Model Of Social Dynamics Research Articles

A Comprehensive Study on the Emerging Role of Neuroeconomics Dynamics in Cybersecurity

As cyber threats continue to evolve, there is growing recognition that effective security requires a deeper scientific understanding of the complex drivers of human judgment and decision-making. The emerging field of neuroeconomics, combining economics, psychology, and neuroscience, provides new theoretical frameworks, measurement techniques, and models to elucidate the neural foundations underpinning human motivations and behaviors critical to cyber contexts. This paper reviews key neuroeconomic concepts including dual-process thinking, prospect theory, and social decision neuroscience and highlights their potential for generating new insights and interventions to strengthen cybersecurity. Ethical considerations surrounding neuroeconomic monitoring and potential manipulations also demand careful governance. Overall, neuroeconomic research promises to advance cybersecurity practices and policies by grounding them in realistic neural models of human cognition, emotions, and social dynamics. Careful interdisciplinary collaboration will be key to validating applications and avoiding pitfalls as neuroeconomic tools and theories are integrated into both cybersecurity scholarship and practice. This neuro-cognitive approach represents a compelling frontier with immense opportunities to transform cybersecurity through enhanced appreciation of the human dimension.

Dynamics of Information Flow and Task Allocation of Social Insect Colonies: Impacts of Spatial Interactions and Task Switching.

Models of social interaction dynamics have been powerful tools for understanding the efficiency of information spread and the robustness of task allocation in social insect colonies. How workers spatially distribute within the colony, or spatial heterogeneity degree (SHD), plays a vital role in contact dynamics, influencing information spread and task allocation. We used agent-based models to explore factors affecting spatial heterogeneity and information flow, including the number of task groups, variation in spatial arrangements, and levels of task switching, to study: (1) the impact of multiple task groups on SHD, contact dynamics, and information spread, and (2) the impact of task switching on SHD and contact dynamics. Both models show a strong linear relationship between the dynamics of SHD and contact dynamics, which exists for different initial conditions. The multiple-task-group model without task switching reveals the impacts of the number and spatial arrangements of task locations on information transmission. The task-switching model allows task-switching with a probability through contact between individuals. The model indicates that the task-switching mechanism enables a dynamical state of task-related spatial fidelity at the individual level. This spatial fidelity can assist the colony in redistributing their workforce, with consequent effects on the dynamics of spatial heterogeneity degree. The spatial fidelity of a task group is the proportion of workers who perform that task and have preferential walking styles toward their task location. Our analysis shows that the task switching rate between two tasks is an exponentially decreasing function of the spatial fidelity and contact rate. Higher spatial fidelity leads to more agents aggregating to task location, reducing contact between groups, thus making task switching more difficult. Our results provide important insights into the mechanisms that generate spatial heterogeneity and deepen our understanding of how spatial heterogeneity impacts task allocation, social interaction, and information spread.

A case of intercommunity lethal aggression by chimpanzees in an open and dry landscape, Issa Valley, western Tanzania

Intercommunity (lethal) aggression is a familiar component of the behavioural repertoire of many forest-dwelling chimpanzee (Pan troglodytes) communities. However, until now, the absence of intercommunity attacks – including killings – in communities that live in open, mosaic environments has supported hypotheses of reduced resource competition in drier habitats, and informed referential models of early hominin social dynamics in a similar habitat. In June 2020, we observed the first instance of intercommunity lethal aggression, a male-committed infanticide, by the Issa chimpanzee community, which live in a savannah-mosaic habitat in the Issa Valley, western Tanzania. The carcass was recovered by researchers after it was abandoned by the attackers. Here, we give a detailed account of the events leading up to and including the infanticide, and contextualise our observations with what has been described for other chimpanzee communities. Notably, in contrast to the majority of reported intercommunity infanticides, the infant male victim was castrated (and not cannibalised), making this the youngest reported castration. This observation of intercommunity aggression disproves its hypothesised absence in savannah-dwelling chimpanzees, which by extension, has implications for early hominin evolution. We suggest that the near absence of observations of intercommunity aggression in savannah chimpanzee communities is most likely due to the lack of long-term study communities, and in some cases geographic isolation. We hypothesise that food-rich areas within a habitat with otherwise widely distributed food sources may select for intense intercommunity aggression despite the low population density characteristic of savannah communities. Anecdotes such as this add to the comparative database available on intercommunity killings in chimpanzee society, improving our ability to draw inferences about their evolutionary significance.

