Authors: Ian F. Wilkinson
Complexity science is relevant to Antitrust Policy because the economy is a complex adaptive system (CAS) of economic and non-economic actors (Arthur et al. 1997). The study of complexity focuses our attention on the way complex behaviour in natural, biological, social or economic systems can and does result from the connections between the parts not from any inherent complexity of the parts. In other words, the overall behaviour and evolution of a system is not a linear function of the behaviour of its parts. Both history and interactions matter. Simple rules of interaction can give rise to complex system behaviour over time. The study of complexity also directs our attention to the importance of feedback effects and evolutionary processes by which the rules of interaction and mix of players in a system changes over time. As Stuart Kauffman nicely expresses it – the winning games are the games the winners play.My argument is to extend the main case for antitrust policy. To date, the main case focuses on economizing, including market power as a key filter for identifying suspect cases. Both production and transaction costs are considered as part of economizing and other factors are use to consider the benefits of different industry structures. CAS analysis focuses attention on dynamics, evolution and networks and I suggest “evolvability” as an additional main case consideration. Evolvability can be thought of in terms of various types of network impacts that go beyond the traditional focus on production and transaction costs. Network costs and benefits stem from the connections between transactions and relations over time and place, including how business arrangements at one time, limit or enable arrangements in the future. Such considerations, I argue, can and should be included in the rules of antitrust and in the processes of antitrust case analysis and decision making.
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