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The social epistemology of science has adopted agent-based computer simulations as one of its core methods for investigating the dynamics of scientific inquiry. The epistemic status of these highly idealized models is currently under active debate in which they are often associated either with predictive or the argumentative functions. These two functions roughly correspond to interpreting simulations as virtual experiments or formalized thought experiments, respectively. This paper advances the argumentative account of modeling by proposing that models serve as a means to (re)conceptualize the macro-level dynamics of complex social epistemic interactions. I apply results from the epistemology of scientific modeling and the psychology of mental simulation to the ongoing debate in the social epistemology of science. Instead of considering simulation models as predictive devices, I view them as artifacts that exemplify abstract hypothetical properties of complex social epistemic processes in order to advance scientific understanding, hypothesis formation, and communication. Models need not be accurate representations to serve these purposes. They should be regarded as pragmatic cognitive tools that engender rather than replace intuitions in philosophical reasoning and argumentation. Furthermore, I aim to explain why the community tends to converge around few model templates: Since models have the potential to transform our intuitive comprehension of the subject of inquiry, successful models may literally capture the imagination of the modeling community. [ABSTRACT FROM AUTHOR]

In a networked anagram game, players are provided letters with possible actions of requesting letters from their neighbours, replying to letter requests, or forming words. The objective is to form as many words as possible as a team. The experimental data show that behaviours among players can vary significantly. However, simulations using agent-based models (ABM) in the literature often have not incorporated proper uncertainty quantification methods to characterise diverse behaviours of players. In this work, we propose an uncertainty quantification framework to build, exercise, and evaluate agent behaviour models and simulations for networked group anagram games. Specifically, using the data of game experiments, the proposed framework considers the clustering of game players based on their performance to reflect players' heterogeneity. Moreover, we also quantify uncertainty within each cluster through statistical modelling and inference. Numerical studies of networked game configurations are conducted to demonstrate the merits of the proposed framework. [ABSTRACT FROM AUTHOR]

Many bees visit just one flower species during a foraging trip, i.e. they show flower constancy. Flower constancy is important for plant reproduction but it could lead to an unbalanced diet, especially in biodiversity-depleted landscapes. It is assumed that flower constancy does not reduce dietary diversity in social bees, such as honeybees or bumblebees, but this has not yet been tested. We used computer simulations to investigate the effects of flower constancy on colony diet in plant species-rich and species-poor landscapes. We also explored if communication about food sources, which is used by many social bees, further reduces forage diversity. Our simulations reveal an extensive loss of forage diversity owing to flower constancy in both species-rich and species-poor environments. Small flower-constant colonies often discovered only 30–50% of all available plant species, thereby increasing the risk of nutritional deficiencies. Communication often interacted with flower constancy to reduce forage diversity further. Finally, we found that food source clustering, but not habitat fragmentation impaired dietary diversity. These findings highlight the nutritional challenges flower-constant bees face in different landscapes and they can aid in the design of measures to increase forage diversity and improve bee nutrition in human-modified landscapes. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new method based on a library of chemical reactions for constructing a system of ordinary differential equations from stochastic simulations arising from an agent-based model. The advantage of this approach is that this library respects any coupling between systems components, whereas the SINDy algorithm (introduced by Brunton et al.) treats the individual components as decoupled from one another. Another advantage of our approach is that we can use a non-negative least squares algorithm to find the non-negative rate constants in a very robust, stable and simple manner. We illustrate our ideas on an agent-based model of tumour growth on a 2D lattice. [ABSTRACT FROM AUTHOR]

