Continuous transition from the extensive to the non-extensive statistics in an agent-based herding model

An infinite bent chain of nanospheres connected by wires is considered. We assume that there are δ-like potentials at the contact points. A solvable mathematical model based on the theory of self-adjoint extensions of symmetric operators is constructed. The spectral equation for the model operator is derived in an explicit form. It is shown that the Hamiltonian has non-empty point spectrum. The positions of the eigenvalues for different values of the system parameters (the length of the connecting wires, the intensities of δ-interactions and the bent angle) are found.     

Understanding the Nature of the Long-Range Memory Phenomenon in Socioeconomic Systems

In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other related works on the modeling of the long-range memory phenomenon in physical, economic, and other social complex systems. Our group has shown that the long-range memory phenomenon can be reproduced using various Markov processes, such as point processes, stochastic differential equations, and agent-based models—reproduced well enough to match other statistical properties of the financial markets, such as return and trading activity distributions and first-passage time distributions. Research has lead us to question whether the observed long-range memory is a result of the actual long-range memory process or just a consequence of the non-linearity of Markov processes. As our most recent result, we discuss the long-range memory of the order flow data in the financial markets and other social systems from the perspective of the fractional Lèvy stable motion. We test widely used long-range memory estimators on discrete fractional Lèvy stable motion represented by the auto-regressive fractionally integrated moving average (ARFIMA) sample series. Our newly obtained results seem to indicate that new estimators of self-similarity and long-range memory for analyzing systems with non-Gaussian distributions have to be developed.