Complex Human Behavior Research Articles

A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning

Computational models that formalize complex human behaviors enable study and understanding of such behaviors. However, collecting behavior data required to estimate the parameters of such models is often tedious and resource intensive. Thus, estimating dataset size as part of data collection planning (also known as Sample Size Determination) is important to reduce the time and effort of behavior data collection while maintaining an accurate estimate of model parameters. In this article, we present a sample size determination method based on Uncertainty Quantification (UQ) for a specific Inverse Reinforcement Learning (IRL) model of human behavior, in two cases: (1)pre-hocexperiment design—conducted in the planning stage before any data is collected, to guide the estimation of how many samples to collect; and (2)post-hocdataset analysis—performed after data is collected, to decide if the existing dataset has sufficient samples and whether more data is needed. We validate our approach in experiments with a realistic model of behaviors of people with Multiple Sclerosis (MS) and illustrate how to pick a reasonable sample size target. Our work enables model designers to perform a deeper, principled investigation of the effects of dataset size on IRL model parameters.

Heterogeneity is a key factor describing the initial outbreak of COVID-19

Assessing the transmission potential of emerging infectious diseases, such as COVID-19, is crucial for implementing prompt and effective intervention policies. The basic reproduction number is widely used to measure the severity of the early stages of disease outbreaks. The basic reproduction number of standard ordinary differential equation models is computed for homogeneous contact patterns; however, realistic contact patterns are far from homogeneous, specifically during the early stages of disease transmission. Heterogeneity of contact patterns can lead to superspreading events that show a significantly high level of heterogeneity in generating secondary infections. This is primarily due to the large variance in the contact patterns of complex human behaviours. Hence, in this work, we investigate the impacts of heterogeneity in contact patterns on the basic reproduction number by developing two distinct model frameworks: 1) an SEIR-Erlang ordinary differential equation model and 2) an SEIR stochastic agent-based model. Furthermore, we estimated the transmission probability of both models in the context of COVID-19 in South Korea. Our results highlighted the importance of heterogeneity in contact patterns and indicated that there should be more information than one quantity (the basic reproduction number as the mean quantity), such as a degree-specific basic reproduction number in the distributional sense when the contact pattern is highly heterogeneous.

Relational Frame Theory (RFT): An Empirical Approach to Language and Cognition

The effort of understanding human language and cognition, which distinguishes humans from other organisms and provides them with the opportunities they have on earth, dates back to ancient times. In line with this effort a theory of language and cognition, which is fairly comprehensive and has empirical support, has been developed based on a functional contextualistic view in the last century. This theory, called Relational Frame Theory (RFT), considers language as a verbal behavior and argues that the basis of human language and cognition is the behavior known as Arbitrarily Applicable Relational Responding (AARR). This behavior includes responding based on arbitrary relations independent of the non-arbitrary (physical) relations between stimuli, established by social whim and that do not make sense outside the social context in which they are located. AARR is characterized by three core properties such as mutual entailment, combinatorial mutual entailment and transformation of stimulus functions, and this behavior itself needs to be learned. This review aims to explain RFT, a very complex theory, in a comprehensive and understandable way. The historical process that brought about RFT will be mentioned, the basic principles and concepts of it will be explained, and the contributions of this theory to the understanding of complex human behaviors and its approach to psychopathology will be discussed.

Engineering a social contract: Rawlsian distributive justice through algorithmic game theory and artificial intelligence