Simple Summary: Targeted therapies can induce strong tumor regression, but they typically fail to eradicate metastatic cancers. The elucidation of the causes that enable cancers to survive within a residual disease is essential for finding eradication strategies. The ability of cancers to survive eradication reflects not only cell-intrinsic sensitivities, but also microenvironmental effects. Paracrine signals produced by fibroblast, non-neoplastic cells that make tumor stroma, can provide strong but spatially limited therapy protection. Even though this phenomenon is well-established, its contribution to the ability of tumors to escape eradication remains poorly understood. To address this gap of knowledge, we developed an in silico model that recapitulates the effect of stroma on therapy responses in tumor tissues. This model enabled us to evaluate the contribution of spatial aspects of stroma-mediated resistance. Our analyses reveal that stroma dispersal might be the most important yet overlooked aspect of stromal resistance that determines the overall tumor responses to therapy. The response of tumors to anti-cancer therapies is defined not only by cell-intrinsic therapy sensitivities but also by local interactions with the tumor microenvironment. Fibroblasts that make tumor stroma have been shown to produce paracrine factors that can strongly reduce the sensitivity of tumor cells to many types of targeted therapies. Moreover, a high stroma/tumor ratio is generally associated with poor survival and reduced therapy responses. However, in contrast to advanced knowledge of the molecular mechanisms responsible for stroma-mediated resistance, its effect on the ability of tumors to escape therapeutic eradication remains poorly understood. To a large extent, this gap of knowledge reflects the challenge of accounting for the spatial aspects of microenvironmental resistance, especially over longer time frames. To address this problem, we integrated spatial inferences of proliferation-death dynamics from an experimental animal model of targeted therapy responses with spatial mathematical modeling. With this approach, we dissected the impact of tumor/stroma distribution, magnitude and distance of stromal effects. While all of the tested parameters affected the ability of tumor cells to resist elimination, spatial patterns of stroma distribution within tumor tissue had a particularly strong impact. [ABSTRACT FROM AUTHOR]

Abstract This research introduces a novel global sensitivity analysis (GSA) framework for agent-based models (ABMs) that explicitly handles their distinctive features, such as multi-level structure and temporal dynamics. The framework uses Grassmannian diffusion maps to reduce output data dimensionality and sparse polynomial chaos expansion (PCE) to compute sensitivity indices for stochastic input parameters. To demonstrate the versatility of the proposed GSA method, we applied it to a non-linear system dynamics model and epidemiological and economic ABMs, depicting different dynamics. Unlike traditional GSA approaches, the proposed method enables a more general estimation of parametric sensitivities spanning from the micro level (individual agents) to the macro level (entire population). The new framework encourages the use of manifold-based techniques in uncertainty quantification, enhances understanding of complex spatio-temporal processes, and equips ABM practitioners with robust tools for detailed model analysis. This empowers them to make more informed decisions when developing, fine-tuning, and verifying models, thereby advancing the field and improving routine practice for GSA in ABMs.

Social structure may have changed from hierarchical to egalitarian and back along the evolutionary line of humans. Within the tradition of sociophysics, we construct a mathematical model of a society of agents subject to competing cognitive and social navigation constraints and predict, using statistical mechanics methods, that its degree of hierarchy decreases with encephalization and increases with group size, hence suggesting human societies were driven from hierarchical to egalitarian structures by the encephalization during the last few million years and back to hierarchical due to fast demographic changes during the Neolithic. In addition, applied to a different problem, the theory leads to the following predictions for modern pre-literary humans: (i) an intermediate hierarchy degree in mild climates. In harsher climates, societies will be (ii) more egalitarian if organized in small groups (of less than 100 persons) but (iii) more hierarchical if in larger (of more than 1000 persons) groups. The predicted bifurcation, characteristic of a phase transition, is also seen in the empirical cross-cultural record (248 cultures in the Ethnographic Atlas).

This research introduces a novel global sensitivity analysis (GSA) framework for agent-based models (ABMs) that explicitly handles their distinctive features, such as multi-level structure and temporal dynamics. The framework uses Grassmannian diffusion maps to reduce output data dimensionality and sparse polynomial chaos expansion (PCE) to compute sensitivity indices for stochastic input parameters. To demonstrate the versatility of the proposed GSA method, we applied it to a non-linear system dynamics model and epidemiological and economic ABMs, depicting different dynamics. Unlike traditional GSA approaches, the proposed method enables a more general estimation of parametric sensitivities spanning from the micro level (individual agents) to the macro level (entire population). The new framework encourages the use of manifold-based techniques in uncertainty quantification, enhances understanding of complex spatio-temporal processes, and equips ABM practitioners with robust tools for detailed model analysis. This empowers them to make more informed decisions when developing, fine-tuning, and verifying models, thereby advancing the field and improving routine practice for GSA in ABMs. [ABSTRACT FROM AUTHOR]

