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Climate change has rapidly altered marine ecosystems and is expected to continue to push systems and species beyond historical baselines into novel conditions. Projecting responses of organisms and populations to these novel environmental conditions often requires extrapolations beyond observed conditions, challenging the predictive limits of statistical modeling capabilities. Bioenergetics modeling provides the mechanistic basis for projecting climate change effects on marine living resources in novel conditions, has a long history of development, and has been applied widely to fish and other taxa. We provide our perspective on 4 opportunities that will advance the ability of bioenergetics-based models to depict changes in the productivity and distribution of fishes and other marine organisms, leading to more robust projections of climate impacts. These are (1) improved depiction of bioenergetics processes to derive realistic individual-level response(s) to complex changes in environmental conditions, (2) innovations in scaling individual-level bioenergetics to project responses at the population and food web levels, (3) more realistic coupling between spatial dynamics and bioenergetics to better represent the local- to regional-scale differences in the effects of climate change on the spatial distributions of organisms, and (4) innovations in model validation to ensure that the next generation of bioenergetics-based models can be used with known and sufficient confidence. Our focus on specific opportunities will enable critical advancements in bioenergetics modeling and position the modeling community to make more accurate and robust projections of the effects of climate change on individuals, populations, food webs, and ecosystems.

Supply chain finance (SCF), which has gained prominent attention from academicians, is a complex process involving various participants. However, previous studies have not provided a comprehensive framework integrating these participants or a consensus on how participants' strategies influence SCF. Here, a comprehensive framework is proposed to investigate SCF through an agent-based approach. We chose the Chinese market to proxy key model parameters. We find that although banks prefer large businesses, trade credit, and partial credit guarantee still play a pair of main complementary financing channels for small and medium enterprises (SMEs). The cautious strategies of agents indeed significantly decrease the risk contagions in the supply chain. However, marketisation negatively moderates this effect for SMEs. From the banks' perspective, although longer-term credit policies bring lower risk contagions, they increase the uncertainty of the banks' risk. In addition, our extended framework also shows that the market-oriented financial system benefits either large business or SMEs as well as smooths the risk fluctuation for banks. This study not only verifies some economic phenomena but also provides a new systematic angle for understanding SCF. We also discuss in detail this study' important practical insights and theoretical contribution. [ABSTRACT FROM AUTHOR]

The existing theories of post-traumatic stress disorder (PTSD) have inspired large volumes of research and have contributed substantially to our current knowledge base. However, most of the theories are of a qualitative and verbal nature, and may be difficult to evaluate and compare with each other. In this paper, we propose that one way forward is to use computational modelling to formulate more precise theories of PTSD that can be evaluated by (1) assessing whether the model can explain fundamental phenomena related to PTSD, and (2) comparing simulated outcomes with real data. Computational modelling can force us to describe processes more precisely and achieve stronger theories that are viable for testing. Establishing the theoretical groundwork before undertaking empirical studies can help us to avoid doing research with low probability of valid results, and counteract the replicability crisis in psychology. In conclusion, computational modelling is a promising avenue for advancing the understanding and treatment of PTSD.

Predator–prey interactions are a fundamental part of community ecology, yet the relative importance of consumptive and nonconsumptive effects (NCEs) (defined as a risk‐induced response that alters prey fitness) has not been resolved. Theory suggests that the emergence and subsequent predominance of consumptive or NCEs depend on the given habitat's complexity as well as predator hunting mode and spatial domain sizes of both predator and prey, but their relative influence on the outcome of predator–prey interactions is unknown. We built agent‐based models in NetLogo to simulate predator–prey interactions for three hunting modes—sit‐and‐wait, sit‐and‐pursue, and active—while concurrently simulating large versus small spatial domain sizes for both predators and prey. We studied (1) how hunting mode and spatial domain size interact to influence the emergence of consumptive or NCEs and (2) how, when NCEs do dominate, hunting mode and spatial domain separately or additively determine prey shifts in time, space, and habitat use. Our results indicate consumptive effects only dominate for active predators when prey habitat domains overlap completely with the predator's spatial domain and when sit‐and‐wait and sit‐and‐pursue predators and their prey both have large spatial domains. Prey are most likely to survive when they shift their time but most frequently shift their habitat. Our paper helps to better understand the underlying mechanisms that drive consumptive or NCEs to be most dominant. [ABSTRACT FROM AUTHOR]

