Dynamical Jurisprudence: Law as a Complex System

• Citation: Vol. 24 No. 4
• Publication year: 2010

Georgia State University Law Review

Volume 24 j 6

Issue 4 Summer 2008

3-21-2012

Dynamical Jurisprudence: Law as a Complex System

Gregory Todd Jones

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Recommended Citation

Jones, Gregory Todd (2007) "Dynamical Jurisprudence: Law as a Complex System," Georgia State University Law Review: Vol. 24: Iss. 4, Article 6.

Available at: http://digitalarchive.gsu.edu/gsulr/vol24/iss4/6

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DYNAMICAL JURISPRUDENCE: LAW AS A COMPLEX SYSTEM

Gregory Todd Jones*

Vast flocks of English starlings gather over the roost at dusk and glide through the air in a spectacular display of spatial coherence.¹ Both the evolutionary, or ultimate cause, of flocking behavior, and the proximate mechanisms that makes this performance possible are still relatively poorly understood.² Flocking, along with schooling in fish³ and swarming in insects,⁴ was until recently believed to be driven by one or more leaders, whose followers percolated the behavior through the group. We are now beginning to discover that this collective behavior results not from leadership, but emerges from individuals following simple sets of local rules.⁵

Highly multidisciplinary teams of scientists from anthropology, biology, computer science, ecology, economics, physics, political

* Faculty Research Fellow, Georgia State University College of Law; Director of Research, Consortium on Negotiation and Conflict Resolution; Director, Computational Laboratory for Complex Adaptive Systems.

1. For an excellent resource summarizing an European Union study of starling flocking for the purpose of employing complex systems principles to shed light on collective animal behavior, including dramatic video footage and still photographs, see http://angel.elte.hu/starling/index.html (last visited Mar 23,2008).

2. See generally, Michele Ballerini, et al., An Empirical Study of Large, Naturally Occurring Starling Flocks: A Benchmark in Collective Animal Behaviour, Animal Behaviour (forthcoming, 2008), available at http://arxiv.org/ftp/arxiv/papers/0802/0802.1667.pdf (last visited Mar 23,2008).

3. See generally, Yoshinobu Inada & Keiji Kawachi, Order and Flexibility in the Motion of Fish Schools, 214 J. ofTheor. Biol. 371 (2002).

4. See generally, ERIC BONABEAU, swarm INTELLIGENCE: from NATURAL TO ARTIFICIAL

Systems (1999).

5. Indeed, recent work has demonstrated that three simple local rules can capture essential flocking behavior:

1. Separation: steer to avoid crowding local flockmates.

2. Alignment: steer towards the average heading of local flockmates.

3. Cohesion: steer to move toward the average position of local flockmates.

For a web site that includes a simulation allowing experimentation with these rules, an excellent summary of the relevant theory, and an exhaustive catalog of resources related to collective group movement, see http://www.red3d.com/cwr/boids/ (last visited mar 23,2008). For another simulation of this behavior, with access to the underlying code, see Uri WlLENSKY, NetLogo FLOCKING MODEL, Center for Connected Learning and Computer-based Modeling, Northwestern University, Evanston, IL. (1998), http://ccl.northwestern.edu/netlogo/models/Flocking.

874 GEORGIA STATE UNIVERSITY LAW REVIEW [Vol. 24:4

science, psychology, mathematics, sociology, and numerous other fields have begun to recognize that large systems of interacting heterogeneous agents often display very complex behaviors that cannot be easily deduced by studying the behaviors of the individual agents. Lessons from these emergent collective behaviors are now being applied to help us rethink the dynamics of human behavior.

Wouldn't it be worthwhile if we could identify and understand individual human behaviors that result in dramatic human group behavior⁶- and use this understanding to optimally design institutions¹ that constrain this behavior in ways that promote social welfare?

A close examination of the dynamics of forest fires reveals that the extent of damage is closely related to the density of the trees.⁸ This is not particularly surprising. However, computer simulations reveal that this observation offers more nuance.⁹ Holding other variables such as combustibility, rainfall, etc. constant, computational results show that a fire started on one end of an artificial forest with 57% density would typically result in about 10% of the forest burning before the fire burned itself out.¹⁰ The same artificial forest with 60% density would result in more than 75% of the forest destroyed.¹¹

6. See generally, Robert a. Stallings, On Theory in Collective Behavior and Empirical Patterns in a Riot Process, 41 am. Sociological Rev. 749 (1976); Mark Granovetter, Threshold Models of Collective Behavior, 83 a. J. S. 1420 (1978).

7. See generally, Marcel Fafchamps, Spontaneous Market Emergence, 2 topics in Theoretical economics 1 (2002) available at http://www.bepress.com/bejte (last visited Mar 23,2008).

8. See generally, Siegfried Clar, Barbara Drossel & Franz Schwabl, Forest Fires and Other Examples of Self-Organized Criticality, 8 J. phys.: condens. matter 6803 (1996).

9. See generally, Barbara Drossel & Franz Schwabl, Self-Organized Critical Forest-Fire Model, 69 Phys. Rev. Letters 1629 (1992). For a simulation of these dynamics, with access to the underlying code, see Uri Wilensky, netLogo Fire Model, Center for Connected Learning and

computer-based modeling, northwestern university, evanston, IL. (1998), http://ccl.northwestem.edu/netlogo/models/Fire.

10. These models are stochastic and results reported are typical averages over a series of simulation runs.

11. Clearly, there are many more variables that may affect outcomes and these damage...