Agent-based Research Articles

Engaging stakeholders to address a complex water resource management issue in the Western Cape, South Africa

Engaging water users when developing water management plans is increasingly being embraced by a wide range of scientists in the water sector as imperative for dealing with present-day water resources challenges. However, the complexity presented by unique catchment situations makes it difficult to prescribe a ‘one size fits all’ solution to localised water resource challenges. South Africa is well-known for its population heterogeneity, which spreads into catchments, layering complexity in terms of water user types, needs and interests. This research presents a novel conjunctive use of classical and newly developed tools within a stakeholder engagement framework. This application of a suite of tools responds to the complex conflict of water user needs and interests in the Koue Bokkeveld catchment in the Western Cape Province of South Africa. We implement the Adaptive Planning Process, and the Actors, Resources, Dynamics, and Interactions approaches to unite conflicting stakeholders and develop a shared vision as the foundation for co-developing and negotiating a shared water management plan. We present the outcomes of a series of participatory workshops in which stakeholders developed a shared catchment vision. Stakeholders united around the vision and actively engaged in participatory Agent-based Model co-development with the research team. Increased attendance and active participation in the latter workshops demonstrate a positive progression in the engagement process. Outcomes of the stakeholder feedback evaluation validate increased participant satisfaction with the process. We then reflect on the practicalities of stakeholder engagement based on our experience. Learnings from this conjunctive application of approaches suggest that providing a conducive engagement platform, facilitation, and appropriate tools can enable conflicting water users to develop shared solutions collaboratively.

Explaining the impact of parameter combinations in agent-based models

Simulation is a useful and effective way to analyze and study complex, real-world systems, allowing researchers, practitioners, and decision makers to make sense of the inner working of a system involving many factors, often resulting in some sort of emergent behavior. The number of parameter value combinations grows exponentially and it quickly becomes infeasible to test them all or even to explore a suitable subset. How does one then efficiently identify the parameter value combinations that matter for a particular simulation study, and determine their impact on the result? In addition, is it possible to train a machine learning model to predict the outcome of an agent-based model (ABM) with a systematically chosen small subset of parameter value combinations, such that the result could be predicted without running the ABM? We use covering arrays to create t-way (t = 2, 3, etc.) combinations of parameter values to significantly reduce an ABM’s parameter value exploration space, which is supported by our prior work. In our ICCS 2023 paper (Olsen et al., 2023) we built on that work by applying it to Wilensky’s Heatbugs model and training a random forest machine learning model to predict simulation results by using the covering arrays to select our training and test data. Our results show that a 2-way covering array provides sufficient training data to train our random forest to predict three different simulation outcomes. Our process of using covering arrays to decrease parameter space to then predict ABM results using machine learning is successful. In this paper that extends the ICCS 2023 paper (Olsen et al., 2023), we analyze the role of parameter combinations and parameter values in determining model output via combination frequency difference (CFD) analysis and Shapley values. CFD has not previously been applied to agent-based models; we provide a process for using this approach and compare and contrast with Shapley values and random forest feature importance.

Parameterizing agent-based models using an online game

Agent-based models (ABMs) of human systems are often parameterized using real-world data. For some ABMs this is not possible because the reality upon which the models are based does not exist or is not generalizable from one setting to another. In this paper we implement an online decision game to parameterize an agent-based model of pedestrian and cyclist route choice decisions in a neighbourhood. Our conceptual framework is to use an experimental game to log decision-making behaviour, summarize this behaviour into a decision model, and then transfer this model to an ABM. The product of this framework is an ABM with agents informed by human decision making made within the game, rather than the real world. The results of our analysis suggest that the decision model is consistent with some general theory about decision making, but the ABM illustrates some unique and contextually specific patterns of flow. ABMs parameterized with game data may be useful for forecasting the effects of change on urban transportation infrastructure.

