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Incentive contracts often do not govern all task elements for which an employee is responsible. Prior research, particularly in the tradition of principal-agent theory, has studied incomplete incentive contracts as multi-task problems focusing on how to motivate the employee to incur effort for a not-contracted task element. Thus, emphasis is on the "vertical" relation between superior and subordinate, where both are modeled as gifted economic actors. This paper takes another perspective focusing on the "horizontal" interferences of—contracted and not-contracted—task elements across various employees in an organization and, hence, on the complexity of an organization's task environment. In order to disentangle the interactions among tasks from agents' behavior, the paper pursues a minimal intelligence approach. An agent-based simulation model based on the framework of NK fitness landscapes is employed. In the simulation experiments, artificial organizations search for superior performance, and the experiments control for the complexity of the task environment and the level of contractual incompleteness. The results suggest that the complexity of the task environment in terms of interactions among task elements may considerably shape the effects of incomplete incentive contracts. In particular, the results indicate that moderate incompleteness of incentive contracts may be beneficial with respect to organizational performance when intra-organizational complexity is high. This is caused by stabilization of search resulting from incomplete contracts. Moreover, interactions may induce that the not-contracted task elements could serve as means objectives, i.e., contributing to achieving contracted task elements. [ABSTRACT FROM AUTHOR]

Optimizing Resource Allocation in Fire Departments (RAFD) is crucial for enhancing Fire Protection Services (FPS) and ultimately saving lives. Efficient RAFD ensures that fire departments have the necessary resources to respond effectively to emergencies. This paper presents a method for optimizing RAFD based on performance assessment results, examining its impact on Fire Department (FD) efficiency in Portugal. Evaluating data from 353 FDs, two RAFD optimization methods were assessed: one adhering to Portuguese regulations and constraints, such as budget allocation limitations, and another without such constraints. Integrating a slack-based data envelopment analysis model and mixed-integer linear programming, the study found that incorporating FD efficiency scores in RAFD improved overall efficiency at national, district, and FD levels. While adherence to Portuguese regulations led to balanced resource allocation and a 4% performance improvement at the national level, relaxing constraints yielded an 8% improvement, albeit with potential performance deterioration in some FDs. The detailed budget and efficiency metric analysis provided in this paper offers actionable insights for fire protection services enhancement. This underscores the importance of diverse optimization strategies to enhance FD efficiency, with implications for decision-makers at the Portuguese National Authority for Emergency and Civil Protection and similar organizations globally. [ABSTRACT FROM AUTHOR]

This paper explores the impact of an increasing number of prosumers in electricity supply systems and investigates how market mechanisms can mitigate the negative effects. The Regional Energy Market Model simulates a supply system based on cellular structures, employing agent-based modeling to capture individual behaviors and simulate real market dynamics. This study includes various supply scenarios, such as a solely photovoltaic scenario and a technically diversified scenario with biogas-fueled combined heat and power units. For each scenario, fixed and flexible pricing scenarios are simulated to analyze their effects. The findings reveal that systems heavily reliant on photovoltaics experience negative effects at certain points due to seasonal limitations, while technically diversified supply scenarios demonstrate fewer drawbacks. Flexible pricing systems stimulate demand in a manner beneficial to the system, creating regional added value, and contributing to the balance between generation and consumption, depending on the supply scenario. However, the study underscores that economic incentives alone are insufficient for balancing generation and consumption. The results highlight the importance of exploring opportunities through the interplay of economic incentive mechanisms and technical possibilities. [ABSTRACT FROM AUTHOR]

Background: Simulations, ludic or otherwise, have so far struggled to gain a foothold in mainstream historiography. Some authors suggest there may be fundamental incompatibilities between history and the language of simulations and scholarly games. Others believe that designing, employing, and validating historical simulations may be simply too costly and/or labor-intensive to justify their widespread adoption. Intervention: This paper intends to identify points of friction between historiography and simulation-based research and suggest practical solutions to these issues. Methods: My discussion is based on the description and analysis of a case study, the ThomondSim/ The Triumphs of Turlough research project. The initiative consisted of the development and application of an agent-based computational model (ABM) and a scholarly board game to investigate the possible associations between economic, environmental, and military hazards in 13th and 14th centuries Ireland. Results and Discussion: Ensuring the simulations matched historical evidence to a standard deemed acceptable by the historiographical community limited their phase space, compromising their capacity to explore emergent phenomena. The intricacy of the underlying conceptual model suited the ABM better than the board game, which struggled to reconcile complexity with good game design practices. The board game, however, proved to be an effective validating tool for the ABM. Limitations and Suggestions for Further Research: The project espoused an overt simulation- and game-centric approach, paying little attention to unguided play. Recent literature suggests that fostering, rather than hindering, playful exploration could address some of the pitfalls identified by this project. Conclusion: Play could be a means of reconciliation between simulational, ludic, and historiographical practices. However, to ensure that projects adhere to epistemic standards, it is recommended that a methodology is developed to integrate it into research in ways that can be tested and evaluated. [ABSTRACT FROM AUTHOR]