The potential for artificial intelligence algorithms and game theory concepts to offer prescriptive and decision-making capability for humankind is increasingly recognized. This derives from the increasing availability of granular, multivariable, well-curated data offering analytical insights for necessarily complex human behaviors and activities. Of the multitude of situations that this decision-making aptitude presents, the application to governmental policy offers a commanding case. This would allow decisions to be made for the benefit of societies and citizens based on rigorous objective information devoid of the traditional approach of choosing policies and societal values based on the opinion of a handful of selected representatives who may be exposed to a lack of comprehensive data analysis capacity and subject to personal biases. There would need to be a critical requirement of wider socially responsible data practices here, beyond those of technical considerations and the incorporation of wider societal fairness approaches. Amongst the schools of political thought particularly acquiescent to the application by this approach would be the egalitarian approach of John Rawls. Here an Original Position’s pre-determination tool of Veil of Ignorance and ensuing Difference Principal presents a method of distributive justice that can be clearly mathematically defined in economics theory through Wald’s Maximin principle. This offers an opportunity to apply algorithmic game theory and artificial intelligence computational approaches to implement Rawlsian distributive justice that are presented and discussed. The outputs from the algorithmic acquaintance of Rawlsian egalitarianism with applicable state data, protected with appropriate privacy, security, legal, ethical and social governance could in turn lead to automated direct governmental choices and an objective Social Contract for citizens of digitally literate nations.

A Study on the Evaluation of Developments in the Field of HRM within the Scope of Organization Theories

In order to compel the limits of other production factors in competitive environments, there is a need for the full use of the potential of human resources and the use of human creative intelligence and talents. In this case, the organization should be able to develop a holistic understanding or culture to understand, interpret, develop and manage multifaceted, variable, complex, and diverse human behaviors. This understanding includes the human resources approach. It is seen that the evolution in human management passes through the stages of personnel management, human resources management (HRM), and strategic HRM, respectively. It is observed that various approaches in organizational theories are also effective in this development. The aim of this study is to examine the developments in the field of HRM in light of the developments in organizational theories. Organizational theories also contain different approaches within themselves. In this respect, the pioneers of each approach will be discussed and their effects on the development of HRM will be examined. It is aimed to contribute to the field literature with this study since organizational theories and the discipline of HRM have been studied separately so far and there is a lack of study in the literature for comparative consideration of these subjects.

Parallel Factories for Smart Industrial Operations: From Big AI Models to Field Foundational Models and Scenarios Engineering

Briefing: The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence, internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to the lack of interpretability and reliability guarantees, it is extremely challenging to apply these technologies directly to real-world industrial systems. Here we present a new paradigm for establishing parallel factories in metaverses to accelerate the deployment of intelligent technologies in real-world industrial systems: QAII-1.0. Based on cyber-physical-social systems, QAII-1.0 incorporates complex social and human factors into the design and analysis of industrial operations and is capable of handling industrial operations involving complex social and human behaviors. In QAII-1.0, a field foundational model called EuArtisan combined with scenarios engineering is developed to improve the intelligence of industrial systems while ensuring industrial interpretability and reliability. Finally, parallel oil fields in metaverses are established to demonstrate the operating procedure of QAII-1.0.

Phenomics in sport: Can emerging methodology drive advanced insights?

Methodologies in applied sport science have predominantly driven a reductionist grounding to component-specific mechanisms to drive athlete training and care. While linear mechanistic approaches provide useful insights, they have impeded progress in the development of more complex network physiology models that consider the temporal and spatial interactions of multiple factors within and across systems and subsystems. For this, a more sophisticated approach is needed and the development of such a methodological framework can be considered a Sport Grand Challenge. Specifically, a transdisciplinary phenomics-based scientific and modeling framework has merit. Phenomics is a relatively new area in human precision medicine, but it is also a developed area of research in the plant and evolutionary biology sciences. The convergence of innovative precision medicine, portable non-destructive measurement technologies, and advancements in modeling complex human behavior are central for the integration of phenomics into sport science. The approach enables application of concepts such as phenotypic fitness, plasticity, dose-response dynamics, critical windows, and multi-dimensional network models of behavior. In addition, profiles are grounded in indices of change, and models consider the athlete's performance or recovery trajectory as a function of their dynamic environment. This new framework is introduced across several example sport science domains for potential integration. Specific factors of emphasis are provided as potential candidate fitness variables and example profiles provide a generalizable modeling approach for precision training and care. Finally, considerations for the future are discussed, including scaling from individual athletes to teams and additional factors necessary for the successful implementation of phenomics.

