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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]

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]

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]

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]

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.

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]

In agent-based simulation methods and applications, discrete timestep approaches prevail. To support continuous-time agent-based simulation, we analyze how methods for simulating population-based Continuous- Time Markov Chains (CMTCs) can be adopted and derive implications for the concrete realization. To corroborate our findings, we develop an efficient internal domain-specific language (DSL) based on ML3, a modeling language for linked lives in demography. The design as an internal DSL, implemented within the Rust programming language, allows the modeler to exploit the complete feature set of the host language, such as data types and structures, when programming decision processes. A concise and expressive modeling of an agent’s discrete decisions and behavior introducing exponentially distributed sojourn times can be supported by adapting the concept of guarded commands from population-based CTMCs. The execution of models relies on an optimized version of the direct method. This method is a variant of stochastic simulation algorithms, an established method for executing population-based CTMCs in other application areas, notably biochemistry. To efficiently handle the large set of possible transitions inherent to continuous-time agent-based models, we use a dependency graph whose updating scheme caters to the dynamic dependencies within agent-based models and the need for efficient implementation. The presented case studies include implementations of a continuous-time, agent-based migration model and a comparative performance study based on an extended SIR model of infection spread, allowing us to draw conclusions about the impact of different design choices on efficiency. [ABSTRACT FROM AUTHOR]

To effectively manage complex human-environment fisheries systems, it is necessary to understand the psychology of fisher agents. While bio-economic models typically provide simple, abstract approaches for human behaviour (e.g., fully informed profit maximisers), fisher agents are of course neither simple nor perfect. Imperfections of learning, memory, and information availability, combined with the diversity of value preferences within populations, can lead to substantial deviations and unanticipated effects of interventions. This paper presents a computational model of fisher agents’ decision-making that draws on theoretical and empirical psychological insights to enrich this critical component. The model includes mechanisms for information integration (learning), social comparisons, and thresholds for economic satisfaction. In offering this enriched account, the model captures how fishers may adapt behaviourally given changes in policy, economic conditions, or social pressures. Furthermore, the model can be parameterised to capture the effects of different socio-cultural contexts can be simulated. The model of fisher agents has been implemented as part of POSEIDON (an agent-based fisheries management model), showing that fishers imbued with the model learn and adapt when responding dynamically to changing conditions. The model is thus demonstrated in a fisheries environment, but we discuss how its architecture could be implemented for simulation in other human-environment systems, such as designing policies to combat the human-environment problems. [ABSTRACT FROM AUTHOR]

Some authors claim that minimal models have limited epistemic value (Fumagalli, 2016; Grüne-Yanoff, 2009a). Others defend the epistemic benefits of modelling by invoking the role of robustness analysis for hypothesis confirmation (see, e.g., Levins, 1966; Kuorikoski et al., 2010) but such arguments find much resistance (see, e.g., Odenbaugh & Alexandrova, 2011). In this paper, we offer a Bayesian rationalization and defence of the view that robustness analysis can play a confirmatory role, and thereby shed light on the potential of minimal models for hypothesis confirmation. We illustrate our argument by reference to a case study from macroeconomics. At the same time, we also show that there are cases in which robustness analysis is detrimental to confirmation. We characterize these cases and link them to recent investigations on evidential variety (Landes, 2020b, 2021; Osimani and Landes, forthcoming). We conclude that robustness analysis over minimal models can confirm, but its confirmatory value depends on concrete circumstances. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this article is to draw attention to the specificity of emergent phenomena in organization and management, the study of which requires an approach derived from complex adaptive systems. Design/methodology/approach: The article uses a critical analysis of research on emergent phenomena in organizations from the perspective of complex social systems. This analysis made it possible to distinguish types of emergence in organization and management depending on the type of agents (members) and interactions that take place in the organization and indicated the causes of emergent phenomena. Findings: The article creates a research framework for complex emergent phenomena and shows how to use computer simulation to study complex emergent phenomena in organizations. Originality/value: A relatively new direction of research are analyzes within the theory of complex adaptive systems, the aim of which is to model and explain the behavior of systems of interconnected objects based on knowledge about the laws of individual elements (at the local level) and the structure of their connections. Therefore, it seems interesting to use computer tools developed based on complexity theory, especially agent-based models, as proposed in this article to study emergent phenomena in organizations. Since there are many approaches and ways of using computer simulations to study complex adaptation phenomena, a method of creating a simulation model and a method of conducting research using it were proposed. [ABSTRACT FROM AUTHOR]

