Agent-based Modeling Research Articles

A critical realist approach to agent-based modeling: Unlocking prediction in non-positivist paradigms

Information systems (IS) scholarship and practice aim to predict phenomena and outcomes of IS use. These phenomena of IS use are typically set in multi-leveled, dynamic, and complex contexts that lend explanation to the non-positivist tradition in IS research. However, limited methodological options exist to make predictions. In this research, we propose stratified agent-based modeling, a step-by-step approach that enables prediction in non-positivist paradigms. Drawing upon the critical realist philosophy of science, which suggests ontological stratification and assumes open systems, we adopt a retroduction-based explanation formation and agent-based modeling to simulate different potential states of a complex system. The critical step in combining critical realism with agent-based modeling involves identifying and codifying the underlying generative mechanisms (i.e., causal powers) into various components of the agent-based model. We propose four steps toward prediction under the critical realist paradigm: (1) capturing the phenomenon, (2) identifying the generative mechanism, (3) building the agent-based model, and (4) simulating states of the system. We present an exemplar of our proposed approach that investigates the effectiveness of strategies to combat malicious content propagation in social networks.

Regulating data platforms from a value of data approach

Purpose This study aims to investigate the impact of ex-ante regulatory interventions on emerging digital markets related to data sharing and combination practices. Specifically, it evaluates how such interventions influence market contestability by considering data network effects and the economic value of data. Design/methodology/approach The research uses agent-based modeling and simulations to analyze the dynamics of value generation and market competition related to the regulatory obligations on data sharing and combination practices. Findings Results show that while the promotion of data sharing through data portability and interoperability has a positive impact on the market, restricting data combination may damage value generation or, at best, have no positive impact even when it is imposed only on those platforms with very large market shares. More generally, the results emphasize the role of regulators in enabling the market through interoperability and service multihoming. Data sharing through portability fosters competition, while the usage of complementary data enhances platform value without necessarily harming the market. Service provider multihoming complements these efforts. Research limitations/implications Although agent-based modeling and simulations describe the dynamics of data markets and platform competition, they do not provide accurate forecasts of possible market outcomes. Originality/value This paper presents a novel approach to understanding the dynamics of data value generation and the effects of related regulatory interventions. In the absence of real-world data, agent-based modeling provides a means to understand the general dynamics of data markets under different regulatory decisions that have yet to be implemented. This analysis is timely given the emergence of regulatory concerns on how to stimulate a competitive digital market and a shift toward ex-ante regulation, such as the regulatory obligations to large gatekeepers set in the Digital Markets Act.

Understanding the Residential Water Demand Response to Price Changes: Measuring Price Elasticity with Social Simulations

Water pricing is an economic instrument traditionally used to reduce water demand. However, its effective implementation requires knowledge of the extent to which users reduce water consumption with increasing water prices. The price elasticity of water demand has been estimated using econometric regression, which relies on cross-sectional and time-series water data. As an alternative, we propose the use of agent-based modelling, which does not require reliable historical data on water prices and consumption and enables the simulation of multiple scenarios with different consumer profiles, behaviour profiles and water price changes, thereby allowing comprehensive understanding of price elasticity estimates. To illustrate the potential use of agent-based modelling for the estimation of water demand price elasticity, we performed an empirical application to a residential area in Chile. Price elasticity estimates ranged from −0.0159 to −0.1036 (mean −0.0250), indicating that residential water consumption is inelastic to price changes. This result is consistent with previous findings. Agent-based modelling is an alternative for the ex-ante assessment of the potential effectiveness of water pricing policies intended to reduce residential water demand.

A Coupled Human and Natural Systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation

To address the challenge of escalating urban flood risk and the deficiency in effective flood emergency management, this study introduces a novel Coupled Human and Natural Systems (CHANS) modelling framework that employs hierarchical reinforcement learning to optimise mobile pump scheduling and placement for urban flood risk mitigation. The CHANS framework integrates hydrodynamic and agent-based models within a multi-GPU computing environment for high-resolution, real-time flood inundation modelling and risk assessment to enrich Reinforcement Learning (RL) training. In the application to Ninh Kieu District in Can Tho City, Vietnam, the new RL-enabled modelling framework is used to evaluate optimal mobile pumping strategies for concurrent pluvial flooding and post-flooding events against the traditional deployment approaches. Results demonstrate that RL-based strategies can significantly enhance flood risk reduction, outperforming traditional methods by achieving 2× and 4× improvements in the concurrent and post-flooding periods/events, respectively. Incorporating human factors and adapting to local conditions, the RL agent provides valuable insights into mobile pump scheduling and deployment strategies. Sensitivity analysis confirms the robustness of the CHANS modelling framework and underscores the role of RL in optimising mobile pump scheduling and placement where traditional rule-based strategies are challenging.

