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In the domain of participatory research and co-creation, understanding the dynamic interplay between group cohesion and diversity is pivotal for fostering innovation. While diversity fuels the generation of novel ideas, cohesion ensures these ideas are effectively synthesized and implemented. This study aims to explore the nuanced role that facilitators play in navigating the balance between cohesion and diversity, particularly in groups characterized by pronounced faultlines. Employing agent-based modeling, the study examines how facilitators affect the cohesion-diversity nexus. The findings reveal a complex facilitator's paradox. While facilitative actions can successfully enhance group cohesion by mitigating the negative effect of faultlines, such efforts often inadvertently reduce the within-group diversity that is crucial for sparking innovative outcomes. These findings challenge the conventional view of faultlines as merely divisive and underscore the intricate role of facilitators in modulating group dynamics. This research discusses a novel framework for dynamic facilitation strategies, emphasizing the need for facilitators to skillfully balance cohesion and diversity. This framework not only enriches the theoretical understanding of group facilitation but also offers practical insights for optimizing collaborative innovation in diverse settings. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'. [ABSTRACT FROM AUTHOR]

Currently, there are increasing attempts to better involve citizens in political decision processes. A successful approach in that regard has been participatory budgeting (PB), which allows citizens to propose projects and then decide how to distribute a given budget over them. Meanwhile, the literature on collective intelligence (CI) has also shown the ability of groups to solve complex problems. Thus, by combining CI and PB, it should be possible for citizens to identify problems and create their own solutions. In this article, we study this possibility by using agent-based models. Specifically, we first show that a system combining CI and PB produces solutions that strongly penalize minorities if the solution quality depends on group size. Then, we introduce an approach that can overcome this issue. Indeed, by using a common knowledge base for the storage of partial solutions, the quality of solutions of minorities can benefit from the work of the majority, thereby promoting fairness. Interestingly, this approach also benefits majorities, as the quality of their solutions is further improved by the work of the minorities, thus reaching better solutions for everyone. This stresses the potential and importance of an open innovation approach, which is committed to information sharing. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'. [ABSTRACT FROM AUTHOR]

Background: Traditional epidemiological models tend to oversimplify the transmission dynamics of Mycobacterium tuberculosis (M.tb) to replicate observed tuberculosis (TB) epidemic patterns. This has led to growing interest in advanced methodologies like agent-based modelling (ABM), which can more accurately represent the complex heterogeneity of TB transmission. Objectives: To better understand the use of agent-based models (ABMs) in this context, we conducted a systematic review with two main objectives: (1) to examine how ABMs have been employed to model the intricate heterogeneity of M.tb transmission, and (2) to identify the challenges and opportunities associated with implementing ABMs for M.tb. Search methods: We conducted a systematic search following PRISMA guidelines across four databases (MEDLINE, EMBASE, Global Health, and Scopus), limiting our review to peer-reviewed articles published in English up to December 2022. Data were extracted by two investigators using a standardized extraction tool. Prospero registration: CRD42022380580. Selection criteria: Our review included peer-reviewed articles in English that implement agent-based, individual-based, or microsimulation models of M.tb transmission. Models focusing solely on in-vitro or within-host dynamics were excluded. Data extraction targeted the methodological, epidemiological, and computational characteristics of ABMs used for TB transmission. A risk of bias assessment was not conducted as the review synthesized modelling studies without pooling epidemiological data. Results: Our search initially identified 5,077 studies, from which we ultimately included 26 in our final review after exclusions. These studies varied in population settings, time horizons, and model complexity. While many incorporated population heterogeneity and household structures, some lacked essential features like spatial structures or economic evaluations. Only eight studies provided publicly accessible code, highlighting the need for improved transparency. Authors' conclusions: ABMs are a versatile approach for representing complex disease dynamics, particularly in cases like TB, where they address heterogeneous mixing and household transmission often overlooked by traditional models. However, their advanced capabilities come with challenges, including those arising from their stochastic nature, such as parameter tuning and high computational expense. To improve transparency and reproducibility, open-source code sharing, and standardised reporting are recommended to enhance ABM reliability in studying epidemiologically complex diseases like TB. [ABSTRACT FROM AUTHOR]

Many nations are hoping to shift to less unsustainable transport systems via adoption of electric vehicles (EVs). Developing sufficient EV charging stations (EVCS) is central to widespread adoption and use. Co-locating private and public EVCS cords is a promising way to lower costs, increase access, and facilitate EVCS developments. This study explores potential sites for and estimates actual costs of co-locating public-private (PP) charging hubs across the City of Austin, reflecting demand and supply features. Existing EVCS sites, sizes, and charging speeds are used inside the agent-based POLARIS model for microsimulation of travel choices and EV use. Site design details are also provided. [ABSTRACT FROM AUTHOR]

