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Complexity Economics in a Time of Crisis: Heterogeneous Agents, Interconnections, and Contagion. SYSTEMS 2021. [DOI: 10.3390/systems9040073] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In this article, we consider a variety of different mechanisms through which crises such as COVID-19 can propagate from the micro-economic behaviour of individual agents through to an economy’s aggregate dynamics and subsequently spill over into the global economy. Our central theme is one of changes in the behaviour of heterogeneous agents, agents who differ in terms of some measure of size, wealth, connectivity, or behaviour, in different parts of an economy. These are illustrated through a variety of case studies, from individuals and households with budgetary constraints, to financial markets, to companies composed of thousands of small projects, to companies that implement single multi-billion dollar projects. In each case, we emphasise the role of data or theoretical models and place them in the context of measuring their inter-connectivity and emergent dynamics. Some of these are simple models that need to be ‘dressed’ in socio-economic data to be used for policy-making, and we give an example of how to do this with housing markets, while others are more similar to archaeological evidence; they provide hints about the bigger picture but have yet to be unified with other results. The result is only an outline of what is possible but it shows that we are drawing closer to an integrated set of concepts, principles, and models. In the final section, we emphasise the potential as well as the limitations and what the future of these methods hold for economics.
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Olbryś J, Ostrowski K. An Entropy-Based Approach to Measurement of Stock Market Depth. ENTROPY 2021; 23:e23050568. [PMID: 34063670 PMCID: PMC8147648 DOI: 10.3390/e23050568] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/15/2021] [Accepted: 05/01/2021] [Indexed: 11/27/2022]
Abstract
The aim of this study is to investigate market depth as a stock market liquidity dimension. A new methodology for market depth measurement exactly based on Shannon information entropy for high-frequency data is introduced and utilized. The proposed entropy-based market depth indicator is supported by an algorithm inferring the initiator of a trade. This new indicator seems to be a promising liquidity measure. Both market entropy and market liquidity can be directly measured by the new indicator. The findings of empirical experiments for real-data with a time stamp rounded to the nearest second from the Warsaw Stock Exchange (WSE) confirm that the new proxy enables us to effectively compare market depth and liquidity for different equities. Robustness tests and statistical analyses are conducted. Furthermore, an intra-day seasonality assessment is provided. Results indicate that the entropy-based approach can be considered as an auspicious market depth and liquidity proxy with an intuitive base for both theoretical and empirical analyses in financial markets.
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Harré MS. Information Theory for Agents in Artificial Intelligence, Psychology, and Economics. ENTROPY 2021; 23:e23030310. [PMID: 33800724 PMCID: PMC8001993 DOI: 10.3390/e23030310] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/02/2021] [Accepted: 03/04/2021] [Indexed: 12/05/2022]
Abstract
This review looks at some of the central relationships between artificial intelligence, psychology, and economics through the lens of information theory, specifically focusing on formal models of decision-theory. In doing so we look at a particular approach that each field has adopted and how information theory has informed the development of the ideas of each field. A key theme is expected utility theory, its connection to information theory, the Bayesian approach to decision-making and forms of (bounded) rationality. What emerges from this review is a broadly unified formal perspective derived from three very different starting points that reflect the unique principles of each field. Each of the three approaches reviewed can, in principle at least, be implemented in a computational model in such a way that, with sufficient computational power, they could be compared with human abilities in complex tasks. However, a central critique that can be applied to all three approaches was first put forward by Savage in The Foundations of Statistics and recently brought to the fore by the economist Binmore: Bayesian approaches to decision-making work in what Savage called ‘small worlds’ but cannot work in ‘large worlds’. This point, in various different guises, is central to some of the current debates about the power of artificial intelligence and its relationship to human-like learning and decision-making. Recent work on artificial intelligence has gone some way to bridging this gap but significant questions remain to be answered in all three fields in order to make progress in producing realistic models of human decision-making in the real world in which we live in.
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Affiliation(s)
- Michael S Harré
- Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Sydney 2006, Australia
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Phillips B, Anand M, Bauch CT. Spatial early warning signals of social and epidemiological tipping points in a coupled behaviour-disease network. Sci Rep 2020; 10:7611. [PMID: 32376908 PMCID: PMC7203335 DOI: 10.1038/s41598-020-63849-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 04/06/2020] [Indexed: 01/12/2023] Open
Abstract
The resurgence of infectious diseases due to vaccine refusal has highlighted the role of interactions between disease dynamics and the spread of vaccine opinion on social networks. Shifts between disease elimination and outbreak regimes often occur through tipping points. It is known that tipping points can be predicted by early warning signals (EWS) based on characteristic dynamics near the critical transition, but the study of EWS in coupled behaviour-disease networks has received little attention. Here, we test several EWS indicators measuring spatial coherence and autocorrelation for their ability to predict a critical transition corresponding to disease outbreaks and vaccine refusal in a multiplex network model. The model couples paediatric infectious disease spread through a contact network to binary opinion dynamics of vaccine opinion on a social network. Through change point detection, we find that mutual information and join count indicators provided the best EWS. We also show the paediatric infectious disease natural history generates a discrepancy between population-level vaccine opinions and vaccine immunity status, such that transitions in the social network may occur before epidemiological transitions. These results suggest that monitoring social media for EWS of paediatric infectious disease outbreaks using these spatial indicators could be successful.