The differential transmission of COVID-19 depending on the socio-economic status of a neighborhood is well established. For example, several studies have shown that COVID-19 transmission was higher in poorer and denser neighborhoods than in wealthier ones. However, what is less well known is how this varied rate of transmission interacted with established health measures, i.e., face masks and lockdowns, in the context of developing countries to reduce pandemic cases and hence resulted in fewer deaths. This study uses an Agent-Based Model (ABM) simulation to examine the context and impacts of COVID-19 mitigation efforts (i.e., lockdowns combined with masks) on the transmission of COVID-19 across a single neighborhood in Ahmedabad, a city in the state of Gujarat, India. The model is parameterized using real-world population data, which allows us to simulate the spread of COVID-19 to find conditions that most closely match the realities of COVID-19 in the spring of 2020. Consequently, the simulation can be used to understand the impact of nation-wide lockdown on the spread of COVID cases across Ahmedabad as a function of housing density. Thus, invaluable insight into the effectiveness of a lockdown as a mitigation measure can be derived. Further information about how the effectiveness of the lockdown varied by neighborhood, as well as other factors that impacted it, can be ascertained. [ABSTRACT FROM AUTHOR]

Social structure may have changed from hierarchical to egalitarian and back along the evolutionary line of humans. Within the tradition of sociophysics, we construct a mathematical model of a society of agents subject to competing cognitive and social navigation constraints and predict, using statistical mechanics methods, that its degree of hierarchy decreases with encephalization and increases with group size, hence suggesting human societies were driven from hierarchical to egalitarian structures by the encephalization during the last few million years and back to hierarchical due to fast demographic changes during the Neolithic. In addition, applied to a different problem, the theory leads to the following predictions for modern pre-literary humans: (i) an intermediate hierarchy degree in mild climates. In harsher climates, societies will be (ii) more egalitarian if organized in small groups (of less than 100 persons) but (iii) more hierarchical if in larger (of more than 1000 persons) groups. The predicted bifurcation, characteristic of a phase transition, is also seen in the empirical cross-cultural record (248 cultures in the Ethnographic Atlas). [ABSTRACT FROM AUTHOR]

In this paper, we present an agent-based model for studying the impact of ‘myside bias’ on the argumentative dynamics in scientific communities. Recent insights in cognitive science suggest that scientific reasoning is influenced by ‘myside bias’. This bias manifests as a tendency to prioritize the search and generation of arguments that support one’s views rather than arguments that undermine them. Additionally, individuals tend to apply more critical scrutiny to opposing stances than to their own. Although myside bias may pull individual scientists away from the truth, its effects on communities of reasoners remain unclear. The aim of our model is two-fold: first, to study the argumentative dynamics generated by myside bias, and second, to explore which mechanisms may act as a mitigating factor against its pernicious effects. Our results indicate that biased communities are epistemically less successful than non-biased ones, and that they also tend to be less polarized than non-biased ones. Moreover, we find that two socio-epistemic mechanisms help communities to mitigate the effect of the bias: the presence of a common filter on weak arguments, which can be interpreted as shared beliefs, and an equal distribution of agents for each alternative at the start of the scientific debate. [ABSTRACT FROM AUTHOR]

Copyright of Mexican Journal of Behavioral Analysis / Revista Mexicana de Análisis de la Conducta is the property of Sociedad Mexicana de Analisis de la Conducta and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A bounded version of the Lotka – Volterra model based on logistic differential equations is suggested to emulate cyclic behaviour in complex evolutionary and other systems. The model provides reasonable approximations for the so-called leaping cycles and other oscillational processes when the main state variables are limited by physical constraints. The model represents a basic model-building block that can be used in system dynamics and other frameworks and applications that require fundamental understanding and modelling of principal features of complex cyclic processes. Two application areas – technological progress and industrial safety – are specifically considered here. The model is shown to emulate techno-economic cycles reasonably well and give an appropriate qualitative description of risk and safety variations in the industrial environment. Simulations using the suggested model are quantitatively compared with the results of agent-based simulations. While a simple model cannot match all details of complex agent-based simulations of the risk and benefit dilemma, the bounded Lotka – Volterra model still gives a reasonable approximation of the process. The conceptual implications of the model are, nevertheless, significant, pointing to common cyclic mechanisms taking place in complex evolutionary systems that may belong to different branches of science. [ABSTRACT FROM AUTHOR]