Pedestrian movement has always been one of the main concerns for urban planning and design, but it has become more important within the sustainable development agenda, as walking is crucial to reducing urban emissions and fostering liveable cities. Therefore, urban planners need to take pedestrian movement into consideration as part of the workflow of planning and designing cities. This study outlines a comprehensive workflow tailored for urban planners. It proposes a hybrid model that integrates an agent-based model, which simulates the micro-scale movement of pedestrians in outdoor urban environments, with a network model, which predicts the aggregated pedestrian flows on a macro-scale. The hybrid model is applied to a pedestrian precinct in the city centre of Gothenburg, Sweden, and is compared to real-world measurements. The reasonable agreement between the simulation results and the real-world data supports the reliability of the proposed workflow, underscoring the model's ability to statistically predict pedestrian movement on a large scale and individually on a local scale. Furthermore, the model enables the analysis of flow distributions and movement restrictions and facilitates the analysis of different design scenarios and specific pedestrian behaviour. This functionality is valuable for urban design and planning practice, contributing to the optimisation of pedestrian flow dynamics. [ABSTRACT FROM AUTHOR]

A Viral Infection Risk Indoor Simulator (VIRIS) has been developed to quickly assess and compare mitigations for airborne disease spread. This agent-based simulator combines people movement in an indoor space, viral transmission modelling and detailed architectural design, and it is powered by topologicpy, an open-source Python library. VIRIS generates very fast predictions of the viral concentration and the spatiotemporal infection risk for individuals as they move through a given space. The simulator is validated with data from a courtroom superspreader event. A sensitivity study for unknown parameter values is also performed. We compare several non-pharmaceutical interventions (NPIs) issued in UK government guidance, for two indoor settings: a care home and a supermarket. Additionally, we have developed the user-friendly VIRIS web app that allows quick exploration of diverse scenarios of interest and visualisation, allowing policymakers, architects and space managers to easily design or assess infection risk in an indoor space. [ABSTRACT FROM AUTHOR]

In recent years we have gained insight into the impact of minimum unit pricing (MUP)—a legal floor price below which a given volume of alcohol cannot be sold—on population‐level reductions in alcohol sales, consumption and harm. However, several questions remain unanswered including how individual‐level purchasing changes impact the local economy (e.g., balance between on‐licence and off‐licence outlets), lead to long‐term population‐level trends (e.g., youth drinking) and social harms (e.g., violence). Agent‐based modelling captures heterogeneity, emergence, feedback loops and adaptive and dynamic features, which provides an opportunity to understand the nuanced effects of MUP. Agent‐based models (ABM) simulate heterogeneous agents (e.g., individuals, organisations) often situated in space and time that interact with other agents and/or with their environment, allowing us to identify the mechanisms underlying social phenomena. ABMs are particularly useful for theory development, and testing and simulating the impacts of policies and interventions. We illustrate how ABMs could be applied to generate novel insights and provide best estimates of social network effects, and changes in purchasing behaviour and social harms, due to the implementation of MUP. ABMs like other modelling approaches can simulate alternative implementations of MUP (e.g., policy intensity [£0.50, £0.60] or spatial scales [local, national]) but can also provide an understanding of the potential impact of MUP on different population groups (e.g., alcohol exposure of young people who are not yet drinking). Using ABMs to understand the impact of MUP would provide new insights to complement those from traditional epidemiological and other modelling methods. [ABSTRACT FROM AUTHOR]

Context: Invasive species cause widespread species extinction and economic loss. There is an increasing need to identify ways to efficiently target control efforts from local to regional scales. Objectives: Our goal was to test whether prioritizing managed habitat using different treatments based on spatial measures of connectivity, including graph-theoretic measures, can improve management of invasive species and whether the level of control effort affects treatment performance. We also explored how uncertainty in biological variables, such as dispersal ability, affects measures performance. Methods: We used a spatially-explicit, individual-based model (sIBM) based on the American bullfrog (Lithobates catesbeianus), a globally pervasive invasive species. Simulations were informed by geographic data from part of the American bullfrog’s non-native range in southeastern Arizona, USA where they are known to pose a threat to native species. Results: We found that total bullfrog populations and occupancy declined in response to all treatments regardless of effort level or patch prioritization methods. The most effective spatial prioritization was effort-dependent and varied depending on spatial context, but frequently a buffer strategy was most effective. Treatments were also sensitive to dispersal ability. Performance of treatments prioritizing habitat patches using betweenness centrality improved with increasing dispersal ability, while performance of eigenvalue centrality improved as dispersal ability decreased. Conclusions: With the careful application of connectivity measures to prioritize control efforts, similar reductions in invasive species population size and occupancy could be achieved with less than half the effort of sub-optimal connectivity measures at higher effort rates. More work is needed to determine if trait-based generalities may define appropriate connectivity measures for specific suites of dispersal abilities, demographic traits, and population dynamics. [ABSTRACT FROM AUTHOR]