Evaluating the resilience of the innovation ecosystem to maintain its performance, in the sense of resistance to disruption and recovery after it, has recently received more attention. Several studies have been conducted to model different ecosystems and evaluate their resilience. However, modeling the innovation ecosystem from a holistic perspective and performing a quantitative assessment of its resilience have received less attention. This paper models the innovation ecosystem holistically and evaluates its resilience index using a quantitative approach through five main steps. In the first step, a case study related to the innovation ecosystem of Iran’s Ministry of Energy, called the Power Innovation Ecosystem, is modeled by combining system dynamics and agent-based modeling. Upon validating the model in the second step, the disruption of the loss of experts is investigated in the third step, and all possible actions to recover each actor are analyzed. In the fourth step, the performance of the ecosystem is simulated before and after the disruption using the data gathered in the previous steps. Finally, resilience is calculated in two different ways in the fifth step. Several improvement solutions are also suggested when considering that the resilience index of the innovation ecosystem is at a medium level. This research may assist policymakers in observing the resilience level of the innovation ecosystem based on the proposed model. By applying strategic changes to this model, they can determine the effects of their policies and make the most appropriate decisions to increase the resilience of the innovation ecosystem. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive agent-based model for the spread of an infection in a network of cities. Directional mobility is defined between each two cities and can take different values. The work examines the role that such mobility levels play in containing the infection with various vaccination coverage and age distributions. The results indicate that mobility reduction is sufficient to control the disease under all circumstances and full lockdowns are not a necessity. It has to be reduced to different ratios depending on the vaccination level and age distribution. A key finding is that increasing vaccination coverage above a certain level does not affect the mobility suppression level required to control the infection anymore for the cases of young population and heterogeneous age distributions. By investigating several migration and commuting patterns, it is found that shutting mobility in a few local places is favored against reducing mobility over the entire country network. In addition, commuting -and not migration-influences the spread level of the infection. The work offers an exclusive combined network-based and agent-based model that makes use of randomly generated mobility matrices. [ABSTRACT FROM AUTHOR]

This paper introduces an Agent-Based Model (ABM) designed to investigate the dynamics of the Internet of Things (IoT) ecosystem, focusing on dynamic coalition formation among IoT Service Providers (SPs). Drawing on insights from our previous research in 5G network modeling, the ABM captures intricate interactions among devices, Mobile Network Operators (MNOs), SPs, and customers, offering a comprehensive framework for analyzing the IoT ecosystem's complexities. In particular, to address the emerging challenge of dynamic coalition formation among SPs, we propose a distributed Multi-Agent Dynamic Coalition Formation (MA-DCF) algorithm aimed at enhancing service provision and fostering collaboration. This algorithm optimizes SP coalitions, dynamically adjusting to changing demands over time. Through extensive experimentation, we evaluate the algorithm's performance, demonstrating its superiority in terms of both payoff and stability compared to three classical coalition formation algorithms: static coalition, non-overlapping coalition, and random coalition. This study significantly contributes to a deeper understanding of the IoT ecosystem's dynamics and highlights the potential benefits of dynamic coalition formation among SPs, providing valuable insights and opening future avenues for exploration. [ABSTRACT FROM AUTHOR]

Disasters associated with natural hazards as well as climate change are happening within complex socio-economic systems and desired system states, including sustainable development and resource management, are formulated on the global as well as regional and national levels. However, complex system approaches are yet only rudimentarily incorporated in related applications, and we discuss modeling as well as policy challenges focusing on fiscal risk. As an intermediate step we suggest a gap approach which we relate to fiscal stress levels a complex system may experience due to natural hazard events. We argue that in case of no gaps one can assume a no stress situation and therefore modeling of disruptions including cascading effects is less necessary. However, at the same time we also acknowledge that there is an urgent need to address corresponding challenges with complex systemmethods. Policy-wise our paper responds to concerns for real-world applications and can provide insights to support current discussions within the UNFCCC and Paris Agreement around both adaptation finance and the new funding arrangements for loss and damage from climate impacts established at COP27. [ABSTRACT FROM AUTHOR]

The rise of social media has led to a new degree of spontaneous volunteering in the aftermath of disasters, even in border areas. However, spontaneous volunteering across national borders creates further obstacles that need to be addressed and prepared for. This paper introduces an agent-based simulation to examine coordination procedures of spontaneous volunteers in crisis scenarios, particularly the specifics of travel restrictions imposed after the COVID-19 pandemic. Moreover, research is conducted on the effects of insufficient or delayed coordination of the population's offer of voluntary help, which reduces the motivation of volunteers and leads to missed request for help.8. The results show that coordination of spontaneous volunteers is a key factor for helping the affected population, especially in a pandemic, where the possibilities to help are limited. These results are discussed with regard to costs that need to be considered in order to establish coordination, as well as health issues and motivation of spontaneous volunteers in different coordination scenarios. [ABSTRACT FROM AUTHOR]

The recent development of Social Networking Services (SNS) has changed the way of information sharing and interchanging. With countless amounts of information broadcast daily, many people use it every time, even during a crisis and disaster situation. However, the trustworthiness of information has become one of the issues on SNS. In this paper, we propose a trust model consisting of identity-based, behavior-based, relation-based, feedback factors, and information-based trust, in which the Big-Five personality traits are also considered. We conducted an agent-based modeling simulation for the proposed trust model, investigating users' behavior according to the Big-Five personality traits and several users' aspects: knowledge level and psychopathy. The experiment is based on online surveys and related works representing social network users' behavior. We compare the overall trust and trustworthiness in the numerical results, validating our proposed trust model. Moreover, we systematically compare the occurrence of fake news under conditions where the initial news is either a truthful or fabricated fake news. Numerical results also show that overall trust is sensitive to information-based trust, whereas it is not affected by behavior-based trust. Furthermore, openness, conscientiousness, and extroversion were correlated with overall trust, while the effect of agreeableness and neuroticism on overall trust were insignificant. [ABSTRACT FROM AUTHOR]

