Complexity theory


The Network Law Review seeks to advance the understanding of markets and digital laws through the lens of complexity science. Complexity studies how “micro-level interactions lead to the emergence of macro-level patterns of behavior” and how these patterns influence back micro-level interactions. Another conventional description of complexity science stresses its focus on systems and how they adaptively change through the backpropagation of the context they create.

The Network Law Review is the world’s first to dedicate a space for legal scholarship grounded on complexity science. We seek not only to use the tools of complexity science to understand legal evolutions, but we also commit to studying how legal rules and standards should learn from complexity science on a substantial level. This page, put together by Thibault Schrepel, serves as an entry door. Welcome to complexity!

Definition, History, and Applications

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In the 19th century, Darwin pioneered works on complexity by studying the relationship between species, varieties, and their environment. Though not phrased in such terms, Darwin laid down the foundations of what would become systems thinking, multilevel analysis, and evolutionary theory. In the following century, complexity science irrigated various fields, including biology, political economy, physics, game theory, archeology, finance, sociology, biochemistry, history, musicology, trading networks, biochemistry, medicine, cultural studies, etc.

In so far as economics is concerned, complexity science has also gained momentum. Since the 1980s, an increasing number of studies have considered the economic system as a living organism instead of a machine. The economy is looked at as a set of systems made of components that combine and recombine. A complexity perspective considers that economic systems and their elements grow, shrink, and change. The ambition is to provide insights into the determinants of evolutionary processes in the economy. In complexity economics, the focus of analysis is on:

  1. Organizational characteristics of the firm, e.g., resources, capabilities, management, ownership, etc.
  2. Business strategy, e.g., products and services sold; transactional relations with suppliers, customers, and consumers; learning from experience, routines,
  3. Competitive environment, e.g., industrial, institutional, and technological forces.
  4. Interaction between the above-mentioned variables.

Mainstream economics has a troubled relationship with complexity science. The idea of incommensurability of complex phenomena has been a hard pill to swallow in a field that reifies measurement and quantitative analysis. Friedrich Hayek explained the problem:

“Unlike the position that exists in the physical sciences, in economics and other disciplines that deal with essentially complex phenomena, the aspects of the events to be accounted for about which we can get quantitative data are necessarily limited and may not include the important ones. While in the physical sciences it is generally assumed, probably with good reason, that any important factor which determines the observed events will itself be directly observable and measurable, in the study of such complex phenomena as the market, which depend on the actions of many individuals, all the circumstances which will determine the outcome of a process, for reasons which I shall explain later, will hardly ever be fully known or measurable.”

This predicament has long ostracized complexity science in subfields like Austrian economics, evolutionary economics, or institutional economics. But a wind of change can be felt. Progress in techniques — essentially computational — like agent-based and pattern-oriented modeling are allowing improved observation, estimation, and prediction. Fruitful applications of complexity theory arise in fields such as the economics of technological change, ecological economics, economics of disease transmission, economics of climate change, economics of human activities and physical environments, economics of public-good management.

Of course, computational techniques capture at best a fraction of economic complexity. Even in advanced computational models, many aspects of economic systems are ignored. But computational techniques walk in the right direction. They highlight the necessity (and difficulty) of considering more dimensions of economic systems. They also stress the relevance of change, dynamism, and processes. In some important fields, like financial economics, experts are today opening their eyes to the relevance of complex, evolutionary, and multi-level dynamics. The same new perspectives can inform a reexamination of antitrust methods.