We should remember that our definition of "vega" is broader, the sensitivity to volatility is not only the derivative of an option value with respect to the volatility of the underlying asset, as depicted in (1).
\(v=\frac{dV}{d\sigma}\)
As discussed previously, the effects work in both ways, the deviations in service usage can also affect price. We must also acknowledge that for those services hosted in centralized networks there are defacto equivalents to "option values" abscribed due to operational activities --either because it has been assigned for internal budgetary purposes, or because attention is actually traded in public markets such as two-sided ad marketplaces. But since the ultimate purpose of migrating to blockchain technology-based digital assets is to tokenize value, we can conceive that in the near term options priced as a multiple of a certain engagement metric will be measurable inputs to attention-based risk frameworks. And given the theoretically unlimited number of assets that can be created, the adaptive analysis approach should remain observant of one of the pilars of Kolmogorov Complexity, the Occam's razor principle: among the theories that are consistent with the observed phenomena, one should select the simplest theory \cite{Li_1993,Li_2008,Li_1997}. Ultimately, the goal is to enable prediction under above-average degrees of uncertainty, but with attainable resources --the optimal trade-off of accuracy and complexity.
Conclusions
Cryptocurrency markets appear to showcase the malaise of boom, bust, and failures to learn in experimental markets \cite{Paich_1993}:
Word of mouth, marketing, and learning curve effects can fuel rapid growth, often leading to overcapacity, price war, and bankruptcy. Previous experiments suggest such dysfunctional behavior can be caused by systematic 'misperceptions of feedback', where decision makers do not adequately account for critical feedbacks, time delays, and nonlinearities which condition system dynamics. However, prior studies often failed to vary the strength of these feedbacks as treatments, omitted market processes, and failed to allow for learning.
But one should be encouraged with the anti-fragility characteristics discovered in the system, albeit if these are defined in broad terms. The open problem remaining is that fragility is K-specific, i.e. we are only concerned with adverse events below a certain pre-specified level, the breaking point. The multipe exposures, of materially different natures, make the task of defining that level more daunting. As an example, among the limitations of this study is the case of specialized social networks. Given the complexity space analyzed, the high contributors operating an hybrid model of blog platform and social network were not discussed. Some of those services are instrumental on shaping public opinion and influencing investor behavior, however the relevant signals are "tacit" knowledge embeded in the network. Here is where the formulation of economic complexity indexes, with their treatment of ubiquity of services and divertisity of economies, serves as a foundation to characterize and quantify drivers of growth. Enhancing this methodological foundation with an adaptive factors-based analytical approach provides the tools to understand fragility, and appropiately price risk, in the cryptocurrency markets.
Datasets