Agent-based Modeling Paradigm Research Articles

Green Urban Scenarios: A Framework for Digital Twin Representation and Simulation for Urban Forests and Their Impact Analysis

Abstract Background Trees are a critical part of urban infrastructure. Cities worldwide are pledging afforestation objectives due to net-zero targets; however, their realisation requires a comprehensive framework that combines science, policy, and practice. Methods The paper presents the Green Urban Scenarios (GUS) framework for designing and monitoring green infrastructures. GUS considers weather, maintenance, tree species, diseases, and spatial distributions of trees to forecast their impacts. The framework uses agent-based modelling (ABM) and simulation paradigm to integrate green infrastructure into a city’s ecological, spatial, economic, and social context. ABM enables the creation of digital twins for urban ecosystems at any level of granularity, including individual trees, to accurately predict their future trajectories. Digital representation of trees is created using a combination of datasets such as earth observations from space, street view images, field surveys, and qualitative descriptions of typologies within existing and future projects. Machine learning and statistical models calibrate biomass growth patterns and carbon release schemes. Results The paper examines various green area typologies, simulating several hypothetical scenarios based on Glasgow’s urban forests. It exhibits the emergence of heterogeneity features of the forests due to interactions among trees. The growth trajectory of trees has a non-linear transition phase toward stable growth in its maturity. Reduced maintenance deteriorates the health of trees leading to lower survival rate and increased CO2emissions, while the stormwater alleviation capacity may differ among species. Conclusions The paper demonstrates how GUS can facilitate policies and maintenance of urban forests with environmental, social, and economic benefits.

Simulated Global Empty Containers Repositioning Using Agent-Based Modelling

Due to an ever-increasing movement of containers across the globe in line with the economic boom, the trade imbalance and issues related to empty containers have become inevitable. Empty containers accumulated at specific ports cannot only generate profit but also increase the environmental footprint. This research proposes a Maritime Empty Container Reposition Modelling Framework by integrating the agent-based modelling (ABM) paradigm to model the global movements of empty containers. An agent-based maritime logistic empty container redistribution model was developed to help minimize the total relevant costs for empty container movement in the planning horizon. In the system, ports, shipping companies, customers, and empty containers were identified as critical agents. Using simulated annealing (SA), shipping line agents were able to optimise empty container repositioning to determine the best sequence for moving containers. The model was applied to the Asia–Middle East region to simulate global empty containers repositioning in the region. The results comparison shows that the proposed optimised empty container repositioning framework can significantly reduce the shipping line’s costs and make full use of empty containers.

Applicability of Artificial Societies to Evaluate Health Care Policies.

We propose the use of artificial societies to support health care policymakers in understanding and forecasting the impact and adverse effects of policies. Artificial societies extend the agent-based modeling paradigm using social science research to allow integrating the human component. We simulate individuals as socially capable software agents with their individual parameters in their situated environment including social networks. We describe the application of our method to better understand policy effects on the opioid crisis in Washington, DC, as an example. We document how to initialize the agent population with a mix of empiric and synthetic data, calibrate the model, and make forecasts of possible developments. The simulation forecasts a rise in opioid-related deaths as they were observed during the pandemic. This article demonstrates how to take human aspects into account when evaluating health care policies.

Handling multiple levels in agent-based models of complex socio-environmental systems: A comprehensive review

