This short article serves as an introduction to Thibault Schrepel’s latest working paper, “Being an Arthurian: Complexity Economics, Law, and Science” (open-access)
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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 the importance of increasing returns for antitrust scholars. As he describes, “[i]ncreasing returns generate not equilibrium but instability: If a product or a company or a technology—one of many competing in a market—gets ahead by chance or clever strategy, increasing returns can magnify this advantage, and the product or company or technology can go on to lock in the market.” The lock-in is then typically intensified by path dependency, i.e., the ‘autocatalytic’ or self-reinforcing outcomes or structures under increasing returns. “[H]istorical ‘small events’ are not averaged away and ‘forgotten’ by the dynamics – they may decide the outcome.” Arthur calls that phenomenon ‘non-ergodicity.’ I derive several policy implications from it.
First, Arthur’s work opens up space for legal enforcement to unfreeze markets stuck in inferior outcomes. It gives legal institutions an important role to play, unlike the Chicagoans and even Schumpeter’s work, which does not. In fact, his work informs what policymakers can do. Under constant and diminishing returns, “the evolution of the market reflects only a-priori endowments, preferences, and transformation possibilities; small events cannot sway the outcome. But while this is comforting, it reduces history to the status of mere carrier — the deliverer of the inevitable.” If they intervene to avoid the inevitable, policymakers must first choose “which technologies to bet on,” and second, do more than inject a random event into the ecosystem. Arthur warns that “because a superior planning authority cannot know in advance which technology will turn out to be best, chance may lock in inferior technologies.” The best policymakers can do is to provide access to business opportunities for several of the competing technologies, e.g., by reducing legal barriers to entry or development.
Under increasing returns, “[p]olicies that are appropriate to success in high-tech production and international trade would encourage industries to be aggressive in seeking out product and process improvements. They would strengthen the national research base on which high-tech advantages are built. They would encourage firms in a single industry to pool their resources in joint ventures that share upfront costs, marketing networks, technical knowledge and compatibility conventions. And they might even extend to strategic alliances among companies in several countries to enter a complex industry that none could tackle alone.” Policymakers need not choose which technologies to bet on, but they must be prepared for unpredictable outcomes. Their interventions will require little interference if they act early, or stronger remedies that unlock positive feedback loops if they intervene late.
Applied to competition law, Arthur’s findings on increasing returns trigger a minimum of two observations. First, Arthur’s work on market dynamics has pointed implications for all related rules and standards, including antitrust. In the short run, a complexity-minded antitrust regime sees positive feedback loops as generating economic uncertainty (i.e., competition) because they change the business environment and thus force agents to “cognize” – rather than rationally devise – new strategies. In the medium to long term, a complexity-minded antitrust sees that positive feedback loops have the potential to lead to lock-ins and path dependency. Positive feedback loops can then turn into negative feedback loops, i.e., the state of the ecosystem is frozen. The role of antitrust agencies becomes to ensure (or even encourage) the emergence of positive feedback loops in the short and long term.
Second, Arthur’s observations support one of the main tenets of legal institutionalism: regulation can help create dynamic markets. But regulation can only contribute to dynamism if two conditions are met. The first condition is for regulation to be adapted to the nature of the market. The presence of decreasing or increasing returns varies the type and timing of the intervention required. The second condition derives from Arthur’s views on complexity: policymakers should be concerned with how firms respond to these rules. As he points out, economic agents respond to the context (legal, economic, etc.) in which they evolve. They adapt to it, which means that regulation can only bend inefficient monopolies if it adapts to how agents interpret the rules, play with the rules, and eventually bend the rules. The DMA is a good example of a regulation that does the opposite by proving incapable of adapting to its effects.
For the rest, access to all references and a discussion of other implications for antitrust scholars, I refer you back to “Being an Arthurian: Complexity Economics, Law, and Science”
Thibault Schrepel
@ProfSchrepel
