Special Issue "Information Theory for Human and Social Processes"

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

Deadline for manuscript submissions: closed (30 June 2020).

Special Issue Editor

Prof. Dr. Martin Hilbert
E-Mail Website
Guest Editor
Department of Communication, GG Computer Science, University of California, 370 Kerr Hall, 1 Shields Avenue, Davis, CA 95616, USA
Interests: computational social science; digitalization; algorithmification; complex social systems; international development; United Nations; computational mechanics; social change; mathematical theory of communication

Special Issue Information

Dear Colleagues,

Shannon famously applied his “mathematical theory of communication” to human communication, allegedly having his wife, Betty, estimating word probabilities to calculate the first approximation of the entropy of English. The following decades have seen creative further applications to humans and social processes (e.g., Miller, 1956; Attneave, 1959; Coleman, 1975; Ellis and Fisher, 1975; Cappella, 1979). These efforts lost steam in the 1980s, mainly because of the lack of adequate data, and limited computational power. Both limitations do not apply anymore. The increase in human interactions taking place in digital environments has led to an abundance of behavioral “big data”, enough even to calculate measures that converge rather slowly.

This Special Issue compiles creative research on the innovative uses of information theory, and its extensions, to better understand human behavior and social processes. Among other topics, the focus is set on human communication, social organization, social algorithms, human–machine interaction, artificial and human intelligence, collaborative teamwork, social media dynamics, information societies, digital development, and cognitive and machine biases—all online and/or offline.

Prof. Martin Hilbert
Guest Editor

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 papers will be 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 1800 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

  • social science
  • behavioral science
  • human dynamics
  • human communication
  • social algorithms
  • human–machine interaction
  • social media
  • economics, sociology
  • antropology
  • political science
  • social psychology
  • social change

Published Papers (12 papers)

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Editorial

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Open AccessEditorial
Information Theory for Human and Social Processes
Entropy 2021, 23(1), 9; https://doi.org/10.3390/e23010009 - 23 Dec 2020
Viewed by 432
Abstract
Ever since its earliest years, information theory has enjoyed both a promising and complicated relationship with the social sciences [...] Full article
(This article belongs to the Special Issue Information Theory for Human and Social Processes)