An agent-based cooperative co-evolutionary framework for optimizing the production planning of energy supply chains under uncertainty scenarios

Nowadays, energy and power companies compete to get the raw materials and equipment they need on time, as project times lengthen, costs spiral, stock-out continues to plague plans to a decarbonized energy future. The risks reflect the impact of uncertainty and volatility on the resilience of the supply chains. Therefore, there is a need for the enhancement of the production planning in Energy Supply Chains (ESCs), as it enables affordable energy supplies and supports the companies transition to a clean, secure and sustainable energy mix. This study aims to understand the interactive behavior among individuals and optimize their production planning under uncertainty scenarios. In particular, we propose a novel framework to couple an Agent-based Modelling (ABM) and a Co-evolutionary Algorithm (CEA), to realize its capacity to solve a Many-objective Optimization Problem (MaOP) where the profits of multiple agents are concurrently maximized in their interactive transaction processes under normal conditions and uncertain disruption events.For demonstration, we illustrate the proposed approach by considering a five-layer oil and gas ESC model, where uncertainties from multiple sources and the structural dynamics challenge the balance between supply and demand. The results obtained by an integration of a Cooperative Co-evolutionary Particle Swarm Optimizer (CCPSO) algorithm into ABM show the pricing and orders of the target agents are optimized while the loss of ESC resilience is minimized under uncertainty scenarios, proving its capacity of improving the diversity and the convergence, compared to the classic evolutionary algorithms.

Assessing the suitability of groundwater resources based on farmers’ predicted intentions and human–water feedbacks

In this study, a behavioral −hydrological simulation modeling framework is developed to explore the functioning of human-water systems. The main objective is to identify the psycho-social factors affecting water conservation intentions among farmers, and then realize the interactions and bidirectional feedback between humans and water resources. For this purpose, the agent-based modeling (ABM) and extended theory of planned behavior (TPB) is recruited to reflect on the agents’ behaviors. As the first step, the framework of the extended TPB together with the agent behavior was designed via field researches through the completion of 169 questionnaires upon proportional-to-size (PPS) random sampling. The partial least squares-structural equation modeling (PLS-SEM) is then implemented, at the second step, to analyze the data and obtain the weights of the extended TPB constructs. The results reveal that the constructs supplemented to the TPB, including moral norms (MNs) and perceived risk (PR), could have significant effects on water conservation intentions and behaviors among farmers. At the last step, the farmers and their decision-making as the dynamic elements by obtaining feedbacks of water resources were modeled using the ABM. To evaluate the proposed framework, the recorded data of groundwater fluctuations and cultivated areas in the Lenjanat plain is utilized and compared with the socio-hydrological simulation modeling outcomes using the Nash-Sutcliffe efficiency (NSE) coefficient. Then, five scenarios are defined to shed light on the changes in the agent behavior on the aquifer suitability, including water consumption rate and groundwater levels in the aquifer. The results correspondingly demonstrated that merging the second and third scenarios in an effort to raise the prices of crops with low water need along with the increase in utility costs (here, water and electricity) could lead to a 75% increase of farmers’ income and a 21.5% reduction in the surface and groundwater uses in this region, implying the highest level of water conservation among other scenarios.

Co-evolving networks for opinion and social dynamics in agent-based models.

The rise of digital social media has strengthened the coevolution of public opinions and social interactions that shape social structures and collective outcomes in increasingly complex ways. The existing literature often explores this interplay as a one-directional influence, focusing on how opinions determine social ties within adaptive networks. However, this perspective overlooks the intrinsic dynamics driving social interactions, which can significantly influence how opinions form and evolve. In this work, we address this gap, by introducing the co-evolving opinion and social dynamics using stochastic agent-based models. Agents' mobility in a social space is governed by both their social and opinion similarity with others. Similarly, the dynamics of opinion formation is driven by the opinions of agents in their social vicinity. We analyze the underlying social and opinion interaction networks and explore the mechanisms influencing the appearance of emerging phenomena, such as echo chambers and opinion consensus. To illustrate the model's potential for real-world analysis, we apply it to General Social Survey data on political identity and public opinion regarding governmental issues. Our findings highlight the model's strength in capturing the coevolution of social connections and individual opinions over time.

