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Entropy
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Explaining Economic and Social Science Phenomena through Physical Models
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Explaining Economic and Social Science Phenomena through Physical Models

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 10732

Special Issue Editors

Dr. Marcin Makowski

E-Mail Website
Guest Editor
Faculty of Physics, University of Bialystok, ul. Ciołkowskiego 1L, 15-245 Białystok, Poland
Interests: quantum information processing; quantum game theory; econophysics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jan Sładkowski

E-Mail Website
Guest Editor
Institute of Physics, University of Silesia, 75 Pułku Piechoty 1, Pl 41-005 Chorzów, Poland
Interests: quantum field theory; quantum computing; mathematical physics; game theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The idea that physics might play an important role in understanding and explaining various aspects of life has long been promoted by physicists. Let us mention, for example, such publications as Light and Life (N. Bohr) or What Is Life? (E. Schrödinger). Recently, we have observed a growing interest in modeling economic and social phenomena with physical methods. This is because traditional models used, for example, in economics fail, especially in the face of recent crises. Attempts to find better, more accurate models are leading to the emergence of entirely new fields, such as quantum information science related to this quantum game theory or more broadly econophysics. Searching for new, more precise risk measures seems to be particularly important. Can entropy present advantages as a measure of uncertainty or risk? Do quantum games or quantum information provide us with better tools for modeling social and economic phenomena? The present Special Issue is open to novel contributions on these topics, but also to others related to them, taking into account the wideness of physics and its applications.

Dr. Marcin Makowski
Prof. Dr. Jan Sładkowski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • entropy and fisher information
  • quantum games
  • econophysics
  • decision making/analysis
  • quantum market game
  • quantum-like models
  • social science
  • quantum finances

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Related Special Issue

Published Papers (6 papers)