Toward multi-granularity spatiotemporal simulation modeling of crowd movement for dynamic assessment of tourist carrying capacity

ABSTRACT Dynamic process simulation and prediction of crowd movement are effective approaches to understanding the complex human behavior system in GIScience. At present, obtaining full-sample individual trajectory data still faces challenges because of privacy and cost constraints, thereby resulting in difficulty solving geographic modeling problems that require full-sample individual data. In this paper, a general model for crowd movement simulation is proposed by taking the dynamic evaluation of tourist carrying capacity as an example. Such method is a multi-granularity coupling model, which considers behavioral process and spatiotemporal heterogeneity of tourists. First, a secrete event-based logic model of tourist behavior is proposed. Second, a social force-based inference method of tourist path is designed. Finally, the simulation and evaluation model of remaining spatial carrying capacity of tourists based on a behavioral dynamic system is achieved. In addition, the correctness and applicability of the model are demonstrated through a case study. The proposed model will positively affect time- and space-sharing analysis and assessment of crowd flow within a specific area of activity.

Scenario Model Predictive Control for Data-Based Energy Management in Plug-In Hybrid Electric Vehicles

One of the major limitations of optimization-based strategies for allocating the power flow in hybrid powertrains is that they rely on predictions of future power demand. These predictions are inherently uncertain as they are dependent on complex human behaviours that are challenging to model accurately. This paper proposes a data-based scenario model predictive control framework, where the inputs are determined at each control update by optimizing the power allocation over multiple previous examples of a route being driven. The proposed energy management optimization is convex, and results from scenario optimization are used to bound the confidence that the one-step-ahead optimization will be feasible with given probability. It is shown through numerical simulation that scenario model predictive control (MPC) obtains the same reduction in fuel consumption as nominal MPC with full preview of future driver behaviour, and that the scenario MPC optimization can be solved efficiently using a tailored optimization algorithm.

Anthology: A Social Simulation Framework

Social simulation research seeks to understand the dynamics of complex human behavior by simulating populations of individual decision-makers as multi-agent systems. However, prior work in games and entertainment fail to account for interactions between social behavior, geography, and relationships in a manner that allows researchers to easily reuse their frameworks and model social characters. We present Anthology, an extensible software framework for modeling human social systems, within the context of an ongoing research agenda to integrate AI techniques from social simulation games and computational social science to enable researchers to model and reason about the complex dynamics of human social behavior. Anthology comprises a motive-based agent decision making algorithm; a knowledge representation system for relationships; a flexible specification language for precondition-effect-style actions; a user interface to inspect and interact with the simulation as it runs in real-time; and an extensive user documentation and reference manual. We describe our participatory research design process used for the developing Anthology, the state of the current system, it's limitations and our future development directions.

Proof of concept study using GPS-based tracking data to build agent-based models of visitors’ off-trail behavior in nature-based tourism settings

Spatial components of visitor behavior in nature-based tourism settings have the potential to influence both the biophysical environmental and recreational experience. Previous efforts to model visitor spatial behavior in these settings have largely been deterministic and probabilistic. Comparatively, agent-based models (ABM) are often considered a more accurate technique for representing complex human behaviors such as visitor use. A key challenge in the development of ABM in nature-based tourism settings has been the lack of detailed, individually-based, georeferenced data on visitors, especially in locations where visitors leave trails. This study is a proof of concept exercise using global position system (GPS) data, collected from visitors in Yosemite National Park, to develop an example ABM of visitor use. Results from this study demonstrate that it is feasible to use GPS tracking data to provide insight into the generation of agent rules, yet knowledge gaps remain for the further development of ABMs in nature-based tourism research.