The paper deals with the extension of the logistics simulation within an agent-based simulation framework to be able to consider the delivery across several supply chains. The logistics simulation is placed in the context of the underlying freight transportation simulation. The use of multiple possible logistics chains addresses a key limitation of previous studies. Logistics chains represent the various transportation options of a Logistic Service Provider (LSP) and are made up of resources such as depots, logistics hubs and carriers that the logistics service provider can commission. When implementing the new functionality, the existing flow of the simulation process must be considered. In particular, changes need to be made to the assignment of freight requests to logistics chains, the iterative optimization of this assignment and scheduling. Appropriate assignment methods and rescheduling strategies are developed and presented. These innovations reveal compatibility problems within the existing simulation framework. The functionality developed is presented in constructed scenarios, highlighting the advantages of using multiple logistics chains. In addition, the applicability is demonstrated in a comprehensive real scenario that includes the delivery of supermarkets. [ABSTRACT FROM AUTHOR]

Shared mobility solutions such as bike sharing services play a key role to reduce greenhouse gas emissions in urban areas. In this paper, we present an approach to model station-based bike sharing in the multi-modal agent-based travel demand model mobiTopp. We compare different implementations of how agents choose their bike pick-up and drop-off stations. In addition to two variations of distance minimization, we also present a gravity approach to represent the reliability of a system. By also comparing different behavioral attitudes of the agents towards walking, a total of six scenarios were implemented and tested. The presented approach allows to easily test scenarios with a varying number of bikes and stations. We apply our algorithm to a model for the city of Hamburg, Germany, where the mobility behavior of a total of 1.9 million agents is modeled. Our simulations show plausible results. The average distances, utilization shares of each station, and other parameters match with values from the actual service. While the different strategies result in significantly different access times, and provide further new valuable insights and options for parameterization, differences in resulting demand are small. Overall, this model provides new methods to simulate bike sharing in travel demand models, thus helps to simulate an important mode of transport of the future. [ABSTRACT FROM AUTHOR]

This agent-based simulation study investigates pedestrian dynamics with a focus on the impacts of behaviour idling on pedestrian flows. It also examines the influence of psychological, social, and environmental factors on pedestrian flows. Our research categorises pedestrian behaviour into three types: time-sensitive (Type A), mobility-constrained (Type B), and 'wandering' type (Type C), defined as pedestrians moving without a specific destination, which includes tourists, shoppers, and leisure walkers. We demonstrate how behaviour heterogeneity influences flow and movement patterns through simulations in unidirectional, bi-directional, and multi-directional pedestrian facilities. We find that Type C pedestrians significantly slow down Type A pedestrians, leading to a speed reduction of up to 30% in high-density tourist scenarios, and cause prolonged stationary periods for Type B pedestrians, particularly in less crowded settings where Type C's tendency to idle is more pronounced. Our results show a linear relationship between density and speed reduction, with tourist behaviour notably exacerbating congestion in high-density environments. Key insights highlight the critical role of wandering (Type C) behaviours in affecting pedestrian flow, emphasising the necessity for urban planning and infrastructure design to accommodate this variability. Future research aims to apply these findings to real-world contexts, further refining urban design strategies to accommodate the full spectrum of pedestrian behaviours.