Agent-based modeling (ABM) has been successfully used, since its emergence in the 1990s, to model and simulate the dynamics at work in complex socio-environmental systems, in many domains and applications where interactions between people and their environments give rise to emergent phenomena that are difficult to study otherwise (urban planning, land-use change, adaptation to environmental changes, biodiversity protection in socio-ecosystems, environmental pollution control, etc.). The inclusion of multiple levels of analysis, abstraction, and representation in these models, however, is much more recent and is still the subject of many proposals and discussions within a relatively informal field, Multilevel Agent-Based Modeling (ML-ABM), which is most often presented as an approach that extends the classical ABM paradigm to include multilevel concepts. Over the past decade, ML-ABM has been increasingly adopted and explored by researchers as an effective paradigm for framing and defining the mechanisms underlying multilevel dynamics. However, due to the youth of the field, no single definition, methodology, or tool unifies studies in this rapidly expanding area. This review will begin with an introduction to socio-environmental systems (SES) and the challenges that modeling approaches face in representing them properly, especially regarding the complexity of human behaviors and organizations. ABM presents opportunities for modeling SESs with respect to these challenges, including the simulation of individual and social behavior and their ability to provide a descriptive and generative representation of the simulated system. However, ABM is limited in its ability to represent levels and scales, as these concepts are absent from the classical ABM metamodel. A complete review of the ML-ABM literature will be carried out, structured around a continuum that emerged during the review: that of the distribution of behaviors (and thus, from a software engineering perspective, of control) across the levels, from approaches that allow only one level to be active at a time, to approaches that rely on simultaneous activity and feedback loops between several levels. Different design choices will, thus, be presented to meet the different needs of multi-level representation, focusing on the interest on modelers and the strengths and limitations of each. In particular, we will highlight a limitation shared by all the reviewed approaches, namely their inability to represent several parallel hierarchies of levels and their interactions, a capability that appears more and more crucial to finely represent social behaviors in SES. A new perspective on the interest that the AGR approach could represent to allow this representation of hierarchies allows us to conclude on the research perspectives are still open.

A systematic review of the agent-based modelling/simulation paradigm in mobility transition

Simulations and agent-based modelling (ABM) have gained momentum as techniques in the transport, energy, and technology diffusion literature to analyse the mobility transition as a complex emergent phenomenon. This study systematically reviews the application of the ABM paradigm in 86 mobility transition studies. The study reveals several research gaps and proposes avenues for future research. Our review highlights that (i) the field has considerably matured in studying the diffusion of electric vehicles, (ii) both price-based and preference-based scenarios for mobility transition should be considered in future research, (iii) most of the empirical model calibrations have been confined to Western countries. Not only will the literature benefit from similar research in other Western regions, but also from non-Western nations with their unique mobility transition pathways, (iv) the conceptual modelling framework of studies can be divided into the two categories of theory-driven and heuristic models. The theory-driven models, which include psychological and non-psychological (e.g., activity-based travel) models, tend to use well-established behavioural rules, (v) most of the models have used the random utility maximization concept, social psychological models, and other simple assumptions/thresholds for the decision-making process of agents, (vi) half of the studies did not validate their models, and (vii) two-thirds of studies omitted to discuss interaction topology among agents. Major remaining challenges and gaps are identified in the review.

An Approach to the Modeling and Simulation of Intra-Hospital Diseases

This publication presents an approach to a simulator to recreate a large number of scenarios and to make agile decisions in the planning of a real emergency room system. A modeling and simulation focused on the point prevalence of intrahospital infections in an emergency room and how it is affected by different factors related to hospital management. To carry out the simulator modeling, the Agent-based Modeling and Simulation (ABMS) paradigm was used. Thus, different intervening agents in the emergency room environment — patients and doctors, among others— were classified. The user belonging to the health system has different data to configure the simulation, such as the number of patients, the number of available beds, etc.
 Based on the tests carried out and the measurements obtained, it is concluded that the disease propagation model relative to the time and contact area of the patients has greater precision than the purely statistical model of the intensive care unit.

A Theoretical Framework for Analysing Technology Transfer Processes Using Agent-Based Modelling: A Case Study on Massive Technology Adoption (AMTEC) Program on Rice Production

The technology transfer (TT) process has been studied from different approaches to improve productivity and competitiveness in agricultural chains. However, the process is not always presented successfully due to heterogeneity and inequality in the technological capacities (TC) of the agents that are part of the transfer process, in addition to the geographical context, the interaction networks and decision rules, which are key factors to understand the TT phenomenon. In this context and as a case study, the Colombian National Federation of rice growers promoted the development and adoption of technology that increased crop competitiveness and sustainability by implementing a technology transfer program known as the Massive Technology Adoption Program (AMTEC—Adopción Masiva de Tecnología) on rice. With the AMTEC program, average production costs were reduced by 26% (USD 119 per hectare), and it increased average yields by 23% (1.27 tonnes per hectare), which shows the importance of introducing technologies in productive chains in developing countries. This research provides a better understanding of the TT processes, based on the analysis of the interaction dynamics and behaviour patterns between the agents (i.e., generators, intermediaries, or users) in the TT processes. As an analysis tool, the agent-based modelling paradigm (ABM) was proposed to study the emergence at the macro-level of behaviour patterns of a system from the interactions of semi-intelligent agents at the micro-level, using experiments.