This paper advocates the use of compositional specifications based on formal languages as a means of modelling and analysing sophisticated collective behaviour in natural systems. With the use of appropriate linguistic constructs, models can be developed that are both compact and intuitive, and can be easily refined and extended in small steps. Automated workflows can be implemented on top of this methodology to provide quick feedback, enabling rapid design iterations. To support our argument, we present three examples from the natural world, focusing on flocks of birds and colonies of ants, which feature well-known examples of emergent behaviour in collective adaptive systems. We use an agent-based language to develop simple models that aim at capturing these collective phenomena, and discuss the specific language constructs that we use in the process. Then, we adapt an existing verification tool for the language to simulate our models, and show that our simulations do display emergent behaviour. [ABSTRACT FROM AUTHOR]

Many solid tumors are characterized by a dense extracellular matrix (ECM) composed of various ECM fibril proteins. These proteins provide structural support and a biological context for the residing cells. The reciprocal interactions between growing and migrating tumor cells and the surrounding stroma result in dynamic changes in the ECM architecture and its properties. With the use of advanced imaging techniques, several specific patterns in the collagen surrounding the breast tumor have been identified in both tumor murine models and clinical histology images. These tumor-associated collagen signatures (TACS) include loosely organized fibrils far from the tumor and fibrils aligned either parallel or perpendicular to tumor colonies. They are correlated with tumor behavior, such as benign growth or invasive migration. However, it is not fully understood how one specific fibril pattern can be dynamically remodeled to form another alignment. Here, we present a novel multi-cellular lattice-free (MultiCell-LF) agent-based model of ECM that, in contrast to static histology images, can simulate dynamic changes between TACSs. This model allowed us to identify the rules of cell–ECM physical interplay and feedback that guided the emergence and transition among various TACSs. [ABSTRACT FROM AUTHOR]

Traffic in urban areas contributes significantly to congestion and air pollution, which contributes to climate change issues and causes economic losses and fuel wastage. Agent-based models have significant advantages for analyzing urban transportation and its sustainability. The main objective of this paper is to provide a critical review of research on agent-based models for traffic simulation in urban areas. This article reviews the literature on the subject and examines earlier case studies that dealt with agent-based models for micro-mobility and traffic simulation considering six criteria. The study analyzes multiple publications obtained from databases such as Google Scholar, Scopus, and Web of Science. These publications span from 2014 to 2022 and are scrutinized to fulfill the stated objectives. Furthermore, a thorough critical evaluation is performed on a chosen set of 16 publications. The research also proposes traffic simulation tools based on insights gathered from case studies. Further, it discusses how to choose a decent data set through a balanced and objective summary of study findings on the topic and recommends future work in this topic.Article Highlights: This paper discusses computational models that can be used to understand and control traffic. This paper discusses how various studies have explored using the mentioned models to manage urban traffic using scooters, bikes etc. This paper reviews how computer simulations are useful for dealing with different kinds of traffic [ABSTRACT FROM AUTHOR]

Agent-based evacuation models provide useful data of the evacuation process, but they are not primarily designed for use during an emergency. The paper aims to test predicting RSET using a surrogate ML model trained on a simulation dataset with 60 samples. A total of 9 machine learning algorithms were tested on 3 simple geometries: bottleneck, stairway and walkway. A set of 7 spatial features was used to train the surrogate models. The results showed a relatively good ability of Artificial Neural Network to learn in scenarios involving bottlenecks and stairways, with an R2: 0.99 on the testing dataset. In the walkway scenario, all models experienced a significant drop in performance, with Gradient Boost performing the best (R2: 0.92). The paper demonstrated ability to generalize effectively in bottleneck-type tasks with training on a relatively small dataset containing spatial parameters obtainable in real-time from camera systems.