Agent-based evolutionary game dynamics uncover the dual role of resource heterogeneity in the evolution of cooperation

Cooperation is a cornerstone of social harmony and group success. Environmental feedbacks that provide information about resource availability play a crucial role in encouraging cooperation. Previous work indicates that the impact of resource heterogeneity on cooperation depends on the incentive to act in self-interest presented by a situation, demonstrating its potential to both hinder and facilitate cooperation. However, little is known about the underlying evolutionary drivers behind this phenomenon. Leveraging agent-based modeling and game theory, we explore how differences in resource availability across environments influence the evolution of cooperation. Our results show that resource variation hinders cooperation when resources are slowly replenished but supports cooperation when resources are more readily available. Furthermore, simulations in different scenarios suggest that discerning the rate of natural selection acts on strategies under distinct evolutionary dynamics is instrumental in elucidating the intricate nexus between resource variability and cooperation. When evolutionary forces are strong, resource heterogeneity tends to work against cooperation, yet relaxed selection conditions enable it to facilitate cooperation. Inspired by these findings, we also propose a potential application in improving the performance of artificial intelligence systems through policy optimization in multi-agent reinforcement learning. These explorations promise a novel perspective in understanding the evolution of social organisms and the impact of different interactions on the function of natural systems.

Advanced Agent-Based Modelling of Subsurface Migration and Accumulation Dynamics of Hydrocarbons

Advanced Agent-Based Modelling of Subsurface Migration and Accumulation Dynamics of Hydrocarbons

Exploring the diffusion of low-carbon power generation and energy storage technologies under electricity market reform in China: An agent-based modeling framework for power sector

Promoting the widespread adoption of multi-complementary low carbon power generation technologies is a fundamental strategy for attaining carbon neutrality within the electricity sector. In the context of electricity market reform, this study develops an agent-based modeling framework integrated simulation with optimization. The model uses agent-based simulation to analyze annual market dynamics and low-carbon technology diffusion, with a two-stage optimization for energy storage and spot market simulation. The research findings indicate: (1) For different power generation technologies, the spot market can establish differentiated average prices, making the market price of carbon-captured coal power higher than that of conventional coal power, which are advantageous in reducing the subsidies required to promote the diffusion of carbon capture technology. (2) Expanding the spot market scale and implementing differentiated capacity compensation mechanisms both promote carbon capture technology diffusion. Nevertheless, with the marginal clearing mechanism, thermal power holds a average price advantage than renewable energy. Failing to control the growth of thermal power capacity will result in increased carbon emissions. (3) After 2030, energy storage's role in balancing supply and demand grows. Storage capacity should align with renewable energy scale and the regional characteristics of wind and solar resources to prevent overbuilding and stranded assets.

Agent-Based Modeling for Sustainable Urban Passenger Vehicle Mobility: A Case of Tehran

In response to escalating congestion and deteriorating air quality in urban centers worldwide, exacerbated by overburdened transportation systems, there is an urgent need for accurate traffic forecasting and effective sustainable urban development strategies. This study employs agent-based modeling through four distinctive scenarios for Tehran, I. R. Iran. A synthetic population is meticulously crafted using simulated annealing, enabling the emulation of daily commuting patterns. Results show that by bolstering cycling infrastructure and enhancing public transportation services, reliance on private cars is reduced up to 46%. The introduction of flexible working hours reduces the traffic volumes during peak traffic hours by 47% and significantly altering the daily distances traveled by personal cars, as evidenced by a 1:6 ratio in car volume increase between scenarios emphasizing flexible working hours and those with more conventional traffic patterns. The results provide powerful insights for decisionmakers to manage the traffic especially in high polluted air conditions.

Predicting population-level impacts of projected climate heating on a temperate freshwater fish.

Climate heating has the potential to drive changes in ecosystems at multiple levels of biological organization. Temperature directly affects the inherent physiology of plants and animals, resulting in changes in rates of photosynthesis and respiration, and trophic interactions. Predicting temperature-dependent changes in physiological and trophic processes, however, is challenging because environmental conditions and ecosystem structure vary across biogeographical regions of the globe. To realistically predict the effects of projected climate heating on wildlife populations, mechanistic tools are required to incorporate the inherent physiological effects of temperature changes, as well as the associated effects on food availability within and across comparable ecosystems. Here we applied an agent-based bioenergetics model to explore the combined effects of projected temperature increases for 2100 (1.4, 2.7, and 4.4°C), and associated changes in prey availability, on three-spined stickleback (Gasterosteus aculeatus) populations representing latitudes 50, 55, and 60°N. Our results showed a decline in population density after a simulated 1.4°C temperature increase at 50°N. In all other modeled scenarios there was an increase (inflation) in population density and biomass (per unit area) with climate heating, and this inflation increased with increasing latitude. We conclude that agent-based bioenergetics models are valuable tools in discerning the impacts of climate change on wild fish populations, which play important roles in aquatic food webs.

