Agent-based Modeling And Simulation Research Articles

Interactive Agent-Based Simulation for Experimentation: A Case Study with Cooperative Game Theory

Incorporating human behavior is a current challenge for agent-based modeling and simulation (ABMS). Human behavior includes many different aspects depending on the scenario considered. The scenario context of this paper is strategic coalition formation, which is traditionally modeled using cooperative game theory, but we use ABMS instead; as such, it needs to be validated. One approach to validation is to compare the recorded behavior of humans to what was observed in our simulation. We suggest that using an interactive simulation is a good approach to collecting the necessary human behavior data because the humans would be playing in precisely the same context as the computerized agents. However, such a validation approach may be suspectable to extraneous effects. In this paper, we conducted a correlation research experiment that included an investigation into whether game theory experience, an extraneous variable, affects human behavior in our interactive simulation; our results indicate that it did not make a significant difference. However, in only 42 percent of the trials did the human participants’ behavior result in an outcome predicted by the underlying theory used in our model, i.e., cooperative game theory. This paper also provides a detailed case study for creating an interactive simulation for experimentation.

Agent-based modeling and simulation in the research of environmental sustainability. A bibliography

In the context of the alarming news about climate changes, resource depletion, ecological disasters, and demographic trends, the interest of the researchers and the scientific literature on topics related to sustainability grew exponentially in the past two decades. In 2020 only, the number of publications indexed in the Web of Science (WoS) having the keywords “sustainability” or “sustainable development” in the topic was higher than 42,000. Facing this flood of literature, the present work aims to ease the effort of locating relevant studies about the applications of Agent-based Modeling and Simulation (ABMS) in the research of environmental sustainability. This goal is accomplished by: (a) providing a comprehensive bibliography of recent relevant articles on this topic, (b) identifying the most popular domains of application of ABMS in this field, and (c) building a list of the available simulation tools. We found that most of the selected studies (52%) are designed for management related acticities (decision support, policy making etc.), the application domain addressed by the largest number of articles (27%) is agriculture and related activities (pastoralism, hunting, fishery, and forestry), and the most popular simulation platform is NetLogo (15%).

Agent-based modeling and simulation of edible fungi growers' adoption behavior towards fungal chaff recycling technology

CONTEXTEdible fungus is considered a green, recyclable, and highly-efficient industry, which prolongs energy flow and improves the effectiveness of resource utilization. However, the disposal of post-harvest waste is a thorny problem. OBJECTIVETo explore the characteristics and regular technology diffusion patterns of fungal chaff recycling technology, this study modeled and simulated edible fungi growers' adoption behavior towards recycling technology based on the combination of Logit regression and Agent-based modeling and simulation (ABMS). METHODSThe main factors affecting the technology adoption behavior of edible fungi growers were identified, and an agent-based model was constructed to simulate farmers' adoption behavior. Then, a farmer investigation was conducted in Beijing to determine the parameters in the simulation model. Finally, four scenarios were designed and the corresponding simulation experiments were performed to simulate growers' adoption behavior for recycling technologies in different situations. RESULTS AND CONCLUSIONSThe results revealed that the main influencing factors were the production scale, farmers' perceived ease of use and perceived risk, others' recommendations, and social network communication. At the current adoption rate, technology diffusion presented an overall attenuation trend under the effect of individual learning and social norms. The final adoption rate of recycling technology increased along with the increment of initial adopters, yet the elastic efficiency was reduced if the increase was too excessive. The optimal increase in the number of initial adopters of the fungal chaff recycling technology was 10%, and the corresponding distribution mode for new initial adopters should be randomly distributed. Social networks had two-sided effects on technology promotion and should be expanded within a certain extent. SIGNIFICANCEThese research findings provide useful suggestions and measures to improve the promotion performance of recycling technology.

Human Decision-making of Coalition Formation: An Experiment Using a Hybrid Agent-Based Model

Incorporating human behavior is a current challenge for agent-based modeling and simulation (ABMS). Human behavior is not a single phenomenon and includes many different aspects, nuances, and scenario-related effects; one scenario is strategic group formation, which is traditionally modeled using cooperative game theory. Human behavior is heterogeneous, which makes ABMS a good choice for modeling its wider effects; however, understanding this heterogeneity is key to effectively modeling human behavior in the first place. This paper presents the results of an experiment to investigate what human characteristics affect strategic group formation. This experiment was conducted using a human-in-the-loop hybrid simulation of a standard cooperative game called the glove game. Demographic information was considered in the statistical analysis, as well as participants' gaming experience. The results showed that these characteristics did not impact the results. The results also showed that the participants achieved stable coalitions, as determined by cooperative game theory, only about 40% of the time. Also, the paper presents a novel approach for conducting human-in-the-loop experiments using a hybrid ABMS approach.

A hierarchical multi-resolution agent-based modeling and simulation framework for household electricity demand profile

Analyzing demand behavior of end consumers is pivotal in long term energy planning. Various models exist for simulating household load profiles to cater different purposes. A macroscopic viewpoint necessitates modeling of a large-scale population at an aggregate level, whereas a microscopic perspective requires measuring loads at a granular level, pertinent to the individual devices of a household. Both aspects have lucrative benefits, instigating the need to combine them into a modeling framework which allows model scalability and flexibility, and to analyze domestic electricity consumption at different resolutions. In this applied research, we propose a multi-resolution agent-based modeling and simulation (ABMS) framework for estimating domestic electricity consumption. Our proposed framework simulates per minute electricity consumption by combining large neighborhoods, the behavior of household individuals, their interactions with the electrical appliances, their sociological habits and the effects of exogenous conditions such as weather and seasons. In comparison with the existing energy models, our framework uniquely provides a hierarchical, multi-scale, multi-resolution implementation using a multi-layer architecture. This allows the modelers flexibility in order to model large-scale neighborhoods at one end, without any loss of expressiveness in modeling microscopic details of individuals’ activities at house level, and energy consumption at the appliance level, at the other end. The validity of our framework is demonstrated using a case study of 264 houses. A validated ABMS framework will support: (a) Effective energy planning; (b) Estimation of the future energy demand; (c) and the analysis of the complex dynamic behavior of the consumers.

Agent-based modeling and simulation of the electricity market with residential demand response

Currently, critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response (RDR) programs. In typical RDR programs, residential customers react to the price or incentive-based signals, but the actions can fall behind flexible market situations. For those residential customers equipped with smart meters, they may contribute more DR loads if they can participate in DR events in a proactive way. In this paper, we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market (DAM). We model and evaluate the interactions between generation companies (GenCos), retailers, residential customers, and the independent system operator (ISO) via an agent-based modeling and simulation (ABMS) approach. The simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning (RL) procedure. The bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR actions. Retailers and GenCos optimize their bidding strategies via the RL procedure. The modified optimization approach in this procedure can prevent the training results from falling into local optimum solutions. The ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker (KKT) conditions. Based on realistic residential data in China, the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential households. Results show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand sides. The models and methods in this paper may be used by utility companies, electricity retailers, market operators, and policymakers to evaluate the consequences of a proactive RDR and the interactions among multi-entities.

Quantitatively assessing the benefits of model-driven development in agent-based modeling and simulation

The agent-based modeling and simulation (ABMS) paradigm has been used to analyze, reproduce, and predict phenomena related to many application areas. Although there are many agent-based platforms that support simulation development, they rely on programming languages that require extensive programming knowledge. Model-driven development (MDD) has been explored to facilitate simulation modeling, by means of high-level modeling languages that provide reusable building blocks that hide computational complexity, and code generation. However, there is still limited knowledge of how MDD approaches to ABMS contribute to increasing development productivity and quality. We thus in this paper present an empirical study that quantitatively compares the use of MDD and ABMS platforms mainly in terms of effort and developer mistakes. Our evaluation was performed using MDD4ABMS—an MDD approach with a core and extensions to two application areas, one of which developed for this study—and NetLogo, a widely used platform. The obtained results show that MDD4ABMS requires less effort to develop simulations with similar (sometimes better) design quality than NetLogo, giving evidence of the benefits that MDD can provide to ABMS.

A Two-Tier Partition Algorithm for the Optimization of the Large-Scale Simulation of Information Diffusion in Social Networks

As online social networks play a more and more important role in public opinion, the large-scale simulation of social networks has been focused on by many scientists from sociology, communication, informatics, and so on. It is a good way to study real information diffusion in a symmetrical simulation world by agent-based modeling and simulation (ABMS), which is considered an effective solution by scholars from computational sociology. However, on the one hand, classical ABMS tools such as NetLogo cannot support the simulation of more than thousands of agents. On the other hand, big data platforms such as Hadoop and Spark used to study big datasets do not provide optimization for the simulation of large-scale social networks. A two-tier partition algorithm for the optimization of large-scale simulation of social networks is proposed in this paper. First, the simulation kernel of ABMS for information diffusion is implemented based on the Spark platform. Both the data structure and the scheduling mechanism are implemented by Resilient Distributed Data (RDD) to simulate the millions of agents. Second, a two-tier partition algorithm is implemented by community detection and graph cut. Community detection is used to find the partition of high interactions in the social network. A graph cut is used to achieve the goal of load balance. Finally, with the support of the dataset recorded from Twitter, a series of experiments are used to testify the performance of the two-tier partition algorithm in both the communication cost and load balance.

Eliciting agents’ behaviour using scenario-based questionnaire in agent-based dairy supply chain simulation

ABSTRACT A scenario-based questionnaire is a survey method that aims to identify the respondents’ decision rules using their responses to a series of scenarios. It is rarely used in agent-based modelling and simulation (ABMS) with most researchers preferring a survey with closed questions as the data collection method. This is particularly true for ABMS studies in agri-food supply chains. In our paper, we design a scenario-based questionnaire to elicit the behaviour of agents in ABMS and apply it in a dairy supply chain case. Our findings suggest that respondents respond well to a scenario-based questionnaire as it relates more closely to their actual decision-making process. Furthermore, our experiment shows that the decision rules extracted using a scenario-based questionnaire improve ABMS validity.

Simulation of trends in the use of e-payment using agent based models

Bank Indonesia (BI) as the central bank in Indonesia has launched a movement to use non-cash instruments in conducting transactions on economic activities. The majority of Indonesian people are increasingly ready to trade without cash or cashless society. The country's economic policy factors, the availability of various non-cash payments, and online sales and purchases, encourage the tendency to use non-cash transactions (e-payment). One way to find out these trends is to use a model. Models can help understand and explain real phenomena more easily and efficiently than directly observing. One model that can be used is Agent Based Modeling and Simulation (ABMS). By using ABMS, the development of models with complex behaviors, dependencies, and interactions can be developed more easily. ABMS is able to describe processes, phenomena, and situations. In this study, the factors that influence the tendency to use e-payment are obtained from various references. From these factors, then created a scenario as a sub-purpose of this model. In simulations using ABMS, detailed descriptions explained based on ODD Protocol elements can be more easily understood and complete. ODD systematically evaluates a model. The advantage is that ODD can improve the accuracy of model formulas and make the theoretical basis more visible.

Evaluating the operational resilience of small and medium-sized enterprises to flooding using a computational modelling and simulation approach: a case study of the 2007 flood in Tewkesbury.

The resilience of small and medium-sized enterprises (SMEs) to disruptive events is significant as this highly prevalent category of business forms the economic backbone in developed countries. This article provides an overview of the application of a computational modelling and simulation approach to evaluate SMEs' operational resilience to flooding based on combinations of structural and procedural mitigation measures that may be implemented to improve their premises' resistance to flooding and safeguard their business continuity. The approach integrates flood modelling and simulation with agent-based modelling and simulation (ABMS) within a modelled geographical environment. SMEs are modelled as agents based on findings of semi-structured interviews with SMEs that have experienced flooding or are at risk of flooding. In this paper, the ABMS has been applied to a new case study of the major flood event of 2007 in Tewkesbury. Furthermore, to enable an evaluation of the operational resilience of manufacturing SMEs in terms of the relative effectiveness of flood mitigation measures, a new coefficient based on production loss is introduced. Results indicate structural mitigation measures are more effective than procedural measures. While this result is intuitive, the approach provides a means of evaluating the relative effectiveness of combinations of mitigation measures that SMEs may implement to enhance their operational resilience to flooding.This article is part of the theme issue ‘Urban flood resilience’.

Agent-based modelling and simulation for ship unloading processes: determining the number of trucks and container cranes

Loading and unloading activities generate nearly fifty per cent of the total cost in port. The loading and unloading process of the container at the port is considered as a complex process since it involves several interrelated components, such as ships, cranes, and trucks. The uncertainty of these component activities might impact the loading and unloading time and cost. Agent-based modelling and simulation (ABMS) approach is a method for analyzing and modelling a complex system. This study aims to simulate the unloading process to determine a strategy to reduces unloading process time in the largest port in Indonesian using ABMS approach. The results show that the agent-based simulation approach is feasible to be applied in port activities. This approach can assist decision-makers in predicting the number of facilities that must be used to minimize processing time.

Research on Synchronization Mechanism of Parallel Time Propulsion Based on ABMS Overlap Area

Abstract The paper proposes a parallel time advance synchronization mechanism based on the overlap region of ABMS (Agent-based Modeling and Simulation). It is driven by events. Combines with the ABMS overlap region, it could ensure the correct execution event causal logic relationship and improve the simulation parallelism and simulation rate. The simulation results shows that the mechanism could be implemented on the X10 high-performance programming language and obtains a good acceleration effect.

Simulation-Based Evolutionary Optimization of Air Traffic Management

In the context of aerospace engineering, the optimization of processes may often require to solve multi-objective optimization problems, including mixed variables, multi-modal and non-differentiable quantities, possibly involving highly-expensive objective function evaluations. In Air Traffic Management (ATM), the optimization of procedures and protocols becomes even more complicated, due to the involvement of human controllers, which act as final decision points in the control chain. In this article, we propose the use of computational intelligence techniques, such as Agent-Based Modelling and Simulation (ABMS) and Evolutionary Computing (EC), to design a simulation-based distributed architecture to optimize control plans and procedures in the context of ATM. We rely on Agent-Based fast-time simulations to carry out offline what-if analysis of multiple scenarios, also taking into account human-related decisions, during the strategic or pre-tactical phases. The scenarios are constructed using real-world traffic data traces, while multiple optimization variables governed by an EC algorithm allow to explore the search space to identify the best solutions. Our optimization approach relies on ad-hoc multi-objective performance metrics which allow to assess the goodness of the control of aircraft and air traffic regulations. We present experimental results which prove the viability of our approach, comparing them with real-world data traces, and proving their meaningfulness from an Air Traffic Control perspective.

Simulation Research on the Effectiveness of a Multiagent Mine Safety Supervision System and Its Verification

A Computable Mine Safety Supervision (CMSS) model is constructed based on agent-based modeling and simulation (ABMS) technology and the conservation of resources (COR). This model aims to solve the mining safety problems involved with illegal mining operations and burnout among mining supervisors, in China. The model includes several types of agents: supervision agents, decision support agents, functional coordination agents, and miner agents, and it uses the Netlogo simulation platform to simulate the influence of reward and punishment on agent behavior. The simulation determines the decision support degree to gauge the influence of functional coordination and miner behavior on the burnout rate of supervision agents. We analyze the macroscopic emergence law of the simulation results. The results show the following: (1) Job Situation Adaptability (JSA) ∈ [−6.02, 2.64] ∪ [16.9, 21.93], which uses a reward strategy to guide miners to choose safe behavior and (2) JSA ∈ [2.64, 16.9], which uses a punishment strategy to restrict unsafe behavior. The decision support coefficient Sc has the greatest influence on the supervision agent’s job burnout. The functional coordination coefficient Fc has the second highest influence on job burnout and the processing effectiveness coefficient Ec has the least influence. According to the simulation results, suggestions for improving the mine safety supervision system are put forward and an improved safety management decision-making basis for reducing mine accidents is provided.

Energy-saving behavior of urban residents in China: A multi-agent simulation

Abstract With recent improvements in residents’ quality of life and the implementation of the two-child policy, guiding the energy-saving behavior of urban residents has become a focus for achieving the national goals of the sustainable development strategy in China. Considering the subjective initiatives of individuals in a realistic environment is the key to studying energy-saving behavior and guiding policy making. This study builds a simulation model of the energy-saving behavior of urban residents using agent-based modeling and simulation (ABMS) methods, which are based on complex adaptive theory. By means of artificial neural networks and the Netlogo simulation platform, the subsequent effect of behavioral outcomes due to the short- and long-term influence of energy-saving behavior and intentions is analyzed in different policy situation. The results show that energy-saving intentions and behavior are poorly matched in the absence of an external policy framework. In the optimal policy situation, residents’ energy-saving intentions and behavior have improved significantly. Policies can significantly encourage energy-saving intentions to become behavior. Different kinds of situational factors have different effects on intentions and the four types of energy-saving behavior. Finally, relevant policy implications are proposed based on analysis of the simulation results.

A multilevel and dynamic model of intragroup conflict and decision making: application of agent-based modeling

Although intragroup conflict has both multilevel and dynamic natures, less attention has been paid to establishing a holistic model of intragroup conflict that emerges across levels and unfolds over time. To address this research gap, we extend the multilevel view of intragroup conflict (Korsgaard et al. 2008) to develop a multilevel and dynamic model of intragroup conflict that explicitly includes (1) the role of time and (2) the feedback loop to encompass the dynamic aspect of intragroup conflict. We further instantiate the extended model in the context of team decision-making. To achieve this and systematically examine the complex relationships, we use agent-based modeling and simulation (ABMS). We directly investigate how two types of intragroup conflict—task and relationship conflict—interplay with cross-level antecedences, interrelate and develop over time, and affect team outcomes. This study adds to the intragroup conflict research by extending the field with multilevel and dynamic views.

Large-scale Agent Data Partitioning Based on DensityRepel-K_medoids

Large-scale Agent data partitioning is the premise of parallel distributed computing in the process of ABMS (Agent-based Modeling and Simulation) . Based on the distance-based K_medoids clustering algorithm, this paper proposes an improved K_medoids algorithm (DensityRepel-K_medoids), which is implemented by high-performance programming language X10 and applied to large-scale Agent data partitioning simulation based on distance interaction. The DensityRepel-Kmedoids algorithm first determines the density value and the repulsion value of each Agent data in the Agent data set, and secondly pre-selects the cluster center according to the density value and the repulsion value of each Agent data, and finally uses the pre-selected cluster centers as the initial cluster centers for continuous iterative clustering until convergence. The algorithm avoids the defects of K_means clustering algorithm sensitive to outliers, and avoids the shortcomings of K_medoids clustering algorithm for large-scale data processing. By comparing and analyzing the simulation experiments of Agent data sets of different scales, the algorithm presents a better performance.

Optimal Allocation Planning for Public EV Charging Station Considering AC and DC Integrated Chargers

The adoption of AC and DC integrated charger(ADC) is considered as a mean to increase the comprehensive utilization rate and reduce the total cost of charging stations. In this paper a simulation-based optimization framework is proposed to solve the allocation problem for public charging stations equipped with multi-type of facilities, including AC slow chargers(ACCs), DC rapid chargers(DCCs) and ADCs. First of all, the characteristics of AC and DC charging demand are predicted, considering private EVs(PEVs) and electric taxis(TEVs). Then a stochastic queuing and service simulation model of multi-type chargers is developed based on agent-based modeling and simulation(ABMS) method, in order to model the complicated interactions among different types of EVs and chargers in detail. On the basis of the simulation model, an allocation optimization model is formulated to minimize the comprehensive annual cost, including charger investment cost, land purchase cost, grid reinforcement cost and queuing time cost. The simulation-based allocation optimization model is solved by embedded the simulation model as a black box into the meta-heuristics solver engine OptQuest. The proposed optimization framework is implemented in different allocation planning scenarios under different situations. The effects of ADC adoption in public charging stations are discussed by comparing the optimal plans with and without ADCs.

Models Within Models – Agent-Based Modelling and Simulation in Energy Systems Analysis

This paper tries to show the various roles agent-based modeling and simulation (ABMS) can play in technology and policy assessment of energy systems. We examine the advantages of ABMS methods using three case studies of electricity market models as example (AMIRIS, EMLab-Generation and PowerACE). In particular, we argue why ABMS might serve as framework for many future energy system models that integrate many different algorithms. We then discuss practical and theoretical problems in the development, validation and assessment of energy-system-analytical ABMS and conclude with an outlook and recommendations for energy system modellers who consider incorporating ABMS into their modelling toolbox.