Pedestrian traffic in a city is subject to fluctuations throughout the day due to a variety of factors. The understanding of these variations can be achieved using properly calibrated agent-based simulation models that capture the dynamics of pedestrian movement. However, despite their significance, such models are currently underrepresented in scientific discussions. In addition, acquiring real-world pedestrian localization data for model calibration poses challenges. To address these issues, this paper presents an agent-based model specifically designed to examine pedestrian traffic fluctuations at a mesoscale level. The model uses popular times data from the Google Places service and population data from the Geographic Information System (GIS) for accurate calibration. As a result, it effectively captures the real-world dynamics of pedestrian movement in the city center. By harnessing the advantages of agent-based modeling (ABM), the model generates several valuable insights into daily pedestrian traffic. It estimates the capacity and speed of pedestrian flows and determines the daily load within the simulated area. Moreover, it enables the identification of bottlenecks and areas characterized by varying levels of pedestrian density. The model's validation process involves comparing its output with empirical studies and pedestrian traffic data from selected points of interest (POIs). The model successfully captures key aspects associated with fundamental diagrams of pedestrian flow. Furthermore, the dynamics of pedestrians closely align with Google Places popular times data for the chosen POIs. Overall, this research contributes to advancing pedestrian traffic management and optimizing public transport organization by employing empirically calibrated agent-based simulation models. [ABSTRACT FROM AUTHOR]

Despite ample research devoted to the non-linear q-voter model and its extensions, little or no attention has been paid to the relationship between the composition of the influence group and the resulting dynamics of opinions. In this paper, we investigate two variants of the q-voter model with independence. Following the original q-voter model, in the first one, among the q members of the influence group, each given agent can be selected more than once. In the other variant, the repetitions of agents are explicitly forbidden. The models are analyzed by means of Monte Carlo simulations and via analytical approximations. The impact of repetitions on the dynamics of the model for different parameter ranges is discussed. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Climate change is a global phenomenon with far-reaching consequences, and its impact on human health is a growing concern. The intricate interplay of various factors makes it challenging to accurately predict and understand the implications of climate change on human well-being. Conventional methodologies have limitations in comprehensively addressing the complexity and nonlinearity inherent in the relationships between climate change and health outcomes. OBJECTIVES: The primary objective of this paper is to develop a robust theoretical framework that can effectively analyze and interpret the intricate web of variables influencing the human health impacts of climate change. By doing so, we aim to overcome the limitations of conventional approaches and provide a more nuanced understanding of the complex relationships involved. Furthermore, we seek to explore practical applications of this theoretical framework to enhance our ability to predict, mitigate, and adapt to the diverse health challenges posed by a changing climate. METHODS: Addressing the challenges outlined in the objectives, this study introduces the Complex Adaptive Systems (CAS) framework, acknowledging its significance in capturing the nuanced dynamics of health effects linked to climate change. The research utilizes a blend of field observations, expert interviews, key informant interviews, and an extensive literature review to shape the development of the CAS framework. RESULTS AND DISCUSSION: The proposed CAS framework categorizes findings into six key sub-systems: ecological services, extreme weather, infectious diseases, food security, disaster risk management, and clinical public health. The study employs agent-based modeling, using causal loop diagrams (CLDs) tailored for each CAS sub-system. A set of identified variables is incorporated into predictive modeling to enhance the understanding of health outcomes within the CAS framework. Through a combination of theoretical development and practical application, this paper aspires to contribute valuable insights to the interdisciplinary field of climate change and health. Integrating agent-based modeling and CLDs enhances the predictive capabilities required for effective health outcome analysis in the context of climate change. CONCLUSION: This paper serves as a valuable resource for policymakers, researchers, and public health professionals by employing a CAS framework to understand and assess the complex network of health impacts associated with climate change. It offers insights into effective strategies for safeguarding human health amidst current and future climate challenges.

• A framework is presented to explore the influence of friendships in a social network on drivers' route choice. • This paper simulates the evolution of drivers' route choice in a hypothetical transportation network under different assumptions. • This research offers insight into the route choice process when drivers can access information about other drivers' travel experience in a social network. • The simulation results show that the information types mainly affect the change rate, and the memory length mainly affects the time for convergence. Route choice is a decision that drivers face daily under varying traffic conditions. Although many studies have focused on route choice, the actual behavior considering real-world networks and the mechanisms that govern it have eluded complete characterization. Part of the difficulty has been in accounting for the growing availability of information sources that may influence drivers' route choices, precluding collection of adequate observational data. In addition, with the continuing emergence of new forms and sources of information, such as social media with varying degrees of interactivity, engagement, and immediacy, existing frameworks and models for representing these choices and the influence of new information sources have been lacking. This study uses an agent-based modeling approach to investigate the effects of social influence on drivers' route choices. In the model, all drivers communicate with each other in the same social network, and this information is combined with their previous experiences. The first aim of this study is to investigate the impact of social network connectivity and information exchange on route choice. The second is to characterize situations in which all drivers in the social network are satisfied with their route choices. The simulation results show that an equilibrium is achieved when all drivers in the given social network are satisfied with their current route choices. [ABSTRACT FROM AUTHOR]

