Abstract
This conference is devoted to the theme of the design, prediction, and control of collectives. Many of the collectives that we are implicitly concerned with (and for good reason) are collectives composed of software agents or combinations of software and hardware agents. Collectives of agents that remotely gather information from distant planets and then transmit that information to Earth are one example. Of interest to the military are collectives of small, cheap sensors distributed on a battlefield or in a city that measure some aspect of local conditions and then relay that information to a central repository near a command center. Another example is a collection of sensors and actuators that control the flow of oil or electricity through a complex network by sensing local conditions and responding to them. One common architecture for the interaction of these local agents is through some sort of analogy with economic systems. Here it is supposed that the local agents compete for some scarce resource (bandwidth in the case of agents whose job it is to transmit information, or fluid pressure in the case of those agents whose job it is to regulate oil flow), possibly by a bidding mechanism or by some other strategic architecture that rewards agents for “buying low” or “selling high.”
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D. Challet and Y.-C. Zhang, Physica A, 246, 407 (1997). R. Savit, R. Manuca, and R. Riolo, Phys. Rev. Lett. 82, 2203 (1999). D. Challet, M. Marsili, and R. Zecchina, Phys. Rev. Lett. 84, 1824 (2000). See also the references on the excellent Web site http://www.unifr.ch/ econophysics/ minority.
It is not quite N/2 in a given game because there are biases built into the game due to the particular (random) strategies that were assigned to the agents at the beginning of the game. This bias is not important for our discussion here.
See note 1.
See note 1.
Y. Li, R. Riolo, and R. Savit, Physica A 276, 234 (2000); Physica A 276, 265 (2000).
The fact that there is a dramatic fall-off in the wealth per agent and in the number of agents playing with a given memory at m = 6 is not an accident. The value of m at the fall-off depends on N and is related to the value of z c in the games played with fixed N and m. See Y. Li, R. Riolo, and R. Savit, Physica A 276, 265 (2000), for more details.
R. Savit, K. Koelle, Y. Li, and R. Gonzalez, to appear.
See note 7.
There is good reason to suppose that m = 3 is about the largest value of m that most people will use in making their decisions. There is a famous notion in psychology called 7 ± 2 that asserts that people typically can pay attention to between five and nine different pieces of information. Because m = 3 encompasses eight pieces of information, this is a reasonable value to take as a maximum number of lags that can be used to determine a person's choice.
See note 5.
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Savit, R., Koelle, K., Treynor, W., Gonzalez, R. (2004). Man and Superman: Human Limitations, Innovation, and Emergence in Resource Competition. In: Tumer, K., Wolpert, D. (eds) Collectives and the Design of Complex Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8909-3_8
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