Sound planning for urban mobility is a key facet of securing a sustainable future for our urban systems, and requires the careful and comprehensive assessment of its components, such as the status of the cities' public transportation network, and how urban planners should invest in developing it. We use agent-based modelling, a tried and true method for such endeavours, for studying the history, planned future works and possible evolution of the tram line network in the Greater Geneva region. We couple these models with knowledge graphs, in a way that both are able to mutually enrich each other. Results show that the information organisation powers of knowledge graphs are highly relevant for effortlessly recounting past events and designing scenarios to be directly incorporated inside the agent-based model. The model features all 5 tram lines from the current real-world network, servicing a total of 15 communes. In turn, the model is capable of replaying past events, predicting future developments and exploring user-defined scenarios. It also harnesses its self-organisation properties to autonomously reconstruct an artificial public transportation network for the region based on two different initial networks, servicing up to 29 communes depending on the scenario. The data gathered from the simulation is effortlessly imported back into the initial knowledge graphs. The artificial networks closely resemble their real-world counterparts and demonstrate the predictive and prescriptive powers of our agent-based model. They constitute valuable assets towards a comprehensive assessment of urban mobility systems, compelling progress for the agent-based modelling field, and a convincing demonstration of its technical capabilities. [ABSTRACT FROM AUTHOR]

The successful adoption of social innovations, such as renewable energy systems or pollution reduction plans in cities, depends, to a large extent, on the willingness and participation of the population in their development and implementation. We present an agent‐based model (ABM) to analyze the process of citizen acceptability of a social innovation that uses a variety of agents to represent individual citizens and relevant groups of citizens. Citizen agents make use of the HUMAT cognitive decision‐making model, based on psychosocial theories, to decide on their support for the social innovation considering how their needs will be satisfied if they decide to support (or not) the innovation project, and the influence exerted by the agents in their environment. The ABM was initially developed to represent the urban and transport planning superblock project in the city of Vitoria‐Gasteiz (Spain). The ABM simulations make it possible to study the evolution of public acceptance of social innovation, with the results providing insights to the social dynamics and individual factors that affect the acceptance of the project, enabling an evaluation of how to devise new policies that increase public acceptance. Sufficiently generic to be easily adaptable to different types of social innovations, the ABM is a powerful tool to explore different scenarios and design strategies that foster the acceptance and sustainable adoption of social innovations. [ABSTRACT FROM AUTHOR]

The second pillar of the European Union's (EU) Common Agricultural Policy (CAP) aims at supporting rural areas by meeting the economic, environmental and social challenges. To deal with these challenges, countries are faced with the question of selecting the best tools among a large set of policy instruments. The problem of choosing the best policy instruments is aggravated by the very heterogeneous character of the societal demands that differ among member countries with very different economic and institutional structures. This study aims to introduce the agent-based modelling platforms that have been widely used in the impact analysis of recent rural development policies in the EU in a comparative manner. It also aims to explain how the above-mentioned sources of heterogeneity are handled in these models. To achieve the stated objectives, the study first examines the historical development of rural development policies within the EU. Subsequently, it proceeds to analyse several agent-based platforms that have been employed for the purpose of assessing the impact of agricultural policies with respect to certain features such as integration of land market, modelling unit, decision rule, rules of exit, labour market and price formation. To conclude, it is observed that as the rural development policies are formulated on farmbasis and as farms have a heterogeneous structure within themselves, in addition, the expansion of databases and the development of empirical analysis tools and technologies have led to a shift in empirical analyses towards agent-based models. However, these modelling platforms still embody various problems, especially in terms of database adjustments and parameterization and calibration of the model. [ABSTRACT FROM AUTHOR]

Abstract Background Traditional epidemiological models tend to oversimplify the transmission dynamics of Mycobacterium tuberculosis (M.tb) to replicate observed tuberculosis (TB) epidemic patterns. This has led to growing interest in advanced methodologies like agent-based modelling (ABM), which can more accurately represent the complex heterogeneity of TB transmission. Objectives To better understand the use of agent-based models (ABMs) in this context, we conducted a systematic review with two main objectives: (1) to examine how ABMs have been employed to model the intricate heterogeneity of M.tb transmission, and (2) to identify the challenges and opportunities associated with implementing ABMs for M.tb. Search methods We conducted a systematic search following PRISMA guidelines across four databases (MEDLINE, EMBASE, Global Health, and Scopus), limiting our review to peer-reviewed articles published in English up to December 2022. Data were extracted by two investigators using a standardized extraction tool. Prospero registration: CRD42022380580. Selection criteria Our review included peer-reviewed articles in English that implement agent-based, individual-based, or microsimulation models of M.tb transmission. Models focusing solely on in-vitro or within-host dynamics were excluded. Data extraction targeted the methodological, epidemiological, and computational characteristics of ABMs used for TB transmission. A risk of bias assessment was not conducted as the review synthesized modelling studies without pooling epidemiological data. Results Our search initially identified 5,077 studies, from which we ultimately included 26 in our final review after exclusions. These studies varied in population settings, time horizons, and model complexity. While many incorporated population heterogeneity and household structures, some lacked essential features like spatial structures or economic evaluations. Only eight studies provided publicly accessible code, highlighting the need for improved transparency. Authors’ conclusions ABMs are a versatile approach for representing complex disease dynamics, particularly in cases like TB, where they address heterogeneous mixing and household transmission often overlooked by traditional models. However, their advanced capabilities come with challenges, including those arising from their stochastic nature, such as parameter tuning and high computational expense. To improve transparency and reproducibility, open-source code sharing, and standardised reporting are recommended to enhance ABM reliability in studying epidemiologically complex diseases like TB.