Disease and decision

At age 16, I injected morphine for the first time, and then started injecting heroin. By most standards, I was highly functioning, although I eventually became addicted. I was and remain socioeconomically privileged, but my relationship to heroin resulted in behaviors and consequences that I never could have conceived of, and which I sometimes strain to remember occurred. My life now is stable and conventional. Some aspects of my past addiction are unerasable, but the most salient of those are the social and legal consequences of having a criminal record—not any hallmarks of a chronic brain disease or disorder. I do not consider myself “in recovery.” Rather, I am recovered, by standards both my own and derived from clinical nosology. I have been in sustained remission for over a decade. Yet feelings are not facts, as is often said. I still use alcohol, and occasionally (though not recently) I have used other drugs, so there remains the possibility that my brain is indeed “diseased” and I am not objectively recovered, my self-assessment notwithstanding. My aim in writing about my lived experience of drug use, addiction, and recovery is to highlight the heterogeneity of people's experiences and the insight that personal narratives can provide. Debates about the brain disease model of addiction are often confined to academia, with the real-world, unintended consequences of the “disease” label seldom considered. Stigmatization of people with addiction comes from moralizing about drug use but may also originate from well-intended labels. I posit that we should not need labels to care about addicted people and make scientifically informed treatment accessible. Addicted people deserve help because they either need or want it, regardless of labels that presume to describe the etiology or likely trajectory of their problems. I conclude that some labels, even those needed for clinical classification of human behavior, may be pernicious. Clinicians and researchers have an obligation to reflect more deeply on the implications of the disease conceptualization of complex human behaviors such as addiction.

Vocal Learning and Songbirds: An Evolutionary Tale of Singing

Vocal Learning and Songbirds: An Evolutionary Tale of Singing Heidi Moss Erickson (bio) MINDING THE GAP [Introduction to the "Minding the Gap" series. Research on the neurobiological underpinnings of vocalization is growing at a rapid pace. Scientists from varied disciplines contribute to this field, elucidating the process from such diverse angles as evolutionary biology, molecular biology, genetics, neuroscience, and psychology.1 Singing is one of our most complex behaviors, involving coordination of more than 100 muscles, integrating both our musical and linguistic selves. Influences are derived from a looped brain-body-environment continuum and serve as the foundation for vocalization and artistic expression. It is therefore essential that singers, pedagogues, and voice scientists capitalize on this wealth of data to fully understand our instruments. An integrative view of how and why we sing can refine the art of voice pedagogy, demystify long held myths, and yield greater vocal efficiency, making singers better faster. The goal of this series, "Minding the Gap," is to bridge the divide between traditional voice pedagogy with the most current research on the brain. This is a dynamic process, and the hope is that this snapshot in scientific time will encourage singers and teachers to follow some of this work as it continues to evolve. Given the constant output in neuroscience research, paradigm shifts are the norm. To that end, it is important to follow the latest work from the diverse minds in the field. The first installment of "Minding the Gap" will explore the field of vocal learning from both an evolutionary and biological perspective. These concepts set the stage for future reviews on the neuroscience of breath, learning, articulation, phonation, emotion, gesture, and more. Each survey will include integrated voice pedagogy and voice science components so that readers can directly apply concepts to their field of study. The hope is that this series will open the door to new lines of inquiry and curiosity, moving our field into a new era of exploration.] The singing instinct, which men have in common with the birds is, without a doubt, at the root of the vocal art. —David Ffrangcon-Davies, The Singing of the Future (1905) Vocalization has both biological and anthropological roots. Humans speak and sing in order to communicate and socially bond. Each individual gains this ability through listening to acoustic models, forming auditory templates, and consolidating sensory information. This results in an identifiable vocal output, as unique as a [End Page 87] fingerprint. When we analyze the parallel evolution that gives rise to our vocal abilities, we gain insight into the inner workings of our complex instrument. Traditional voice pedagogy focuses on the question of how to sing; however, by analyzing our evolutionary story, we gain a greater understanding of why we sing. Ultimately, the why serves the how, leading to a deeper appreciation for our instrument by all who care for the voice. Much discussion around the evolution of complex human behaviors focuses on our primate cousins. We have inherited a great deal from our simian counterparts; chimpanzees, for example, share 99% of our DNA, they laugh when tickled, create strong social bonds, use hand gestures to communicate, mimic,2 and even demonstrate elements of emotion.3 However, two critical components for higher order vocalizations—a flexible vocal apparatus and a brain that coordinates complex sounds—are noticeably absent or severely compromised in our close relatives. Regarding structural elements of the voice, the vocal limitations of nonhuman primates have been partially attributed to the anatomy and configuration of their vocal tract (i.e., a high position of the larynx and a less flexible tongue). This was described in a seminal 1969 paper by Lieberman et al., who used a computer algorithm to extract the phonetic capability of subjects, and thus serve as a model for other nonhuman primates.4 Their measurements concluded that monkeys do not have a physical capacity for complex speech. Although in recent years scientists proved that there is more variability than initially thought,5 the fact remains that adult humans possess a more flexible vocal tract capable of more diverse vocalizations than our primate cousins. A more critical difference, however, lies in the correlative brain regions for...