Research

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Open AccessArticle
Source of Knowledge Dynamics—Transition from High School to University
Entropy 2020, 22(9), 918; https://doi.org/10.3390/e22090918 - 21 Aug 2020
Cited by 2 | Viewed by 714
Abstract
The paper addresses the dynamics of education by using Markov chains, a powerful probabilistic model able to make predictions on how sources of knowledge either change or stabilize over adulthood. To this end, each student filled in a survey that rated, on a [...] Read more.
The paper addresses the dynamics of education by using Markov chains, a powerful probabilistic model able to make predictions on how sources of knowledge either change or stabilize over adulthood. To this end, each student filled in a survey that rated, on a scale from 1 to 5, the utility of five different sources of knowledge. They completed this survey twice, once for their previous and once for their current education. The authors then fitted a Markov chain to these data—essentially, calculating transition probabilities from one ranking of sources of knowledge to another—and inferred the final maximum utility sources of knowledge via the stationary distribution. The overall conclusion is the following: even if the professor used to play a crucial role in early development, students have the tendency to become independent in their learning process, relying more on online materials and less on printed books and libraries. Full article
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
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Open AccessFeature PaperArticle
Inferring an Observer’s Prediction Strategy in Sequence Learning Experiments
Entropy 2020, 22(8), 896; https://doi.org/10.3390/e22080896 - 15 Aug 2020
Cited by 1 | Viewed by 880
Abstract
Cognitive systems exhibit astounding prediction capabilities that allow them to reap rewards from regularities in their environment. How do organisms predict environmental input and how well do they do it? As a prerequisite to answering that question, we first address the limits on [...] Read more.
Cognitive systems exhibit astounding prediction capabilities that allow them to reap rewards from regularities in their environment. How do organisms predict environmental input and how well do they do it? As a prerequisite to answering that question, we first address the limits on prediction strategy inference, given a series of inputs and predictions from an observer. We study the special case of Bayesian observers, allowing for a probability that the observer randomly ignores data when building her model. We demonstrate that an observer’s prediction model can be correctly inferred for binary stimuli generated from a finite-order Markov model. However, we can not necessarily infer the model’s parameter values unless we have access to several “clones” of the observer. As stimuli become increasingly complicated, correct inference requires exponentially more data points, computational power, and computational time. These factors place a practical limit on how well we are able to infer an observer’s prediction strategy in an experimental or observational setting. Full article
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
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Open AccessArticle
Economics of Disagreement—Financial Intuition for the Rényi Divergence
Entropy 2020, 22(8), 860; https://doi.org/10.3390/e22080860 - 03 Aug 2020
Cited by 4 | Viewed by 1060
Abstract
Disagreement is an essential element of science and life in general. The language of probabilities and statistics is often used to describe disagreements quantitatively. In practice, however, we want much more than that. We want disagreements to be resolved. This leaves us with [...] Read more.
Disagreement is an essential element of science and life in general. The language of probabilities and statistics is often used to describe disagreements quantitatively. In practice, however, we want much more than that. We want disagreements to be resolved. This leaves us with a substantial knowledge gap, which is often perceived as a lack of practical intuition regarding probabilistic and statistical concepts. Here, we propose to address disagreements using the methods of financial economics. In particular, we show how a large class of disagreements can be transformed into investment opportunities. The expected financial performance of such investments quantifies the amount of disagreement in a tangible way. This provides intuition for statistical concepts such as the Rényi divergence, which becomes connected to the financial performance of optimized investments. Investment optimization takes into account individual opinions as well as attitudes towards risk. The result is a market-like social mechanism by which funds flow naturally to support a more accurate view. Such social mechanisms can help us with difficult disagreements (e.g., financial arguments concerning the future climate). In terms of scientific validation, we used the findings of independent neurophysiological experiments as well as our own research on the equity premium. Full article
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
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Open AccessArticle
An Objective-Based Entropy Approach for Interpretable Decision Tree Models in Support of Human Resource Management: The Case of Absenteeism at Work
Entropy 2020, 22(8), 821; https://doi.org/10.3390/e22080821 - 27 Jul 2020
Cited by 4 | Viewed by 943
Abstract
The negative impact of absenteeism on organizations’ productivity and profitability is well established. To decrease absenteeism, it is imperative to understand its underlying causes and to identify susceptible employee subgroups. Most research studies apply hypotheses testing and regression models to identify features that [...] Read more.