Abstract Traffic in urban areas contributes significantly to congestion and air pollution, which contributes to climate change issues and causes economic losses and fuel wastage. Agent-based models have significant advantages for analyzing urban transportation and its sustainability. The main objective of this paper is to provide a critical review of research on agent-based models for traffic simulation in urban areas. This article reviews the literature on the subject and examines earlier case studies that dealt with agent-based models for micro-mobility and traffic simulation considering six criteria. The study analyzes multiple publications obtained from databases such as Google Scholar, Scopus, and Web of Science. These publications span from 2014 to 2022 and are scrutinized to fulfill the stated objectives. Furthermore, a thorough critical evaluation is performed on a chosen set of 16 publications. The research also proposes traffic simulation tools based on insights gathered from case studies. Further, it discusses how to choose a decent data set through a balanced and objective summary of study findings on the topic and recommends future work in this topic.

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 Objective This paper aims to provide a comprehensive overview of the current state of agent-based models, focusing specifically on their application in urban transportation research. It identifies research gaps and challenges while outlining the key potential directions for future research. Methodology To conduct this study, a bibliometric analysis has been performed on 309 documents obtained from the Scopus database. The resulting clustering analysis has been further supplemented with content analysis. Findings The analysis reveals the existence of nine distinct clusters representing a wide range of research methods and problem interpretations in the field. In-depth examination of selected publications within each cluster has helped to identify key challenges faced by agent-based modelling approaches. These challenges include enhancing computing efficiency, developing unified calibration and validation methods, ensuring reproducibility of work, and incorporating various modules or frameworks into models to accurately replicate the complexities of the transport system and travel behaviour within specific application contexts.

We investigate an agent-based model for the emergence of corruption in public contracts. There are two types of agents: business people and public servants. Both business people and public servants can adopt two strategies: corrupt or honest behavior. Interactions between business people and public servants take place through defined payoff rules. Either type of agent can switch between corrupt or honest strategies by comparing their payoffs after interacting. We measure the level of corruption in the system by the fractions of corrupt and honest agents for asymptotic times. We study the effects of the group size of the interacting agents, the dispersion with respect to the average salary of the public servants, and a parameter representing the institutional control of corruption. We characterize the fractions of honest and corrupt agents as functions of these variables. We construct phase diagrams for the level of corruption in the system in terms of these variables, where three collective states can be distinguished: i) a phase where corruption dominates; ii) a phase where corruption remains in less than 50% of the agents; and iii) a phase where corruption disappears. Our results indicate that a combination of large group sizes of interacting servants and business people and small dispersion of the salaries of public servants, contributes to the decrease of systemic corruption in public contracts.

Decades of research seek to understand how people form perceptions of risk by modeling either individual-level psychological processes or broader social and organizational mechanisms. Yet, little formal theoretical work has focused on the interaction of these 2 sets of factors. In this paper, I contribute to closing this gap by modifying a psychologically rich individual model of probabilistic reasoning to account for the transmission and collective emergence of risk perceptions. Using data from 357 individuals, I present experimental evidence in support of my main building assumptions and demonstrate the empirical validity of my model. Incorporating these results into an agent-based setting, I simulate over 1.5 billion social interactions to analyze the emergence of risk perceptions within organizations under different information frictions (i.e., limits on the availability and precision of social information). My results show that by focusing on information quality (rather than availability), groups and organizations can more effectively boost the accuracy of their emergent risk perceptions. This work offers researchers a formal framework to analyze the relationship between psychological and organizational factors in shaping risk perceptions.