AbstractIn probabilistic risk assessment (PRA) of nuclear power plants (NPPs), human reliability analysis (HRA) is conducted to identify potential human failure events that could contribute to risk scenarios and estimate human error probabilities. Lessons learned from the 2011 Fukushima Daiichi NPP accident underscored that for PRA, it is critical to model external control room (Ex-CR) human actions. The state-of-practice HRA methods, historically developed for the main control room HRA, are limited in capturing the unique nature of Ex-CR human actions, such as location dependence (in addition to the time dependence) of human actions and spatiotemporal interactions of human performance with the surrounding physical environments, for instance, hazard propagation.To advance the Ex-CR HRA in the context of the fire PRA for NPPs, the authors’ team developed a simulation-based fire crew performance model using an agent-based modeling (ABM) technique. The ABM fire crew simulation was coupled with a fire progression model through a spatiotemporal interface using a geographic information system. This paper focuses on the validation of the ABM simulation, which is the key requirement for the simulation-based Ex-CR human performance model to be utilized in PRA. The existing validation approach, initially developed for physical models in the fire PRA of NPPs, is extended for validation of the simulation-based Ex-CR human performance model.Model uncertainty is used as a measure of model validity, which facilitates the incorporation of the validation result into the PRA. The degree of the model uncertainty is characterized by a lognormal error model whose parameters are quantified based on a pairwise comparison between empirical data and model predictions.The proposed validation approach is demonstrated using a case study of the fire PRA of NPPs. This study makes two research contributions: (1) it is the first to validate the simulation-based Ex-CR human performance model against empirical human performance data and incorporate the validation result into the PRA of NPPs, and (2) this study, for the first time, conducts a controlled experimental test to collect empirical data for fire crew performance at NPPs. [ABSTRACT FROM AUTHOR]

The increased complexity of construction projects has caused various management challenges. To clarify the mechanism of construction system complexity and improve the ability to manage the complexity of construction projects, the Agent-based modeling (ABM) method has been introduced and used in the construction management field. Nevertheless, a systematic, holistic, and panoramic understanding of the use of the ABM model in the construction management field is still lacking. To address this research gap, this study reviewed 133 historical explorations retrieved from the database of Web of Science. By using the multiple objectives of construction management as the literature classification framework, the study described the research status of the agent-based modeling method in the field of construction management. On this basis, this paper suggested the improvement paths in the application of this method from three aspects. It is expected that this study will provide a theoretical basis for enhancing understanding of the use of the ABM method in construction management, and also provide insights for future explorations in the area. [ABSTRACT FROM AUTHOR]

Adopting an integrated production, maintenance, and quality policy in production systems is of great importance due to their interconnected influence. Consequently, investigating these aspects in isolation may yield an infeasible solution. This paper aims to address the joint optimal policy of production, maintenance, and quality in a two-machine-single-product production system with an intermediate buffer and final product storage. The production machines have degradation levels from as-good-as-new to the breakdown state. The failures increase the production machine's degradation level, and maintenance activities change the status to the initial state. Also, the quality of the final product depends on the level of degradation of the machines and the correlation between the degradation level of the production machines and the product's quality in the case that high degradation of the previous production machines leads to a high probability to produce wastage by the following machines is considered. The production system studied in this research has been modeled using the agent-based simulation, and the Reinforcement Learning (RL) algorithm has obtained the optimal integrated policy. The goal is to find an integrated optimal policy that minimizes production costs, maintenance costs, inventory costs, lost orders, breakdown of production machines, and low-quality production. The meta-heuristic technique evaluates the joint policy obtained by the decision-maker agent. The results show that the acquired joint policy by the RL algorithm offers acceptable performance and can be applied to the autonomous real-time decision-making process in manufacturing systems. [ABSTRACT FROM AUTHOR]

In this research, we present the first steps toward developing a data-driven agent-based model (ABM) specifically designed for simulating infectious disease dynamics in Greece. Amidst the ongoing COVID-19 pandemic caused by SARS-CoV-2, this research holds significant importance as it can offer valuable insights into disease transmission patterns and assist in devising effective intervention strategies. To the best of our knowledge, no similar study has been conducted in Greece. We constructed a prototype ABM that utilizes publicly accessible data to accurately represent the complex interactions and dynamics of disease spread in the Greek population. By incorporating demographic information and behavioral patterns, our model captures the specific characteristics of Greece, enabling accurate and context-specific simulations. By using our proposed ABM, we aim to assist policymakers in making informed decisions regarding disease control and prevention. Through the use of simulations, policymakers have the opportunity to explore different scenarios and predict the possible results of various intervention measures. These may include strategies like testing approaches, contact tracing, vaccination campaigns, and social distancing measures. Through these simulations, policymakers can assess the effectiveness and feasibility of these interventions, leading to the development of well-informed strategies aimed at reducing the impact of infectious diseases on the Greek population. This study is an initial exploration toward understanding disease transmission patterns and a first step towards formulating effective intervention strategies for Greece. [ABSTRACT FROM AUTHOR]

Power outages can cause severe disruption to critical infrastructure. With the predicted increase in the electrification of the transport sector, society will become even more vulnerable to the effects of power outages. While increased electric vehicle (EV) adoption will contribute to the electrification process, EVs can also offer capabilities to provide services during an outage. This paper studied the use of a fleet of EVs during the aftermath of a disaster to provide disaster relief by donating power to a shelter, delivering critical supplies and people in need, and providing transport for personnel or performing inspections. While the bulk of the past work has focused on using EVs to increase the resilience of the distribution grid, or individual buildings, to a power outage, this paper was novel in its use of an agent-based model to study EVs that are performing functions to increase the resilience of a community to an outage. The fleet of EVs were provided access to a microgrid with a solar array, one or two EV fast chargers, and three possible sizes of a storage. Useful outputs were produced and studied for such features as daily energy donated to a shelter, daily energy used at the microgrid, and the length of outages that can be supported before the energy is depleted at the microgrid. Results showed that increasing storage size at the microgrid led to substantial increases in the outage length that could be support. Additionally, it was found that focusing a fleet on delivery and transport tasks, as opposed to energy donation, could also increase the length of outages that could be supported.

