Themes Dynamics (Time series analysis and prediction, temporal correlations; interaction between internal dynamics and external inputs; programmability of self-organization), Methodology (data-driven research ; machine learning; Soft Computing)
Human Social & Economic Systems, economies and markets; financial systems; risk management
Literature
behavioral modeling of networks \cite{Altshuler_2014} , applications in businessand information security
economic complexity \cite{2014}
how "crypto nations" learn ...
the evolutionary foundations of economics have been studied by authors \cite{Dopfer}, understanding the processes which generate particular forms of economic activities and structures
controllability of complex networks \cite{Liu_2011}
fields finance \cite{Krabec_2015} ,
a soft computing approach to Population balance modeling \cite{2014a} - a balance on the number of particles of a particular state (in this example, attention units, such as visits).
particularly the applications of Coase theorem \cite{Mikami_2013}
crypto economic complexity \cite{Venegas}
genetic programming \cite{Kotanchek_2012} , real world data modeling, useful to searching for solutions in the space of algorithms ...Soft computing deals with uncertainty and approximation to achieve practicability
Even high-benefit, high-probability outcomes do not outweigh
the existence of low probability, infinite cost options—i.e. ruin.
... \cite{Zander}
Results
Methodology
Mathematica \cite{2017}
At the time of writing, March 6th 2018, 3:38 PM (UTC) the Total Market Cap: $405,939,039,748, BTC $187,065,686,782, ETH $81,602,911,777
page hits
Systemically important websites in cryptocurrency markets
GP detected invariance, capture general relationships
analytical model,
binary system- network stats
behavioral finance: in the retail segment, people are using some service before checking or performing a transaction
The common sources in both sets 1000 to 196
Distributions of main contributors
models that are already in the knee of the pareto
\(\)
Fragility
... Detection
of (Anti)Fragility \cite{Taleb_2013} ,
exposure to price shocks
pagehits
sources of fragility: overlapping exposures
shared risk drivers for more control over aggregate factor exposure
Systemic risk
Multivariate volatility, covariance matrix
learning distributions of web traffic from data
secondary effects: a wallet service will be more affected by activity in selected exchanges, than directly by prices
X13 appears to be a spurious relationship, but is the Chinese exchange
Cyber risk
...
technology used by websites
"tacit knowledge," which is the knowledge that cannot be communicated but can only be embedded in people.
Discussion
..
Getting Big Too Fast problem \cite{Sterman_2007} , when market dynamics are rapid relative to capacity adjustment, forecasting errors lead to excess capacity, overwhelming the advantage conferred by increasing returns.
prospect theory, aversion to losses \cite{Liu_2014} ,
(risk-reward ratio r, win-loss holding time ratio s, and win-loss
ROI ratio u
Medium-internal search
economic entropy vs economic equilibirum \cite{Bheemaiah_2017} ,
Conclusions
...
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.
Limitations: include social networks
Datasets