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Research

16 pages, 617 KiB  
Article
A Non-Stochastic Special Model of Risk Based on Radon Transform
by Marcin Makowski and Edward W. Piotrowski
Entropy 2024, 26(11), 913; https://doi.org/10.3390/e26110913 - 28 Oct 2024
Viewed by 736
Abstract
The concept of risk is fundamental in various scientific fields, including physics, biology and engineering, and is crucial for the study of complex systems, especially financial markets. In our research, we introduce a novel risk model that has a natural transactional–financial interpretation. In [...] Read more.
The concept of risk is fundamental in various scientific fields, including physics, biology and engineering, and is crucial for the study of complex systems, especially financial markets. In our research, we introduce a novel risk model that has a natural transactional–financial interpretation. In our approach, the risk of holding a financial instrument is related to the measure of the possibility of its loss. In this context, a financial instrument is riskier the more opportunities there are to dispose of it, i.e., to sell it. We present a model of risk understood in this way, introducing, in particular, the concept of financial time and a financial frame of reference, which allows for associating risk with the subjective perception of the observer. The presented approach does not rely on statistical assumptions and is based on the transactional interpretation of models. To measure risk, we propose using the Radon transform. The financial concept of risk is closely related to the concepts of uncertainty, entropy, information, and error in physics. Therefore, the well-established algorithmic aspects of the computed tomography method can be effectively applied to the broader field of uncertainty analysis, which is one of the foundational elements of experimental physics. Full article
(This article belongs to the Special Issue Explaining Economic and Social Science Phenomena through Physical Models)
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13 pages, 3726 KiB  
Article
Nonlinear Economic State Equilibria via van der Waals Modeling
by Max-Olivier Hongler, Olivier Gallay and Fariba Hashemi
Entropy 2024, 26(9), 727; https://doi.org/10.3390/e26090727 - 27 Aug 2024
Viewed by 675
Abstract
The renowned van der Waals (VDW) state equation quantifies the equilibrium relationship between the pressure P, volume V, and temperature kBT of a real gas. We assign new variable interpretations adapted to the economic context: P→Y, [...] Read more.
The renowned van der Waals (VDW) state equation quantifies the equilibrium relationship between the pressure P, volume V, and temperature kBT of a real gas. We assign new variable interpretations adapted to the economic context: P→Y, representing price; V→X, representing demand; and kBT→κ, representing income, to describe an economic state equilibrium. With this reinterpretation, the price elasticity of demand (PED) and the income elasticity of demand (YED) are non-constant factors and may exhibit a singularity of the cusp-catastrophe type. Within this economic framework, the counterpart of VDW liquid–gas phase transition illustrates a substitution mechanism where one product or service is replaced by an alternative substitute. The conceptual relevance of this reinterpretation is discussed qualitatively and quantitatively via several illustrations ranging from transport (carpooling), medical context (generic versus original medication), and empirical data drawn from the electricity market in Germany. Full article
(This article belongs to the Special Issue Explaining Economic and Social Science Phenomena through Physical Models)
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17 pages, 3307 KiB  
Article
A Dynamic Network Model of Societal Complexity and Resilience Inspired by Tainter’s Theory of Collapse
by Florian Schunck, Marc Wiedermann, Jobst Heitzig and Jonathan F. Donges
Entropy 2024, 26(2), 98; https://doi.org/10.3390/e26020098 - 23 Jan 2024
Cited by 3 | Viewed by 2457
Abstract
In recent years, several global events have severely disrupted economies and social structures, undermining confidence in the resilience of modern societies. Examples include the COVID-19 pandemic, which brought unprecedented health challenges and economic disruptions, and the emergence of geopolitical tensions and conflicts that [...] Read more.
In recent years, several global events have severely disrupted economies and social structures, undermining confidence in the resilience of modern societies. Examples include the COVID-19 pandemic, which brought unprecedented health challenges and economic disruptions, and the emergence of geopolitical tensions and conflicts that have further strained international relations and economic stability. While empirical evidence on the dynamics and drivers of past societal collapse is mounting, a process-based understanding of these dynamics is still in its infancy. Here, we aim to identify and illustrate the underlying drivers of such societal instability or even collapse. The inspiration for this work is Joseph Tainter’s theory of the “collapse of complex societies”, which postulates that the complexity of societies increases as they solve problems, leading to diminishing returns on complexity investments and ultimately to collapse. In this work, we abstract this theory into a low-dimensional and stylized model of two classes of networked agents, hereafter referred to as “laborers” and “administrators”. We numerically model the dynamics of societal complexity, measured as the fraction of “administrators”, which was assumed to affect the productivity of connected energy-producing “laborers”. We show that collapse becomes increasingly likely as the complexity of the model society continuously increases in response to external stresses that emulate Tainter’s abstract notion of problems that societies must solve. We also provide an analytical approximation of the system’s dominant dynamics, which matches well with the numerical experiments, and use it to study the influence on network link density, social mobility and productivity. Our work advances the understanding of social-ecological collapse and illustrates its potentially direct link to an ever-increasing societal complexity in response to external shocks or stresses via a self-reinforcing feedback. Full article
(This article belongs to the Special Issue Explaining Economic and Social Science Phenomena through Physical Models)
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24 pages, 555 KiB  
Article
Capacity, Collision Avoidance and Shopping Rate under a Social Distancing Regime