This paper enhances the agent model of ordinary individuals by incorporating the roles of places in the transmission, prevention, and control in the process, establishing a fundamental connection between these two types of agents through individual travel rules. The impact of real-world prevention and control measures on regional epidemic transmission is studied based on this model. Firstly, based on the analysis of place elements in typical cases of major infectious diseases, we give the classification of places. Secondly, the resident agent and the place agent are constructed and rules are established to form an agent-based major infectious disease transmission model considering the place. Then, the simulation process is established and an ideal regional environment is constructed to simulate overall changes in major infectious disease scale and spread speed. Additionally, evolutionary simulations are conducted for individual isolation intensity and place control time during emergency management stages to compare infection outcomes, and we analyze different roles and application scenarios of specific prevention and control measures. Finally, simulation results from real major infectious disease cases in Yangzhou, China, validate the effectiveness of this model in predicting major infectious disease development trends as well as evaluating prevention and control measure effects. [ABSTRACT FROM AUTHOR]

Many of the world's fisheries are "data-limited" where the information does not allow precise determination of fish stock status and limits the development of appropriate management responses. Two approaches are proposed for use in data-limited stock management strategy evaluations to guide the evaluations and to understand the sources of uncertainty: rejection sampling methods and the incorporation of more complex socio-economic dynamics into management evaluations using agent-based models. In rejection sampling (or rejection filtering) a model is simulated many times with a wide range of priors on parameters and outcomes are compared multiple filtering criteria. Those simulations that pass all the filters form an ensemble of feasible models. The ensemble can be used to look for robust management strategies, robust to both model uncertainties. Agent-based models of fishery economics can be implemented within the rejection framework, integrating the biological and economic understanding of the fishery. A simple artificial example of a difference equation bio-economic model is given to demonstrate the approach. Then rejection sampling is applied to an agent-based model for the hairtail (Trichiurus japónicas) fishery, where an operating model is constructed with rejection/agent-based methods and compared to known data and analyses of the fishery. The usefulness of information and rejection filters are illuminated and efficacy examined. The methods can be helpful for strategic guidance where multiple states of nature are possible as a part of management strategy evaluation. [ABSTRACT FROM AUTHOR]

When we survey the current theoretical landscape, we find two distinct approaches to the analysis of worldviews. The systemic approach centers on responses to fundamental worldview questions (aka “big questions”); the cognitive‐behavioral approach focuses on the processes that give rise to behaviors that express worldviews. If we think of worldviews as subjective representations of the environment, that is, subjective “worlds,” we can think of the first approach as a means of eliciting, documenting, and comparing “worlds‐made” and the second as a framework for understanding the nonconscious processes of “world‐making.” It is not clear, however, how the two approaches are related. If human answers to the fundamental worldview questions are simply reflective additions to underlying cognitive processes, we would anticipate that worldview conflicts could be resolved relatively easily. If the implicit answers are embedded in nonconscious processes that are presupposed by various ways of life, we would expect that the process of resolving conflicts would be much more complex. An evolutionary approach, which views world‐making as an evolved capacity, not only suggests that the latter is the case, but also offers a way to integrate the two approaches. If, as an evolutionary approach would suggest, all mobile organisms must implicitly answer basic, species‐appropriate versions of the big questions in order to survive, then we can integrate the two approaches by defining worldviews in terms of simplified big questions that allow for both proximate and ultimate answers. This allows us to embed the systemic framework in an agent‐based cognitive‐behavioral process grounded in the everyday life and behavior of humans and other animals. The article is divided into three parts. The first demonstrates how we can use simplified versions of the big questions to integrate the systemic and cognitive‐behavioral approaches, ground the big questions in ways of life, and shift between systemic and agent‐based perspectives. The second offers more refined analytic concepts—modes, scale, and scope—for characterizing this dynamic, multilevel approach to worldviews. The third offers several comparisons to illustrate the benefits of this more‐nuanced approach in the context of conflict resolution.

