Agent-Based Models and Multi-Agent Systems: A Comprehensive Review of Distinctions, Synergies and Applications

An agent is an autonomous computer system situated in an environment to fulfill a design objective. Multi-Agent Systems aim to solve problems in a flexible and robust way by assembling sets of agents interacting in cooperative or competitive ways for the sake of possibly common objectives. Multi-Agent Systems have been applied to several domains ranging from many industrial sectors, e-commerce, health and even entertainment. Agent-Based Modeling, a sort of Multi-Agent Systems, is a technique used to study complex systems in a wide range of domains. A natural or social system can be represented, modeled and explained through a simulation based on agents and interactions. Such a simulation can comprise a variety of agent architectures like reactive and cognitive agents. Despite cognitive agents being highly relevant to simulate social systems due their capability of modelling aspects of human behaviour ranging from individuals to crowds, they still have not been applied extensively. A challenging and socially relevant domain are the Disaster-Rescue simulations that can benefit from using cognitive agents to develop a realistic simulation. In this paper, a Multi-Agent System applied to the Disaster-Rescue domain involving cognitive agents based on the Belief–Desire–Intention architecture is presented. The system aims to bridge the gap in combining Agent-Based Modelling and Multi-Agent Systems approaches by integrating two major platforms in the field of Agent-Based Modeling and Belief-Desire Intention multi-agent systems, namely, NetLogo and Jason.

In this chapter we will review on the models utilized in analyzing urban policies, and compare the Constraint Cellular Automata (CCA) approach, Agent-based Model (ABM) approach and some traditional approaches such as statistical analysis, Geographical Information System (GIS) in urban policy analysis. It is showed that such kind models are developing from aggregated to disaggregated, from stable to spatial-temporal dynamic. Due to its capacity in simulating the interaction between individuals, ABM approach become a hot trend in urban policy analysis. Regarding to this purpose, we suggest that when employ ABM approach for planning support of targeted urban policy, a decision-making process is quite necessary for combining with the ABM development. In the meantime, the differences between ABM and traditional individual-based model in simulating individual behavior will also be illustrated r.KeywordsConstraint cellular automata (CCA)Agent-based model (ABM)Individual behaviorSustainable developmentPlanning supportUrban policy analysis

This chapter traces the lineage of organizational theory to methodological individualism as a mode of explanation in social sciences. In this framework, an organization is viewed as a multiagent system, where an observed phenomenon at the aggregate organizational level is explained as the result of the behaviour of and interactions among the constituent individuals within the organization. In this context, a formal theory as an explanation through deductive logic can be expressed in two distinct ways: (1) mathematical model and analysis and (2) computational model and simulation. This chapter offers a comparative review of these two approaches to theory development in organizational science. It first provides a brief review of the mathematical modelling literature, often referred to as organizational economics. This is followed by a review of the progress made in agent-based models of organizations. This sequence of reviews allows us to highlight the complementary roles that the agent-based modelling and formal mathematical modelling approaches play with one another. The lacunae in the organizational theory literature that could be filled with the agent-based modelling approach are identified, thus charting a path forward.