Debugging collective digging in Drosophila melanogaster using an agent-based model

Being a member of a group entails a range of possible costs and benefits; observing when and with whom groups form can help identify how these are weighted. Clustering behaviour in fruit fly (Drosophila melanogaster) third-instar larvae has been shown to provide individuals that cluster with improved foraging opportunities on otherwise inaccessible food. To isolate how environmental and genetic factors affect who clusters and when, we created an on-lattice agent-based model and systematically explored the effects of several variables, such as population density, resource distribution, and the phenotypic composition of the group. Our simple model reproduces most of the key features of larval clustering and demonstrates drastic differences in collective behaviour and fitness in response to changes in environmental variables, in ways that align with the limited empirical data available. This model can improve our understanding of this complex social behaviour and make testable predictions about the behavioural and ecological mechanisms of larval clustering.

Agent-Based Modeling of COVID-19 Transmission: A Case Study of Housing Densities in Sankalitnagar, Ahmedabad

The differential transmission of COVID-19 depending on the socio-economic status of a neighborhood is well established. For example, several studies have shown that COVID-19 transmission was higher in poorer and denser neighborhoods than in wealthier ones. However, what is less well known is how this varied rate of transmission interacted with established health measures, i.e., face masks and lockdowns, in the context of developing countries to reduce pandemic cases and hence resulted in fewer deaths. This study uses an Agent-Based Model (ABM) simulation to examine the context and impacts of COVID-19 mitigation efforts (i.e., lockdowns combined with masks) on the transmission of COVID-19 across a single neighborhood in Ahmedabad, a city in the state of Gujarat, India. The model is parameterized using real-world population data, which allows us to simulate the spread of COVID-19 to find conditions that most closely match the realities of COVID-19 in the spring of 2020. Consequently, the simulation can be used to understand the impact of nation-wide lockdown on the spread of COVID cases across Ahmedabad as a function of housing density. Thus, invaluable insight into the effectiveness of a lockdown as a mitigation measure can be derived. Further information about how the effectiveness of the lockdown varied by neighborhood, as well as other factors that impacted it, can be ascertained.

Port Accessibility Depends on Cascading Interactions between Fleets, Policies, Infrastructure, and Hydrodynamics

Reducing waiting times is crucial for ports to be efficient and competitive. Important causes of waiting times are cascading interactions between realistic hydrodynamics, accessibility policies, vessel-priority rules, and detailed berth availability. The main challenges are determining the cause of waiting and finding rational solutions to reduce waiting time. In this study, we focus on the role of the design depth of a channel on the waiting times. We quantify the performance of channel depth for a representative fleet rather than the common approach of a single normative design vessel. The study relies on a mesoscale agent-based discrete-event model that can take processed Automatic Identification System and hydrodynamic data as its main input. The presented method’s validity is assessed by hindcasting one year of observed anchorage area laytimes for a liquid bulk terminal in the Port of Rotterdam. The hindcast demonstrates that the method predicts the causes of 73.4% of the non-excessive laytimes of vessels, thereby correctly modelling 60.7% of the vessels-of-call. Following a recent deepening of the access channel, cascading waiting times due to tidal restrictions were found to be limited. Nonetheless, the importance of our approach is demonstrated by testing alternative maintained bed level designs, revealing the method’s potential to support rational decision-making in coastal zones.

How can the lead industry improve sustainability based on the resource-economy-carbon nexus system?