Düşük Trafikli Mahalle Tasarımı İçin Planlanmış Davranış Teorisi’nin ve Zaman Coğrafyası ile Sentezinin İncelenmesi

This article presents an evaluation of the contribution of the synthesis of the Theory of Planned Behaviour (TPB) with Time Geography to the Low Traffic Neighbourhood (LTN) design stages. In the LTN design scheme, the evaluation of the human factor has importance at all stages of the design. However, the LTN design is currently based on stakeholder meetings, verbal statements and maps created with stakeholders in general. Social psychology, which includes concepts and theories to understand complex human behaviour, has been used in many transportation studies. TPB, one of the most well-known theories on this subject, its contributions to transportation studies and its primary deficiencies were identified within the article. It has been evaluated that the lack of spatial and temporal scope, one of these primary deficiencies, can be eliminated by synthesizing the Time Geography approach. As a result, this synthesis has the potential to increase the effects of the LTN design by integrating TPB, which has the potential to provide a basis for guiding people's behaviour, and Time Geography, which can reflect its spatial and temporal projection, in each of the stages of LTN design namely street classification, determination of neighbourhood boundaries, prioritization of neighbourhoods and determination of measures.

FROM CONFLICT TO INTEGRATION: AN ANTHROPOLOGICAL STUDY ON THE PSYCHOLOGICAL AND BEHAVIORAL CHANGES OF MANCHU URBAN PLANNING AND NATIONAL INTEGRATION IN THE QING DYNASTY (1644-1911)

FROM CONFLICT TO INTEGRATION: AN ANTHROPOLOGICAL STUDY ON THE PSYCHOLOGICAL AND BEHAVIORAL CHANGES OF MANCHU URBAN PLANNING AND NATIONAL INTEGRATION IN THE QING DYNASTY (1644-1911)

Modifications of Behavior and Inflammation in Mice Following Transplant with Fecal Microbiota from Children with Autism

Autism spectrum disorder (ASD) is a neurodevelopmental disorder displaying the modification of complex human behaviors, characterized by social interaction impairments, stereotypical/repetitive activities and emotional dysregulation. In this study, fecal microbiota transplant (FMT) via gavage from autistic children donors to mice, led to the colonization of ASD-like microbiota and autistic behaviors compared to the offspring of pregnant females exposed to valproic acid (VPA). Such variations seemed to be tightly associated with increased populations of Tenericutes plus a notable reduction (p < 0.001) of Actinobacteria and Candidatus S. in the gastrointestinal region of FMT mice as compared to controls. Indeed altered behaviors of FMT mice was reported when evaluated in the different maze tests (light dark, novel object, three chamber tests, novel cage test). Contextually, FMT accounted for elevated expression levels of the pro-inflammatory factors IL-1β, IL-6, COX-1 and TNF-α in both brain and small intestine. Villous atrophy and inflammatory infiltration (Caspase 3 and Ki67) were increased in the small intestine of FMT and VPA mice compared to controls. Moreover, the observed FMT-dependent alterations were linked to a decrease in the methylation status. Overall, findings of the present study corroborate a key role of gut microbiota in ASD. However, further investigations are required before any possible manipulation of gut bacteria with appropriate diets or probiotics can be conducted in ASD individuals.