Agent-based models have been developed and widely employed to assess the impact of disturbances or conservation management on animal habitat use, population development, and viability. However, the direct impacts of canopy disturbance on the arboreal movement of individual primates have been less studied. Such impacts could shed light on the cascading effects of disturbances on animal health and fitness. Orangutans are an arboreal primate that commonly encounters habitat quality deterioration due to land-use changes and related disturbances such as forest fires. Forest disturbance may, therefore, create a complex stress scenario threatening orangutan populations. Due to forest disturbances, orangutans may adapt to employ more terrestrial, as opposed to arboreal, movements potentially prolonging the search for fruiting and nesting trees. In turn, this may lead to changes in daily activity patterns (i.e., time spent traveling, feeding, and resting) and available energy budget, potentially decreasing the orangutan's fitness. We developed the agent-based simulation model BORNEO (arBOReal aNimal movEment mOdel), which explicitly describes both orangutans' arboreal and terrestrial movement in a forest habitat, depending on distances between trees and canopy structures. Orangutans in the model perform activities with a motivation to balance energy intake and expenditure through locomotion. We tested the model using forest inventory data obtained in Sebangau National Park, Central Kalimantan, Indonesia. This allowed us to construct virtual forests with real characteristics including tree connectivity, thus creating the potential to expand the environmental settings for simulation experiments. In order to parameterize the energy related processes of the orangutans described in the model, we applied a computationally intensive evolutionary algorithm and evaluated the simulation results against observed behavioral patterns of orangutans. Both the simulated variability and proportion of activity budgets including feeding, resting, and traveling time for female and male orangutans confirmed the suitability of the model for its purpose. We used the calibrated model to compare the activity patterns and energy budgets of orangutans in both natural and disturbed forests . The results confirm field observations that orangutans in the disturbed forest are more likely to experience deficit energy balance due to traveling to the detriment of feeding time. Such imbalance is more pronounced in males than in females. The finding of a threshold of forest disturbances that affects a significant change in activity and energy budgets suggests potential threats to the orangutan population. Our study introduces the first agent-based model describing the arboreal movement of primates that can serve as a tool to investigate the direct impact of forest changes and disturbances on the behavior of species such as orangutans. Moreover, it demonstrates the suitability of high-performance computing to optimize the calibration of complex agent-based models describing animal behavior at a fine spatio-temporal scale (1-m and 1-s granularity).

Framework for Modelling Multi-stakeholder City Logistics Domain Using the Agent based Modelling Approach

Purpose This paper aims to provide a systematic review of how agent-based modeling (ABM) has been applied in maintenance contexts across various industries. By consolidating findings from a range of peer-reviewed studies, this review identifies current research trends and pinpoints underexplored opportunities for future work. Design/methodology/approach A systematic literature review was carried out following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Relevant papers were gathered from Web of Science and supplementary sources such as Google Scholar, applying predefined search queries and exclusion criteria. A final set of 69 papers was analyzed in detail, focusing on industry applications, methodological combinations, software usage and stated future research directions. Findings The review reveals that ABM and multi-agent systems (MAS) approaches are widely used, in the context of maintenance, in energy, infrastructure, aviation and manufacturing, often integrating simulation, optimization and machine learning techniques. Less attention has been given to healthcare, oil and gas and non-aviation military maintenance, highlighting areas with significant potential for further study. Researchers emphasize the need for real-world validation, larger-scale modeling and the incorporation of human and environmental factors. Additionally, variations in software choice reflect differing priorities, from visualization and simplicity (e.g. NetLogo) to robust coordination in multi-agent environments (e.g. JADE) and comprehensive, hybrid simulation capabilities (e.g. AnyLogic). Originality/value This review uniquely synthesizes current research on ABM in maintenance, providing a comprehensive overview of its applications, benefits and future research directions.

There are numerous biological scenarios in which populations of cells migrate in crowded environments. Typical examples include wound healing, cancer growth, and embryo development. In these crowded environments cells are able to interact with each other in a variety of ways. These include excluded-volume interactions, adhesion, repulsion, cell signaling, pushing, and pulling. One popular way to understand the behavior of a group of interacting cells is through an agent-based mathematical model. A typical aim of modellers using such representations is to elucidate how the microscopic interactions at the cell-level impact on the macroscopic behavior of the population. At the very least, such models typically incorporate volume-exclusion. The more complex cell-cell interactions listed above have also been incorporated into such models; all apart from cell-cell pulling. In this paper we consider this under-represented cell-cell interaction, in which an active cell is able to "pull" a nearby neighbor as it moves. We incorporate a variety of potential cell-cell pulling mechanisms into on- and off-lattice agent-based volume exclusion models of cell movement. For each of these agent-based models we derive a continuum partial differential equation which describes the evolution of the cells at a population level. We study the agreement between the agent-based models and the continuum, population-based models and compare and contrast a range of agent-based models (accounting for the different pulling mechanisms) with each other. We find generally good agreement between the agent-based models and the corresponding continuum models that worsens as the agent-based models become more complex. Interestingly, we observe that the partial differential equations that we derive differ significantly, depending on whether they were derived from on- or off-lattice agent-based models of pulling. This hints that it is important to employ the appropriate agent-based model when representing pulling cell-cell interactions.

