Complexity science provides a general framework for approaching all fields of science. Unlike other scientific methods, complexity looks at how multiple interactions between agents (be they humans, insects, animals, companies, etc.) create a context to which they respond. Complexity does not see ecosystems in equilibrium. Agents face ill-defined problems to which they respond with not always optimal, fully rational behavior. Ecosystems depend on time and history; complexity science looks at the messy vitality of ecosystems. Economists and lawyers, among others, logically have much to gain from considering complexity science because they deal with lively ecosystems. Fortunately, they can build on previous research to guide their efforts, starting with the work of W. Brian Arthur.
W. Brian Arthur is an economist, engineer, and mathematician who obtained his first tenured position as a Professor of Economics and Population Studies at Stanford University after receiving his Ph.D. in Operations Research from Berkeley (50 years ago this year). From development economics and demography, Arthur moved to the Santa Fe Institute where he led a research program on complex systems applied to economics, and while remaining there as an External Faculty Member, he became a visiting researcher in the Intelligent Systems Lab at PARC (Palo Alto Research Center; formerly Xerox PARC), where he currently conducts his research. He has received the Schumpeter Prize in Economics, the Lagrange Prize in Complexity Science, and two honorary doctorates. Against this background, I would argue that being an Arthurian — I am Arthurian myself — implies a number of substantive and methodological interests. I note three of them. My latest working paper, “Being an Arthurian: Complexity Economics, Law, and Science”, explores them in turn with the hope of contributing to the diffusion of W. Brian Arthur’s ideas, and of inspiring others to embrace his research interest and scientific approach.
In this short contribution, I want to insist on a single point. Applied to legal scholarship, Arthur’s work on complexity science and economics informs three trends. First, the legal system, like the economy, can be studied as a biological ecosystem. The combination of rules and standards exhibits a messy vitality that a complexity mindset can begin to comprehend. Using Arthur’s methodology, legal scholars can document how small events trigger chains of legal responses, resulting in a complex network of laws.
Second, the legal system is part of a larger ecosystem — including market, norms and architecture — that constrains everyone’s behavior. Legal scholars may be interested in studying the dynamics between these constraints, how agents respond to them, and how agents’ responses in turn affect these constraints. Legal studies that mostly focus on equilibrium, i.e., static, non-adaptive agents. Arthur’s work in complexity science informs the need to combine constraints when addressing robust issues (i.e., persistent issues due to agent adaptation).
Third, given that agents’ experimentation evolves over time in response to ill-defined problems, legal scholars might consider creating complex adaptive regulations that co-evolve with technology to document and respond to their experimentation. Policymakers and regulators typically ignore the strategies and resources of the agents they regulate. But the ability to adapt to how agents respond to new regulation make them more effective. It makes the law inductive rather than treating rules and standards as perfectly informed solutions to well-defined problems. For example, regulations can document their effects, and adapt (e.g., become stricter) on that basis. Regulating the speed of cars on the highway in order to reduce accidents, the regulation could automatically adapt to the number of cars on the road, the type of cars, the weather, the type of road, the time of the day, etc. Such adaptive regulations allow for real-time laws and standards rather than lagging behind or creating laws ahead of technology. Building on the literature dedicated to epistemic game theory and social systems theory, real-time regulation effectively addresses ill-defined problems because it evolves based on effects that emerge from known and unknown parameters together.