A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach

Virtual commissioning is a key technology in Industry 4.0 that can address issues faced by engineers during early design phases. The process of virtual commissioning involves the creation of a Digital Twin—a dynamic, virtual representation of a corresponding physical system. The digital twin model can be used for testing and verifying the control system in a simulated virtual environment to achieve rapid set-up and optimization prior to physical commissioning. Additionally, the modular production control systems, can be integrated and tested during or prior to the construction of the physical system. This paper describes the implementation of a digital twin emulator of an automated mechatronic modular production system that is linked with the running programmable logic controllers and allow for exchanging near real-time information with the physical system. The development and deployment of the digital twin emulator involves a novel hybrid simulation- and data-driven modeling approach that combines Discrete Event Simulation and Agent Based Modeling paradigms. The Digital Twin Emulator can support design decisions, test what-if system configurations, verify and validate the actual behavior of the complete system off-line, test realistic reactions, and provide statistics on the system’s performance.

Agent System Mining: Vision, Benefits, and Challenges

In the Agent-Based Modeling (ABM) paradigm, an organization is a Multi-Agent System (MAS) composed of autonomous agents inducing business processes. Process Mining automates the creation, update, and analysis of explicit business process models based on event data. Process Mining techniques make simplifying assumptions about the processes discovered from data. However, actual business processes are often more complex than those restricted by Process Mining assumptions. Several Process Mining approaches relax these standard assumptions by discovering more realistic process models. These approaches can discover more realistic process models. However, these models are often difficult to visualize and, consequently, to understand. Many MASs induce processes whose behaviors become more complex with each next embraced time step, while the complexities of these MASs remain constant. Thus, the ABM paradigm can cope naturally with the increasing complexity of the discovered process models. This paper proposes Agent System Mining (ASM) and ASM Framework. ASM combines Process Mining and ABM in the Business Process Management (BPM) context to infer MAS models of operational business processes from real-world event data, while ASM Framework maps ASM activities to different phases of the MAS modeling lifecycle. The paper also discusses the benefits of using ASM and outlines challenges associated with the implementation of the ASM Framework.

Finding Core Members of Cooperative Games Using Agent-Based Modeling

Agent-based modeling (ABM) is a powerful paradigm to gain insight into social phenomena. One area that ABM has rarely been applied is coalition formation. Traditionally, coalition formation is modeled using cooperative game theory. In this paper, a heuristic algorithm is developed that can be embedded into an ABM to allow the agents to find coalition. The resultant coalition structures are comparable to those found by cooperative game theory solution approaches, specifically, the core. A heuristic approach is required due to the computational complexity of finding a cooperative game theory solution which limits its application to about only a score of agents. The ABM paradigm provides a platform in which simple rules and interactions between agents can produce a macro-level effect without the large computational requirements. As such, it can be an effective means for approximating cooperative game solutions for large numbers of agents. Our heuristic algorithm combines agent-based modeling and cooperative game theory to help find agent partitions that are members of a games' core solution. The accuracy of our heuristic algorithm can be determined by comparing its outcomes to the actual core solutions. This comparison achieved by developing an experiment that uses a specific example of a cooperative game called the glove game. The glove game is a type of exchange economy game. Finding the traditional cooperative game theory solutions is computationally intensive for large numbers of players because each possible partition must be compared to each possible coalition to determine the core set; hence our experiment only considers games of up to nine players. The results indicate that our heuristic approach achieves a core solution over 90% of the time for the games considered in our experiment.

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.

Virtualising Space – New Directions for Applications of Agent-Based Modelling in Spatial Economics

Due to enormous technological progress, socio‑economic science has gained new possibilities of investigating complex and not well‑known socio‑economic phenomena. One of the recent promising research approaches is agent‑based modelling (ABM) with connection to geographical (GIS) data. ABM is a bottom‑up research method concerning individuals that live and interact in the artificial environment. In this type of simulation, evolution of the whole system and macro‑level patterns results from individual behaviour of autonomous entities. Combining ABM with GIS data moves the simulation into the real geographical space. Applying this approach provides powerful possibilities of more realistic socio‑economic simulations concerning urban and spatial economics, sociology and psychology. Geosimulation also helps to answer questions about dependencies between geographical space and economic performances of modern cities. In this paper, a closer look at this topic is presented. We deal with the problem of implementation of GIS data into agent‑based modelling software. In the first step of our research procedure, we compare ABM programming platforms, then we chose three of them which provide GIS data support. In the second step, we implement OpenStreetMap GIS data for one of the districts of Poznań into these programming platforms. Finally, we compare the performance of ABM platforms regarding three major criteria: difficulty of programming, GIS data compatibility and available technical support. Our research is the first step in developing a comple Xsocio‑economic urban system under the ABM paradigm.

