundamental theoretical issues,
                  particularly in the realms of multi-agent learning
                  and dynamic optimization.},
  keywords =     {multiagent auctions},
  url =
                  {http://www.research.ibm.com/infoecon/paps/html/rudin/rudin.html},
  cluster = 	 {2529745234797111548}
}