Self-organized complexity in economics and finance

Abstract

This article discusses some of the similarities between work being done by economists and by physicists seeking to contribute to economics. We also mention some of the differences in the approaches taken and seek to justify these different approaches by developing the argument that by approaching the same problem from different points of view, new results might emerge. In particular, we review two newly discovered scaling results that appear to be universal, in the sense that they hold for widely different economies as well as for different time periods: (i) the fluctuation of price changes of any stock market is characterized by a probability density function, which is a simple power law with exponent ؊4 extending over 10 2 SDs (a factor of 10 8 on the y axis); this result is analogous to the Gutenberg-Richter power law describing the histogram of earthquakes of a given strength; and (ii) for a wide range of economic organizations, the histogram shows how size of organization is inversely correlated to fluctuations in size with an exponent Ϸ0.2. Neither of these two new empirical laws has a firm theoretical foundation. We also discuss results that are reminiscent of phase transitions in spin systems, where the divergent behavior of the response function at the critical point (zero magnetic field) leads to large fluctuations.

The Emerging Global Tourism Geography—An Environmental Sustainability Perspective

“…However, there is also a class of phenomena that follow a much different probability curve that has a very high probability at one end of the range of occurrences and very low at the other end. These power curve probabilities are often suggested for varying physical, natural, economic and social phenomena [75][76][77][78][79][80][81]. Examples of phenomena following a power distribution are earthquakes [75], the number of species in general [79], the use of words [77] and the distribution of wealth [82].…”

Aging Process Modulates Nonlinear Dynamics in Liver Cell Metabolism

“…As a direct consequence of modulated redox/ROS dynamics, the aged cells up-regulate uncoupling protein homolog possibly for mitigating increased oxidative stress, which in turn contributes to diminished bioenergetics during substrate metabolism. Complex behaviors have been reported recently in various physiological signals such as heart beat intervals, gait dynamics, and respiratory signals, thus challenging the classical paradigm of homeostatic regulation (2,(7)(8)(9)(10)(11)(12)(13). Our data demonstrate for the first time that similar physiologically relevant nonlinear dynamical features are operative at the level of single cells for efficient regulation of energy metabolism.…”

“…Complexity in a dynamic system is characterized by mutually interacting components (nonlinear network dynamics), hypersensitivity to initial conditions (deterministic chaos), and longterm memory processes (power-law scaling) (1,2). Because of the fact that multiple enzymes, cofactors, and signaling molecules contribute to efficient cell survival amid spatially/temporally varying stimuli, metabolic networks in a living cell are unique biological candidates for studying the role that complexity plays in regulatory enzyme kinetics.…”

“…Mitochondria serve dual roles in cellular metabolism; that is, as the hub of cellular energetics as well as the centers of programmed cell death (3). The mitochondrial theory of aging postulates that intracellular reactive oxygen species (ROS) 2 produced during normal cellular metabolism cumulatively contributes to the progressive damage of macromolecules and, hence, to organ dysfunction that collectively define aging in organisms (4 -6). Earlier studies in isolated mitochondria from young/aged animals were unable to provide a realistic picture since they ignored physiologically relevant regulatory cross-talks between mitochondria and cytosol/nucleus.…”

Construction of Complex Network with Multiple Time Series Relevance

“…The financial market is naturally a complex system, and the economy and financial systems have a self-organized complexity [5]. Many studies have shown that complex networks have been successfully used to describe financial systems [6].…”