Agent action diagram: Toward a model for emergency management system

Agent-based modeling (ABM) has become a useful tool in describing microcosmic action mode of emergency management system (EMS). However, one of key challenges in multi-agent model for EMS could be how to prevent inconsistency between the simulation model established by technicians of computer simulation engineering and the conceptual model established by domain experts. In this paper, a novel multi-view modeling paradigm, agent action diagram (AAD), which has a normalized conceptual model driven architecture and combines the high-layer conceptual model and simulation model, is proposed by establishing its rule symbol system to resolve the above technology problem. Visualization representations of AADs including agent entity organization diagram, agent entity action attribute diagram, single-agent action diagram and multi-agent action diagram are presented. These different views depend on each other according to their functions and form a mutually coupled entirety, thus giving a complete description of EMS with different perspectives. Subsequently, agent model template is constructed, thus affording an automatic mechanism of transforming this graphical conceptual model to its simulation model. Finally, scenarios of emergency command for a great traffic accident and an explosion of a chemical plant are set and two case studies of EMS modeling are implemented, thus verifying feasibility and effectiveness of the proposed approach. This fact shows that the approach has more advantages in model credibility and development efficiency compared with the traditional ABM paradigm.

A Comparison of Economic Agent-Based Model Calibration Methods

Interest in agent-based models of financial markets and the wider economy has increased consistently over the last few decades, in no small part due to their ability to reproduce a number of empirically-observed stylised facts that are not easily recovered by more traditional modelling approaches. Nevertheless, the agent-based modelling paradigm faces mounting criticism, focused particularly on the rigour of current validation and calibration practices, most of which remain qualitative and stylised fact-driven. While the literature on quantitative and data-driven approaches has seen significant expansion in recent years, most studies have focused on the introduction of new calibration methods that are neither benchmarked against existing alternatives nor rigorously tested in terms of the quality of the estimates they produce. We therefore compare a number of prominent ABM calibration methods, both established and novel, through a series of computational experiments in an attempt to determine the respective strengths and weaknesses of each approach and the overall quality of the resultant parameter estimates. We find that Bayesian estimation, though less popular in the literature, consistently outperforms frequentist, objective function-based approaches and results in reasonable parameter estimates in many contexts. Despite this, we also find that agent-based model calibration techniques require further development in order to definitively calibrate large-scale models. We therefore make suggestions for future research.

Adoption of energy efficient technologies by households – Barriers, policies and agent-based modelling studies

Increasing the adoption of energy efficient technologies by households is one of the formulated strategies to reduce greenhouse gas emissions. This paper presents a systematic review of agent-based modelling studies on the adoption of energy efficiency by households. It starts with an overview of barriers for adoption, of energy efficiency policies, energy efficiency model types. Afterwards, an analysis is given of technologies modelled, policies simulated, decision-making theories included, and the use of empirical data. An overview is presented of how technologies, barriers and policies relate in the models. Furthermore, the core policy recommendations from existing models are presented. The analysis shows that the reviewed studies predominantly focus on a subset of barriers – a lack of capital, a lack of information, high upfront cost, ignorance, inertia and other priorities. So far, agent-based models have focused on how subsidies, technology bans and information campaigns influence energy efficiency adoption. There is ample opportunity for future agent-based modelling research on energy efficiency adoption policy by studying other residential technologies, other barriers, and other policies that fit the agent-based modelling paradigm well.

A hybrid simulation modeling framework for regional food hubs

As demand for regionally produced food has increased, regional food hubs have helped to facilitate connections between consumers and small-scale food producers. However, food hubs often struggle to achieve the logistical and operational efficiencies that characterize conventional large-scale food distribution. In many cases, implementation of innovations adopted by conventional food distributors has proved to be challenging and even counterproductive for food hubs, due to their distinct business structure and mission. To address this problem, an empirical agent-based and discrete-event hybrid simulation model was developed to determine the effects of incorporating various efficiency-enhancing practices into food hub warehousing operations. The model was validated using data from a food hub in central Iowa. Experimental results demonstrate the potential usefulness of this model in supporting food hub managers’ operational planning decisions, as well as the effectiveness of incorporating agent-based and discrete-event simulation modeling paradigms to study warehousing operations.