Skin contraction is an important biophysical process that takes place during and after recovery of deep tissue injury. This process is mainly caused by fibroblasts (skin cells) and myofibroblasts (differentiated fibroblasts which exert larger pulling forces and produce larger amounts of collagen) that both exert pulling forces on the surrounding extracellular matrix (ECM). Modelling is done in multiple scales: agent-based modelling on the microscale and continuum-based modelling on the macroscale. In this manuscript we present some results from our study of the connection between these scales. For the one-dimensional case, we managed to rigorously establish the link between the two modelling approaches for both closed-form solutions and finite-element approximations. For the multi-dimensional case, we computationally evidence the connection between the agent-based and continuum-based modelling approaches.

Background and Purpose: The purpose of this study is to describe the principles of the development of parallel system-dynamics and agent-based models of organic farming for the case of Slovenia. The advantage of agent-based modeling is demonstrated by including geospatial information as an agent attribute. The models were compared by the validation, confirming the appropriate level of similarity. Design/Methodology/Approach: Both system-dynamics and agent-based modeling approaches were applied. Statistical methods were used in the validation. Results: The results of the validation confirm the appropriateness of the proposed agent-based model. Introducing additional attributes into the agent-based model provides an important advantage over the system-dynamics model, which serves as the paradigmatic example. Conclusion: A thorough validation and comparison of the results of the system-dynamics and agent-based models indicates the proper approach to combining the methodologies. This approach is promising, because it enables the modeling of the entire agricultural sector, taking each particular farm into account.

European mountain regions are becoming more vulnerable to natural hazards due to global change, climate change, and land-use change. Therefore, it is essential to understand their resilience. Currently, quantitative and dynamic models of coupled human-landscape interactions are in their infancy. However, agent-based modelling (ABM) approaches have high potential to advance the analysis of the interplay of natural and social factors affecting socio-ecological resilience in European mountain communities. The Socio-Ecological Land Agent-Based Model (SECLAND) integrates information from qualitative interviews and spatial data into a quantitative modelling environment. This enriches the diversity of scenario modelling beyond economic rationales by incorporating individual agent's motivations for land-use decisions. The outputs from this model have been used as input to hydrological or ecological models on multiple occasions.SECLAND has been used to model the potential success of various adaptation strategies for coping with climate-induced natural hazards. In a study conducted in the department of Ariège, France, we analysed the potential impacts of intensified livestock grazing on mountain pastures under scenarios with strong climate change effects and increased extreme events. In this scenario, farmers use mountain pastures to seek additional forage resources in specific years. However, these grazing areas require considerate management in years when they are not needed for food provision. Our study also found that the utilization patterns of mountain pastures are strongly influenced by farm succession, vegetation regrowth on unused mountain pastures, and the search for cost-efficient forage resources. In a case study conducted in Eastern Austria, we found that adaptive learning moderates the decline in the number of active farms and farmland, regardless of the scenario conditions, compared to scenarios without adaptive learning. However, the results also indicate that adaptation increases the workload of farmers. This highlights the importance of considering more than just simplistic economic rationales when making land-use decisions. Agent-based models can be used to model socio-ecological responses and help cope with adaptation in complex socio-ecological systems.Both studies emphasise that in the context of risk management and socio-ecological resilience, learning and managing additional workload are key factors for achieving adaptive success. To further improve, it is necessary to couple agent-based models with climatic and landscape models, allowing for bi-directional feedback between social and natural systems. SECLAND has been adapted to integrate adaptive learning processes, demonstrating the possibility of capturing mutual system dynamics and feedback loops. This allows the full capacity of agent-based models to be used to assess the resilience of mountain communities to cope with natural hazards, using a scenario approach that includes heterogeneous agents, different trajectories of socio-economic conditions, as well as global and climate change dynamics. This presentation outlines a conceptual framework for operationalizing an interdisciplinary effort within a modelling environment that integrates human decision-making, socio-economic conditions, and climatic and landscape dynamics.