AbstractAcross contemporary and historical human groups, there is variation in how cultures interact with the environment and this variation is inherited through culture. Some human-environment relationships maximize group fitness relative to other relationships. Thus, there is heritable, fitness-associated variation in human-environment interactions and so human-environment interactions may be understood to be an evolved trait. However, one fitness-maximizing human-environment relationship could be unsustainable resource extraction. Anthroecological theory proposes that human cultures and genotypes have been selected for unsustainability, although several mechanisms have been proposed for how this selection may have worked. Here, I extend anthroecological theory and argue that energy inheritances are critical to understanding the evolution of human unsustainability and the human-environment relationship. I develop an agent-based model to illustrate the hypotheses and find that the ability to pass energy to successive generations has impacts on sustainability. Simulations in which energy cannot be passed to subsequent generations are more stable and sustainable than those that can pass energy to offspring, but simulations in which energy cannot be inherited evolve higher reproductive rates than those with energy inheritance. A significant loss of diversity occurs in the simulations and I tentatively compare this loss to the cultural hegemony associated with historic colonization.

Agent-based modelling allows researchers to build artificial pastoral systems that are spatially explicit and allow for the examination of complex interactions between households, herds, and rangelands over long time periods. However, agent-based modelling also necessarily reduces the complexity of the pastoral systems. The question that we examine in this paper is how researchers model pastoral systems and what artificial pastoral systems they create. To answer that question, we systematically reviewed 35 agent-based modelling studies of pastoral systems. We examined how the studies describe the focal pastoral system, how the focal system is represented in a conceptual model, implemented in computer code, and how it emerges as an artificial pastoral system from the simulations. Our review indicates that most models are built by interdisciplinary teams, integrated into empirical studies of pastoral systems, and use a specific pastoral system as its focal system. The research problems explored in the models range from resource management, wealth dynamics, herd demography, sustainability, adaptation, mobility, and conflict. The artificial pastoral systems that emerge from these agent-based modelling studies mostly confirm current theoretical understandings that are based on empirical studies of pastoral systems. There are a few emergent patterns that have not been validated extensively in empirical studies. We conclude with a discussion of the theoretical, methodological, and practical implications of using agent-based models to create artificial pastoral systems. [ABSTRACT FROM AUTHOR]

Governments worldwide are investing in innovative transport technologies to foster their development and widespread adoptions. Since accurate predictions are essential for evaluating public policies, great efforts have been devoted to forecast the potential demand and adoption times of these innovations. However, this proves to be challenging, and it often fails to deliver accurate predictions. Learning a lesson to guide future work is critical but difficult because forecast figures depend on modelling methods and assumptions, and exhibit a great variability in methodologies, data and contexts. This paper provides a critical review of the models and methods employed in the literature to forecast the demand for electric vehicles (EVs), with a focus on the methods for incorporating choice behaviour into diffusion modelling. The review complements and extends previous works in three ways: (1) it focuses specifically on the ways in which fuel type choice has been incorporated into diffusion models or vice-versa; (2) it includes a discussion on forecast accuracy, contrasting the predictions with the actual figures available and estimating an average root mean square error and (3) it compares models and methods in terms of their strengths and limitations, and their implications in forecasting accuracy. In doing that, it also contributes discussing the literature published between 2019 and 2021. The analysis shows that EV demand estimation requires solving the non-trivial issue of jointly modelling the factors that induce diffusion in a social network and the instrumental and psychological elements that might favour household adoption considering the available alternatives. Mixed models that integrate disaggregate micro-simulation tools to capture social interaction and discrete choice models for individual behaviour appear as an interesting approach, but like almost all methods analysed failed to deliver satisfactory results or accurate predictions even when using sophisticated modelling techniques. Further improvement in various components is still needed, in particular in the input data, which regardless of the method used, is key to the accuracy of any forecasting exercise. [ABSTRACT FROM AUTHOR]