Empirically grounded agent-based models of innovation diffusion: a critical review

Innovation diffusion has been studied extensively in a variety of disciplines, including sociology, economics, marketing, ecology, and computer science. Traditional literature on innovation diffusion has been dominated by models of aggregate behavior and trends. However, the agent-based modeling (ABM) paradigm is gaining popularity as it captures agent heterogeneity and enables fine-grained modeling of interactions mediated by social and geographic networks. While most ABM work on innovation diffusion is theoretical, empirically grounded models are increasingly important, particularly in guiding policy decisions. We present a critical review of empirically grounded agent-based models of innovation diffusion, developing a categorization of this research based on types of agent models as well as applications. By connecting the modeling methodologies in the fields of information and innovation diffusion, we suggest that the maximum likelihood estimation framework widely used in the former is a promising paradigm for calibration of agent-based models for innovation diffusion. Although many advances have been made to standardize ABM methodology, we identify four major issues in model calibration and validation, and suggest potential solutions.

AN ALGEBRA FOR MODELLING THE SIMULTANEITY IN AGENTS' BEHAVIOR IN SPATIALLY EXPLICIT SOCIAL-ENVIRONMENTAL MODELS

Humanity is the major driver of spatial changes resulting from interactions between social and environmental systems. Environmental models usually apply the agent-based modeling paradigm to describe the social aspects of spatial changes. For this reason, these models have incorporated challenges inherent to this paradigm. One of these challenges is how to provide a semantically correct way to describe and simulate the simultaneity in agents' behavior. In this context, this work describes an algebra to the development of spatially explicit agent-based models in a way that the algebra operators implicitly treat the simultaneity in agents' behavior.

Underwater acoustic impacts of shipping management measures: Results from a social-ecological model of boat and whale movements in the St. Lawrence River Estuary (Canada)

The recovery of whale species at risk requires the implementation of protection measures designed to mitigate the risks posed by various stressors. In the St. Lawrence Estuary (Canada), several whale species are threatened by navigation activities in various ways. Since 2013, seasonal voluntary ship strike mitigation measures, including a speed reduction area (SRA) and a no-go area, were implemented annually and largely adopted by the maritime industry to reduce the risks of lethal collisions with four species of baleen whales. While the endangered St. Lawrence beluga population is unlikely to be subject to collisions with large merchant ships, it is known to be negatively affected by vessel-generated underwater noise. To assess how these protection measures modify the beluga’s soundscape throughout their critical habitat, we implemented an underwater acoustic module within an existing agent-based model (3MTSim) of ship-whale movements and interactions in the St. Lawrence Estuary. We ran multiple simulations for two scenarios 1) without and 2) with the protection measures to compare the level of noise received by belugas before and after 2013. Overall, the simulations showed a statistically-significant 1.6% decrease in the total amount of noise received by belugas in their critical habitat following the implementation of the protection measures. Although slowing down ships reduces instantaneous radiated noise, it also increases the total amount of acoustic energy released in the environment by extending the time spent in the SRA. Accordingly, our simulations showed a 2.4% increase in the cumulative noise from shipping received by beluga in the SRA. Conversely, belugas located in the Upper Estuary, mostly females and calves, i.e., the most valuable individuals experienced a 5.4% reduction in the cumulative received level of shipping noise. Although refinements are required to improve the modelling of noise sources and propagation for finer scale projections in this complex nearshore environment, this agent-based modelling paradigm of 3MTSim proved informative for underwater acoustic impact assessments.

A method for malware propagation in industrial critical infrastructures

The main goal of this work is to propose a model for the study and analysis of malware propagation in an industrial critical infrastructure. Specifically, an individual-based mathematical model is introduced and its parameters, variables and equations are explicitly defined and constructed. This model follows the agent based modeling paradigm which makes it extremely suitable for use within the small and heterogeneous environments where individual characteristics of all devices can be captured. The main strengths of this model are its ease of computational implementation (both in hardware and software), and its ability to simulate control measures (which can be of great help for security system administrators); however, this model strongly depends on a proper identification of the parameters.