As a key component of the balancing market, balance settlement links the physical layer of electricity balance with financial fund equilibrium. Imbalance funds pertain to costs that emerge from the balancing settlement process and, lacking a clearly defined responsible entity, require distribution or reimbursement among market participants. Under the current dual-track market system in China, the issue of imbalance funds has become increasingly prominent. The allocation of these funds is crucial, directly impacting the revenue of these participants and playing a pivotal role in ensuring a fair and efficient market. In this context, this paper, integrating the current dual-track electricity market environment in China, establishes a comprehensive framework to understand the balancing settlement process. Its aim is to explore effective methods for designing imbalance fund allocation mechanisms. Viewing through the lens of balancing service providers, the paper introduces an agent-based model for the balancing market. This model is proficient in capturing the complex dynamics of market behaviors and the resulting imbalance funds during the settlement process. Grounded in existing market regulations and the progressive evolution of China's spot market, the paper meticulously crafts various scenarios with unique imbalance fund allocation mechanisms. It demonstrates that a well-considered allocation mechanism can significantly enhance the efficiency of balancing market operations and reduce the overall costs of system balancing. Hopefully, the proposed framework and method will provide beneficial support and reference for the formulation of related policies and the design of market settlement mechanisms.

Optimizing Resource Allocation in Fire Departments (RAFD) is crucial for enhancing Fire Protection Services (FPS) and ultimately saving lives. Efficient RAFD ensures that fire departments have the necessary resources to respond effectively to emergencies. This paper presents a method for optimizing RAFD based on performance assessment results, examining its impact on Fire Department (FD) efficiency in Portugal. Evaluating data from 353 FDs, two RAFD optimization methods were assessed: one adhering to Portuguese regulations and constraints, such as budget allocation limitations, and another without such constraints. Integrating a slack-based data envelopment analysis model and mixed-integer linear programming, the study found that incorporating FD efficiency scores in RAFD improved overall efficiency at national, district, and FD levels. While adherence to Portuguese regulations led to balanced resource allocation and a 4% performance improvement at the national level, relaxing constraints yielded an 8% improvement, albeit with potential performance deterioration in some FDs. The detailed budget and efficiency metric analysis provided in this paper offers actionable insights for fire protection services enhancement. This underscores the importance of diverse optimization strategies to enhance FD efficiency, with implications for decision-makers at the Portuguese National Authority for Emergency and Civil Protection and similar organizations globally.

As a key component of the balancing market, balance settlement links the physical layer of electricity balance with financial fund equilibrium. Imbalance funds pertain to costs that emerge from the balancing settlement process and, lacking a clearly defined responsible entity, require distribution or reimbursement among market participants. Under the current dual-track market system in China, the issue of imbalance funds has become increasingly prominent. The allocation of these funds is crucial, directly impacting the revenue of these participants and playing a pivotal role in ensuring a fair and efficient market. In this context, this paper, integrating the current dual-track electricity market environment in China, establishes a comprehensive framework to understand the balancing settlement process. Its aim is to explore effective methods for designing imbalance fund allocation mechanisms. Viewing through the lens of balancing service providers, the paper introduces an agent-based model for the balancing market. This model is proficient in capturing the complex dynamics of market behaviors and the resulting imbalance funds during the settlement process. Grounded in existing market regulations and the progressive evolution of China's spot market, the paper meticulously crafts various scenarios with unique imbalance fund allocation mechanisms. It demonstrates that a well-considered allocation mechanism can significantly enhance the efficiency of balancing market operations and reduce the overall costs of system balancing. Hopefully, the proposed framework and method will provide beneficial support and reference for the formulation of related policies and the design of market settlement mechanisms. • The causes of imbalance funds in the dual track electricity market in China are analyzed. • An agent-based model is proposed to capture the intricate dynamics of the balancing market processes. • The interaction between market behaviours of BSP and the imbalance funds are investigated. • The impact of marketization and the balancing services demand on the imbalance funds are quantified. • Adequate imbalance funds allocation mechanism should represent market contributions. [ABSTRACT FROM AUTHOR]

Recent years have seen an explosion of theoretical interest, as well as increasingly fraught real-world debate, around issues to do with discourse participation. For example, marginalised groups may find themselves excluded or may exclude themselves from discourse contexts that are hostile. This not only has ethical implications, but likely impacts epistemic outcomes. The nature and scale of such outcomes remain difficult to estimate in practice. In this paper, we use agent-based modelling to explore the implications of a tendency toward `agreeableness' whereby agents might shape their communication so as to reduce direct conflict. Our simulations show that even mild tendencies to avoid disagreement can have significant consequences for information exchange and the resultant beliefs within a population.

Contemporary formal models aim to capture group polarization as the result of deliberation between rational agents. Paradigmatic models do, however, rely on rather limited agents, casting doubt on the conclusion that group polarization can be rationally reconstructed. In this paper, we use a recently developed Bayesian agent-based model of deliberation to investigate this conclusion. This model avoids problems we identify in a group of influential Bayesian polarization models. Our case study shows that a simple mechanism produces realistic patterns of group polarization: limited exchange of evidence across a sparse social network. We reflect on what our results mean for our formal understanding of rational group polarization.

This paper presents an approach to model the internal cognitive state of decision-makers when interacting with AI to understand exchanges between agents and improve future interactions. We focus on understanding how AI suggestions are perceived by a human agent using an approach based on the technology acceptance model. The variation in the user's state is investigated when perceiving the interaction with AI by considering it as a hidden (latent) state. Using human evaluation data collected from two cases of clinical decision-making and software development scenarios, we analyse and explore the user's perceptional state during interaction. The experiment conducted employs the Bayesian belief network to represent the human perceptional model and provide a prediction of the usefulness of AI model's suggestions in the considered case. Upon introduction of cognitive states in the model, we observed an increase in predictive performance by 76–77%. Our investigation can be concluded as an attempt to identify implicit static and dynamic cognitive characteristics of users to provide personalized assistance in human-AI interaction (HAI) and collaboration in complex domains of decision-making

Opinion averaging is a common means of judgment aggregation that is employed in the service of crowd wisdom effects. In this paper, we use simulations with agent-based models to highlight contexts in which opinion averaging leads to poor outcomes. Specifically, we illustrate the conditions under which the optimal posterior prescribed by a normative model of Bayesian argument exchange diverges from the mean belief that would be arrived at via simple averaging. The theoretical and practical implications of this are discussed.