Abstract A Viral Infection Risk Indoor Simulator (VIRIS) has been developed to quickly assess and compare mitigations for airborne disease spread. This agent-based simulator combines people movement in an indoor space, viral transmission modelling and detailed architectural design, and it is powered by topologicpy, an open-source Python library. VIRIS generates very fast predictions of the viral concentration and the spatiotemporal infection risk for individuals as they move through a given space. The simulator is validated with data from a courtroom superspreader event. A sensitivity study for unknown parameter values is also performed. We compare several non-pharmaceutical interventions (NPIs) issued in UK government guidance, for two indoor settings: a care home and a supermarket. Additionally, we have developed the user-friendly VIRIS web app that allows quick exploration of diverse scenarios of interest and visualisation, allowing policymakers, architects and space managers to easily design or assess infection risk in an indoor space.

The second pillar of the European Union’s (EU) Common Agricultural Policy (CAP) aims at supporting rural areas by meeting the economic, environmental and social challenges. To deal with these challenges, countries are faced with the question of selecting the best tools among a large set of policy instruments. The problem of choosing the best policy instruments is aggravated by the very heterogeneous character of the societal demands that differ among member countries with very different economic and institutional structures. This study aims to introduce the agent-based modelling platforms that have been widely used in the impact analysis of recent rural development policies in the EU in a comparative manner. It also aims to explain how the above-mentioned sources of heterogeneity are handled in these models. To achieve the stated objectives, the study first examines the historical development of rural development policies within the EU. Subsequently, it proceeds to analyse several agent-based platforms that have been employed for the purpose of assessing the impact of agricultural policies with respect to certain features such as integration of land market, modelling unit, decision rule, rules of exit, labour market and price formation. To conclude, it is observed that as the rural development policies are formulated on farm-basis and as farms have a heterogeneous structure within themselves, in addition, the expansion of databases and the development of empirical analysis tools and technologies have led to a shift in empirical analyses towards agent-based models. However, these modelling platforms still embody various problems, especially in terms of database adjustments and parameterization and calibration of the model.

Sustainability in cities, and its accurate and exhaustive assessment, represent a major keystone of environmental sciences and policy making in urban planning. This study aims to provide methods for a reproducible, descriptive, predictive and prescriptive analysis of urban residential choices and mobility, which are key components of an urban system's sustainability. Using the DPSIR framework for building agent evolution rules, we design an agent-based model of the canton of Geneva, Switzerland. The model leverages real geographical data for the canton of Geneva and its public transportation network. The resulting simulations show the dynamics of the relocation choices of commuters, in terms of the function of their travel time by public transportation to their workplace. Results show that areas around the city centre are generally preferred, but high rent prices and housing availability may prevent most residents from relocating to these areas. Other preferred housing locations are distributed around major tram and train lines and where rent prices are generally lower. The model and its associated tools are capable of spatialising aggregated statistical datasets, inferring spatial correlations, and providing qualitative and quantitative analysis of relocation dynamics. Such achievements are made possible thanks to the efficient visualisation of our results. The agent-based modelling methodology represents an adequate solution for understanding complex phenomena related to sustainability in urban systems, which can be used as guidance for policy making. [ABSTRACT FROM AUTHOR]

Coral reefs, vital ecosystems supporting diverse marine life, are primarily shaped by the clonal expansion of coral colonies. Although the principles of coral clonal growth, involving polyp division for spatial extension, are well-understood, numerical modelling efforts are notably scarce in the literature. In this article, we present a parsimonious numerical model based on the cloning of polyps, using five key parameters to simulate a range of coral shapes. The model is agent-based, where each polyp represents an individual. The colony's surface expansion is dictated by the growth mode parameter (s), guiding the preferred growth direction. Varying s facilitates the emulation of diverse coral shapes, including massive, branching, cauliflower, columnar and tabular colonies. Additionally, we introduce a novel approach for self-regulatory branching, inspired by the intricate mesh-like canal system and internode regularity observed in Acropora species. Through a comprehensive sensitivity analysis, we demonstrate the robustness of our model, paving the way for future applications that incorporate environmental factors, such as light and water flow. Coral colonies are known for their high plasticity, and understanding how individual polyps interact with each other and their surroundings to create the reef structure has been a longstanding question in the field. This model offers a powerful framework for studying these interactions, enabling a future implementation of environmental factors and the possibility of identifying the key mechanisms influencing coral colonies' morphogenesis. [ABSTRACT FROM AUTHOR]