Agent-Based Model for Situational Awareness in the Workplace: Enhancing Neural Networks with Direct Feedback Alignment

The integration of direct feedback alignment (DFA) into neural networks presents a paradigm shift in computational models, enhancing situational awareness within professional settings. This paper explores the application of DFA in agent-based computational models, demonstrating its efficiency and biological plausibility over traditional backpropagation methods. The research highlights the significant impact of direct feedback alignment on reinforcing situational awareness, evidenced through comprehensive simulations that show marked improvements in agents' ability to effectively navigate and comprehend complex work environments. The study suggests that direct feedback alignment revolutionizes neural network learning processes and significantly enhances cognitive aspects critical to employee safety and operational efficiency. It lays the groundwork for future investigations into the integration of neural computation techniques with organizational psychology and behavior, offering a new perspective on fostering safer, more aware, and efficient workplace environments. The potential application of direct feedback alignment across various professional scenarios opens new avenues for research in computational neuroscience, cognitive psychology, and organizational behavior, with a focus on optimizing human-environment interactions in complex systems.

Enhancing healthcare facility resilience: utilizing machine learning model for airborne disease infection prediction

During this pandemic, advanced epidemiological models have been widely used to determine intervention strategies for controlling the spread of the disease in public and healthcare settings. These models played a crucial role by providing predictive insights into disease transmission dynamics, informing resource allocation and guiding policy decisions. However, the accuracy of these predictions depends on the substantial amount of input data, which was not readily available at the onset of the pandemic. Another concern with the existing models is their inability to adequately account for the complex indoor built environments, which has been shown to significantly impact infection risk. To tackle these issues, this paper discusses the potential of developing a joint modelling technique that integrates machine learning models, building information models and agent-based models to assess the risk of nosocomial airborne infections. With limited available data, machine learning models can determine infection risk with high confidence.

Measuring the effectiveness of incorporating mobile charging services into urban electric vehicle charging network: An agent-based approach

Mobile charging service (MCS) has emerged as a cost-efficient solution to compensate for the limitations of fixed charging stations (FCSs) in service capacity and coverage. Nevertheless, the impacts of incorporating the MCS into the charging infrastructure network have not been well understood. Therefore, this study introduces an exhaustive framework for devising Agent-based Modeling (ABM) to delineate electric vehicle (EV) drivers' daily travels and charging behaviors within the integrated charging network. Specifically, the developed ABM framework encompasses both internal attributes of individuals and external variables of charging and transport networks that potentially shape EV drivers’ public charging decisions. It generates authentic daily charging demands for FCSs and MCSs and provides a streamlined process to further incorporate more charging modes into the EV charging ecosystem. Subsequently, we opted for the Salt Lake City metropolitan area to implement our framework and showcase the effectiveness of MCS integration on the charging network over various EV adoption levels and network settings. The findings of the designed case study unravel the choice patterns of EV drivers when facing diverse charging options and pinpoint the strengths and weaknesses of MCS incorporating into the urban charging network at different stages.

Agent-Based Modeling of Virtual Tumors Reveals the Critical Influence of Microenvironmental Complexity on Immunotherapy Efficacy.

Since the introduction of the first immune checkpoint inhibitor (ICI), immunotherapy has changed the landscape of molecular therapeutics for cancers. However, ICIs do not work equally well on all cancers and for all patients. There has been a growing interest in using mathematical and computational models to optimize clinical responses. Ordinary differential equations (ODEs) have been widely used for mechanistic modeling in immuno-oncology and immunotherapy. They allow rapid simulations of temporal changes in the cellular and molecular populations involved. Nonetheless, ODEs cannot describe the spatial structure in the tumor microenvironment or quantify the influence of spatially-dependent characteristics of tumor-immune dynamics. For these reasons, agent-based models (ABMs) have gained popularity because they can model more detailed phenotypic and spatial heterogeneity that better reflect the complexity seen in vivo. In the context of anti-PD-1 ICIs, we compare treatment outcomes simulated from an ODE model and an ABM to show the importance of including spatial components in computational models of cancer immunotherapy. We consider tumor cells of high and low antigenicity and two distinct cytotoxic T lymphocyte (CTL) killing mechanisms. The preferred mechanism differs based on the antigenicity of tumor cells. Our ABM reveals varied phenotypic shifts within the tumor and spatial organization of tumor and CTLs despite similarities in key immune parameters, initial simulation conditions, and early temporal trajectories of the cell populations.

