Abstract
It is widely believed that a group of cooperating agents engaged in problem solving can solve a task faster than either a single agent or the same group of agents working in isolation from each other. As a matter of fact, that cooperation leads to improvements in the performance of a group of individuals underlies the founding of the firm, the existence of scientific and professional communities, and the establishing of committees charged with solving particular problems. In the realm of computation, the emergence of massively parallel machines underscores the assumed power of concurrency for solving very complex tasks that can be decomposed into smaller pieces, and a large effort is being devoted to the design of parallel algorithms for the solution of computationally hard problems.
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References
Aitchison J., Brown J.A.C. (1957): The Lognormal Distribution. Cambridge University Press, Cambridge.
Aitchison J., Brown J.A.C. (1969): The Lognormal Distribution. Cambridge University Press, Cambridge.
Axelrod R., Hamilton W. (1981): The Evolution of Cooperation, Science, 211 1390–1396.
Bollobas B. (1985): Random Graphs, Academic Press, NY.
Bree D.S. (1975): The Distribution of Problem-Solving Times: An Examination of the Stages Model, Br. J. Math. Stat. Psychol., 28 177–200.
Clearwater S.H., Huberman B.A., Hogg T. (1991): Cooperative Solution of Constraint Satisfaction Problems, Sciences 254 1181–1183.
Crow E.L., Shimizu K. editors (1988): Lognormal Distributions: Theory and Applications, Marcel Dekker, New York.
Engelmore R., Morgan T. (1988): Blackboard Systems, Addison Wesley, Reading.
Goldberg D.E. (1989): Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading.
Huberman B.A., Hogg T. (1987): Phase Transitions in Artificial Intelligence Systems, Artificial Intelligence, 33 155–171.
Huberman B.A., Hogg T. (1988): The Behavior of Computational Ecologies. In The Ecology of Computation, B.A. Huberman ed., 77–115. North-Holland, Amsterdam.
Huberman B.A. (1990): The Performance of Cooperative Processes, Physica D, 42 3847.
Huberman B.A., Clearwater S.H., Hogg T. (1992): Cooperative Problem Solving, Computation: The Micro and the Macro View 33–70.
Huberman B.A. and Hogg T (1995): Communities of Practice: Performance and Evolution, Computational and Mathematical Organization Theory, 1 73–92.
Kornfeld W.A. (1982): Combinatorially Implosive Algorithms, Communications of the ACM, 25 (10) 734–738.
Krebs C.J. (1972): Ecology, Harper and Row, New York.
Montroll E.W., Shlesinger M.F. (1982): On l/f Noise and other Distributions with Long Tails, Proc. Natl. Acad. Sci. (USA), 79 3380–3383.
Pearl J. (1984): Heuristics: Intelligent Search Strategies for Computer Problem Solving, Addison-Wesley, Reading, Mass.
Shockley W. (1957): On the Statistics of Individual Variations of Productivity in Research Laboratories, Proceedings of the IRE, 45 279–290.
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© 1997 Springer-Verlag Berlin Heidelberg
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Huberman, B.A. (1997). The Power of Cooperation. In: Garrido, P.L., Marro, J. (eds) Fourth Granada Lectures in Computational Physics. Lecture Notes in Physics, vol 493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-14148-9_2
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DOI: https://doi.org/10.1007/978-3-662-14148-9_2
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