In hierarchical perception-action (PA) learning, agents discover invariants between percepts and actions that are structured hierarchically, from very basic immediate links to higher-level, more abstract notions. In practice, existing work tends to either focus on the general theory at the expense of details of the proposed mechanisms, or specify a-priori the contents of some layers. Here, we introduce a framework that does without such constraints. We demonstrate the framework in a simple 2D environment using an agent that has minimal perceptual and action abilities. We vary the perceptual abilities of the agent to explore how the specifics of this aspect of the agent's body might affect PA learning and find unexpected consequences. The contribution of this paper is therefore twofold, (1) we add a novel framework to the literature on PA learning, using, in particular curiosity-based reinforcement learning (RL) to implement the necessary learning mechanisms, and (2) we demonstrate that even for very simple agents, the relation between the specifics of an agent's body and its cognitive abilities is not straightforward.

In this paper, we first introduce the `complex hidden profile', a previously overlooked category of hidden profiles that arises from complex inferential relations among arguments.Second, in order to investigate the conditions under which interrelated arguments can generate hidden profiles, we introduce a novel Bayesian agent-based framework for collective reasoning with complex argument structures.Finally, we show that that many possible argument structures can generate hidden profiles, even when agents do not have any information in common.

Food systems are dominated by multiple risks inflicted by their surrounding environments that pose pressures on the continuous and efficient supply of food products. Market dynamics represented by competitive and cooperative behaviors of the system's main players is one of the uncertainties that influence the availability and affordability of food products. Efficient modeling of these risks can assist decision-makers in simulating and predicting future actions that mitigate the impact that market dynamics have on the provision of food products. This paper suggests an Agent-based model-based framework that simulates the behavior and tracks the economic and environmental performances of the tomato supply system in the State of Qatar. It considers three different scenarios representing the current situation of the market in addition to a competitive and a cooperative trade network. Interactions between trade partners are modeled using two game theoretic approaches counting Cournot competition and Cartel collusion. Based on the research findings, a competitive global market can bring economic benefits to Qatar as an importing nation, but this approach also entails substantial water consumption and results in significant environmental emissions. Conversely, collaboration among exporting countries demonstrates a potential 20% reduction in water usage and a 15% decrease in environmental emissions compared to the competitive scenario, offering enhanced environmental performance albeit at higher economic costs. This study provides a prescriptive analysis that enables policymakers to quantify the trade-offs between economic efficiency and environmental sustainability. These insights are crucial for guiding strategic planning for food supply security and environmental conservation. By understanding the implications of market structure changes, policymakers can navigate potential challenges more effectively and develop targeted policy interventions that enhance the resilience of the food supply system against market volatility and environmental impacts. This analysis supports proactive policy-shaping that aligns with their desired outcomes, ensuring a balanced approach to managing economic benefits and environmental considerations. [ABSTRACT FROM AUTHOR]

• Using the ADOPT algorithm as a conflict resolution model for urban flood management. • Grouping agents as active and passive considering their investment on flood control. • Developing some quadratic functions for simulating active agents' utilities. • Using SWMM to simulate urban flood quantity and the average flood depth. • Using stakeholder analysis and ADOPT reduces the average flood depth by 58 %. The management of water resources in urban environments is challenging due to the existence of numerous stakeholders and the large number of affected people. In this paper, agents in an urban environment are categorized as active, passive, and monitoring agents, and a new mechanism is proposed to determine the flood control motivation functions (FCMFs) of active agents. An FCMF shows the motivation of an agent for investing and participating in a flood control activity. Also, for the first time, the effectiveness of a conflict resolution model based on asynchronous distributed constraint optimization (ADOPT) is evaluated for urban flood management. To evaluate the effectiveness of the proposed framework, it is applied to the eastern drainage catchment of Tehran, the capital city of Iran. The FCMFs of active stakeholders are modeled using some mathematical functions. To determine the FCMFs of active agents, an economic optimum point is suggested. This point is determined based on the construction costs of flood control infrastructures and flood damage costs. The utility functions of passive agents are modeled using a regression analysis. Flood management scenarios include a combination of water transfer canals, pipes, and ponds with real-time operating methods. ADOPT is used for solving Distributed Constraint Optimization Problems (DCOPs). The goal in DCOPs is to assign values to these variables in an approach that maximizes a global objective function while satisfying the constraints of each agent. The results show that by using the ADOPT-based conflict resolution model, the average depth of flooding is reduced from 24 cm to about 10 cm, and this is associated with the satisfaction of more than 90 percent of stakeholders. A decline in Average Flood Depth (AFD) has been observed with increased usage of technological methods such as constructing ponds or transferring water outside the basin. However, stakeholder desirability is critical and both technical and stakeholder interests should be taken into account to ensure project viability. [ABSTRACT FROM AUTHOR]

