Clustered volatility in multiagent dynamics

Large distributed multiagent systems are characterized by vast numbers of agents trying to gain access to limited resources in an unpredictable environment. Agents in these system continuously switch strategies in order to opportunistically find improvements in their utilities. We have analyzed the fluctuations around equilibrium that arise from strategy switching and discovered the existence of a new phenomenon. It consists of the appearance of sudden bursts of activity that punctuate the fixed point, and is due to an effective random walk consistent with overall stability. This clustered volatility is followed by relaxation to the fixed point but with different strategy mixes from the previous one. The phenomenon is quite general for systems in which agents explore strategies in search of local improvements.

[1]  Richard H. Day.  Adaptive Processes and Economic Theory , 1975 .

[2]  William A. Brock,et al.  Understanding macroeconomic time series using complex systems theory , 1991 .

[3]  W. Arthur.  Inductive Reasoning and Bounded Rationality , 1994 .

[4]  Tad Hogg,et al.  Distributed Computation as an Economic System , 1995 .

[5]  Iqbal Adjali,et al.  Markets in open telecommunication service systems , 1993 .

[6]  S. Clearwater.  Market-based control: a paradigm for distributed resource allocation , 1996 .

[7]  Tatsuya Suda,et al.  Distributed channel allocation in ATM networks , 1993, Proceedings of GLOBECOM '93. IEEE Global Telecommunications Conference.

[8]  Tad Hogg,et al.  Controlling chaos in distributed systems , 1991, IEEE Trans. Syst. Man Cybern..

[9]  E. R. Grannan,et al.  Contrarians and Volatility Clustering , 1994, Complex Syst..

[10]  Bernardo A. Huberman,et al.  The ecology of computation , 1988, Digest of Papers. COMPCON Spring 89. Thirty-Fourth IEEE Computer Society International Conference: Intellectual Leverage.

[11]  H. Simon,et al.  Models of Bounded Rationality: Empirically Grounded Economic Reason , 1997 .

[12]  Mario Tokoro,et al.  The society of objects , 1993, OOPSLA Addendum.

[13]  Ivan E. Sutherland,et al.  A futures market in computer time , 1968, Commun. ACM.

[14]  Tad Hogg,et al.  Spawn: A Distributed Computational Economy , 1992, IEEE Trans. Software Eng..

[15]  Hogg,et al.  Dynamics of computational ecosystems. , 1989, Physical review. A, General physics.

[16]  Harry R. Erwin.  Mixing and Sensitive Dependence on Initial Conditions in Computer Systems , 1989, Comput. Meas. Group Trans..

[17]  Blake LeBaron,et al.  A Dynamic Structural Model for Stock Return Volatility and Trading Volume , 1995 .

[18]  R. Chou,et al.  ARCH modeling in finance: A review of the theory and empirical evidence , 1992 .

[19]  Adjali,et al.  Fluctuations in a decentralized agent-resource system. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[20]  Donald F. Ferguson,et al.  Microeconomic algorithms for load balancing in distributed computer systems , 1988, [1988] Proceedings. The 8th International Conference on Distributed.