The sustainability of the lead industry is threatened by pressures from resource depletion, slowing economic growth, and increasing carbon emissions. Exploring the linkages between resource, economy and carbon to promote synergies between them is essential to achieving sustainability. This study constructed a complex system driven by resource, economy, and carbon by utilizing system dynamics, agent-based model, and life cycle assessment, and further developed resource-economy‑carbon nexus model to evaluate the coordinated sustainability of the lead industry. The results show that due to the impact of carbon neutrality, China's lead demand will rapidly decline after reaching a peak of 7.0 million tonnes in 2028. The widening lead supply-demand gap will result in falling lead prices, thus lowering the economy index. Meanwhile, the decline in lead demand and the increase in the proportion of secondary lead will increase the resource index and carbon index. Such uneven developments between resource, economy, and carbon will constrain rapid growth in coordinated sustainability, which is expected to grow weakly after 2030. To improve the coordinated sustainability of the lead industry, we suggest advancing the substitution of lead-acid batteries as soon as possible before 2030, fully utilizing the advantages of low-intensity substitution after 2030, and introducing a carbon tax while avoiding high taxation rates after 2040.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM).

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

Performance Analysis of Automated Parcel Lockers in Urban Delivery: Combined Agent-Based–Monte Carlo Simulation Approach

Background: The habitat structure, the environmental impact, the market acceptance, the changes in consumers’ preferences, and the pandemic urged for innovative solutions in urban last-mile delivery. Parcel lockers are among the most preferred solutions by customers due to their home proximity, time availability, and cost efficiency. Methods: This paper introduces an agent-based model (ABM) and a Monte Carlo simulation program to analyze in detail the activity of parcel locker points. The ABM describes the behavior of the agents (customers, parcels, lockers, delivery agents). The simulation is realized using ARENA 12 software. Two scenarios are created based on the number of daily delivery shifts; for each scenario, 300 simulation experiments with various input data are conducted. Results: Three measures of performance (MOPs) are selected to assess the system activity: the number of daily delivered parcels, the delivery time of an order, and the daily delayed orders. The simulation outputs reveal significant predictors of MOPs and disclose moments when actions need to be taken to increase system capacity or change customer behavior. Conclusions: The versatility of the simulation model in terms of input variables makes it a useful decision support tool for planning by highlighting quantitative assessments, organizing delivery activity, along with influences due to customer behavior changes.

The role of vehicular considerations in tsunami shelter location optimization

Using vehicles during tsunami evacuation might exacerbate road congestion, potentially increasing casualties. However, since the 2011 Tohoku Earthquake Tsunami, vehicular evacuation during tsunamis has partially been permitted, especially to assist elderly and disabled individuals with mobility challenges. The present study aimed to elucidate the impact of vehicular use on reducing mortality rate by introducing a new tsunami evacuation shelter in a given coastal area. To do so, the authors applied an agent-based tsunami evacuation simulation model that can account for behaviors of both pedestrians and vehicular evacuees to three Japanese coastal cities (Mihama Town, Shingu City, and Minabe Town) considering future Nankai–Tonankai Earthquake Tsunami scenarios. The authors first determined mortality rates under scenarios both with and without vehicular evacuation using current shelter placements. Subsequently, mortality rates with the introduction of a new tsunami evacuation shelter were examined for both scenarios. The findings showed that effective shelter locations for minimizing mortality rates varied depending on whether vehicular evacuation was considered. It was also found that the effectiveness of vehicular use during evacuation and the strategic placement of new tsunami evacuation shelters are highly dependent on local factors such as population distribution, existing shelter locations, and road layouts. These findings underscore the necessity for area-specific tsunami evacuation simulations to construct robust evacuation strategies.

Mathematical properties of the agent-based model of extinction — recolonization for population genetics

Mathematical properties of the agent-based model of extinction — recolonization for population genetics

Assessing the impacts of climate change on agriculture and water systems via coupled human-hydrological modeling