The ability of sociotechnical systems to recover from disruptions through decentralized, bottom-up processes is a crucial element of their overall resilience, but it is also among the most overlooked in resilience research. This paper proposes a computational framework leveraging dynamic multi-agent networks to conceptualize, model, and quantify multi-agent, decentralized recovery process building upon strategic network formation methods. This innovative approach addresses limitations in conventional strategic network formation models by accounting for the fluidity of structural shifts induced by the strategic behaviors of individual agents, and by acknowledging the inherent heterogeneity within agent populations. The central objective of this study is to highlight the critical role of bottom-up recovery and present a systematic, adaptable framework for modeling these processes in diverse real-world scenarios. We achieve this through four key elements: modeling post-disruption responses, exploring their interdependencies, accommodating multiple equilibria, and generating metrics for scenario comparison and forecasting. Our dynamic strategic network formation model offers valuable insights into crucial trade-offs in decentralized recovery processes and facilitates the integration of both bottom-up and top-down resilience strategies. Furthermore, it could serve as a predictive tool informing initial network architectural decisions by estimating likely post-disruption equilibrium states based on the initial network structure. Ultimately, this study provides a robust platform for analyzing network efficiency and resilience trade-offs, setting the stage for more informed decision-making in complex sociotechnical systems. • Bottom-up recovery is crucial for resilience of sociotechnical systems. • Top-down interventions need to incorporate models of bottom-up processes. • We propose a multi-agent network framework for modeling Bottom-up recovery. • Framework includes metrics to evaluate the role of top-down interventions. [ABSTRACT FROM AUTHOR]

Optimizing the cropping structure is of great significance for ensuring efficient water use and ecological sustainability in irrigation districts experiencing water shortages. However, how farmers choose the crops to plant under the intervention of factors such as behavioral economics and policies has been less considered in agricultural land-use planning. This study proposes an agent-based socio-hydrological model embedding a geographic information system (ABSHM-GIS). In ABSHM-GIS, a bottom-up research methodology coupled with farmers' behavioral economics and government policies is used to examine the factors influencing farmers' cropping structure and the impact of cropping decisions on farmers' income and the spatial and temporal distributions of water resources. In this study, ABSHM-GIS was applied in the context of planting structure and crop subsidy policy in Qira oasis, Xinjiang, China as a case study area. Results showed that government intervention in cropping structure was effective in reducing water use for agricultural irrigation (18–30 million m3/year) relative to farmer intervention, but it also reduced farmer returns (17,850–25,860 yuan/ha). During the period of agricultural irrigation, regulating the increase in the lower residual volume of irrigation gates 2–4 in the range of 666–869 m3/day and that of irrigation gates 5–9 in the range of 211–342 m3/day could effectively ensure the even spatial distribution of surface water and groundwater. In the planting structure, an increase in government subsidy by 300 yuan/ha could save an average of 2 million m3/year of water resources for ecological water use to ensure ecological safety. The behavioral economics showed that the learning factor was the main behavioral factor that changed the decision-making behavior of farmers. The ABSHM-GIS model proposed in this paper could help researchers reveal the causes and provide insights into the emergence of macro-patterns, such as changes in cropping structure and irrigated agriculture, as a result of microfarmers' economic behavior in a bottom-up research approach. • A socio-hydrological ABM coupling behavioral economics and water policies was proposed. • Government intervention saves water use via cropping structure, but reducing farmer returns. • Learning factor was main behavioral factor changing decision-making behavior of farmers. • Sensitivity factor and radius of information has a less significant effect on income of farmers. [ABSTRACT FROM AUTHOR]

Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions. This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023. Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed. • Use of multi-agent systems to study biological interactions in oncology. • Multi-agent systems enable modeling of cell signaling network dynamics. • Review of multi-agent systems application for personalized therapeutic strategies. • Meta-heuristic algorithms found to be the most used models in recent studies. [ABSTRACT FROM AUTHOR]

Residential areas in developing arid and semi-arid countries are highly vulnerable to flooding, and water shortages have forced their inhabitants to live close to rivers. While climate change is expected to cause more extreme weather conditions in the future, adopting private loss-reduction measures can diminish flood risk. Although the number of flood models has grown significantly for developing arid and semi-arid lands, these models suffer from being incapable of performing micro-scale flood risk analysis and including household behaviors. This research work presents a novel socio-economic simulation model in the framework of flood risk management (FRM) to couple household adaptive responses with flood risk analysis. Agent-based modeling (ABM) embeds human behaviors in a flood-simulating environment. The focus of this research is the upper Kan catchment in Iran with a long history of severe flash flooding. Our results show the ability of the developed framework to address household participation in FRM activities through private precautionary measures. Moreover, the results indicate the importance of presenting such micro-level behaviors in flood modeling for a more realistic flood risk assessment. It is also demonstrated that household adaptation in a continuous way can lead to less flood risks in the region, even under climate change and the future economy. Finally, the results reveal that the remaining and diminished regional flood risks are influenced by the behavioral framework through which the individuals make decisions in adopting flood-loss-reduction measures. A similar pattern is, however, observed in household contributions over time among the discussed behavioral approaches. [ABSTRACT FROM AUTHOR]

The dynamics of pedestrian behavior within the built environment represent a multifaceted and evolving field of study, profoundly influenced by shifts in industrial and commercial paradigms. This systematic literature review (SLR) is motivated by the imperative to comprehensively investigate and assess the built environment through the lens of pedestrian modeling, employing advanced modeling tools. While previous scholarship has explored the interplay between the built environment and pedestrian dynamics (PD), there remains a conspicuous gap in research addressing the utilization of agent-based modeling (ABM) tools for a nuanced evaluation of PD within these contexts. The SLR highlights the essential and practical benefits of using ABM to study PD in built environments and combine related theories and practical projects. Beyond theoretical discussions, it emphasizes the real-world contributions of ABM in understanding and visualizing how people behave in urban spaces. It aims to provide deep insights for both researchers and urban planners. By thoroughly examining recent research, it not only explores the practical uses of ABM but also reveals its broad implications for various aspects of pedestrian behavior in built environments. As a result, this SLR becomes a key resource for understanding the crucial role of ABM in studying the complexities of our surroundings. The findings and discussion here highlight ABM's vital role in bridging the gap between theory and practice, improving our understanding of pedestrian behavior in urban settings. Furthermore, this study outlines promising avenues for future research, thereby fostering continued exploration and innovation in the dynamic realm of pedestrian behavior within built environments. [ABSTRACT FROM AUTHOR]