by Haitian Zhong and David Sankoff
Entropy 2023, 25(12), 1668; https://doi.org/10.3390/e25121668 - 17 Dec 2023
Viewed by 1444
Abstract
Capacity restrictions in stores, maintained by mechanisms like spacing customer intake, became familiar features of retailing in the time of the pandemic. Shopping rates in a crowded store under a social distancing regime are prone to considerable slowdown. Inspired by the random particle [...] Read more.
Capacity restrictions in stores, maintained by mechanisms like spacing customer intake, became familiar features of retailing in the time of the pandemic. Shopping rates in a crowded store under a social distancing regime are prone to considerable slowdown. Inspired by the random particle collision concepts of statistical mechanics, we introduce a dynamical model of the evolution of the shopping rate as a function of a given customer intake rate. The slowdown of each individual customer is incorporated as an additive term to the baseline value of the shopping time, proportionally to the number of other customers in the store. We determine analytically and via simulation the trajectory of the model as it approaches a Little’s law equilibrium and identify the point beyond which equilibrium cannot be achieved. By relating the customer shopping rate to the slowdown compared with the baseline, we can calculate the optimal intake rate leading to maximum equilibrium spending. This turns out to be the maximum rate compatible with equilibrium. The slowdown due to the largest possible number of shoppers is more than compensated for by the increased volume of shopping. This macroscopic model is validated by simulation experiments in which avoidance interactions between pairs of shoppers are responsible for shopping delays. Full article
(This article belongs to the Special Issue Explaining Economic and Social Science Phenomena through Physical Models)
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16 pages, 334 KiB  
Article
Recurrence and Eigenfunction Methods for Non-Trivial Models of Discrete Binary Choice
by James Holehouse
Entropy 2023, 25(7), 996; https://doi.org/10.3390/e25070996 - 29 Jun 2023
Cited by 1 | Viewed by 1331
Abstract
Understanding how systems relax to equilibrium is a core theme of statistical physics, especially in economics, where systems are known to be subject to extrinsic noise not included in simple agent-based models. In models of binary choice—ones not much more complicated than Kirman’s [...] Read more.
Understanding how systems relax to equilibrium is a core theme of statistical physics, especially in economics, where systems are known to be subject to extrinsic noise not included in simple agent-based models. In models of binary choice—ones not much more complicated than Kirman’s model of ant recruitment—such relaxation dynamics become difficult to determine analytically and require solving a three-term recurrence relation in the eigendecomposition of the stochastic process. In this paper, we derive a concise closed-form solution to this linear three-term recurrence relation. Its solution has traditionally relied on cumbersome continued fractions, and we instead employ a linear algebraic approach that leverages the properties of lower-triangular and tridiagonal matrices to express the terms in the recurrence relation using a finite set of orthogonal polynomials. We pay special attention to the power series coefficients of Heun functions, which are also important in fields such as quantum mechanics and general relativity, as well as the binary choice models studied here. We then apply the solution to find equations describing the relaxation to steady-state behavior in social choice models through eigendecomposition. This application showcases the potential of our solution as an off-the-shelf solution to the recurrence that has not previously been reported, allowing for the easy identification of the eigenspectra of one-dimensional, one-step, continuous-time Markov processes. Full article
(This article belongs to the Special Issue Explaining Economic and Social Science Phenomena through Physical Models)
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12 pages, 1167 KiB  
Article
Precision Measurement of the Return Distribution Property of the Chinese Stock Market Index
by Peng Liu and Yanyan Zheng
Entropy 2023, 25(1), 36; https://doi.org/10.3390/e25010036 - 24 Dec 2022
Cited by 4 | Viewed by 3061
Abstract
In econophysics, the analysis of the return distribution of a financial asset using statistical physics methods is a long-standing and important issue. This paper systematically conducts an analysis of composite index 1 min datasets over a 17-year period (2005–2021) for both the Shanghai [...] Read more.
In econophysics, the analysis of the return distribution of a financial asset using statistical physics methods is a long-standing and important issue. This paper systematically conducts an analysis of composite index 1 min datasets over a 17-year period (2005–2021) for both the Shanghai and Shenzhen stock exchanges. To reveal the differences between Chinese and mature stock markets, we precisely measure the property of the return distribution of the composite index over the time scale Δt, which ranges from 1 min to almost 4000 min. The main findings are as follows: (1) The return distribution presents a leptokurtic, fat-tailed, and almost symmetrical shape that is similar to that of mature markets. (2) The central part of the return distribution is described by the symmetrical Lévy α-stable process, with a stability parameter comparable with a value of about 1.4, which was extracted for the U.S. stock market. (3) The return distribution can be described well by Student’s t-distribution within a wider return range than the Lévy α-stable distribution. (4) Distinctively, the stability parameter shows a potential change when Δt increases, and thus a crossover region at 15 <Δt< 60 min is observed. This is different from the finding in the U.S. stock market that a single value of about 1.4 holds over 1 ≤Δt≤ 1000 min. (5) The tail distribution of returns at small Δt decays as an asymptotic power law with an exponent of about 3, which is a widely observed value in mature markets. However, it decays exponentially when Δt≥ 240 min, which is not observed in mature markets. (6) Return distributions gradually converge to a normal distribution as Δt increases. This observation is different from the finding of a critical Δt= 4 days in the U.S. stock market. Full article
(This article belongs to the Special Issue Explaining Economic and Social Science Phenomena through Physical Models)
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