Abstract Rapid advances in the field of movement ecology have led to increasing insight into both the population‐level abundance patterns and individual‐level behaviour of migratory species. Despite this progress, research questions that require scaling individual‐level understanding of the behaviour of migrating organisms to the population level remain difficult to investigate. To bridge this gap, we introduce a generalizable framework for training full‐annual cycle individual‐based models of migratory movements by combining information from tracking studies and species occurrence records. Focusing on migratory birds, we call this method: Models of Individual Movement of Avian Species (MIMAS). We implement MIMAS to design individual‐based models of avian migration that are trained using previously published weekly occurrence maps and fit via Approximate Bayesian Computation. MIMAS models leverage individual‐ and population‐level information to faithfully represent continental‐scale migration patterns. Models can be trained successfully for species even when little existing individual‐level data is available for parameterization by relying on population‐level information. In contrast to existing mathematical models of migration, MIMAS explicitly represents and estimates behavioural attributes of migrants. MIMAS can additionally be used to simulate movement over consecutive migration seasons, and models can be easily updated or validated as new empirical data on migratory behaviours becomes available. MIMAS can be applied to a variety of research questions that require representing individual movement at large scales. We demonstrate three applied uses for MIMAS: estimating population‐specific migratory phenology, predicting the spatial patterns and magnitude of ectoparasite dispersal by migrants, and simulating the spread of a pathogen across the annual cycle of a migrant species. Currently, MIMAS can easily be used to build models for hundreds of migratory landbird species but can also be adapted in the future to build models of other types of migratory animals.

Many of the world’s fisheries are “data-limited” where the information does not allow precise determination of fish stock status and limits the development of appropriate management responses. Two approaches are proposed for use in data-limited stock management strategy evaluations to guide the evaluations and to understand the sources of uncertainty: rejection sampling methods and the incorporation of more complex socio-economic dynamics into management evaluations using agent-based models. In rejection sampling (or rejection filtering) a model is simulated many times with a wide range of priors on parameters and outcomes are compared multiple filtering criteria. Those simulations that pass all the filters form an ensemble of feasible models. The ensemble can be used to look for robust management strategies, robust to both model uncertainties. Agent-based models of fishery economics can be implemented within the rejection framework, integrating the biological and economic understanding of the fishery. A simple artificial example of a difference equation bio-economic model is given to demonstrate the approach. Then rejection sampling is applied to an agent-based model for the hairtail (Trichiurus japonicas) fishery, where an operating model is constructed with rejection/agent-based methods and compared to known data and analyses of the fishery. The usefulness of information and rejection filters are illuminated and efficacy examined. The methods can be helpful for strategic guidance where multiple states of nature are possible as a part of management strategy evaluation.

Objective: Rapid changes in the world's oceans make assessment of fish population responses to multiple stressors, especially on scales relevant to management, increasingly important. I used an existing agent‐based, spatially explicit model of Atlantic Croaker Micropogonias undulatus in the northern Gulf of Mexico to examine how temperature, hypoxia, and ocean acidification, singly and in combinations, affect long‐term population dynamics. Methods: I performed a factorial simulation experiment with each stressor at three levels and analyzed various treatment combinations to assess the additivity and multiplicity of interactions. The response variables were long‐term equilibrium (final year) values of spawning stock biomass (SSB), recruitment, weight at age, and two measures of stock productivity (recruits per SSB and maximum recruitment) derived from the spawner–recruit relationship fitted to model output. I used the single‐stressor effects from the experiment to predict how the response variables would change when all three stressors were changed. Single‐stressor effects were combined as the sum of the fractional changes (additive scale) and the product of ratios of changes (multiplicative scale) and compared to the responses in simulations with all stressors imposed. Result: Analyzing the factorial design for two‐way and three‐way interactions showed that there were many interactions on the additive scale but very few on the multiplicative scale. Thus, the responses to multiple stressors were well predicted from single stressor effects when combined as multiplicative effects. Conclusion: I discuss how the lack of strong interactions could be due to model assumptions, the structure of the model, or oversimplified representation of stressor effects. Alternatively, the model and analysis may be sufficiently realistic and weak interactions on the multiplicative scale may be common. This would reduce a complicated multi‐factor situation to a series of more tractable single‐factor effects. A critical next step is to determine how we can a priori identify situations of low interactions (i.e., predictable from single‐stressor effects) without having to already know the multi‐stressor response. Impact statementThe prevailing view is that population responses to multiple stressors are complex. Simulations of temperature, dissolved oxygen, and ocean acidification effects using a croaker population dynamics model showed that population responses to all three stressors together were predictable by combining the effects of each stressor alone. [ABSTRACT FROM AUTHOR]