Understanding the intricate relationship between farmers’ water usage and its hydrological effects is crucial for developing adaptable water policies. However, conducting such an analysis proves challenging due to the lack of appropriate modeling tools that comprehensively integrate water policies, water utilization, and hydrological processes. To address this challenge, this study introduces an innovative socio-hydrological framework to investigate the interplay between farmer actions and water resources. This framework integrates an agent-based model, which is based on the Value-Belief-Norm (VBN) theory, linked with a distributed hydrological model (SWAT-MODFLOW) to capture farmer behaviors. The modeling framework is applied to the Mahabad River Basin to assess water use and hydrological impacts. To assess the framework's ability, Nash–Sutcliffe (NS) efficiency and the coefficient of determination (R2) are computed for simulating runoff, while Mean Absolute Residual Error (MAE) and Root Mean Square Error (RMSE) are computed for simulating groundwater head. Results demonstrate the acceptable performance of the proposed model, with NS = 0.61, R2 = 0.69, MAE = 1.16, and RMSE = 1.92. Moreover, this study integrates climate change data from the 6th IPCC report to evaluate the model's responsiveness to altering climate conditions. Findings suggest that farmers facing economic challenges tend to opt for high-profit crops to ameliorate their financial situation. So, without policy changes, climate change will reduce crop yield, farmer income, and water storage. Furthermore, the study evaluates enhancing irrigation efficiency and groundwater extraction restrictions to mitigate climate change effects. Enhancing irrigation efficiency annually conserves 38.39 MCM, boosts crop yields by 6 %, elevates farmer incomes, and encourages a shift toward low-water-consuming crops, contributing to regional groundwater sustainability. Overall, the results of this study can enhance our comprehension of the impact of human activities on hydrological cycles, offering valuable insights for water managers.

What's luck got to do with it? A generative model for examining the role of stochasticity in age-at-death, with implications for bioarchaeology.

The role of "luck" in determining individual exposure to health insults is a critical component of the processes that shape age-at-death distributions in mortality samples but is difficult to address using traditional bioarcheological analysis of skeletal materials. The present study introduces a computer simulation approach to modeling stochasticity's contribution to the mortality schedule of a simulated cohort. The present study employs an agent-based model of 15,100 individuals across a 120 year period to examine the predictive value of birth frailty on age-at-death when varying the likelihood of exposure to health insults. Birth frailty, when accounting for varying exposure likelihood scenarios, was found to account for 18.7% of the observed variation in individual age-at-death. Analysis stratified by exposure likelihood demonstrated that birth frailty alone explains 10.2%-12.1% of the variation observed across exposure likelihood scenarios, with the stochasticity associated with exposure to health insults (i.e., severity of health insult) and mortality likelihood driving the majority of variation observed. Stochasticity of stressor exposure and intrinsic stressor severity are underappreciated but powerful drivers of mortality in this simulation. This study demonstrates the potential value of simulation modeling for bioarchaeological research.

Agent-based modeling to estimate the impact of lockdown scenarios and events on a pandemic exemplified on SARS-CoV-2

In actual pandemic situations like COVID-19, it is important to understand the influence of single mitigation measures as well as combinations to create most dynamic impact for lockdown scenarios. Therefore we created an agent-based model (ABM) to simulate the spread of SARS-CoV-2 in an abstract city model with several types of places and agents. In comparison to infection numbers in Germany our ABM could be shown to behave similarly during the first wave. In our model, we implemented the possibility to test the effectiveness of mitigation measures and lockdown scenarios on the course of the pandemic. In this context, we focused on parameters of local events as possible mitigation measures and ran simulations, including varying size, duration, frequency and the proportion of events. The majority of changes to single event parameters, with the exception of frequency, showed only a small influence on the overall course of the pandemic. By applying different lockdown scenarios in our simulations, we could observe drastic changes in the number of infections per day. Depending on the lockdown strategy, we even observed a delayed peak in infection numbers of the second wave. As an advantage of the developed ABM, it is possible to analyze the individual risk of single agents during the pandemic. In contrast to standard or adjusted ODEs, we observed a 21% (with masks) / 48% (without masks) increased risk for single reappearing participants on local events, with a linearly increasing risk based on the length of the events.