In the cost–benefit analysis of urban transportation investment, a logsum-based benefit calculation is widely used. However, it is rarely applied to inter-regional transportation. In this study, we applied a logsum-based approach to the calculation of benefits for high-speed projects for inter-regional railways in Korea’s long-term transportation plan. Moreover, we applied a behavioral model in which an agent travels beyond the zones assumed by an aggregate model. In the case of South Korea, such a model is important for determining transportation priorities: whether to specialize in mobility improvement by investing in a high-speed railway project, such as the 300 km/h Korea Train eXpress (KTX), or to improve existing facilities, such as by building a relatively slower railroad (150–250 km/h) to enhance existing mobility and accessibility. In this context, if a new, relatively slow railroad were constructed adjacent to a high-speed railroad, the benefits would be negligible since the reduction in travel time would not sufficiently reflect accessibility improvements. Therefore, this study proposes the use of aggregate and agent-based models to evaluate projects to improve intercity railway service and conduct a case study with the proposed new methodology. A logsum was selected to account for the benefits of passenger cars on semi-high-speed and high-speed railroads simultaneously since it has been widely used to estimate the benefits of new modes or relatively slow modes. To calculate the logsum, this study used input data from both the aggregate and individual agent-based models, and found that an analysis of the feasibility of inter-regional railroad investment was possible. Moreover, the agent-based model can also be applied to inter-regional analysis. The proposed methods are expected to enable a more comprehensive evaluation of the transport system. In the case of the agent-based model, it is suggested that further studies undertake more detailed scenario analysis and travel time estimation.

BACKGROUND Complex clinical decision-making in neuro-oncology is a multifaceted process involving numerous specialties influenced by various objective factors and patient preferences. Modeling these multidisciplinary neuro-oncology discussions presents significant challenges, particularly given the multimodal nature of the data. Recent advancements in large language models (LLMs) have enabled the development of LLM “agents”. These agents pave the way for multi-agent systems capable of simulating interacting components, thus capturing the nuances of interdisciplinary clinical discussions. MATERIAL AND METHODS Utilizing GPT-4, we developed conversational agents with an agent-based modeling (ABM) approach; each agent was assigned a specific specialty. We simulated their interactions in various clinical decision-making scenarios. Lastly, we simulated 30 patient-agents and included them in the ABM system. We applied this ABM approach to a dataset of 16 clinical vignettes extracted from the UCSF Clinical Data Warehouse of primary adult glioma patients who underwent tumor board (TB) reviews. The TB decision was used as ground truth for quantitative evaluation. Seven independent clinicians evaluated the final decisions. RESULTS Our quantitative assessment showed that, when blinded to whether ABM or TB generated responses, evaluators preferred ABM recommendations 47.8% of the time, followed by TB recommendations (38%). The highest recommendation concordance was between TB and ABM (50.6%). The ABM was deemed clinically accurate in 76.8% of cases and provided clinically appropriate reasoning in 73.2%. Our qualitative assessment showed that including the patient agent significantly influenced the course of discussions, notably when the patient agents participated in the system by stating their healthcare goals. The most recommended individual treatment was standard-of-care chemotherapy, followed by targeted therapies, clinical trials, immunotherapy, and palliative care. The least frequently recommended management options were radiation, surgery, consultation with other specialties, and devices. CONCLUSION This study underscores the potential of ABM in neuro-oncology, particularly in simulating complex, real-world scenarios. ABM not only empowers patients but also offers clinicians a novel tool for understanding and navigating the multifaceted landscape of patient preferences and treatment options. It provides a platform for clinicians to visualize the potential impacts of various decisions, enhancing their ability to provide personalized, effective care. We envision future work where ABM systems are patient-centric, enabling patients to explore the potential impacts of their individual characteristics on clinical management options.