Rapid advances in the field of movement ecology have led to increasing insight into both the population‐level abundance patterns and individual‐level behaviour of migratory species. Despite this progress, research questions that require scaling individual‐level understanding of the behaviour of migrating organisms to the population level remain difficult to investigate.To bridge this gap, we introduce a generalizable framework for training full‐annual cycle individual‐based models of migratory movements by combining information from tracking studies and species occurrence records. Focusing on migratory birds, we call this method: Models of Individual Movement of Avian Species (MIMAS). We implement MIMAS to design individual‐based models of avian migration that are trained using previously published weekly occurrence maps and fit via Approximate Bayesian Computation.MIMAS models leverage individual‐ and population‐level information to faithfully represent continental‐scale migration patterns. Models can be trained successfully for species even when little existing individual‐level data is available for parameterization by relying on population‐level information. In contrast to existing mathematical models of migration, MIMAS explicitly represents and estimates behavioural attributes of migrants. MIMAS can additionally be used to simulate movement over consecutive migration seasons, and models can be easily updated or validated as new empirical data on migratory behaviours becomes available.MIMAS can be applied to a variety of research questions that require representing individual movement at large scales. We demonstrate three applied uses for MIMAS: estimating population‐specific migratory phenology, predicting the spatial patterns and magnitude of ectoparasite dispersal by migrants, and simulating the spread of a pathogen across the annual cycle of a migrant species. Currently, MIMAS can easily be used to build models for hundreds of migratory landbird species but can also be adapted in the future to build models of other types of migratory animals. [ABSTRACT FROM AUTHOR]

We propose a new agent-based model for studying wealth distribution. We show that a model that links wealth to information (interaction and trade among agents) and to trade advantage is able to qualitatively reproduce real wealth distributions, as well as their evolution over time and equilibrium distributions. These distributions are shown in four scenarios, with two different taxation schemes where, in each scenario, only one of the taxation schemes is applied. In general, the evolving end state is one of extreme wealth concentration, which can be counteracted with an appropriate wealth-based tax. Taxation on annual income alone cannot prevent the evolution towards extreme wealth concentration. [ABSTRACT FROM AUTHOR]

In the past, the bottom-up study of financial stock markets relied on first-generation multi-agent systems (MAS) , which employed zero-intelligence agents and often required the additional implementation of so-called noise traders to emulate price formation processes. Nowadays, thanks to the tools developed in cognitive science and machine learning, MAS can quantitatively gauge agent learning, a pivotal element for information and stock price estimation in finance. In our previous work, we therefore devised a new generation MAS stock market simulator , which implements two key features: firstly, each agent autonomously learns to perform price forecasting and stock trading via model-free reinforcement learning ; secondly, all agents ' trading decisions feed a centralised double-auction limit order book, emulating price and volume microstructures. Here, we study which trading strategies (represented as reinforcement learning policies) the agents learn and the time-dependency of their heterogeneity. Our central result is that there are more ways to succeed in trading than to fail. More specifically, we find that : i- better-performing agents learn in time more diverse trading strategies than worse-performing ones, ii- they tend to employ a fundamentalist, rather than chartist, approach to asset price valuation, and iii- their transaction orders are less stringent (i.e. larger bids or lower asks). [ABSTRACT FROM AUTHOR]

This paper enhances the agent model of ordinary individuals by incorporating the roles of places in the transmission, prevention, and control in the process, establishing a fundamental connection between these two types of agents through individual travel rules. The impact of real-world prevention and control measures on regional epidemic transmission is studied based on this model. Firstly, based on the analysis of place elements in typical cases of major infectious diseases, we give the classification of places. Secondly, the resident agent and the place agent are constructed and rules are established to form an agent-based major infectious disease transmission model considering the place. Then, the simulation process is established and an ideal regional environment is constructed to simulate overall changes in major infectious disease scale and spread speed. Additionally, evolutionary simulations are conducted for individual isolation intensity and place control time during emergency management stages to compare infection outcomes, and we analyze different roles and application scenarios of specific prevention and control measures. Finally, simulation results from real major infectious disease cases in Yangzhou, China, validate the effectiveness of this model in predicting major infectious disease development trends as well as evaluating prevention and control measure effects.