Evaluating the sensitivity and robustness of occupancy models for building energy simulation during design

Occupancy modeling plays a crucial role in building energy simulation in building design, yet studies investigating the influence of model types and their input assumptions remain sparse. This study evaluates the sensitivity of parameters and model robustness to uncertainties in parameter ranges and data availability during the design phase. We assessed three occupancy models—deterministic, probabilistic, and agent-based—using an open dataset with occupancy data across four space types. Our findings reveal that the predictive performance of deterministic and probabilistic models is influenced heavily by a few of the most sensitive parameters. In contrast, more parameters affected the prediction performance of the agent-based model. This suggests a need to prioritize sensitive parameter estimation to enhance prediction accuracy in deterministic and probabilistic models. Specifically, the occupancy density and start/end time, followed by schedule fractions, emerged as highly sensitive due to their significant impact on average occupancy profiles. We also found that for hourly energy predictions, narrowing the range of the top five sensitive parameters decreases the coefficient of variation of the root mean square error (CV(RMSE)) by 3% and reduces its standard deviation by half. These results underscore the importance of careful parameter estimation to improve the reliability of occupancy models in building energy simulations.

Shared Lightweight Autonomous Vehicles for Urban Food Deliveries: A Simulation Study

In recent years, the rapid growth of on-demand delivery services, especially in food deliveries, has spurred the exploration of innovative mobility solutions. In this context, lightweight autonomous vehicles have emerged as a potential alternative. However, their fleet-level behavior remains largely unexplored. To address this gap, we have developed an agent-based model and an environmental impact study assessing the fleet performance of lightweight autonomous food delivery vehicles. This model explores critical factors such as fleet sizing, service level, operational strategies, and environmental impacts. We have applied this model to a case study in Cambridge, MA, USA, where results indicate that there could be significant environmental benefits in replacing traditional car-based deliveries with shared lightweight autonomous vehicle fleets. Lastly, we introduce an interactive platform that offers a user-friendly means of comprehending the model’s performance and potential trade-offs, which can help inform decision-makers in the evolving landscape of food delivery innovation.

Impact of autonomous vehicles on discretionary activities: an agent-based model with space–time accessibility constraints

AbstractThe extensive development of autonomous vehicles (AVs) is set to revolutionise the way of travelling. Research suggests that the introduction of AVs may affect travel behaviour and choices, resulting in long-term changes in land use. Accessibility is an important concept that connects transportation and land use, providing a holistic performance measure for the transport-land use system. However, this concept has not been adequately capitalised in studies that attempt to understand the impact of AVs on location choice decisions. To explore this knowledge gap, we proposed an agent-based simulation framework that integrates with accessibility constraints to study how AVs influence behavioural and location choices. The framework consists of an activity-based travel demand model with accessibility constraints and a dynamic transport assignment model. The accessibility constraints are derived from individuals’ travel time budgets based on activity-travel survey data. We applied the agent-based simulation framework to Clayton, Australia, and focused on discretionary activity location choices. Various values of travel time and vehicle running costs underpinned by the use of AVs were examined. While most studies have concluded that AVs can significantly increase trip lengths for daily activities, our results demonstrate that even when AVs are used, the movement of individuals is still limited by spatio-temporal constraints of accessibility. As a result, we predict that the increase in discretionary trip lengths and their impact on traffic congestion is modest.

The Wound Environment Agent-based Model (WEABM): a digital twin platform for characterization and complex therapeutic discovery for volumetric muscle loss.