Copyright of Social Science Information is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper discusses the history of the development of different logical formalisms, explicitly modelling situations in which disagreement is present. It is structured as follows: (§1) sketches the distinction between two main traditions in logic, that have different relation to disagreement; (§2) presents Jaskowski's discussive logic and its shortcomings; (§3) develops the doctrinal paradox in social choice theory. Then we come to (§4) the probabilistic model of Lehrer-Wagner; (§5) the bounded confidence model of Hegselmann and Krause and (§6) the agent-based model of Douven and Riegler. The aim of the paper is to show that the development of these formalisms raises many questions that are yet not solved in adequate manner. [ABSTRACT FROM AUTHOR]

Computer-based simulations are highly effective in supporting students' deep conceptual understanding of scientific ideas. However, in the unprecedented era of the COVID-19 outbreak, students around the world experienced an induced state anxiety, which may have affected their engagement with the learning environments and ultimately their academic outcomes. This crisis underscores the global need to examine the learning processes and identify means of supporting students' engagement under stressful conditions. With this goal in mind, the current study evaluated the impact of COVID-19 induced anxiety on the learning process of 187 undergraduate students by means of computer-based simulation during a quarantine. Findings show that 56% of the students reported experiencing anxiety following the COVID-19 outbreak. A bivariate parametric analysis demonstrated that this COVID-19 induced anxiety had a negative impact on students' engagement. Indirect model analysis revealed that emotional disaffection in terms of boredom mediated the negative effect of COVID-19 induced anxiety on students' engagement. From a theoretical perspective, these findings highlight the pivotal role of boredom in students' learning processes in times of externally induced anxiety. From a pedagogical perspective, our findings highlight the necessity to implement teaching approaches that attend to boredom to mitigate the negative effects of externally induced anxiety. [ABSTRACT FROM AUTHOR]

Background: The United States is in the midst of an opioid overdose epidemic; 28.3 per 100,000 people died of opioid overdose in 2020. Simulation models can help understand and address this complex, dynamic, and nonlinear social phenomenon. Using the HEALing Communities Study, aimed at reducing opioid overdoses, and an agent-based model, Simulation of Community-Level Overdose Prevention Strategy, we simulated increases in buprenorphine initiation and retention and naloxone distribution aimed at reducing overdose deaths by 40% in New York Counties. Methods: Our simulations covered 2020-2022. The eight counties contrasted urban or rural and high and low baseline rates of opioid use disorder treatment. The model calibrated agent characteristics for opioid use and use disorder, treatments and treatment access, and fatal and nonfatal overdose. Modeled interventions included increased buprenorphine initiation and retention, and naloxone distribution. We predicted a decrease in the rate of fatal opioid overdose 1 year after intervention, given various modeled intervention scenarios. Results: Counties required unique combinations of modeled interventions to achieve a 40% reduction in overdose deaths. Assuming a 200% increase in naloxone from current levels, high baseline treatment counties achieved a 40% reduction in overdose deaths with a simultaneous 150% increase in buprenorphine initiation. In comparison, low baseline treatment counties required 250-300% increases in buprenorphine initiation coupled with 200-1000% increases in naloxone, depending on the county. Conclusions: Results demonstrate the need for tailored county-level interventions to increase service utilization and reduce overdose deaths, as the modeled impact of interventions depended on the county's experience with past and current interventions. [ABSTRACT FROM AUTHOR]

In this paper, we study an agent-based model for multi-round, pay as bid, sealed bid reverse auctions using techniques from partial differential equations and statistical mechanics tools. We assume that in each round a fixed fraction of bidders is awarded, and bidders learn from round to round using simple microscopic rules, adjusting myopically their bid according to their performance. Agent-based simulations show that bidders coordinate in the sense that they tend to bid the same value in the long-time limit. Moreover, this common value is the true cost or the ceiling price of the auction, depending on the level of competition. A discontinuous phase transition occurs when half of the bidders win. We establish the corresponding rate equations, and we obtain a system of ordinary differential equations describing the dynamics. Finally, we derive formally the kinetic equations modeling the dynamics, and we study the asymptotic behavior of solutions of the corresponding first-order, nonlinear partial differential equation satisfied by the distribution of agents. [ABSTRACT FROM AUTHOR]

Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and "social forces" such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modeling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically observed collective phenomena, including cohesion, milling, and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference--without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal nontrivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly. [ABSTRACT FROM AUTHOR]

This paper presents classification and analysis of the mathematical models of the spread of COVID-19 in different groups of population such as family, school, office (3–100 people), town (100–5000 people), city, region (0.5–15 million people), country, continent, and the world. The classification covers major types of models (time-series, differential, imitation ones, neural networks models and their combinations). The time-series models are based on analysis of time series using filtration, regression and network methods. The differential models are those derived from systems of ordinary and stochastic differential equations as well as partial differential equations. The imitation models include cellular automata and agent-based models. The fourth group in the classification consists of combinations of nonlinear Markov chains and optimal control theory, derived by methods of the mean-field game theory. COVID-19 is a novel and complicated disease, and the parameters of most models are, as a rule, unknown and estimated by solving inverse problems. The paper contains an analysis of major algorithms of solving inverse problems: stochastic optimization, nature-inspired algorithms (genetic, differential evolution, particle swarm, etc.), assimilation methods, big-data analysis, and machine learning. [ABSTRACT FROM AUTHOR]

This study uses evolutionary simulations to explore the strategies that emerge to enable populations to cope with random environmental changes in situations where lifetime learning approaches are not available to accommodate them. In particular, it investigates how the average magnitude of change per unit time and the persistence of the changes (and hence the resulting autocorrelation of the environmental time series) affect the change tolerances, population diversities, and extinction timescales that emerge. Although it is the change persistence (often discussed in terms of environmental noise color) that has received most attention in the recent literature, other factors, particularly the average change magnitude, interact with this and can be more important drivers of the adaptive strategies that emerge. Moreover, when running simulations, the choice of change representation and normalization can also affect the outcomes. Detailed simulations are presented that are designed to explore all these issues. They also reveal significant dependences on the associated mutation rates and the extent to which they can evolve, and they clarify how evolution often leads populations into strategies with higher risks of extinction. Overall, this study shows how modeling the effect of environmental change requires more care than may have previously been realized. [ABSTRACT FROM AUTHOR]

Background: Students who are blind are integrated into public schools in many countries, yet are often excluded from full participation in science since most learning materials are visual. To create a compensatory route, an existing model‐based inquiry‐learning environment was adapted by means of sonification (addition of non‐speech sounds that represent dynamic information). The learning environment uses agent‐based models and a complex systems approach to teach the Kinetic Molecular Theory (KMT) and Gas Laws. The models are accompanied by a workbook consisting of text (printed or auditory) and images (printed or tactile). Objectives: The current research examined whether such perceptual compensation creates a comparable learning environment for learners who are blind compared with learners who are sighted using the original learning environment. The aim of the study is to expand knowledge about how the auditory channel may compensate the visual channel among individuals who are blind. Methods: Conceptual learning in science and reasoning about complex systems were assessed using pre‐ and post‐questionnaires. To explore learners' learning progression throughout the unit, four progression analysis 'windows' were selected. These were groups of adjacent or nearly adjacent items in the workbook that permitted a glimpse of learners' progression. Results: The sonified environment not only supported the learning of learners who are blind compared with the learning of learners who are sighted using visual material, but even furthered their learning with respect to diffusion, one of the more challenging concepts in KMT. It seems the types of sonified representations used in this study increased listeners' sensitivity to the micro‐level interactions in a way less accessible in visual representations. Takeaways: Sonified environments can be provide learners who are blind with equitable participation by compensating and complementing the visual channel. Sonification can have implications for students who are blind as well as students who are sighted. Lay Description: What is already known: Sonification is the use of non‐speech sounds to represent dynamic information. Sonification can potentialy be used to allow equitable participation for students who are blind. What this paper adds: To test this, the study compared the outcomes of learners who are blind who used a sonified learning environment (L2C) to that of learners who are sighted who used a visual environment. Implications for practice: Learners who are blind showed equal or better learning outcomes using the sonified models compared with the learners who are sighted using the visual models. There are major implications for integrating students who are blind in public school classrooms. [ABSTRACT FROM AUTHOR]