The negative impact of absenteeism on organizations’ productivity and profitability is well established. To decrease absenteeism, it is imperative to understand its underlying causes and to identify susceptible employee subgroups. Most research studies apply hypotheses testing and regression models to identify features that are correlated with absenteeism—typically, these models are limited to finding simple correlations. We illustrate the use of interpretable classification algorithms for uncovering subgroups of employees with common characteristics and a similar level of absenteeism. This process may assist human resource managers in understanding the underlying reasons for absenteeism, which, in turn, could stimulate measures to decrease it. Our proposed methodology makes use of an objective-based information gain measure in conjunction with an ordinal CART model. Our results indicate that the ordinal CART model outperforms conventional classifiers and, more importantly, identifies patterns in the data that have not been revealed by other models. We demonstrate the importance of interpretability for human resource management through three examples. The main contributions of this research are (1) the development of an information-based ordinal classifier for a published absenteeism dataset and (2) the illustration of an interpretable approach that could be of considerable value in supporting human resource management decision-making. Full article
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
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Open AccessFeature PaperArticle
Discrete Information Dynamics with Confidence via the Computational Mechanics Bootstrap: Confidence Sets and Significance Tests for Information-Dynamic Measures
Entropy 2020, 22(7), 782; https://doi.org/10.3390/e22070782 - 17 Jul 2020
Cited by 1 | Viewed by 878
Abstract
Information dynamics and computational mechanics provide a suite of measures for assessing the information- and computation-theoretic properties of complex systems in the absence of mechanistic models. However, both approaches lack a core set of inferential tools needed to make them more broadly useful [...] Read more.
Information dynamics and computational mechanics provide a suite of measures for assessing the information- and computation-theoretic properties of complex systems in the absence of mechanistic models. However, both approaches lack a core set of inferential tools needed to make them more broadly useful for analyzing real-world systems, namely reliable methods for constructing confidence sets and hypothesis tests for their underlying measures. We develop the computational mechanics bootstrap, a bootstrap method for constructing confidence sets and significance tests for information-dynamic measures via confidence distributions using estimates of ϵ -machines inferred via the Causal State Splitting Reconstruction (CSSR) algorithm. Via Monte Carlo simulation, we compare the inferential properties of the computational mechanics bootstrap to a Markov model bootstrap. The computational mechanics bootstrap is shown to have desirable inferential properties for a collection of model systems and generally outperforms the Markov model bootstrap. Finally, we perform an in silico experiment to assess the computational mechanics bootstrap’s performance on a corpus of ϵ -machines derived from the activity patterns of fifteen-thousand Twitter users. Full article
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
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Open AccessArticle
Labor Market Segmentation and Immigrant Competition: A Quantal Response Statistical Equilibrium Analysis
Entropy 2020, 22(7), 742; https://doi.org/10.3390/e22070742 - 05 Jul 2020
Cited by 3 | Viewed by 867
Abstract
Competition between and within groups of workers takes place in labor markets that are segmented along various, often unobservable dimensions. This paper proposes a measure of the intensity of competition in labor markets on the basis of limited data. The maximum entropy principle [...] Read more.
Competition between and within groups of workers takes place in labor markets that are segmented along various, often unobservable dimensions. This paper proposes a measure of the intensity of competition in labor markets on the basis of limited data. The maximum entropy principle is used to make inferences about the unobserved mobility decisions of workers in US household data. The quantal response statistical equilibrium class of models can be seen to give robust microfoundations to the persistent patterns of wage inequality. An application to labor market competition between native and foreign-born workers in the United States shows that this class of models captures a substantial proportion of the informational content of observed wage distributions. Full article
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
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Open AccessArticle
How Complexity and Uncertainty Grew with Algorithmic Trading
Entropy 2020, 22(5), 499; https://doi.org/10.3390/e22050499 - 26 Apr 2020
Cited by 3 | Viewed by 1678
Abstract
The machine-learning paradigm promises traders to reduce uncertainty through better predictions done by ever more complex algorithms. We ask about detectable results of both uncertainty and complexity at the aggregated market level. We analyzed almost one billion trades of eight currency pairs (2007–2017) [...] Read more.
The machine-learning paradigm promises traders to reduce uncertainty through better predictions done by ever more complex algorithms. We ask about detectable results of both uncertainty and complexity at the aggregated market level. We analyzed almost one billion trades of eight currency pairs (2007–2017) and show that increased algorithmic trading is associated with more complex subsequences and more predictable structures in bid-ask spreads. However, algorithmic involvement is also associated with more future uncertainty, which seems contradictory, at first sight. On the micro-level, traders employ algorithms to reduce their local uncertainty by creating more complex algorithmic patterns. This entails more predictable structure and more complexity. On the macro-level, the increased overall complexity implies more combinatorial possibilities, and therefore, more uncertainty about the future. The chain rule of entropy reveals that uncertainty has been reduced when trading on the level of the fourth digit behind the dollar, while new uncertainty started to arise at the fifth digit behind the dollar (aka ‘pip-trading’). In short, our information theoretic analysis helps us to clarify that the seeming contradiction between decreased uncertainty on the micro-level and increased uncertainty on the macro-level is the result of the inherent relationship between complexity and uncertainty. Full article
