Literature
In their book “Beyond Smart Beta: Index Investment Strategies for Active Portfolio Management” Kula, Raab, and Stahn define Total return as the amount of value an investor earns from a security over a specific period when all distributions are reinvested \cite{Kula_2017}. While it is still early in the development of crypto assets to account for all distributions (dividends, coupons, capital gains), it is customary to use at least the price increase to measure the investment’s performance. Typically, those historical returns would be the “goal” in a predictive model catered to “learn” (in an interactive fashion) what demand signals are also signs of value appreciation. However, in crypto economies prices are taken rather as a measurement of market sentiment, and related quantities such as on-chain transaction volume are difficult or impossible to assess in a trustworthy manner \cite{metrics}. Therefore we may begin to characterize off-chain flows in terms of returns (a common success measure for investors), but soon we should move beyond prices, exchange volumes and transaction counts, and include hard metrics such as fees into our analysis.
An ideal scenario to study trust asymmetry is the case of a cryptocurrency fork, where at t=0 one may assume equal conditions for the two chains (although in practice this is hardly the case, since the different fractions have already grouped around their preferred coin before the split, financial futures may have been trading already, and so on). In our paper on Crypto Economic Complexity \cite{venegas}, we argued 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. And, since a fork is really an event at the macroeconomic level (for instance, the economy of BitcoinCash 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 is demand for attention. We also discussed the practicalities of quantifying economic complexity by ranking economies, focusing on the specific case of cryptocurrencies and tokens. Here we will demonstrate how to develop the heuristics of such an approach, from the perspectives of structure and dynamics of the combined system.
Trust equations
The socio-technical modeling of mass and information flow has usually been accomplished in econometrics, industrial, and, policy planning circles, using Jay Forrester's System Dynamics methodology \cite{Forrester_2007}. The fact that continuous systems contain differential equations is hidden from the user by talking about levels, i.e., quantities that can accumulate (state variables), and rates, i.e., quantities that influence the accumulation and/or depletion of levels (state derivatives) \cite{Cellier_1991}. A typical model for the traditional financial system is shown in Figure 4. However, real-life systems modeling in the context of a digital economy involves a different set of variables, notably, the inclusion of online activity and distributed ledger related records (either online or offline, if the architecture is based on mesh networks).