This study conducts a comprehensive review of macroeconomic models within the Water, Energy, Food, and Ecosystem (WEFE) nexus, considering four different approaches: computable general equilibrium (CGE) models, integrated assessment models (IAMs), agent-based models (ABMs), and dynamic stochastic general equilibrium (DSGE) models. Specifically, we examine how macroeconomic models represent not only the WEFE nexus as a whole but also its individual components and their combinations. Spanning a collection of 77 papers published in the last 20 years, this review underscores the prevalence of CGE models and IAMs, followed by ABMs, as dominant avenues of research within this field. CGE models frequently investigate interconnections between pairs of WEFE elements, while IAMs focus on the whole nexus. At the same time, ABMs do not exhibit a clear pattern, whereas DSGE models predominantly concentrate on the energy component alone. Overall, our findings indicate that the development of DSGE models and ABMs is still in its early stages. DSGE models potentially allow the analysis of uncertainty and risk in this field, while ABMs might offer new insights into the complex interactions between natural and human systems but still lack a common framework.

This paper discussed the multi-projects scheduling problem in Cloud Manufacturing system, where each of the projects is a set of interrelated tasks, and these projects need to be scheduled timely and carefully. However, scheduling massive projects can be challenging due to the uneven distribution of the services and the uncertain arrival of projects. Therefore, we (1) established a dual-objectives optimisation model to minimise both the total makespan and the logistical distance; (2) proposed a Reinforcement Learning based Assigning Policy (RLAP) approach to obtain non-dominated solution set; (3) designed a dynamic state representing an algorithm for agents to determine their decision environment when using RLAP. Experiment results show that RLAP can adjust the distribution of service load according to the nearby tasks, and the schedule quality is improved by and compared with NSGA-II and Q-learning, respectively. Besides, the RLAP method has the ability to schedule stochastically arriving projects. [ABSTRACT FROM AUTHOR]

Bone mechanobiology is the study of the physical, biological and mechanical processes that continuously affect the multiscale multicellular system of the bone from the organ to the molecular scale. Current knowledge derives from experimental studies, which are often limited to gathering qualitative data in a cross-sectional manner, up to a restricted number of time points. Moreover, the simultaneous collection of information about 3D bone microarchitecture, cell activity as well as protein distribution and level is still a challenge. In silico models can expand qualitative information with hypothetical quantitative systems, which allow quantification, testing and comparison to existing quantifiable experimental data. An overview of multiscale, multiphysics, agent-based and hybrid techniques and their applications to bone mechanobiology is provided in the present review. The study analysed how mechanical signals, cells and proteins can be modelled in silico to represent bone remodelling and adaptation. Hybrid modelling of bone mechanobiology could combine the methods used in multiscale, multiphysics and agent-based models into a single model, leading to a unified and comprehensive understanding of bone mechanobiology. Numerical simulations of in vivo multicellular systems aided in hypothesis testing of such in silico models. Recently, in silico trials have been used to illustrate the mechanobiology of cells and signalling pathways in clinical biopsies and animal bones, including the effects of drugs on single cells and signalling pathways up to the organ level. This improved understanding may lead to the identification of novel therapies for degenerative diseases such as osteoporosis.

Peer-to-peer car sharing is a service model in which car owners let others use their vehicle while it is not in use by themselves. To simulate such a service in the agent-based transport simulation framework MATSim, a novel synchronization concept is proposed that considers limited resources in the decision-making of the agents. The concept is introduced in detail and demonstrated for peer-to-peer car sharing on a conceptual use case and a large-scale simulation for Berlin. The concept is generalizable to other constrained resources, such as parking or micromobility. [ABSTRACT FROM AUTHOR]

