Crypto Economy Complexity

We demonstrate that attention flows manifest knowledge, and the distance (similarity) between crypto economies has predictive power to understand whether a fork or fierce competition within the same token space will be a destructive force or not. When dealing with hundreds of currencies and thousands of tokens investors have to face a very practical constraint: attention quickly becomes a scarce resource. To understand the role of attention in trustless markets we use Coase's theorem. For the theorem to hold, the conditions that the crypto communities that will split should meet are: (i)Well defined property rights: the crypto investor owns his attention; (ii) Information symmetry: it is reasonable to assume that up to the moment of the hard fork market participants are at a level ground in terms of shared knowledge. Specialization (who becomes the expert on each new digital asset) will come later; (iii) Low transaction costs: Just before the chains split there is no significant cost in switching attention. Other factors (such as mining profitability) will play a role after the fact, and any previous conditions (e.g. options sold on the future new assets) are mainly speculative. The condition of symmetry refers to the “common knowledge” available at t-1 where all that people know is the existing asset. Information asymmetries do exist at the micro level -we cannot assume full efficiency because transaction costs are really never zero. Say’s Law 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 (in this case, 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. The Economic Complexity Index (ECI) introduced by Hidalgo and Hausmann allows to predicting future economic growth by looking at the production characteristics of the economy as a whole, rather than as the sum of its parts i.e. the present information content of the economy is a predictor of future growth. Say’s Law and the ECI approach are about aggregation of dispersed resources, and that’s what makes those relevant to the study of decentralized systems. While economic complexity is measured by the mix of products that countries are able to make, crypto economy complexity depends on the remixing of activities. Some services are complex because few crypto economies consume them, and the crypto economies that consume those tend to be more diversified. We should differentiate between the structure of output (off-chain events) vs aggregated output (on-chain, strictly transactional events). It can be demonstrated that crypto economies tend to converge to the level of economic output that can be supported by the know-how that is embedded in their economy — and is manifested by attention flows. Therefore, it is likely that a crypto economy complexity is a driver of prosperity when complexity is greater than what we would expect, at a given level of investment return. As members of the community specialize in different aspects of the economy, the structure of the network itself becomes an expression of the composition of attention output. We use genetic programming to find drivers — in other words, to learn the rankings. Such a ranking score function has the form, returns_tokenA > returns_tokenB = f (sources_tokenA > sources_tokenB). Ultimately, the degree of complexity is an issue of trust or lack thereof, and that is what the flow of attention and its conversion into transactional events reveal.