Volumetric Muscle Loss (VML) injuries are characterized by significant loss of muscle mass, usually due to trauma or surgical resection, often with a residual open wound in clinical settings and subsequent loss of limb function due to the replacement of the lost muscle mass with non-functional scar. Being able to regrow functional muscle in VML injuries is a complex control problem that needs to override robust, evolutionarily conserved healing processes aimed at rapidly closing the defect in lieu of restoration of function. We propose that discovering and implementing this complex control can be accomplished by the development of a Medical Digital Twin of VML. Digital Twins (DTs) are the subject of a recent report from the National Academies of Science, Engineering and Medicine (NASEM), which provides guidance as to the definition, capabilities and research challenges associated with the development and implementation of DTs. Specifically, DTs are defined as dynamic computational models that can be personalized to an individual real world "twin" and are connected to that twin via an ongoing data link. DTs can be used to provide control on the real-world twin that is, by the ongoing data connection, adaptive. We have developed an anatomic scale cell-level agent-based model of VML termed the Wound Environment Agent Based Model (WEABM) that can serve as the computational specification for a DT of VML. Simulations of the WEABM provided fundamental insights into the biology of VML, and we used the WEABM in our previously developed pipeline for simulation-based Deep Reinforcement Learning (DRL) to train an artificial intelligence (AI) to implement a robust generalizable control policy aimed at increasing the healing of VML with functional muscle. The insights into VML obtained include: 1) a competition between fibrosis and myogenesis due to spatial constraints on available edges of intact myofibrils to initiate the myoblast differentiation process, 2) the need to biologically "close" the wound from atmospheric/environmental exposure, which represents an ongoing inflammatory stimulus that promotes fibrosis and 3) that selective, multimodal and adaptive local mediator-level control can shift the trajectory of healing away from a highly evolutionarily beneficial imperative to close the wound via fibrosis. Control discovery with the WEABM identified the following design principles: 1) multimodal adaptive tissue-level mediator control to mitigate pro-inflammation as well as the pro-fibrotic aspects of compensatory anti-inflammation, 2) tissue-level mediator manipulation to promote myogenesis, 3) the use of an engineered extracellular matrix (ECM) to functionally close the wound and 4) the administration of an anti-fibrotic agent focused on the collagen-producing function of fibroblasts and myofibroblasts. The WEABM-trained DRL AI integrates these control modalities and provides design specifications for a potential device that can implement the required wound sensing and intervention delivery capabilities needed. The proposed cyber-physical system integrates the control AI with a physical sense-and-actuate device that meets the tenets of DTs put forth in the NASEM report and can serve as an example schema for the future development of Medical DTs.

Incorporating decentralized facilities into the food waste treatment infrastructure in Megacity: A locational optimization in Beijing

Food waste management has emerged as a pivotal issue with the acceleration of urbanization. Inappropriate treatment of food waste causes environmental contamination, resource squandering, and land occupation. This research explores optimal strategies in Beijing with an experimental case study on the bioconversion technology using black soldier fly larvae (BSFL), comparing centralized and decentralized approaches. We studied waste sorting behaviors influenced by community characteristics, employing an Agent-Based Model (ABM) and a mixed integer non-linear programming (MINLP). Results indicate that 29 % of Beijing's food waste could be suitably managed through decentralized methods with BSFL, particularly in outlying areas and densely populated suburbs. Decentralized treatment exhibits the potential to boost waste separation participation in medium-sized, close- knit communities. However, this approach encounters hurdles related to technological implementation, public perception, and urban planning. This study underscores the need for community-specific strategies and a concerted effort from communities, government, and the private sector in advancing sustainable urban food waste management.

An agent-based model of school tracking, accountability, and segregation

ABSTRACT Extant research on the effect of education system characteristics on school socio-economic segregation does not consider education as a complex system. This paper’s contribution lies in using agent-based modelling to simulate the effect of the interaction between families’ strategies for school selection and two education system characteristics: tracking – a system where students of different academic abilities are separated in different schools – and school accountability – the public availability of information on school quality. The model shows that school tracking and accountability tend to at the same time attenuate and increase school socio-economic segregation, but overall both policies tend to exacerbate segregation by eliciting competition for the best schools. The policy implications are: (i) tracking has a stronger exacerbating effect on segregation than accountability, (ii) the two polices interact to create compounding effects and (iii) by reducing residential segregation between families the segregating effect on schools of the two policies diminishes dramatically.