Towards a mathematical theory of behavioral human crowds

The first part of our paper presents a general survey on the modeling, analytic problems, and applications of the dynamics of human crowds, where the specific features of living systems are taken into account in the modeling approach. This critical analysis leads to the second part which is devoted to research perspectives on modeling, analytic problems, multiscale topics which are followed by hints towards possible achievements. Perspectives include the modeling of social dynamics, multiscale problems and a detailed study of the link between crowds and swarms modeling.

Metaverse, SED Model, and New Theory of Value

The metaverse concept constructs a virtual world parallel to the real world. The social economic dynamics (SED) model establishes a systematic model for social economic dynamics simulation that integrates macroeconomy and microeconomy based on modeling mechanism of the new theory of value by analogy with Newtonian mechanics and the modeling approach of Agent‐based computational economics. This article describes the SED model’s modeling mechanisms, modeling rules, and behavior equations. At the same time, this article introduces the methods, testing standards, and some typical cases about using the SED model to generate the economic digital twin systems. By doing so, we hope to demonstrate that the method of computer simulation experiment based on the SED model is a scientific empirical method, which has more advantages than the existing empirical research methods in economics. The SED model, which can be fully used to form an economic engine and construct a virtual economic system by digital twin method, can be integrated with the extant physical engine in the metaverse concept to build a virtual world consisting of physics, economy, culture, and politics that is close to and coexists with reality.

Opinion Dynamics Optimization by Varying Susceptibility to Persuasion via Non-Convex Local Search

A long line of work in social psychology has studied variations in people’s susceptibility to persuasion—the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people’s intrinsic opinions, it is also natural to consider interventions that modify people’s susceptibility to persuasion. In this work, motivated by this fact, we propose an influence optimization problem. Specifically, we adopt a popular model for social opinion dynamics, where each agent has some fixed innate opinion, and a resistance that measures the importance it places on its innate opinion; agents influence one another’s opinions through an iterative process. Under certain conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to modify the resistance of any number of agents (within some given range) such that the sum of the equilibrium opinions is minimized; for the budgeted variant, in addition the algorithm is given upfront a restriction on the number of agents whose resistance may be modified. We prove that the objective function is in general non-convex. Hence, formulating the problem as a convex program as in an early version of this work (Abebe et al., KDD’18) might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Finally, we propose and evaluate experimentally a family of heuristics for the budgeted variant of the problem.

Human social sensing is an untapped resource for computational social science.

The ability to 'sense' the social environment and thereby to understand the thoughts and actions of others allows humans to fit into their social worlds, communicate and cooperate, and learn from others' experiences. Here we argue that, through the lens of computational social science, this ability can be used to advance research into human sociality. When strategically selected to represent a specific population of interest, human social sensors can help to describe and predict societal trends. In addition, their reports of how they experience their social worlds can help to build models of social dynamics that are constrained by the empirical reality of human social systems.

Quantum Contagion: A Quantum-Like Approach for the Analysis of Social Contagion Dynamics with Heterogeneous Adoption Thresholds.