A mob is an event that is organized via social media, email, SMS, or other forms of digital communication technologies in which a group of people (who might have an agenda) get together online or offline to collectively conduct an act and then disperse (quickly or over a long period). In recent years, these events are increasingly happening worldwide due to the anonymity of the internet, affordability of social media, boredom, etc. Studying such a phenomenon is difficult due to a lack of data, theoretical underpinning, and resources. In this research, we use the Agent-Based Modeling (ABM) technique to model the mobbers and the Monte Carlo method to assign random values to the factors extracted from the theory of Collective Action and conduct many simulations. We also leverage our previous research on Deviant Cyber Flash Mobs to implement various scenarios the mobber could face when they decide to act in a mob or not. This resulted in a model that can simulate mobs, estimate the mob success rate, and the needed powerful actors (e.g., mob organizers) for a mob to succeed. We finally evaluate our model using real-world mob data collected from the Meetup social media platform. This research is one step toward fully understanding mob formation and the motivations of its participants and organizers. [ABSTRACT FROM AUTHOR]

In recent years, the rapid growth of on-demand delivery services, especially in food deliveries, has spurred the exploration of innovative mobility solutions. In this context, lightweight autonomous vehicles have emerged as a potential alternative. However, their fleet-level behavior remains largely unexplored. To address this gap, we have developed an agent-based model and an environmental impact study assessing the fleet performance of lightweight autonomous food delivery vehicles. This model explores critical factors such as fleet sizing, service level, operational strategies, and environmental impacts. We have applied this model to a case study in Cambridge, MA, USA, where results indicate that there could be significant environmental benefits in replacing traditional car-based deliveries with shared lightweight autonomous vehicle fleets. Lastly, we introduce an interactive platform that offers a user-friendly means of comprehending the model's performance and potential trade-offs, which can help inform decision-makers in the evolving landscape of food delivery innovation. [ABSTRACT FROM AUTHOR]

The quality of urban green space has an impact on the health and well-being of populations. Previous studies have shown that consideration of crowd activity characteristics is the key premise of landscape space design and planning. However, there is limited research on the correlation between features of the spatial layout of Mini Urban Green Spaces (MUGS) and the behavior of people, and it is difficult to take into account the possible distribution of people and their activity characteristics during the design phase of MUGS. This study aims to construct a technical workflow utilizing the AnyLogic platform and agent-based simulation methods for analyzing the characteristics of landscape spatial layouts considering dynamic human behavior. One MUGS, named 511 Park in Nanjing, China, was selected as the case for the application of the method and exploration of the impacts of spatial elements and layout on crowd activity types and density. We investigated the impact of four types of spatial elements—paths, facilities, nodes, and entrances—on human activities in MUGS. The results showed that path layout emerged as the most significant influencing factor. Changes in nodes and the number of facilities have a relatively minor impact on people's activities. There was an apparent impact of changes in path orientation around nodes on the dynamics of the flow of people. This study could provide valuable insights for landscape designers, aiding informed decision-making during the construction, renovation, and management of MUGS. [ABSTRACT FROM AUTHOR]

Evaluating the resilience of the innovation ecosystem to maintain its performance, in the sense of resistance to disruption and recovery after it, has recently received more attention. Several studies have been conducted to model different ecosystems and evaluate their resilience. However, modeling the innovation ecosystem from a holistic perspective and performing a quantitative assessment of its resilience have received less attention. This paper models the innovation ecosystem holistically and evaluates its resilience index using a quantitative approach through five main steps. In the first step, a case study related to the innovation ecosystem of Iran's Ministry of Energy, called the Power Innovation Ecosystem, is modeled by combining system dynamics and agent-based modeling. Upon validating the model in the second step, the disruption of the loss of experts is investigated in the third step, and all possible actions to recover each actor are analyzed. In the fourth step, the performance of the ecosystem is simulated before and after the disruption using the data gathered in the previous steps. Finally, resilience is calculated in two different ways in the fifth step. Several improvement solutions are also suggested when considering that the resilience index of the innovation ecosystem is at a medium level. This research may assist policymakers in observing the resilience level of the innovation ecosystem based on the proposed model. By applying strategic changes to this model, they can determine the effects of their policies and make the most appropriate decisions to increase the resilience of the innovation ecosystem.

This paper presents a comprehensive agent-based model for the spread of an infection in a network of cities. Directional mobility is defined between each two cities and can take different values. The work examines the role that such mobility levels play in containing the infection with various vaccination coverage and age distributions. The results indicate that mobility reduction is sufficient to control the disease under all circumstances and full lockdowns are not a necessity. It has to be reduced to different ratios depending on the vaccination level and age distribution. A key finding is that increasing vaccination coverage above a certain level does not affect the mobility suppression level required to control the infection anymore for the cases of young population and heterogeneous age distributions. By investigating several migration and commuting patterns, it is found that shutting mobility in a few local places is favored against reducing mobility over the entire country network. In addition, commuting -and not migration-influences the spread level of the infection. The work offers an exclusive combined network-based and agent-based model that makes use of randomly generated mobility matrices.