Participants in socio-economic systems are often ranked based on their performance. Rankings conveniently reduce the complexity of such systems to ordered lists. Yet, it has been shown in many contexts that those who reach the top are not necessarily the most talented, as chance plays a role in shaping rankings. Nevertheless, the role played by chance in determining success, i.e. serendipity, is underestimated, and top performers are often imitated by others under the assumption that adopting their strategies will lead to equivalent results. We investigate the tradeoff between imitation and serendipity in an agent-based model. Agents in the model receive payoffs based on their actions and may switch to different actions by either imitating others or through random selection. When imitation prevails, most agents coordinate on a single action, leading to non-meritocratic outcomes, as a minority of them accumulate the majority of payoffs. Yet, such agents are not necessarily the most skilled ones. When serendipity dominates, instead, we observe more egalitarian outcomes. The two regimes are separated by a sharp transition, which we characterize analytically in a simplified setting. We discuss the implications of our findings in a variety of contexts, ranging from academic research to business.

By collecting and expanding several numerical recipes developed in previous work, we implement an object-oriented Python code, based on the networkX library, for the realization of the configuration model and Newman rewiring. The software can be applied to any kind of network and “target” correlations, but it is tested with focus on scale-free networks and assortative correlations. In order to generate the degree sequence we use the method of “random hubs”, which gives networks with minimal fluctuations. For the assortative rewiring we use the simple Vazquez-Weigt matrix as a test in the case of random networks; since it does not appear to be effective in the case of scale-free networks, we subsequently turn to another recipe which generates matrices with decreasing off-diagonal elements. The rewiring procedure is also important at the theoretical level, in order to test which types of statistically acceptable correlations can actually be realized in concrete networks. From the point of view of applications, its main use is in the construction of correlated networks for the solution of dynamical or diffusion processes through an analysis of the evolution of single nodes, i.e., beyond the Heterogeneous Mean Field approximation. As an example, we report on an application to the Bass diffusion model, with calculations of the time tmax of the diffusion peak. The same networks can additionally be exported in environments for agent-based simulations like NetLogo.

Abstract The success of screening programs depends to a large extent on the adherence of the target population, so it is therefore of fundamental importance to develop computer simulation models that make it possible to understand the factors that correlate with this adherence, as well as to identify population groups with low adherence to define public health strategies that promote behavioral change. Our aim is to demonstrate that it is possible to simulate screening adherence behavior using computer simulations. Three versions of an agent-based model are presented using different methods to determine the agent’s individual decision to adhere to screening: (a) logistic regression; (b) fuzzy logic components and (c) a combination of the previous. All versions were based on real data from 271,867 calls for diabetic retinopathy screening. The results obtained are statistically very close to the real ones, which allows us to conclude that despite having a high degree of abstraction from the real data, the simulations are very valid and useful as a tool to support decisions in health planning, while evaluating multiple scenarios and accounting for emergent behavior.

This study belongs to an emerging area of research seeking ways to depolarize societies in the short run (around events such as elections) as well as in a sustainable fashion. We approach the depolarization process with a model of three homophilic groups (US Democrats, Republicans, and Independents interacting in the context of upcoming federal elections). We expand a previous polarization model, which assumed that each individual interacts with all other individuals in its group with mean-field interactions. We add a depolarization field, which is analogous to the Blume–Capel model’s crystal field. There are currently numerous depolarization efforts around the world, some of which act in ways similar to this depolarization field. We find that for low values of the depolarization field, the system continues to be polarized. When the depolarization field is increased, the polarization decreases.

