The condition of symmetry refers to the common knowledge available at t-1 where all that people know is the existing digital asset. Information asymmetries do exist but can be appreciated only by “zooming-in”-- as one would do by applying gradient descent to take ever smaller steps to reach a goal. This counterintuitive insight was actually Coase’s own frustration with policymakers -- we cannot assume full efficiency because transaction costs are really never zero. To elaborate on the point, we can consider the dynamics of a “phase change”, which can be explained using also complexity science and statistical physics but hold in social systems, as Harmon et al. demonstrated in the work on prediction of collective panic in stock markets \cite{Harmon_2011}. From this perspective, we would be zooming-in into a latent state.

## The macro view: Say’s Law

Say’s Law (also known as the Law of Markets) which has been considered the most fundamental law in classical economic theory \cite{Jonsson_1999}, states that at the macro level, aggregate production inevitably creates an equal aggregate demand. Since a fork is really an event at the macroeconomic level (for instance, the economy of bitcoin cash vs the economy of bitcoin), the aggregate demand for output is determined by the aggregate supply of output — there is a supply of attention before there was demand for attention.
This view is much in the spirit of the use of the Equation of Exchange (MV=PQ) that is commonly applied in the valuation of crypto economies, where each protocol is analyzed as its own separate economy --only that in this case, is not the monetary base and velocity of money which is balanced-out with quantity of crypto and the price of a basket of digital goods, but rather attention flows. When a fork occurs, or a token is issued targeting the same audience and use case, there is a competition for attention that has to be resolved at the macro level because the larger crypto economy cannot produce enough informed investors rapidly enough.

## Economic complexity

The Economic Complexity Index (ECI) introduced by Cesar Hidalgo (MIT Media Lab) and Ricardo Hausmann (Harvard) provides the ability to predict future economic growth by looking at the production characteristics of the economy as a whole, rather than as the sum of its parts \cite{Hidalgo_2009}.