This article argues that the agent-based computational model permits a distinctive approach to social science for which the term “generative” is suitable. In defending this terminology, features distinguishing the approach from both “inductive” and “deductive” science are given. Then, the following specific contributions to social science are discussed: The agent-based computational model is a new tool for empirical research. It offers a natural environment for the study of connectionist phenomena in social science. Agent-based modeling provides a powerful way to address certain enduring—and especially interdisciplinary—questions. It allows one to subject certain core theories—such as neoclassical microeconomics—to important types of stress (e.g., the effect of evolving preferences). It permits one to study how rules of individual behavior give rise—or “map up”—to macroscopic regularities and organizations. In turn, one can employ laboratory behavioral research findings to select among competing agent-based (“bottom up”) models. The agent-based approach may well have the important effect of decoupling individual rationality from macroscopic equilibrium and of separating decision science from social science more generally. Agent-based modeling offers powerful new forms of hybrid theoretical-computational work; these are particularly relevant to the study of non-equilibrium systems. The agentbased approach invites the interpretation of society as a distributed computational device, and in turn the interpretation of social dynamics as a type of computation. This interpretation raises important foundational issues in social science—some related to intractability, and some to undecidability proper. Finally, since “emergence” figures prominently in this literature, I take up the connection between agent-based modeling and classical emergentism, criticizing the latter and arguing that the two are incompatible. ! 1999 John Wiley & Sons, Inc.

Comparing Food Policies and Programs in an Urban Neighborhood – an Agent-Based Modeling Approach (P04-181-19)

Background and objectiveIn-stent restenosis (ISR) following percutaneous coronary intervention with drug-eluting stent (DES) implantation remains an unresolved issue, with ISR rates up to 10%. The use of antiproliferative drugs on DESs has significantly reduced ISR. However, a complete knowledge of the mechanobiological processes underlying ISR is still lacking. Multiscale agent-based modelling frameworks, integrating continuum- and agent-based approaches, have recently emerged as promising tools to decipher the mechanobiological events driving ISR at different spatiotemporal scales. However, the integration of sophisticated drug models with an agent-based model (ABM) of ISR has been under-investigated. The aim of the present study was to develop a novel multiscale agent-based modelling framework of ISR following DES implantation. MethodsThe framework consisted of two bi-directionally coupled modules, namely (i) a drug transport module, simulating drug transport through a continuum-based approach, and (ii) a tissue remodelling module, simulating cellular dynamics through an ABM. Receptor saturation (RS), defined as the fraction of target receptors saturated with drug, is used to mediate cellular activities in the ABM, since RS is widely regarded as a measure of drug efficacy. Three studies were performed to investigate different scenarios in terms of drug mass (DM), drug release profiles (RP), coupling schemes and idealized vs. patient-specific artery geometries. ResultsThe studies demonstrated the versatility of the framework and enabled exploration of the sensitivity to different settings, coupling modalities and geometries. As expected, changes in the DM, RP and coupling schemes illustrated a variation in RS over time, in turn affecting the ABM response. For example, combined small DM – fast RP led to similar ISR degrees as high DM – moderate RP (lumen area reduction of ∼13/17% vs. ∼30% without drug). The use of a patient-specific geometry with non-equally distributed struts resulted in a heterogeneous RS map, but did not remarkably impact the ABM response. ConclusionThe application to a patient-specific geometry highlights the potential of the framework to address complex realistic scenarios and lays the foundations for future research, including calibration and validation on patient datasets and the investigation of the effects of different plaque composition on the arterial response to DES.