Human societies are complex systems and as such have tipping points. They can rapidly transit from one mode of operation to another and thereby change the way they function as a whole. Such transitions appear as financial or economic crises, rapid swings in collective opinion, political regime shifts, or revolutions. In physics collective transitions are known as phase transitions; for example, water exists in states of liquid, ice, and vapor. A few variables determine which state is realized: temperature, pressure, and volume. For social systems it is less clear what determines collective social states. A better understanding of social tipping points would allow us to tackle some of the big challenges more systematically, such as polarization, loss of social cohesion, fragmentation, or the green transition. The physics concept of universality might be key to understanding some tipping points in human societies and why agent-based models (ABMs) might make sense for identifying the transition points. If universality exists in social systems there is hope that relatively simple ABMs will be sufficient for understanding collective social systems in transition; if it does not exist, highly detailed computational models will be unavoidable. Both are possible. Both need new forms of collaboration between the social and natural sciences, and new types of data will be essential. [ABSTRACT FROM AUTHOR]

One of the primary goals of the palaeosciences is to produce robust understandings of palaeoecologies of extinct ecosystems. The time has arrived where such palaeoecologies can be significantly improved—agent-based models (ABMs) that synthesize our modern understandings of animal ecology with past conditions provide a unique opportunity for this. The Palaeo-Agulhas Plain (PAP), a now submerged landscape off the south coast of South Africa, formed a novel ecosystem during the lower sea levels of the Pleistocene. Here we provide a review of the utility of ABMs for understanding herbivores in prehistoric environments using the Paleoscape Model. The Paleoscape Model is fortunate to have features models of climate, geology and vegetation upon which we can understand how herbivores used the PAP environment. To incorporate aspects of herbivore behaviour and habitat suitability into the model requires analogies from extant systems. In addition to reconstructions of the availability of resources and associated risks, data on herbivore behaviour and physiology are imperative to our understanding of these systems. Behaviour, driven by changes in the environment, is interpreted by species, leading to selection of suitable habitats ultimately driving the herbivore's decisions within ABMs. Finally, we discuss herbivore integration into these models that can be used in numerous other scenarios (past, present and future). [ABSTRACT FROM AUTHOR]

The subject of keto‐enol equilibrium has a long history and well‐established position within physical organic chemistry. Nonetheless, one still finds numerous reports of confusing findings and questions of accuracy when dealing with its practical application. In this report, some apparently anomalous recent observations are reviewed and then reexamined using density functional theory computations and agent‐based (cellular automata) models of the keto‐enol‐anion equilibrium system. It becomes apparent that a resolution of many of the results can be achieved by taking into account the fact that although the ketone form is often present in overwhelmingly greater concentration, the enol can still contribute significantly to formation of the anion through its much greater acidity. Thus, in these cases, dissociation data assigned solely to the ketone form should in fact be recognized as representing a mixture of contributions from both the keto and the (neglected) enol form. [ABSTRACT FROM AUTHOR]

This study belongs to an emerging area of research seeking ways to depolarize societies in the short run (around events such as elections) as well as in a sustainable fashion. We approach the depolarization process with a model of three homophilic groups (US Democrats, Republicans, and Independents interacting in the context of upcoming federal elections). We expand a previous polarization model, which assumed that each individual interacts with all other individuals in its group with mean-field interactions. We add a depolarization field, which is analogous to the Blume–Capel model's crystal field. There are currently numerous depolarization efforts around the world, some of which act in ways similar to this depolarization field. We find that for low values of the depolarization field, the system continues to be polarized. When the depolarization field is increased, the polarization decreases. [ABSTRACT FROM AUTHOR]

Deterministic compartment models (CMs) and stochastic models, including stochastic CMs and agent-based models, are widely utilized in epidemic modeling. However, the relationship between CMs and their corresponding stochastic models is not well understood. The present study aimed to address this gap by conducting a comparative study using the susceptible, exposed, infectious, and recovered (SEIR) model and its extended CMs from the coronavirus disease 2019 modeling literature. We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations. Based on this equivalence, we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment. The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics. However, it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs. Additionally, we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents. This model offered a balance between computational efficiency and accuracy. The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling. Furthermore, the results had implications for the development of hybrid models that integrated the strengths of both frameworks. Overall, the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases. [ABSTRACT FROM AUTHOR]