Growing e-commerce activities cause an equally rapid rise in demand for courier, express, and parcel (CEP) transportation, resulting in increased emissions and substantial strain on the road infrastructure, especially in urban areas. Therefore, transportation planners investigate the potential of alternative concepts for parcel and freight transport. Commonly freight demand models are used, however, current models for CEP-based transportation typically focus on private parcel demand. In this paper, we extend the integrated agent-based model logiTopp of last-mile parcel delivery and private travel demand by introducing company agents and, consequently, first-mile deliveries. We present a concept for modeling (the number of) relations between companies and carriers, as these relations highly influence the resulting travel demand in the survey area. Along with implementation techniques, we provide a greedy algorithm to estimate these carrier relations, which takes carrier market shares, customer parcel demand, and carrier capacity into account. All developed model components are solely based on open-source data. [ABSTRACT FROM AUTHOR]

Fabrication areas in semiconductor industry are considered one of the most complex production systems. This complexity is caused by the high-mix of products and end-user market-based demands in that industry. Its dynamic and challenging processing requirements affect the handling capabilities of traditional production management paradigms. In this paper, we propose an application for dispatching and resources allocation through reinforcement learning. The application is based on a discrete-event simulation model for a case study of a real semiconductor manufacturing system. The model is built using both data-driven and agent-based approaches. The model simulates the various processing aspects that are present normally in these complex systems. The model's agents are responsible for dispatching tasks and allocation of the different system's resources. They employ Deep-Q-Network reinforcement learning. They learn simultaneously through the model execution. An independent Deep-Q-Network is trained for each agent. The model provides the training environment for the agents in which their decisions are applied and assessed for their adequacy. Our formulation of the environment's state and the reward function for the learning algorithms creates cooperative decision-making policies for the agents. This results in improving the global performance of the whole system, and the performance of each agent's resources. Our approach is compared to heuristics-based strategies that are applied in our case study. It achieved better production performance than the currently applied strategy. [ABSTRACT FROM AUTHOR]

This paper develops an agent-based model of the spread of highly pathogenic avian influenza in a regional poultry sector. Spatially located flocks and flock owners interact with each other within a realistic regional geography. A specific regional poultry industry in Thailand shapes the model details: production practices that affect bio-containment, key transportation routes and methods, commercial and social networks, and an unusually detailed tabulation of flock types, flock owners, and human behaviors. Model simulations follow disease spread from an initial infection through both direct and indirect transmission pathways. This demonstrates how to realistically calibrate a model of disease spread to a regional poultry industry, while attending to the relative importance of different pathways. Despite its region-specific calibration, the model design facilitates easy adaptation to similar settings. This supports public policy by providing a modeling framework that may be used to simulate interventions to reduce or prevent the regional transmission of avian influenza. [ABSTRACT FROM AUTHOR]

Traditionally, agent-based modelling and simulation relied on using utility function in agents’ decision-making process. Some drawbacks in this process are identified, and a potential remedy to the issue is proposed. This paper introduces a methodological framework for building a hybrid agent-based model that aims to overcome some of the elaborated problems related to the usage of a utility function. In the proposed approach, a machine learning algorithm substitutes the utility function, thus providing a possibility to use various algorithms. The proposed methodological framework has been applied to a case study of churn in a telecommunications company. Three models have been created and used for simulation experiments, two using the proposed methodology and one using utility function. The pros and cons of different approaches are identified and discussed.

IntroductionThe onset of the SARS-CoV-2 pandemic alerted the Philippine government to impose the enhanced community quarantine (ECQ) as a means to hamper human mobility and interaction and eventually diminish transmission. Due to severe limitations in accessibility to basic needs due to ECQ, the government devised amelioration programs. A year after the declaration of the SARS-CoV-2 pandemic, variants of concern were detected locally. Consequently, there is a necessity to prepare reinstatement of strict non-pharmaceutical interventions while meeting the food-related basic needs of the population. Studies related to food distribution during a strict community quarantine have been lacking. The importance of allocating provisions during extreme pandemic measures should be properly analyzed, especially when attempts had been made by local government units.MethodsThis study devised an agent-based model (ABM) to observe the effects of the food relief system in mitigating the disease during Davao City ECQ when two variants are present in two adjacent villages. These relief distribution types are as follows: “regular and sufficient,” “regular but insufficient,” and “irregular” relief type. In total, three barangay scenarios were considered.Results and discussionFor the worst-case scenario, wherein a lot of infections are anticipated, the results show that the “irregular” relief type peaked at the highest number of cases, while the “regular and sufficient” relief type showed little to almost no new cases. The compromise-case scenario showed almost no difference between “regular but insufficient” and “regular and sufficient.” For the best-case scenario, the three relief types showed low average infected cases with almost small variance. The model was then compared, situationally, with Davao City barangays during ECQ and recommended which food relief type applies to the barangays. This could serve as a baseline on how food reliefs could be optimally distributed in cases where barangay conditions differently affect and transmit the SARS-CoV-2 virus of different variants with varying transmission rates within a community. Further development of the model should potentially be useful for decision support not only during pandemics but also in contexts where resource allocation to a community is involved.