Retail distribution is essential for the growth of markets for local food. While online direct‐to‐consumer and local food hubs are becoming more sophisticated, the largest market for local foods remains the traditional intermediation (retailing) sector. We develop an agent‐based model to simulate the expansion, growth and profitability of retailers offering local foods across a landscape populated by consumers and competing retailers. We design a series of experiments to examine how changes in prices and assortment that include local and non‐local options in the fresh produce category impact store market share and profitability. We validate the model, and conduct our experiments, using household fresh produce expenditures data from a retail food‐delivery business in the Mid‐Atlantic region of the US. We find that retailers offering a larger assortment of local foods are able to sustain higher basket‐average retail prices and exhibit higher long‐run profits in comparison to retailers that do not carry local options. These results underscore the importance of local foods in a food retailer's price and assortment strategy. The key implication is that retailers need to be conscious not only of the breadth but also of the quality of their assortment in their pricing strategies. [ABSTRACT FROM AUTHOR]

Early Southern Mesopotamia shows a complex history of expansion of (irrigated) farming in relation to urban developments and changing landscapes. As a first step to study expanding irrigated farming system, an irrigation-related agent-based model was developed to explore farm(land)s and irrigation systems in relation to decision-making processes, both of farms and their farmlands (an agriculture unit) and collective decision-making processes for irrigation system management—especially sharing water between farms. The decision-making processes include options to move farms, expand the system, or start a new system, as these would be options available for Mesopotamian farmers as well. In this text, we report how model parameters contribute to the generation of various patterns of yields and expansion of farms and system. Additionally, the Gini coefficient (based on yields) is applied to estimate levels of inequality among farmers. Our results show how (1) human decision-making determines the level of influence of and benefits for farms, as well as the overall irrigation system; (2) Gini values effectively capture the degree of inequality in yields among farms based on water availability; and (3) our model is a suitable base for further study, by incorporating additional agents into the irrigation system and expanding the spatial–temporal scales of the irrigated landscapes, to reach a more comprehensive understanding of the evolutionary dynamics of irrigation systems in Southern Mesopotamia. [ABSTRACT FROM AUTHOR]

Emergency departments (EDs) are a critical entry gate for infectious agents into hospitals. In this interdisciplinary study, we explore how infection prevention and control (IPC) architectural interventions mitigate the spread of emerging respiratory pathogens using the example of SARS-CoV-2 in a prototypical ED. Using an agent-based approach, we integrated data on patients' and healthcare workers' (HCWs) routines and the architectural characteristics of key ED areas. We estimated the number of transmissions in the ED by modelling the interactions between and among patients and HCWs. Architectural interventions were guided towards the gradual separation of pathogen carriers, compliance with a minimum interpersonal distance, and deconcentrating airborne pathogens (higher air exchange rates (AERs)). Interventions were epidemiologically evaluated for their mitigation effects on diverse endpoints. Simulation results indicated that higher AERs in the ED (compared with baseline) may provide a moderate level of infection mitigation (incidence rate ratio (IRR) of 0.95 (95% confidence interval (CI) 0.93–0.98)) while the overall burden decreased more when rooms in examination areas were separated (IRR of 0.78 (95% CI 0.76–0.81)) or when the size of the ED base was increased (IRR of 0.79 (95% CI 0.78–0.81)). The reduction in SARS-CoV-2-associated nosocomial transmissions was largest when architectural interventions were combined (IRR of 0.61 (95% CI 0.59–0.63)). These modelling results highlight the importance of IPC architectural interventions; they can be devised independently of profound knowledge of an emerging pathogen, focusing on technical, constructive, and functional components. These results may inform public health decision-makers and hospital architects on how IPC architectural interventions can be optimally used in healthcare premises. [ABSTRACT FROM AUTHOR]

Water pricing is an economic instrument traditionally used to reduce water demand. However, its effective implementation requires knowledge of the extent to which users reduce water consumption with increasing water prices. The price elasticity of water demand has been estimated using econometric regression, which relies on cross-sectional and time-series water data. As an alternative, we propose the use of agent-based modelling, which does not require reliable historical data on water prices and consumption and enables the simulation of multiple scenarios with different consumer profiles, behaviour profiles and water price changes, thereby allowing comprehensive understanding of price elasticity estimates. To illustrate the potential use of agent-based modelling for the estimation of water demand price elasticity, we performed an empirical application to a residential area in Chile. Price elasticity estimates ranged from −0.0159 to −0.1036 (mean −0.0250), indicating that residential water consumption is inelastic to price changes. This result is consistent with previous findings. Agent-based modelling is an alternative for the ex-ante assessment of the potential effectiveness of water pricing policies intended to reduce residential water demand. [ABSTRACT FROM AUTHOR]