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Affiliation(s)
- Brendon Phillips
- University of Waterloo, Department of Mathematics, Waterloo, N2L 3G1, Canada.
| | - Madhur Anand
- University of Guelph, School of Environmental Sciences, Guelph, N1G 2W1, Canada
| | - Chris T Bauch
- University of Waterloo, Department of Mathematics, Waterloo, N2L 3G1, Canada
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Svenkeson A, West BJ. Persistent random motion with maximally correlated fluctuations. Phys Rev E 2019; 100:022119. [PMID: 31574651 DOI: 10.1103/physreve.100.022119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Indexed: 11/07/2022]
Abstract
How often should a random walker change its direction of motion in order to maximize correlation in velocity fluctuations over a finite time interval? We address this optimal diffusion problem in the context of the one-dimensional persistent random walk, where we evaluate the correlation and mutual information in velocity trajectories as a function of the persistence level and the observation time. We find the optimal persistence level corresponds to the average number of direction reversals asymptotically scaling as the square root of the observation time. This square-root scaling law makes the relative growth between the average number of direction reversals and the persistence length invariant with respect to changes in the overall time duration of the random walk.
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Affiliation(s)
- Adam Svenkeson
- Vehicle Technology Directorate, Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, Maryland 21005, USA
| | - Bruce J West
- Information Science Directorate, Army Research Office, Research Triangle Park, North Carolina 27703, USA
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Darmon D, Rapp PE. Specific transfer entropy and other state-dependent transfer entropies for continuous-state input-output systems. Phys Rev E 2017; 96:022121. [PMID: 28950488 DOI: 10.1103/physreve.96.022121] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Indexed: 11/07/2022]
Abstract
Since its original formulation in 2000, transfer entropy has become an invaluable tool in the toolbox of nonlinear dynamicists working with empirical data. Transfer entropy and its generalizations provide a precise definition of uncertainty and information transfer that are central to the coupled systems studied in nonlinear science. However, a canonical definition of state-dependent transfer entropy has yet to be introduced. We introduce a candidate measure, the specific transfer entropy, and compare its properties to both total and local transfer entropy. Specific transfer entropy makes possible both state- and time-resolved analysis of the predictive impact of a candidate input system on a candidate output system. We also present principled methods for estimating total, local, and specific transfer entropies from empirical data. We demonstrate the utility of specific transfer entropy and our proposed estimation procedures with two model systems, and find that specific transfer entropy provides more, and more easily interpretable, information about an input-output system compared to currently existing methods.
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Affiliation(s)
- David Darmon
- Department of Military and Emergency Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland 20814, USA and The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland 20817, USA
| | - Paul E Rapp
- Department of Military and Emergency Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland 20814, USA
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Kim EJ, Hollerbach R. Geometric structure and information change in phase transitions. Phys Rev E 2017; 95:062107. [PMID: 28709324 DOI: 10.1103/physreve.95.062107] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Indexed: 11/07/2022]
Abstract
We propose a toy model for a cyclic order-disorder transition and introduce a geometric methodology to understand stochastic processes involved in transitions. Specifically, our model consists of a pair of forward and backward processes (FPs and BPs) for the emergence and disappearance of a structure in a stochastic environment. We calculate time-dependent probability density functions (PDFs) and the information length L, which is the total number of different states that a system undergoes during the transition. Time-dependent PDFs during transient relaxation exhibit strikingly different behavior in FPs and BPs. In particular, FPs driven by instability undergo the broadening of the PDF with a large increase in fluctuations before the transition to the ordered state accompanied by narrowing the PDF width. During this stage, we identify an interesting geodesic solution accompanied by the self-regulation between the growth and nonlinear damping where the time scale τ of information change is constant in time, independent of the strength of the stochastic noise. In comparison, BPs are mainly driven by the macroscopic motion due to the movement of the PDF peak. The total information length L between initial and final states is much larger in BPs than in FPs, increasing linearly with the deviation γ of a control parameter from the critical state in BPs while increasing logarithmically with γ in FPs. L scales as |lnD| and D^{-1/2} in FPs and BPs, respectively, where D measures the strength of the stochastic forcing. These differing scalings with γ and D suggest a great utility of L in capturing different underlying processes, specifically, diffusion vs advection in phase transition by geometry. We discuss physical origins of these scalings and comment on implications of our results for bistable systems undergoing repeated order-disorder transitions (e.g., fitness).
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Affiliation(s)
- Eun-Jin Kim
- School of Mathematics and Statistics, University of Sheffield, Sheffield S3 7RH, United Kingdom
| | - Rainer Hollerbach
- Department of Applied Mathematics, University of Leeds, Leeds LS2 9JT, United Kingdom
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Information Geometry of Non-Equilibrium Processes in a Bistable System with a Cubic Damping. ENTROPY 2017. [DOI: 10.3390/e19060268] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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Rocchi J, Tsui EYL, Saad D. Emerging interdependence between stock values during financial crashes. PLoS One 2017; 12:e0176764. [PMID: 28542278 PMCID: PMC5444585 DOI: 10.1371/journal.pone.0176764] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Accepted: 04/17/2017] [Indexed: 11/19/2022] Open
Abstract
To identify emerging interdependencies between traded stocks we investigate the behavior of the stocks of FTSE 100 companies in the period 2000-2015, by looking at daily stock values. Exploiting the power of information theoretical measures to extract direct influences between multiple time series, we compute the information flow across stock values to identify several different regimes. While small information flows is detected in most of the period, a dramatically different situation occurs in the proximity of global financial crises, where stock values exhibit strong and substantial interdependence for a prolonged period. This behavior is consistent with what one would generally expect from a complex system near criticality in physical systems, showing the long lasting effects of crashes on stock markets.
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Affiliation(s)
- Jacopo Rocchi
- Nonlinearity and Complexity Research Group, Aston University, Birmingham, B4 7ET, United Kingdom
- * E-mail:
| | - Enoch Yan Lok Tsui
- Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - David Saad
- Nonlinearity and Complexity Research Group, Aston University, Birmingham, B4 7ET, United Kingdom
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