One of the important applications of simulation modeling methods is forecasting the spread of epidemic dis- eases. System-dynamic and agent-based approaches are used in this area. The goal of agent-based modeling is to obtain an understanding of these global rules, the general behavior of the system, based on assumptions about the individual, private behavior of its individual active objects and the interaction of these objects in the system. Sys- tem-dynamic and agent-based models of the spread of epidemic diseases have complementary properties, and therefore, the task of jointly using these classes of models to predict the dynamics of epidemic systems is relevant and requires a comprehensive comparison of their characteristics and properties. [ABSTRACT FROM AUTHOR]

Ecosystem modeling is a critical process for understanding complex systems at spatiotemporal scales needed to conserve, manage, and restore ecosystem services (ESs). Although mangrove wetlands are sources of ESs worth billions of dollars, there is a lack of modeling tools. This is reflected in our lack of understanding of mangroves' functional and structural attributes. Here, we discuss the "state of the art" of mangrove models used in the planning and monitoring of R/R projects during the last 30 years. The main objectives were to characterize the most frequent modeling approach, their spatiotemporal resolution, and their current utility/application in management decisions. We identified 281 studies in six broad model categories: conceptual, agent-based (ABM), process-based (PBM), spatial, statistical, and socioeconomic/management (ScoEco). The most widely used models are spatial and statistical, followed by PBM, ScoEco, and conceptual categories, while the ABMs were the least frequently used. Yet, the application of mangrove models in R/R projects since the early 1990s has been extremely limited, especially in the mechanistic model category. We discuss several approaches to help advance model development and applications, including the targeted allocation of potential revenue from global carbon markets to R/R projects using a multi-model and integrated approach. [ABSTRACT FROM AUTHOR]

Animal movement datasets are growing in number and depth, and researchers require a growing number of analytical approaches to adequately answer questions using movement datasets. As the complexity of questions and analyses increase, deciding on the best approach both in terms of study design and analysis can become more difficult. A potential solution is to simulate an array of synthetic datasets under varying study designs and simulation parametrisations to gain insight into the impact of analysis choice(s) in different contexts. The abmAnimalMovement R package provides the means of simulating animal movement for this purpose. The abmAnimalMovement simulations use a discrete time agent-based model and does not require previous movement data as an input. The simulations include a number of key internal and external movement influences, as well as parameters for navigation and mobility capacity of the animal. Internal influences include three predefined behavioural states (e.g., rest, explore, forage) and any number of activity cycles (e.g., diel, seasonal). External influences are implemented via matrices describing landscape characteristics (e.g., shelter quality, foraging resources, movement ease), and predefined points describing shelter sites and points the animal aims to avoid. Navigation capacity is defined by the range the animal can dynamically choose a foraging location to which it is subsequently attracted. Mobility capacity is implemented by user defined distributions, from which step length and turn angles are draw at each time step, governing the possible subsequent locations of the animal. Critically, the navigation capacity (the choice of destination) operates on a different time scale to the mobility capacity, allowing the internal state of the animal to differ from the observed movements. When combined with other emergent properties, such as site fidelity generated via repeated shelter site use, the simulations offer opportunities to test whether movement analyses can accurately recover hidden mechanisms, states, and drivers.