The importance of knowledge for organizational success is widely recognized, leading managers to leverage knowledge actively. Within knowledge transfer, the Absorptive Capacity (ACAP) of Knowledge Recipients (KR) emerges as an unresolved barrier. ACAP is the dynamic capability to absorb knowledge and surpass the aggregation of individual ACAP within an organization. However, more research is needed on individual-level ACAP and its implications for bridging the gap between individual and organizational knowledge transfer. To address this gap, this study employs Agent-Based Modeling (ABM) as a simulation method to replicate individual ACAP within an organization, facilitating the examination of knowledge transfer dynamics. ABM allows for the detailed analysis of interactions between individual KRs and the organizational environment, revealing how uninterrupted time and other factors influence knowledge absorption. The implications of the study are that ABM provides specific insights into how individual ACAP affects organizational learning and performance, emphasizing the importance of uninterrupted time for KR to achieve optimal knowledge exploitation and highlighting the need for organizational practices and policies that foster environments conducive to knowledge absorption. [ABSTRACT FROM AUTHOR]

We provide an experimental design to study how estimates of the distance deterrence parameter in a standard doubly-constrained gravity model respond to different patterns of spatial structure. The design is defined as an agent-based simulation framework that simultaneously considers the response of commuting and migration to changes in the spatial structure, accounting for labor and housing market issues. The agents are equipped with a utility function and are expected to maximize their utilities corresponding to their disposable income net of commuting costs and house value. We study how the estimates of the distance deterrence parameter respond to where the central business district is located, to the clustering of local and basic sector jobs, to changes in the road transportation network, and to the compactness in the system of towns in the geography. [ABSTRACT FROM AUTHOR]

The integration of direct feedback alignment (DFA) into neural networks presents a paradigm shift in computational models, enhancing situational awareness within professional settings. This paper explores the application of DFA in agent-based computational models, demonstrating its efficiency and biological plausibility over traditional backpropagation methods. The research highlights the significant impact of direct feedback alignment on reinforcing situational awareness, evidenced through comprehensive simulations that show marked improvements in agents' ability to effectively navigate and comprehend complex work environments. The study suggests that direct feedback alignment revolutionizes neural network learning processes and significantly enhances cognitive aspects critical to employee safety and operational efficiency. It lays the groundwork for future investigations into the integration of neural computation techniques with organizational psychology and behavior, offering a new perspective on fostering safer, more aware, and efficient workplace environments. The potential application of direct feedback alignment across various professional scenarios opens new avenues for research in computational neuroscience, cognitive psychology, and organizational behavior, with a focus on optimizing human-environment interactions in complex systems. [ABSTRACT FROM AUTHOR]

We propose a novel micro-level and spatially explicit agent-based modeling framework dubbed TranEpiSim to model the spread of infectious diseases through micromobility systems and a baseline population in an urban area. A case study is conducted on the Chicago City public bikesharing system to demonstrate the ability of the model to isolate the role of transportation as a vector and assess the efficacy of the model. Results show that the emergence of viral disease through micromobility transportation is possible, but the overall impact of the system on the disease dynamics in a worst-case scenario, especially with the current size of the system, is rather small. The proposed model offers a comprehensive approach to estimate the impact of micromobility (and more generally, specific transportation modes) on disease spread by considering infections that occur both at destinations and in/on transportation vehicles and infrastructure. The spatial pattern for the risk of exposure shows a higher risk in the central business district and north of it, where most of the shared bike transportation occurs. Because of its intrinsic features, the proposed framework is uniquely placed to assess the efficacy of interventions and make trade-offs between competing scenarios when dealing with epidemics. [ABSTRACT FROM AUTHOR]

Tsunami vertical evacuation (TVE) buildings have the potential to save many human lives in countries exposed to near-field tsunamis. Up to now, TVE research has examined three main topics separately: shelter siting, building benchmarks, and decision-making by evacuees. This study aims to integrate these topics to develop more comprehensive TVE planning frameworks. To this, we examined a catastrophic tsunami evacuation scenario in Viña del Mar, Chile. First, we developed an agent-based model to estimate potential human fatalities in the case of a fully horizontal evacuation. Second, we designed an immersive VR experience, which we applied to a sample of 151 people in the city to collect their potential TVE decision-making, which allowed us to identify 11 buildings that could serve as TVE shelters. Lastly, we incorporated this new evacuation system into the former agent-based model to assess the potential impact of vertical evacuation. Our findings showed that while fatalities ranged from 50 to 72% of the population in the fully horizontal scenario, the inclusion of TVE buildings might decrease human losses by 6.5–13.7%. Complementary questionnaires administered to participants highlighted their lack of previous experience in real-world evacuations (only 45.69% had previously experienced a tsunami-related evacuation process), as well as their knowledge about how to proceed in the case of a future emergency (62.91% declared that they knew where to go during an evacuation). [ABSTRACT FROM AUTHOR]