Modeling the information of social contagion processes has recently attracted a substantial amount of interest from researchers due to its wide applicability in network science, multi-agent-systems, information science, and marketing. Unlike in biological spreading, the existence of a reinforcement effect in social contagion necessitates considering the complexity of individuals in the systems. Although many studies acknowledged the heterogeneity of the individuals in their adoption of information, there are no studies that take into account the individuals’ uncertainty during their adoption decision-making. This resulted in less than optimal modeling of social contagion dynamics in the existence of phase transition in the final adoption size versus transmission probability. We employed the Inverse Born Problem (IBP) to represent probabilistic entities as complex probability amplitudes in edge-based compartmental theory, and demonstrated that our novel approach performs better in the prediction of social contagion dynamics through extensive simulations on random regular networks.

A metapopulation model of social group dynamics and disease applied to Yellowstone wolves

The population structure of social species has important consequences for both their demography and transmission of their pathogens. We develop a metapopulation model that tracks two key components of a species' social system: average group size and number of groups within a population. While the model is general, we parameterize it to mimic the dynamics of the Yellowstone wolf population and two associated pathogens: sarcoptic mange and canine distemper. In the initial absence of disease, we show that group size is mainly determined by the birth and death rates and the rates at which groups fission to form new groups. The total number of groups is determined by rates of fission and fusion, as well as environmental resources and rates of intergroup aggression. Incorporating pathogens into the models reduces the size of the host population, predominantly by reducing the number of social groups. Average group size responds in more subtle ways: infected groups decrease in size, but uninfected groups may increase when disease reduces the number of groups and thereby reduces intraspecific aggression. Our modeling approach allows for easy calculation of prevalence at multiple scales (within group, across groups, and population level), illustrating that aggregate population-level prevalence can be misleading for group-living species. The model structure is general, can be applied to other social species, and allows for a dynamic assessment of how pathogens can affect social structure and vice versa.

Ideology, communication and polarization.

Ideologically committed minds form the basis of political polarization, but ideologically guided communication can further entrench and exacerbate polarization depending on the structures of ideologies and social network dynamics on which cognition and communication operate. Combining a well-established connectionist model of cognition and a well-validated computational model of social influence dynamics on social networks, we develop a new model of ideological cognition and communication on dynamic social networks and explore its implications for ideological political discourse. In particular, we explicitly model ideologically filtered interpretation of social information, ideological commitment to initial opinion, and communication on dynamically evolving social networks, and examine how these factors combine to generate ideologically divergent and polarized political discourse. The results show that ideological interpretation and commitment tend towards polarized discourse. Nonetheless, communication and social network dynamics accelerate and amplify polarization. Furthermore, when agents sever social ties with those that disagree with them (i.e. structure their social networks by homophily), even non-ideological agents may form an echo chamber and form a cluster of opinions that resemble an ideological group. This article is part of the theme issue 'The political brain: neurocognitive and computational mechanisms'.

Optimal majority threshold in a stochastic environment

Within the model of social dynamics determined by collective decisions in a stochastic environment (the ViSE model), we consider the case of a homogeneous society consisting of classically rational economic agents. We obtain analytical expressions for the optimal majority threshold as a function of the parameters of the environment, assuming that the proposals are generated by means of random variables. The cases of several specific distributions of these variables are considered in more detail.

On boundaries in social dynamics models with large number of agents

It is known that community boundaries in social systems evolve in a rather complicated manner. We investigate several well-known models of interpersonal interaction as well as their modifications in order to establish some asymptotic results of the long time behaviour of the edge in large (or infinite) configurations of agents. We also show that the introduction of randomness in such models can lead to promising applications of some powerful probabilistic tools.

