81 research outputs found
Complex networks analysis in socioeconomic models
This chapter aims at reviewing complex networks models and methods that were
either developed for or applied to socioeconomic issues, and pertinent to the
theme of New Economic Geography. After an introduction to the foundations of
the field of complex networks, the present summary adds insights on the
statistical mechanical approach, and on the most relevant computational aspects
for the treatment of these systems. As the most frequently used model for
interacting agent-based systems, a brief description of the statistical
mechanics of the classical Ising model on regular lattices, together with
recent extensions of the same model on small-world Watts-Strogatz and
scale-free Albert-Barabasi complex networks is included. Other sections of the
chapter are devoted to applications of complex networks to economics, finance,
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issues, including results for opinion and citation networks.
Finally, some avenues for future research are introduced before summarizing the
main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared
for Complexity and Geographical Economics - Topics and Tools, P.
Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published
LIPIcs, Volume 244, ESA 2022, Complete Volume
LIPIcs, Volume 244, ESA 2022, Complete Volum
PSA 2016
These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2016
Complexity in economic and social systems: cryptocurrency market at around COVID-19
Social systems are characterized by an enormous network of connections and
factors that can influence the structure and dynamics of these systems. All
financial markets, including the cryptocurrency market, belong to the
economical sphere of human activity that seems to be the most interrelated and
complex. The cryptocurrency market complexity can be studied from different
perspectives. First, the dynamics of the cryptocurrency exchange rates to other
cryptocurrencies and fiat currencies can be studied and quantified by means of
multifractal formalism. Second, coupling and decoupling of the cryptocurrencies
and the conventional assets can be investigated with the advanced
cross-correlation analyses based on fractal analysis. Third, an internal
structure of the cryptocurrency market can also be a subject of analysis that
exploits, for example, a network representation of the market. We approach this
subject from all three perspectives based on data recorded between January 2019
and June 2020. This period includes the Covid-19 pandemic and we pay particular
attention to this event and investigate how strong its impact on the structure
and dynamics of the market was. Besides, the studied data covers a few other
significant events like double bull and bear phases in 2019. We show that,
throughout the considered interval, the exchange rate returns were multifractal
with intermittent signatures of bifractality that can be associated with the
most volatile periods of the market dynamics like a bull market onset in April
2019 and the Covid-19 outburst in March 2020. The topology of a minimal
spanning tree representation of the market also used to alter during these
events from a distributed type without any dominant node to a highly
centralized type with a dominating hub of USDT. However, the MST topology
during the pandemic differs in some details from other volatile periods
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