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
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Open AccessArticle
Spatial-Temporal Characteristic Analysis of Ethnic Toponyms Based on Spatial Information Entropy at the Rural Level in Northeast China
Entropy 2020, 22(4), 393; https://doi.org/10.3390/e22040393 - 30 Mar 2020
Cited by 4 | Viewed by 1376
Abstract
As a symbol language, toponyms have inherited the unique local historical culture in the long process of historical development. As the birthplace of Manchu, there are many toponyms originated from multi-ethnic groups (e.g., Manchu, Mongol, Korean, Hui, and Xibe) in Northeast China which [...] Read more.
As a symbol language, toponyms have inherited the unique local historical culture in the long process of historical development. As the birthplace of Manchu, there are many toponyms originated from multi-ethnic groups (e.g., Manchu, Mongol, Korean, Hui, and Xibe) in Northeast China which possess unique cultural connotations. This study aimed to (1) establish a spatial-temporal database of toponyms in Northeast China using a multi-source data set, and identify their ethnic types and origin times; and (2) explore the geographical distribution characteristics of ethnic toponyms and the evolution of rural settlements by comparing the spatial analysis and spatial information entropy methods. The results found that toponyms reflect not only the spatial distribution characteristics of the density and direction of ethnic groups, but also the migration law of rural settlements. Results also confirm that toponyms contain unique cultural connotations and provide a theoretical basis for the protection and promotion of the cultural connotations of toponyms. This research provides an entropic perspective and method for exploring the spatial-temporal evolutionary characteristics of ethnic groups and toponym mapping. Full article
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
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Open AccessArticle
Entropy as a Measure of Attractiveness and Socioeconomic Complexity in Rio de Janeiro Metropolitan Area
Entropy 2020, 22(3), 368; https://doi.org/10.3390/e22030368 - 23 Mar 2020
Cited by 2 | Viewed by 1662
Abstract
Defining and measuring spatial inequalities across the urban environment remains a complex and elusive task which has been facilitated by the increasing availability of large geolocated databases. In this study, we rely on a mobile phone dataset and an entropy-based metric to measure [...] Read more.
Defining and measuring spatial inequalities across the urban environment remains a complex and elusive task which has been facilitated by the increasing availability of large geolocated databases. In this study, we rely on a mobile phone dataset and an entropy-based metric to measure the attractiveness of a location in the Rio de Janeiro Metropolitan Area (Brazil) as the diversity of visitors’ location of residence. The results show that the attractiveness of a given location measured by entropy is an important descriptor of the socioeconomic status of the location, and can thus be used as a proxy for complex socioeconomic indicators. Full article
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
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Open AccessArticle
The Emergence of Integrated Information, Complexity, and ‘Consciousness’ at Criticality
Entropy 2020, 22(3), 339; https://doi.org/10.3390/e22030339 - 16 Mar 2020
Cited by 4 | Viewed by 2170
Abstract
Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte–Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model. Full article
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
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Open AccessArticle
Using the Maximal Entropy Modeling Approach to Analyze the Evolution of Sedentary Agricultural Societies in Northeast China
Entropy 2020, 22(3), 307; https://doi.org/10.3390/e22030307 - 09 Mar 2020
Cited by 3 | Viewed by 796
Abstract
The emergence of agriculture and the evolution of sedentary societies are among the most important processes in human history. However, although archeologists and social scientists have long been studying these processes, our understanding of them is still limited. This article focuses on the [...] Read more.
The emergence of agriculture and the evolution of sedentary societies are among the most important processes in human history. However, although archeologists and social scientists have long been studying these processes, our understanding of them is still limited. This article focuses on the Fuxin area in present-day Liaoning province in Northeast China. A systematic archeological survey we conducted in Fuxin in recent years located sites from five successive stages of the evolution of agricultural sedentary society. We used the principles of Maximal Entropy to study changes in settlement patterns during a long-term local trajectory, from the incipient steps toward a sedentary agricultural way of life to the emergence of complex societies. Based on the detailed data collected in the field, we developed a geo-statistical model based on Maximal Entropy (MaxEnt) that characterizes the locational choices of societies during different periods. This combination of high-resolution information on the location and density of archeological remains, along with a maximal entropy-based statistical model, enabled us to chart the long-term trajectory of the interactions between human societies and their natural environment and to better understand the different stages of the transition to developed sedentary agricultural society. Full article
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
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