Modeling Driving Experience in Smart Traffic Routing Scenarios: Application to Traffic Multi-Map Routing

The effectiveness of user-oriented traffic routing applications to mitigate traffic congestion in Intelligent Transportation Systems depends on their degree of adoption, which usually evolves depending on subjective and exogenous factors. This paper proposes a user experience and social dynamics model to analyze and evaluate traffic routing methods, based on fuzzy rules and discrete choice theory. The model has been applied to the optimal Traffic-Weighted Multi-Maps (TWM) routing method to evaluate the adoption dynamics and analyze convergence towards the system optimum. Route unfairness and resistance to change are also considered in the model. Experimental results are obtained simulating the evolution of the drivers' population behavior. Simulation is carried over synthetic and real networks, using optimized TWM maps. The experimental results show how the TWM system evolves to a stationary System Optimum, improving overall traffic congestion and showing how User Equilibrium variability is bounded as it depends on user routing choices influenced by behavioral patterns.

Whither Now, Opinion Modelers?

The article describes the current status and potential directions of development of Agent Based Models (ABMs) of social opinion dynamics. Despite extensive effort, the models achieve, at best, only qualitative agreement with social observations. To understand the increasingly pressing issues such as all-encompassing political and social polarization, resurgence of fundamentalist and populist movements, persistence of socially dangerous trends such as denial of climate change or the anti-vaccination activism, the models must be capable of handling much more complex set of agent characteristics, content of the communications (between agents and through media) as well as psychologically adequate reaction mechanisms and realistic influence networks. Moreover, to meet the challenge of understanding the globally growing political polarization and changes brought by increasing reliance on electronic communication the models should adapt to the post-truth era. It is necessary to include in the models phenomena such as fake news, omnipresent exaggerations and stereotypes, trolling and algorithmic biases funnelling personal information universe. We also need to consider that most social systems may be described as transient, out-of-equilibrium ones, where a crucial role is played by the models' initial conditions. In this work we analyse the challenges facing the modelling community and point out certain promising directions for development.

Heterogeneity in social and epidemiological factors determines the risk of measles outbreaks

Political and environmental factors-e.g., regional conflicts and global warming-increase large-scale migrations, posing extraordinary societal challenges to policymakers of destination countries. A common concern is that such a massive arrival of people-often from a country with a disrupted healthcare system-can increase the risk of vaccine-preventable disease outbreaks like measles. We analyze human flows of 3.5 million (M) Syrian refugees in Turkey inferred from massive mobile-phone data to verify this concern. We use multilayer modeling of interdependent social and epidemic dynamics to demonstrate that the risk of disease reemergence in Turkey, the main host country, can be dramatically reduced by 75 to 90% when the mixing of Turkish and Syrian populations is high. Our results suggest that maximizing the dispersal of refugees in the recipient population contributes to impede the spread of sustained measles epidemics, rather than favoring it. Targeted vaccination campaigns and policies enhancing social integration of refugees are the most effective strategies to reduce epidemic risks for all citizens.

Go big or go home: A model-based assessment of general strategies to slow the spread of forest pests via infested firewood.

Invasive pests, such as emerald ash borer or Asian longhorn beetle, have been responsible for unprecedented ecological and economic damage in eastern North America. These and other wood-boring invasive insects can spread to new areas through human transport of untreated firewood. Behaviour, such as transport of firewood, is affected not only by immediate material benefits and costs, but also by social forces. Potential approaches to reduce the spread of wood-boring pests through firewood include raising awareness of the problem and increasing the social costs of the damages incurred by transporting firewood. In order to evaluate the efficacy of these measures, we create a coupled social-ecological model of firewood transport, pest spread, and social dynamics, on a geographical network of camper travel between recreational destinations. We also evaluate interventions aimed to slow the spread of invasive pests with untreated firewood, such as inspections at checkpoints to stop the movement of transported firewood and quarantine of high-risk locations. We find that public information and awareness programs can be effective only if the rate of spread of the pest between and within forested areas is slow. Direct intervention via inspections at checkpoints can only be successful if a high proportion of the infested firewood is intercepted. Patch quarantine is only effective if sufficiently many locations can be included in the quarantine and if the quarantine begins early. Our results indicate that the current, relatively low levels of public outreach activities and lack of adequate funding are likely to render inspections, quarantine and public outreach efforts ineffective.

