Effects of Interaction Topology and Activation Regime in Several Multi-Agent Systems

The effects of distinct agent interaction and activation structures are compared and contrasted in several multi-agent models of social phenomena. Random graphs and lattices represent two limiting kinds of agent interaction networks studied, with so-called 'small-world' networks being an intermediate form between these two extremes. A model of retirement behavior is studied with each network type, resulting in important differences in key model outputs. Then, in the context of a model of multi-agent firm formation it is demonstrated that the medium of interaction--whether through individual agents or through firms--affects the qualitative character of the results. Finally, alternative agent activation 'schedules' are studied. In particular, two activation modes are compared: (1) all agents being active exactly once each period, and (2) each agent having a random number of activations per period with mean 1. In some models these two regimes produce indistinguishable results at the aggregate level, but in certain cases the differences between them are significant.

[1]  Frank Harary,et al.  Graph Theory , 2016 .

[2]  Glenn Ellison.  Learning, Local Interaction, and Coordination , 1993 .

[3]  B A Huberman,et al.  Evolutionary games and computer simulations. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Robert L. Axtell,et al.  Aligning simulation models: A case study and results , 1996, Comput. Math. Organ. Theory.

[5]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[6]  A. Rubinstein.  Modeling Bounded Rationality , 1998 .

[7]  Scott E. Page.  On Incentives and Updating in Agent Based Models , 1997 .

[8]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[9]  H. Young.  Individual Strategy and Social Structure , 2020 .

[10]  H. Young.  Diffusion in Social Networks , 1999 .

[11]  N. Birdsall,et al.  Equity, Savings, and Growth , 1999 .

[12]  Xerox,et al.  The Small World , 1999 .

[13]  G. Burtless,et al.  Effects of growing wage disparities and changing family composition on the U.S. income distribution , 1999 .

[14]  Nancy Birdsall.  Education: The People's Asset , 1999 .

[15]  Robert L. Axtell,et al.  Coordination in Transient Social Networks: An Agent-Based Computational Model of the Timing of Retir , 2012 .

[16]  R. Axtell.  The Emergence of Firms in a Population of Agents , 1999 .

[17]  H. Aaron.  Behavioral dimensions of retirement economics , 1999 .

[18]  G. Burtless.  Social Security Privatization and Financial Market Risk: Lessons from U.S. Financial History , 2002 .

[19]  Duncan J. Watts,et al.  Book Review: Small Worlds. The Dynamics of Networks Between Order and Randomness , 2000 .

[20]  Joshua M. Epstein,et al.  The Emergence of Classes in a Multi-Agent Bargaining Model , 2012 .

[21]  Péter Gács,et al.  Deterministic computations whose history is independent of the order of asynchronous updating , 2001, ArXiv.

[22]  Joshua M. Epstein,et al.  Learning to Be Thoughtless: Social Norms and Individual Computation , 2001 .

[23]  H. Young,et al.  Competition and Custom in Economic Contracts: A Case Study of Illinois Agriculture , 2001 .

[24]  Jie Wu,et al.  Small Worlds: The Dynamics of Networks between Order and Randomness , 2003 .

[25]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .