Multi-agent Simulation Approach Research Articles

Assessing the impact of disease incidence and immunization on the resilience of complex networks during epidemics

Disease severity through an immunized population ensconced on a physical network topology is a key technique for preventing epidemic spreading. Its influence can be quantified by adjusting the common (basic) methodology for analyzing the percolation and connectivity of contact networks. Stochastic spreading properties are difficult to express, and physical networks significantly influence them. Visualizing physical networks is crucial for studying and intervening in disease transmission. The multi-agent simulation method is useful for measuring randomness, and this study explores stochastic characteristics of epidemic transmission in various homogeneous and heterogeneous networks. This work thoroughly explores stochastic characteristics of epidemic propagation in homogeneous and heterogeneous networks through extensive theoretical analysis (positivity and boundedness of solutions, disease-free equilibrium point, basic reproduction number, endemic equilibrium point, stability analysis) and multi-agent simulation approach using the Gilespie algorithm. Results show that Ring and Lattice networks have small stochastic variations in the ultimate epidemic size, while BA-SF networks have disease transmission starting before the threshold value. The theoretical and deterministic aftermaths strongly agree with multi-agent simulations (MAS) and could shed light on various multi-dynamic spreading process applications. The study also proposes a novel concept of void nodes, Empty nodes and disease severity, which reduces the incidence of contagious diseases through immunization and topologies.

Simulation analysis of dual-end queuing ride-hailing system considering driver-side queue management

Ride-hailing services have transformed urban transportation through convenience yet introduced new complexities around efficiency and traffic management. This study investigates the dual-queuing problem in ride-hailing from the driver perspective using a multi-agent simulation approach. The focus is dissecting the dynamics between driver search times and passenger wait times, which critically influence operational efficiency especially during peak demand. Exploring these interactions aims to uncover insights that could improve service efficiency and customer satisfaction. Addressing such ride-hailing challenges is vital not just for individual providers but also for advancing sustainable mobility across rapidly growing metropolitan regions. Enhanced efficiency connects to broader urban development narratives around livability, accessibility, and responsible mobility ecosystems.

Transition from machine intelligence to knowledge intelligence: A multi-agent simulation approach to technology transfer

Abstract The traditional machine intelligence system lacks deep understanding and reasoning ability. This study took the automatic driving system in multi-agent as an example to bring higher-level intelligence and decision-making ability to automatic driving through knowledge intelligence. It obtained real-world geographic information data from OpenStreetMap, preprocessed the data, and built a virtual environment. The inception model was used to identify information in environmental images, and the knowledge information of traffic regulations, road signs, and traffic accidents was expressed to build a knowledge map. The knowledge related to automatic driving was integrated, and automatic driving training was carried out through the reward mechanism and the deep Q-network (DQN) model. About 13 kinds of traffic situations were set up in the virtual environment, and the traditional machine intelligence autonomous driving and knowledge fusion autonomous driving multi-agent were compared. The results show that the average number of accidents in 100,000 km of traditional machine intelligence autonomous driving and knowledge fusion autonomous driving multi-agents was 3 and 1.4, and the average number of violations in 100,000 km was 4.3 and 1.8, respectively. The average graphics processing unit (GPU) utilization rate of knowledge fusion autonomous driving in 13 virtual environments was 75.9%, and the average peak GPU utilization rate was 96.1%. Knowledge fusion of autonomous driving multi-agents can effectively improve the safety of autonomous driving and enable autonomous driving multi-agents to have a higher level of decision-making ability.

Multi-Agent Simulation Approach for Modular Integrated Construction Supply Chain

The shift from traditional on-site to off-site construction marks a significant evolution in the construction industry, characterized by increasing levels of prefabrication. These advancements enhance construction efficiency, reduce lead times, and mitigate environmental impacts, leading to modular integrated construction (MiC). However, MiC presents complex supply chain challenges, particularly in the transportation of prefabricated components and fully integrated modules. This study addresses these challenges by employing a multi-agent simulation using AnyLogic to optimize MiC transport logistics. The simulation models the interactions of various agents involved in the MiC process to improve operational efficiency and reduce costs. Results demonstrate that using three vehicles per supplier minimizes total transport costs, effectively balancing fixed and variable expenses while eliminating penalties for project delays. The findings highlight the cost efficiency of MiC, showing potential savings due to centralized assembly and optimized logistics. These significantly reduce material transportation and related costs, contributing to the overall efficiency and sustainability of construction projects. These insights underscore the value of multi-agent simulation in addressing the complexities of MiC supply chains.

Micro-Household Human Capital Investment Decisions and a Simulation Study from the Intergenerational Conflict Perspective.

Education is highly valued in Asian families. However, as family members age, competition for intra-family resources affects children's actualization in the family, which impacts the family's future capital. However, most existing studies have interpreted the family's intergenerational conflicts in terms of care services for older adults, and few have analyzed and simulated intra-family competition based on the intergenerational conflict. This study introduces a multi-agent simulation approach to observe micro-households' educational investment choices under the dual pressures of retirement and childcare. This measure captures households' investment choices and provides a decision basis for given households. Using data from the China Family Panel Study for 2014, 2016, and 2018, we explore the impact of these dual pressures on household educational expenditures and their differences across urban and rural areas, household aging, and income samples. We also simulated the micro-households' investment choices under these dual pressures to observe that these pressures reduce investments in educational human capital in these "sandwich-like" households. The simulation results suggest that households with high childcare stress invest more in education than those with a high retirement burden. Moreover, income growth can mitigate the dual stress "crowding-out" effect on education, which is most pronounced in low-income, high childcare-stress households.

Stochasticity of disease spreading derived from the microscopic simulation approach for various physical contact networks

COVID-19 has emphasized that a precise prediction of a disease spreading is one of the most pressing and crucial issues from a social standpoint. Although an ordinary differential equation (ODE) approach has been well established, stochastic spreading features might be hard to capture accurately. Perhaps, the most important factors adding such stochasticity are the effect of the underlying networks indicating physical contacts among individuals. The multi-agent simulation (MAS) approach works effectively to quantify the stochasticity. We systematically investigate the stochastic features of epidemic spreading on homogeneous and heterogeneous networks. The study quantitatively elucidates that a strong microscopic locality observed in one- and two-dimensional regular graphs, such as ring and lattice, leads to wide stochastic deviations in the final epidemic size (FES). The ensemble average of FES observed in this case shows substantial discrepancies with the results of ODE based mean-field approach. Unlike the regular graphs, results on heterogeneous networks, such as Erdős–Rényi random or scale-free, show less stochastic variations in FES. Also, the ensemble average of FES in heterogeneous networks seems closer to that of the mean-field result. Although the use of spatial structure is common in epidemic modeling, such fundamental results have not been well-recognized in literature. The stochastic outcomes brought by our MAS approach may lead to some implications when the authority designs social provisions to mitigate a pandemic of un-experienced infectious disease like COVID-19.

Optimizing total passenger waiting time in an urban rail network: A passenger flow guidance strategy based on a multi-agent simulation approach

With the sharp increase of passenger travel demands in an urban rail transit (URT) network, more and more stations suffer an over-saturated and congested situation during peak hours, which often leads to colossal passenger accumulation in platforms, especially in transfer platforms. Under this state, the passenger flow guidance is a useful method to release the passengers’ pressure and balance the passenger accumulation imbalance. In order to calculate the passenger flow traveling data and the passenger flow guidance (PFG) time, a multi-agent simulation model is firstly established. And then, a two-phase integrated passenger flow assignment based on the backtracking algorithm (BA) is proposed for generating guidance information to minimize total passenger waiting time and release passenger congestion. Besides, a passenger compliance degree to the guidance information is defined to stimulate the passengers’ response to the guidance information in the real world. Finally, a real-world example of the Chongqing subway network with five metro lines and 95 stations is implemented to verify the performance and effectiveness of the proposed method. The total passenger waiting time is decreased by 5.62% under the passenger flow guidance approach.

A Multi-Agent Driving Simulation Approach for Evaluating the Safety Benefits of Connected Vehicles

The in-vehicle warning information provided in the CVs environment allow the driver to respond rapidly to upcoming hazardous situations. The main purpose of this study is to evaluate the safety benefits due to the provision of warning information by analyzing vehicle interactions that are defined as the behavior change of the subject vehicle and the preceding vehicle. A notable feature of this study is the use of a multi-agent driving simulation (MADS) method to analyze the vehicle interaction with various vehicle pairs that are composed of the connected vehicle (CV) capable of receiving warning information and the regular vehicle (RV) that does not receive warning information. A total of four scenarios representing different vehicle pairs, which include CV-RV, RV-CV, CV-CV, and RV-RV, are evaluated in this study. The proposed analysis consists of four parts: the characteristics of subject vehicle maneuvering, variation of relative speed, evasive maneuvering by lane change, and overall crash potential. As an example, the result of analyzing crash potential index (CPI) showed that the greatest safety benefits were obtained with the CV-CV case among aforementioned four vehicle pairs. Approximately 45% of CPI reduction was achievable with the CV-CV case, compared to the RV-RV case. In addition to the CPI, useful findings obtained by investigating safety indicators for each vehicle pair are discussed in terms of safety benefits. The results of this study are expected to be used for both deriving valuable policies and developing more effective in-vehicle warning technologies to fully exploit the benefits of CVs.

Multiagent Simulation Approach to Pedestrian Laminar Flow With Group Walking Models

We propose a model that combines existing walking and crowd models (the social-force, following behavior, and Boids models) to simulate pedestrian flow under realistic states of congestion. We recorded pedestrians walking in a passage inside Shinagawa Station to capture the actual conditions and compared this with our simulation results to verify the reliability of the existing and proposed models. This confirmed that the proposed model could reproduce actual pedestrian flow better than the existing models. Using the verified proposed model, we examined the number of rows deep in which pedestrians can comfortably walk down a crowded passage. We then calculated the magnitude of the average number of people leaving as well as their walking speed depending on the number of rows deep in which the pedestrians walked. This analysis confirmed that pedestrians could comfortably walk down a passage that is 16-m wide by 64-m long when there are only two or three rows of pedestrian flow.

Interplay between cost and effectiveness in influenza vaccine uptake: a vaccination game approach.

Pre-emptive vaccination is regarded as one of the most protective measures to control influenza outbreak. There are mainly two types of influenza viruses—influenza A and B with several subtypes—that are commonly found to circulate among humans. The traditional trivalent (TIV) flu vaccine targets two strains of influenza A and one strain of influenza B. The quadrivalent (QIV) vaccine targets one extra B virus strain that ensures better protection against influenza; however, the use of QIV vaccine can be costly, hence impose an extra financial burden to society. This scenario might create a dilemma in choosing vaccine types at the individual level. This article endeavours to explain such a dilemma through the framework of a vaccination game, where individuals can opt for one of the three options: choose either of QIV or TIV vaccine or none. Our approach presumes a mean-field framework of a vaccination game in an infinite and well-mixed population, entangling the disease spreading process of influenza with the coevolution of two types of vaccination decision-making processes taking place before an epidemic season. We conduct a series of numerical simulations as an attempt to illustrate different scenarios. The framework has been validated by the so-called multi-agent simulation (MAS) approach.

Multi-Agent Simulation in the Inter-Organizational Trust Model and Knowledge Sharing in the Automotive Industry

Two organizations can share knowledge to determine the success of their collaboration. Effectiveness in sharing knowledge affect the organizational performance and trust between organizations. This study aims to identify the implementation of multi-agent simulations to increase confidence in sharing knowledge between organizations. A multi-agent simulation approach was used in this study. The research model was adopted from previous research. Several hypotheses and path coefficients had tested in simulation models. The results of this study showed that multi-agent simulation is suitable to be implemented as a problem-solving method for a complex problem such as knowledge sharing. Besides, this study found the role of individual factors such as likability and expertise on the success of sharing knowledge between organizations.

Multi-Agent Simulation in the Inter-Organizational Trust Model and Knowledge Sharing in the Automotive Industry

Two organizations can share knowledge to determine the success of their collaboration. Effectiveness in knowledge sharing affects organizational performance and trust between organizations. This study aims to identify the implementation of multi-agent simulations to increase trust in sharing knowledge between organizations. A multi-agent simulation approach was used in this study. The research model was adopted from previous research. Several hypotheses and path coefficients were tested in simulation models. This study showed that multi-agent simulation is suitable as a problem-solving method for complex issues such as knowledge sharing. In addition, this study identified individual factors such as likability and expertise in the success of knowledge sharing between organizations.

A game theoretic approach to discuss the positive secondary effect of vaccination scheme in an infinite and well-mixed population

Abstract Pre-emptive vaccination policy used in controlling the rapid spreading of infectious diseases is considered as one of the most challenging issues imposed to mankind, causing enormous death tolls over the years. This paper dedicatedly studies the dilemma effect coming from the failure of getting perfect immunity to those individuals who committed vaccination earlier. Therefore, we propose a new theoretical model that slows down the infection spreading and also facilitates quicker recovery time than what the previous model does even if a vaccinator fails to obtain perfect immunity. We name this effect as the “positive secondary effect” of vaccination as it gives a second chance to the vaccinators which in return subdues the rapid spreading that helps in producing better social average payoff as well as keeping the final epidemic size smaller. Moreover, to address the positive secondary effect more precisely, we introduce two different parameters; namely, relaxation parameter (η) and foster parameter (δ) in two different directions to quantify the individual effects resulting from each of the parameter space as well as their superposition effect. An in-depth discussion focuses on the influential role played by our proposed model via discounting and faster recovery effects while a second chance is given to the vaccinators. In addition, we also examine the situation when discounting effect brought by η outperforms very much than its faster recovery controlled by δ as well as the superposition effects. Unlike all previous studies dealing with vaccination game, we pay much attention to investigating the secondary effect of imperfect vaccination policy. Our proposed theoretical scheme completely reproduces the decision-making process of choosing an imperfect provision based on evolutionary game theory entailed with the widely used SIR (Susceptible–Infected–Recovered) epidemic model. Without considering any spatial structure and perfect vaccination policy, our model presumes the population being infinite and well-mixed to represent the infection spreading dynamics mathematically. This study is conducted throughout using the so-called theoretical approach. Besides that, three different updating rules based on evolutionary game theory have also been considered to investigate all possible situations. Later on, we draw 2D full phase diagrams showing the final epidemic size, vaccination coverage, and average social payoff quantitatively. Finally, our theoretical result is compared with the counterpart result obtained from the multi-agent simulation (MAS) approach and a good agreement is found, hence the appropriateness of the proposed model is fully justified.

Corrigendum to “Evolutionary Games in the Agricultural Product Quality and Safety Information System: A Multiagent Simulation Approach”

Corrigendum to “Evolutionary Games in the Agricultural Product Quality and Safety Information System: A Multiagent Simulation Approach”

Exploring the Impact of User Preferences on Shared Autonomous Vehicle Modal Split: A Multi-Agent Simulation Approach

Abstract Shared autonomous vehicles are considered to have a transformative impact on future urban transportation and especially Shared Mobility. In order to assess the transport-related impact of this new mode, various models and simulations are under development. The majority of recently proposed simulations are based on activity-based multi-agent approaches. Thanks to the disaggregated level of data in multi-agent simulation, the traveler decision-making mechanisms might be individualized according the attributes. In this paper, we try to take in advantage of this granularity in order to explore the impact of user preferences on the modal split of shared autonomous vehicles. To illustrate the proposed methodology the transport system of Paris is simulated by using an activity-based multi-agent tool called MATSim. The traveler preferences toward shared autonomous vehicles use are also summarized based on the literature review.

A metaheuristic algorithm and simulation to study the effect of learning or tiredness on sequence-dependent setup times in a parallel machine scheduling problem

This work analyses the effects of learning or tiredness on the setup times in a scheduling problem with identical parallel machines. This problem involves setup times that depend on the sequence of jobs with the goal of minimizing the sum of total completion times. Due to the complexity of the problem and the assumption that high-quality solutions of the problem without effects are also high-quality solutions when these effects are considered, we firstly propose a metaheuristic algorithm aimed at finding high-quality and diverse solutions, ignoring the learning/tiredness issues. Then, we study the effects of learning or tiredness on the obtained solutions in a real-world scenario by using a multi-agent simulation approach. The computational experiments carried out demonstrate that the simulation model developed in this work is valid to handle randomness in a practical scenario, allowing to be adapted to different learning or tiredness effects. Furthermore, the computational experiments underscore the fact that the proposal can be used as a decision support tool aimed at estimating the amount of job to be assigned to the available machines on the basis of the operator profile.