Inbred mouse strain differences in alcohol and nicotine addiction-related phenotypes from adolescence to adulthood

Understanding the genetic basis of a predisposition for nicotine and alcohol use across the lifespan is important for public health efforts because genetic contributions may change with age. However, parsing apart subtle genetic contributions to complex human behaviors is a challenge. Animal models provide the opportunity to study the effects of genetic background and age on drug-related phenotypes, while controlling important experimental variables such as amount and timing of drug exposure. Addiction research in inbred, or isogenic, mouse lines has demonstrated genetic contributions to nicotine and alcohol abuse- and addiction-related behaviors. This review summarizes inbred mouse strain differences in alcohol and nicotine addiction-related phenotypes including voluntary consumption/self-administration, initial sensitivity to the drug as measured by sedative, hypothermic, and ataxic effects, locomotor effects, conditioned place preference or place aversion, drug metabolism, and severity of withdrawal symptoms. This review also discusses how these alcohol and nicotine addiction-related phenotypes change from adolescence to adulthood.

Determinants of Handwashing Without Soap in Tanzania: Evidence from the 2015/16 Tanzania Demographic Health Malaria Indicator Survey

Handwashing with soap can reduce pathogens, leading to a reduction in the spread of diarrhoea and other diseases. Handwashing with soap is insufficiently practised in Tanzania despite its potential health benefits. This study identifies the determinants of high levels of handwashing without soap in rural and urban areas of Tanzania. It used the 2015/16 Tanzania Demographic Health Survey - Malaria Indicator Survey (TDHS-MIS) dataset. A sample of 9,695 households—3,058 from urban areas and 6,637 households from rural areas—were used in the analysis. Pearson chi-square test and multivariate logistics regression analysis were used to determine the association between the independent and dependent variables. The study found that handwashing without soap was more prevalent in rural areas (46%) than in urban areas (28%). Exposure to the media (listening to the radio in rural areas and watching television in urban areas), and improved access to water sources decreased the probability of handwashing without soap, both in rural and urban areas. A household-headed by a female, and a household-head who was married, showed a decreased odd of handwashing with water only in rural areas; while households with 7 and more members showed increased odds of handwashing with water only in rural areas. The study concludes that exposure to the media and improved access to water sources can reduce high rates of handwashing without soap in Tanzania. Efforts to reduce handwashing without soap should target urban and rural communities since handwashing is a complex human behaviour.

Paying the Piper: History, Humanities, and the Scientific Study of Religion

Abstract Here we respond to a recent article in this journal by Leonardo Ambasciano, in which he offers a high-level critique of “big data,” artificial intelligence, and computational approaches in the study of religion. The main thrust of his argument is that these approaches are fundamentally problematic both because of their negative effect on the humanities and because they inappropriately rely on “neoliberal philanthrocapitalist” funding. In our response, we refer to our experience working with computational scientists and humanities scholars in collaborative teams, where they stand shoulder to shoulder in equal collaboration with one another, each side relying on the distinctive value that the other provides as they attempt to create clearer and more valid descriptions, analyses, and explanations of complex human behaviors. We correct several errors of fact in Ambasciano’s article, focusing first on ideological and ethical issues and then on methodological and epistemological issues. We conclude by emphasizing several points on which we agree with his assessment.

Discrete spread model for COVID‐19: the case of Lebanon

BackgroundMathematical models are essential to predict the likely outcome of an epidemic. Various models have been proposed in the literature for disease spreads. Some are individual based models and others are compartmental models. In this study, discrete mathematical models are developed for the spread of the coronavirus disease 2019 (COVID‐19).MethodsThe proposed models take into account the known special characteristics of this disease such as the latency and incubation periods, and the different social and infectiousness conditions of infected people. In particular, they include a novel approach that considers the social structure, the fraction of detected cases over the real total infected cases, the influx of undetected infected people from outside the borders, as well as contact‐tracing and quarantine period for travelers. The first model is a simplified model and the second is a complete model.ResultsFrom a numerical point of view, the particular case of Lebanon has been studied and its reported data have been used to estimate the complete discrete model parameters using optimization techniques. Moreover, a parameter analysis and several prediction scenarios are presented in order to better understand the role of the parameters.ConclusionsUnderstanding the role of the parameters involved in the models help policy makers in deciding the appropriate mitigation measures. Also, the proposed approach paves the way for models that take into account societal factors and complex human behavior without an extensive process of data collection.