Optimizing Municipal Solid Waste (MSW) Collection and Transportation Network Using GIS and Agent-based Modeling Approach: A Case Study in Hung Yen City, Vietnam

Optimizing Municipal Solid Waste (MSW) Collection and Transportation Network Using GIS and Agent-based Modeling Approach: A Case Study in Hung Yen City, Vietnam

Subjective Well-Being of Chief Executive Officers and Its Impact on Stock Market Volatility During the COVID-19 Pandemic in Poland: Agent-Based Model Perspective

This study aimed to connect the behavioral corporate finance perspective (micro level) with complexity theory via agent-based modeling to analyze the impact of selected psychological factors of chief executive officers (CEOs) on stock market volatility (macro level). Specifically, we wanted to explore whether Polish CEOs’ subjective well-being (SWB) influenced their managerial decisions during the COVID-19 pandemic and how it might be related to the volatility of stock prices during this critical period in Poland. Our study was based on a survey of Polish CEOs who managed companies listed on the Warsaw Stock Exchange. In particular, 255 CEOs completed the Satisfaction with Life Scale, the Positive and Negative Affect Scale, and a business survey on the impact of the COVID-19 pandemic on company management. Using the results of this survey, we built an agent-based model to investigate how CEOs’ decision-making, stemming from their SWB levels, influences the perception of prices by individual traders and, in turn, how it is translated into aggregate stock market volatility. The results indicate the pathways through which the microscopic-level SWB of CEOs influences market price formation at a macroscopic level. The findings obtained from our model may shed new light on the rational expectations theory applied to stock market volatility during the financial crisis.

Situating spatial determinism in urban design and planning for sustainable walkability: a simulation of street morphology and pedestrian behaviour

Streets and roads have been constantly identified as key spaces to improve walkability and—by extension—the entire urban environment. Such improvements lie on planning and design theories that are often supported by qualitative evidence and as such, tend to remain highly speculative on how street design influences walkability in more deterministic ways. Drawbacks in further exploring this street-walkability correlation are partly defined by an explicit rejection to ‘spatial determinism’ in urban morphology and social sciences, but also because combining differing epistemological approaches for understanding social processes linked to the physical aspects of the space is complex. In this paper, it is proposed that qualitative (social-based) theories of the space and quantitative (positivist) understandings of people’s behaviour can complement each other to elaborate upon ‘spatial determinism’ with focus on streetscapes and pedestrian behaviour. By using Agent-Based Modelling (ABMs), morphological components of streets linked to population characteristics are analysed to understand walkability from the perspective of spatial determinism. The findings suggest that the street settings directly affect pedestrian behaviour, and that changes in the built environment result in changes on walkability patterns. Ultimately, the modelling exercise shed light on street design and walkability, but also on wider debates on how urban design and social processes correlate in more deterministic and, therefore, predictable ways.

Integrating mechanism-based T cell phenotypes into a model of tumor-immune cell interactions.

Interactions between cancer cells and immune cells in the tumor microenvironment influence tumor growth and can contribute to the response to cancer immunotherapies. It is difficult to gain mechanistic insights into the effects of cell-cell interactions in tumors using a purely experimental approach. However, computational modeling enables quantitative investigation of the tumor microenvironment, and agent-based modeling, in particular, provides relevant biological insights into the spatial and temporal evolution of tumors. Here, we develop a novel agent-based model (ABM) to predict the consequences of intercellular interactions. Furthermore, we leverage our prior work that predicts the transitions of CD8+ T cells from a naïve state to a terminally differentiated state using Boolean modeling. Given the details incorporated to predict T cell state, we apply the integrated Boolean-ABM framework to study how the properties of CD8+ T cells influence the composition and spatial organization of tumors and the efficacy of an immune checkpoint blockade. Overall, we present a mechanistic understanding of tumor evolution that can be leveraged to study targeted immunotherapeutic strategies.