A simulation-based approach to investigate the driver route choice behavior under the connected vehicle environment

Drivers can move with the real-time traffic information under the connected vehicle environment. Meanwhile, individual driving behavior (e.g., route choice) can influence the traffic state of the connected vehicle. This study contributes to investigate the relationship between the performance of traffic system and the driver route choice behavior in the connected vehicle environment. Especially, the heterogeneity of drivers in attention to fuel consumption and travel time is considered. A dynamic process of commuter day-to-day route choice behavior under the connected vehicle environment was proposed, the driving experience and the real-time information that affect the driver route choice are taken into considered simultaneously. Using the multi-agent simulation approach, the microcosmic and macroscopic behaviors of driver-vehicle-unit agents were described. A series of simulation experiments were conducted under the different percentages of drivers who pay enough attention to fuel consumption, and then simulation results were compared. The simulation results demonstrated that the performance of traffic state is better when enough drivers put emphasis on fuel consumption, and the optimal percentage of these drivers is about 60%. It is indicated that the travel time is more sensitive to the change of traffic state than fuel consumption, and the traffic system with enough drivers who care the fuel consumption could bring the better performance. This study has a guidance function to the application and promotion of eco-driving for government to optimize the structure of different types of drivers in the connected vehicle environment.

Evolutionary Games in the Agricultural Product Quality and Safety Information System: A Multiagent Simulation Approach

This paper aims at identifying the key factors to maintain the quality and safety of agricultural products in the agricultural product quality and safety information system (APQSIS). Based on the theoretical framework of information entropy and complexity, this paper uses the dynamic evolutionary game model and the multiagent modeling and simulation to discuss the APQSIS agents’ equilibrium strategies and the effects of their interactive behaviors on the APQSIS evolutionary stability with asymmetric information. The results show that the governmental supervision and intermediary organizations are significant to assuring agricultural product quality and safety (APQS) as well as the effective transmission of APQS information in stable environments with low complexity.

Cooperative survival principles for underground flooding: Vitae System based multi-agent simulation

Evacuation and rescue principles under life-threatening situations are clarified.A multi-agent simulation approach based on the Vitae System model is presented.A person's ability is characterized by survivability, vitality, and conviviality.Three strategies for the evacuation and rescue processes are simulated & compared.The results show that even people at risk (evacuees) can assist one another. Evacuation and rescue principles under life-threatening situations are clarified using a simulation model designed with a Vitae System-based agent. The Vitae System model identifies an individual's ability to maintain his or her life under life-threatening situations as characterized by three functions: survivability, vitality, and conviviality. This simulation model forms a basis for analyzing rescue strategies. As an illustration, these three functions are specifically defined to operationalize the Vitae System conceptual model employing an underground inundation case study in which people must be evacuated. Three strategies for the evacuation and rescue processes under life-threatening flooding are simulated and compared. The results show that even people at risk (evacuees) can assist one another if there are sufficient and appropriate strategies and equipment for survival. In addition to the current mitigation plan, the suggested preparations for an extreme disaster provide another type of safety net for saving human life.

Modeling human behavior during emergency evacuation using intelligent agents: A multi-agent simulation approach

It is costly and takes a lot of time for disaster employees to execute several evacuation drills for a building. One cannot glean information to advance the plan and blueprint of forthcoming buildings without executing many drills. We have developed a multi-agent system simulation application to aid in running several evacuation drills and theoretical situations. This paper combines the genetic algorithm (GA) with neural networks (NNs) and fuzzy logic (FL) to explore how intelligent agents can learn and adapt their behavior during an evacuation. The adaptive behavior focuses on the specific agents changing their behavior in the environment. The shared behavior of the agent places an emphasis on the crowd-modeling and emergency behavior in the multi-agent system. This paper provides a fuzzy individual model being developed for realistic modeling of human emotional behavior under normal and emergency conditions. It explores the impact of perception and emotions on the human behavior. We have established a novel intelligent agent with characteristics such as independence, collective ability, cooperativeness, and learning, which describes its final behavior. The contributions of this paper lie in our approach of utilizing a GA, NNs, and FL to model learning and adaptive behavior of agents in a multi-agent system. The planned application will help in executing numerous evacuation drills for what-if scenarios for social and cultural issues such as evacuation by integrating agent characteristics. This paper also compares our proposed multi-agent system with existing commercial evacuation tools as well as real-time evacuation drills for accuracy, building traffic characteristics, and the cumulative number of people exiting during evacuation. Our results show that the inclusion of GA, NNs, and fuzzy attributes made the evacuation time of the agents closer to the real-time evacuation drills.