The introduction of Marine Non-Indigenous Species (NIS) poses a significant threat to global marine biodiversity and ecosystems. To mitigate this risk, the Ballast Water Management Convention (BWMC) was adopted by the UN International Maritime Organisation (IMO), setting strict criteria for discharges of ballast water. However, the BWMC permits exemptions for shipping routes operating within a geographical area, known as a Same-Risk-Area (SRA). An SRA can be established in areas where a risk assessment (RA) can conclude that the spread of NIS via ballast water is low relative to the predicted natural dispersal. Despite the BWMC's requirement for RAs to be based on modelling of the natural dispersal of NIS, no standard procedures have been established. This paper presents a methodology utilizing biophysical modelling and marine connectivity analyses to conduct SRA RA and delineation. Focusing on the Kattegat and Øresund connecting the North Sea and Baltic Sea, we examine two SRA candidates spanning Danish and Swedish waters. We provide an example on how to conduct an RA including an RA summary, and addressing findings, challenges, and prospects. Our study aims to advance the development and adoption of consistent, transparent, and scientifically robust SRA assessments for effective ballast water management. [ABSTRACT FROM AUTHOR]

The goal of the paper is to propose an abstract but formalised model of how Schwartz higher order values may influence individual decisions on sharing an individual effort among alternative economic activities. Subsequently, individual decisions are aggregated into the total (collective) economic output, taking into account interactions between the agents. In particular, we explore the relationship between individual higher order values: Self-Enhancement, Self-Transcendence, Openness to Change and Conservation – measured according to Schwartz's universal human values theory – and individual and collective economic performance, by means of a theoretical agent based model. Furthermore, based on empirical observations, Openness to Change (measured by the population average in the case of collective output) is positively associated with individual and collective output. These relations are negative for Conservation. Self-Enhancement is positively associated with individual output but negatively with collective output. In case of Self-Transcendence, this effect is opposite. The model provides the potential explanations, in terms of individual and population differences in propensity for management, willingness to change and skills (measured by an educational level) for the empirically observed relations between Schwartz higher order values and individual and collective output. We directly calibrate the micro-level of the model using data from the ninth round of the European Social Survey (ESS9) and present the results of numerical simulations. [ABSTRACT FROM AUTHOR]

The rapid dissemination of misinformation has become one of the most pressing issues of the modern world, as social media platforms enable misinformation to be spread incredibly quickly and unpredictably. To combat the misinformation epidemic, many misinformation models have been developed to simulate and study the spread of misinformation so that it may be understood and mitigated more effectively. While many survey papers review misinformation holistically, there is a gap in the literature for a survey of models in particular, mainly because the field is still relatively young. To overcome this gap, we compile a dataset of 28 papers, each using its model to study misinformation. In reviewing trends amongst these models, we find that a. there is a shortage of papers that evaluate countermeasures against misinformation or take their influence into account, and b. platforms other than Twitter/X are underused as sources for model validation data, considering their real-world importance. [ABSTRACT FROM AUTHOR]

Autonomous systems, such as drones and rescue robots, are increasingly used during emergencies. They deliver services and provide situational awareness that facilitate emergency management and response. To do so, they need to interact and cooperate with humans in their environment. Human behaviour is uncertain and complex, so it can be difficult to reason about it formally. In this article, we propose IDEA: an adaptive software architecture that enables cooperation between humans and autonomous systems, by leveraging the social identity approach. This approach establishes that group membership drives human behaviour. Identity and group membership are crucial during emergencies, as they influence cooperation among survivors. IDEA systems infer the social identity of surrounding humans, thereby establishing their group membership. By reasoning about groups, we limit the number of cooperation strategies the system needs to explore. IDEA systems select a strategy from the equilibrium analysis of game-theoretic models that represent interactions between group members and the IDEA system. We demonstrate our approach using a search-and-rescue scenario, in which an IDEA rescue robot optimises evacuation by collaborating with survivors. Using an empirically validated agent-based model, we show that the deployment of the IDEA system can reduce median evacuation time by 13.6%. [ABSTRACT FROM AUTHOR]

Minimally Intelligent Agent-Based Manufacturing concerns the provision of agents with the minimal intelligence required to autonomously negotiate and broker work across machines and jobs. The minimal intelligence of agents can be updated in real-time and when coupled with technologies, such as Additive Manufacturing (AM), robotics, automated inventory, and Computer Numerical Control (CNC) affords highly flexible, re-configurable, resilient and responsive systems. The concept has become topical as changes to global supply chains are necessitating a shift to responsive and resilient on-demand manufacturing. However, understanding and characterising how these systems operate under various demand profiles is required to support operators operating these future systems. This paper reports a numerical study into the operating behaviour of minimally intelligent agent-based manufacturing systems operating across the Average Demand Interval and Coefficient of Variation (ADI-CV) demand state space. An established state space used in spare part supply chain research. The results show minimally intelligent manufacturing systems are stable across much of the ADI-CV demand space 85%, 63% and 76–84% for constant, triangular and lognormal manufacturing time distribution profiles respectively. The stability is largely independent of the combination of intelligence. Rather, the combination of intelligence impacts job Time-in-System and system response.