In the dynamic field of Artificial Intelligence (AI), strategic integration is crucial to avoid the pitfall of optimizing peripheral processes at the expense of organizational goals. This paper presents a nuanced AI strategy framework that combines Causal Loop Diagrams (CLD) with Agent-Based Modeling (ABM) to address the intricacies of complex, growth-oriented organizations. By marrying empirical data with theoretical constructs and broad meta-knowledge, this approach enables the practical application of common sense to AI planning. The use of CLDs helps reveal and tackle ingrained patterns that could hinder organizational progress, while ABM facilitates the testing of AI strategies through extensive simulations within varied market and product scenarios. This study exemplifies the benefits of a causal-model-enabled AI strategy for strategic alignment and sustainable growth, suggesting a shift from mere efficiency-driven AI applications to those grounded in a thorough comprehension of complex system interdependencies. The proposed framework paves the way for organizations to develop a robust, anti-fragile AI ecosystem that is attuned to the nuances of their operational environment. [ABSTRACT FROM AUTHOR]

By collecting and expanding several numerical recipes developed in previous work, we implement an object-oriented Python code, based on the networkX library, for the realization of the configuration model and Newman rewiring. The software can be applied to any kind of network and "target" correlations, but it is tested with focus on scale-free networks and assortative correlations. In order to generate the degree sequence we use the method of "random hubs", which gives networks with minimal fluctuations. For the assortative rewiring we use the simple Vazquez-Weigt matrix as a test in the case of random networks; since it does not appear to be effective in the case of scale-free networks, we subsequently turn to another recipe which generates matrices with decreasing off-diagonal elements. The rewiring procedure is also important at the theoretical level, in order to test which types of statistically acceptable correlations can actually be realized in concrete networks. From the point of view of applications, its main use is in the construction of correlated networks for the solution of dynamical or diffusion processes through an analysis of the evolution of single nodes, i.e., beyond the Heterogeneous Mean Field approximation. As an example, we report on an application to the Bass diffusion model, with calculations of the time t m a x of the diffusion peak. The same networks can additionally be exported in environments for agent-based simulations like NetLogo. [ABSTRACT FROM AUTHOR]

This paper presents methods to proactively choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers. We show that by a smart integration of exploration/exploitation balancing, contact tracing, and location-based sampling, one can effectively mitigate the disease spread and significantly reduce the infection rates and death rates. Under different vaccination policies and under different compliance levels to quarantine order and/or testing requests, our smart testing algorithm can bring down the death rate significantly by 20% to 30%, as well as the percentage of infected drops by approximately 30%. The load on hospitals at peak times, a crucial aspect of managing COVID-19, drops, by 50% when implementing smart testing. We also show how procedural fairness can be incorporated into our method and present results that show that this can be done without hurting the effectiveness of the mitigation that can be achieved. This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers. The smart-testing ideas presented here are motivated by active learning and multi-armed bandit techniques in machine learning. Our active sampling method works in conjunction with quarantine policies, can handle different objectives, and is dynamic and adaptive in the sense that it continually adapts to changes in real-time data. The bandit algorithm uses contact tracing, location-based sampling and random sampling in order to select specific individuals to test. Using a data-driven agent-based model simulating New York City we show that the algorithm samples individuals to test in a manner that rapidly traces infected individuals. Experiments also suggest that smart-testing can significantly reduce the death rates as compared with current methods, with or without vaccination. While smart testing strategies can help mitigate disease spread, there could be unintended consequences with fairness or bias when deployed in real-world settings. To this end we show how procedural fairness can be incorporated into our method and present results that show that this can be done without hurting the effectiveness of the mitigation that can be achieved. History: Ahmed Abbasi, Senior Editor; Maytal Saar-Tsechansky, Associate Editor. Funding: W. Yahav is supported by the Jeremy Coller Foundation and the Henry Crown Institute of Business Research in Israel. Supplemental Material: The e-companion is available at https://doi.org/10.1287/isre.2023.1215. [ABSTRACT FROM AUTHOR]

Repulsion between individuals within a finite radius is encountered in numerous applications, including cell exclusion, i.e., avoidance of overlapping cells, bird flocks, or microscopic pedestrian models. We define such individual-based particle dynamics in one spatial dimension with minimal assumptions of the repulsion force f as well as their external velocity v and prove their characteristic properties. Moreover, we are able to perform a rigorous limit from the microscopic to the macroscopic scale, where we could recover the finite interaction radius as a density threshold. Specific choices for the repulsion force f lead to well-known nonlinear diffusion equations on the macroscopic scale, as, e.g., the porous medium equation. At both scaling levels, numerical simulations are presented and compared to underline the analytical results. [ABSTRACT FROM AUTHOR]