From disorganized equality to efficient hierarchy: how group size drives the evolution of hierarchy in human societies.

A manifest trend is that larger and more productive human groups shift from distributed to centralized decision-making. Voluntary theories propose that human groups shift to hierarchy to limit scalar stress, i.e. the increase in cost of organization as a group grows. Yet, this hypothesis lacks a mechanistic model to investigate the organizational advantage of hierarchy and its role on its evolution. To fill this gap, we describe social organization by the distribution of individuals' capacity to influence others. We then integrate this formalization into models of social dynamics and evolutionary dynamics. First, our results demonstrate that hierarchy strongly reduces scalar stress, and that this benefit can emerge solely because leaders and followers differ in their capacity to influence others. Second, the model demonstrates that this benefit can be sufficient to drive the evolution of leader and follower behaviours and ultimately, the transition from small egalitarian to large hierarchical groups.

The polarization within and across individuals: the hierarchical Ising opinion model

AbstractPolarization of opinions is a societal threat. It involves psychological processes as well as group dynamics, a popular topic in statistical physics. However, the interaction between the within individual dynamics of attitude formation and across person polarization is rarely studied. By modelling individual attitudes as Ising networks of attitude elements, and approximating this behaviour by the cusp singularity, we developed a fundamentally new model of social dynamics. In this hierarchical model, agents behave either discretely or continuously depending on their attention to the issue. At the individual level, the model reproduces the mere thought effect and resistance to persuasion. At the social level, the model implies polarization and the persuasion paradox. We propose a new intervention for escaping polarization in bounded confidence models of opinion dynamics.

Organisational Social Influence on Directed Hierarchical Graphs, from Tyranny to Anarchy

Coordinated human behaviour takes place within a diverse range of social organisational structures, which can be thought of as power structures with “managers” who influence “subordinates”. A change in policy in one part of the organisation can cause cascades throughout the structure, which may or may not be desirable. As organisations change in size, complexity and structure, the system dynamics also change. Here, we consider majority rule dynamics on organisations modelled as hierarchical directed graphs, where the directed edges indicate influence. We utilise a topological measure called the trophic incoherence parameter, q, which effectively gauges the stratification of power structure in an organisation. We show that this measure bounds regimes of behaviour. There is fast consensus at low q (e.g. tyranny), slow consensus at mid q (e.g. democracy), and no consensus at high q (e.g. anarchy). These regimes are investigated analytically, numerically and empirically with diverse case studies in the Roman Army, US Government, and a healthcare organisation. Our work demonstrates the usefulness of the trophic incoherence parameter when considering models of social influence dynamics, with widespread consequences in the design and analysis of organisations.

Transferring the impacts of pilot-scale studies to other scales: Understanding the role of non-biophysical factors using field-based irrigation studies

Researchers are challenged to design research that can generate credible claims regarding cross-scale impact and adoption. However, the context in which new knowledge or innovation is developed and tested may differ from that for the uptake and use of those findings. This paper reports insight into the problem of designing impactful research and proposes a model to assist bio-physical researchers in accounting for non-biophysical context when moving between scales or settings. We treat the scaling problem as the one shifting contexts. The use of the model is illustrated by application in two New Zealand-based irrigation water use efficiency (WUE) field studies. We hypothesised that to successfully transfer the learnings from these two pilot studies to other scales it would be important to understand the influence of context on WUE practices. To support this process, we developed a social dynamics model (Composite Context Model, CCM) from existing social systems frameworks. The CCM maps influential non-biophysical dynamics to help interpret the WUE field study findings for other scales. The paper represents a contribution to researchers addressing two related challenges: that of making credible claims regarding the possible future impact of their research, and that of translating innovations across scales. By demonstrating the use of our CCM for documenting key non-biophysical variables, we aim to equip researchers with a practical tool to assist in the interpretation of findings across contexts, that include both biophysical and non-biophysical factors.