Fitting statistical models to aggregate data is still the dominant approach in many demographic and biodemographic applications. Although these macro-level models have proven useful for a variety of tasks, they often have no demographic interpretation. Individual-level modelling, on the other hand, offers a deeper understanding of the mechanisms underlying observed patterns. Their parameters represent quantities in the real world, instead of pure mathematical abstractions. However, estimating these parameters using real-world data has remained a challenge. The approach we introduce in this article attempts to overcome this limitation. Using a likelihood-free inference technique, we show that it is possible to estimate the parameters of a simple but demographically interpretable individual-level model of the reproductive process by exclusively relying on the information contained in a set of age-specific fertility rates. By estimating individual-level models from widely available aggregate data, this approach can contribute to a better understanding of reproductive behaviour and its driving mechanisms, bridging the gap between individual-level and population-level processes. We illustrate our approach using data from three natural fertility populations.

Pedestrian movement has always been one of the main concerns for urban planning and design, but it has become more important within the sustainable development agenda, as walking is crucial to reducing urban emissions and fostering liveable cities. Therefore, urban planners need to take pedestrian movement into consideration as part of the workflow of planning and designing cities. This study outlines a comprehensive workflow tailored for urban planners. It proposes a hybrid model that integrates an agent-based model, which simulates the micro-scale movement of pedestrians in outdoor urban environments, with a network model, which predicts the aggregated pedestrian flows on a macro-scale. The hybrid model is applied to a pedestrian precinct in the city centre of Gothenburg, Sweden, and is compared to real-world measurements. The reasonable agreement between the simulation results and the real-world data supports the reliability of the proposed workflow, underscoring the model’s ability to statistically predict pedestrian movement on a large scale and individually on a local scale. Furthermore, the model enables the analysis of flow distributions and movement restrictions and facilitates the analysis of different design scenarios and specific pedestrian behaviour. This functionality is valuable for urban design and planning practice, contributing to the optimisation of pedestrian flow dynamics.

Background Agent-based modelling provides an appealing methodological choice for simulating human behaviour and decisions. The currently dominant approaches based on static transition rates or unverified assumptions are restrictive, and could be enhanced with insights from cognitive experiments on actual decision making. Here, one common concern is that standard surveys or experiments may lack ecological validity, limiting the extent to which research findings can be generalised to real-life settings. For complex, highly emotive decision-making scenarios, such as those related to irregular migration, the typically used short, methodical survey questions may not appropriately map onto complex real-world decisions of interest. Immersive contexts may offer more accurate representations of reality, potentially enhancing the usefulness of experimental information in multi-disciplinary modelling endeavours. Methods This preregistered study, aimed directly at examining the effect of immersion on risk-taking in the context of migration decisions, and indirectly at informing a multi-disciplinary construction of an agent-based model of migration, presents a choice-based interactive fiction game in which players make migration decisions to advance through a story. Participants (N = 1000 Prolific users) took part in one of four experimental conditions, three involving different renditions of the game attempting to create immersion, with the last condition presenting the decisions in standard survey format. Results Although addressing the lack of ecological validity in survey data is important for improving agent-based modelling methodology, the experimental design used to tackle this issue, while responding directly to modelling needs, proved too complex. The created experimental conditions ended up too distinct from each other, involving stimuli that differed in quantity and content. This introduced several unintended and uncontrolled confounds, making it impossible to meaningfully interpret the results of this experiment on its own. Our results act as a cautionary tale for agent-based modellers, highlighting that the modelling needs should not override the principles of experimental design, and provide motivation for more rigorous research on this topic.

Simulation models of social processes may require data that are not readily available, have low accuracy, are incomplete or biased. The paper presents a formal process for collating, assessing, selecting, and using secondary data as part of creating, validating, and documenting an agent-based simulation model of a complex social process, in this case, asylum seekers’ journeys to Europe. The process starts by creating an inventory of data sources, and the associated metadata, followed by assessing different aspects of data quality according to pre-defined criteria. As a result, based on the typology of available data, we are able to produce a thematic map of the area under study, and assess the uncertainty of key data sources, at least qualitatively. We illustrate the process by looking at the data on Syrian migration to Europe in 2011–21. In parallel, successive stages of the development of a simulation model allow for identifying key types of information which are needed as input into empirically grounded modelling analysis. Juxtaposing the available evidence and model requirements allows for identifying knowledge gaps that need filling, preferably by collecting additional primary data, or, failing that, by carrying out a sensitivity analysis for the assumptions made. By doing so, we offer a way of formalising the data collection process in the context of model-building endeavours, while allowing the modelling to be predominantly question-driven rather than purely data-driven. The paper concludes with recommendations with respect to data and evidence, both for modellers, as well as model users in practice-oriented applications.