Agent-based Modeling Research Articles

Exploring the efficiency of a source-level model to predict hydrophone-based received noise levels of the St. Lawrence Estuary’s merchant fleet

The Enhancing Cetacean Habitat and Observation (ECHO) source-level model was statistically derived using nearly 10,000 vessel transits along Canada’s western coast. Before using this model in other areas, it is important to examine its efficiency in new environments and on other fleets. In this work, hydrophone-based acoustic recordings collected between August 3rd and September 16th of 2022 within the Saguenay-St. Lawrence Marine Park were used to investigate the applicability of the ECHO source-level model to Canada’s eastern fleet. Opportunistic transits of 71 merchant ships near (i.e., ≲3 km) the hydrophone were obtained. As an attempt to reduce computing times in the context of agent-based modeling, the reliability of the proposed analytic seabed critical angle (SCA) method for propagation losses calculations was examined and compared to robust, although time-consuming, numerical methods. Results generally show that the combination of the ECHO source-level model and our best estimates for propagation losses calculations does allow to reliably predict the acoustic footprint of the radiated noise levels by merchant ships in the acoustic module of the Marine Mammal and Maritime Traffic Simulator (3MTSim). However, computational gains could be marginal at best due to the analytic SCA method quickly becoming inconsistent with the numerical approach in range-dependent scenarios that often translate into long-range encounters.

Enhancing Agricultural Sustainability Through Rhizomicrobiome: A Review.

Sustainable agriculture represents the responsible utilization of natural resources while safeguarding the well-being of the natural environment. It encompasses the objectives of preserving the environment, fostering economic growth, and promoting socioeconomic equality. To achieve sustainable development for humanity, it is imperative to prioritize sustainable agriculture. One significant approach to achieving this transition is the extensive utilization of microbes, which play a crucial role due to the genetic reliance of plants on the beneficial functions provided by symbiotic microbes. This review focuses on the significance of rhizospheric microbial communities, also known as the rhizomicrobiome (RM). It is a complex community of microorganisms that live in the rhizosphere and influence the plant's growth and health. It provides its host plant with various benefits related to plant growth, including biocontrol, biofertilization, phytostimulation, rhizoremediation, stress resistance, and other advantageous properties. Yet, the mechanisms by which the RM contributes to sustainable agriculture remain largely unknown. Investigating this microbial population presents a significant opportunity to advance toward sustainable agriculture. Hence, this study aims to provide an overview of the diversity and applications of RM in sustainable agriculture practices. Lately, there has been growing momentum in various areas related to rhizobiome research and its application in agriculture. This includes rhizosphere engineering, synthetic microbiome application, agent-based modeling of the rhizobiome, and metagenomic studies. So, developing bioformulations of these beneficial microorganisms that support plant growth could serve as a promising solution for future strategies aimed at achieving a new green revolution.

Modeling COVID-19 Transmission in Closed Indoor Settings: An Agent-Based Approach with Comprehensive Sensitivity Analysis

Computational simulation models have been widely used to study the dynamics of COVID-19. Among those, bottom-up approaches such as agent-based models (ABMs) can account for population heterogeneity. While many studies have addressed COVID-19 spread at various scales, insufficient studies have investigated the spread of COVID-19 within closed indoor settings. This study aims to develop an ABM to simulate the spread of COVID-19 in a closed indoor setting using three transmission sub-models. Moreover, a comprehensive sensitivity analysis encompassing 4374 scenarios is performed. The model is calibrated using data from Calabria, Italy. The results indicated a decent consistency between the observed and predicted number of infected people (MAPE = 27.94%, RMSE = 0.87 and χ2(1,N=34)=(44.11,p=0.11)). Notably, the transmission distance was identified as the most influential parameter in this model. In nearly all scenarios, this parameter had a significant impact on the outbreak dynamics (total cases and epidemic peak). Also, the calibration process showed that the movement of agents and the number of initial asymptomatic agents are vital model parameters to simulate COVID-19 spread accurately. The developed model may provide useful insights to investigate different scenarios and dynamics of other similar infectious diseases in closed indoor settings.

Efficiency and energy consumption of the automated container yard with twin rail-mounted gantry cranes considering crane scheduling strategies and handshake area designs

This study investigates the effects of twin rail-mounted gantry crane (RMG) scheduling strategies and handshake area designs on container yard efficiency and energy consumption. A handshake area is set for temporary storage of containers within each block, enabling the cooperation and interference avoidance of twin RMGs. Considering the complexity and dynamics, we establish a simulation model for the twin RMG operations with agent-based and discrete event simulation modeling. Container relocation, microscopic movements of RMGs, and stochastic arrivals of automated guided vehicles (AGVs) and external trucks (ETs) are simulated, and the corresponding efficiency and energy consumption are quantitatively estimated. By testing the six RMG scheduling strategies and thirteen handshake area designs, the results show that the 2-OPT RMG scheduling strategy (an optimization heuristic that attempts to find the schedule with the minimum makespan) outperforms the other strategies in terms of efficiency. However, the optimal RMG strategy for minimal energy consumption depends on the ratio of landside and seaside requests and handshake area settings. Furthermore, the location of the handshake area significantly affects the efficiency and power consumption of AGVs and RMGs. This study can provide decision support to improve the efficiency and reduce energy consumption of the twin RMGs by selecting a specified combination of the RMG scheduling strategy and the handshake area design.

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible.

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification - a markup language - that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open-source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a GUI that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years, which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open-source software and contributions from the community are encouraged.

Trajectory-based global sensitivity analysis in multiscale models

This research introduces a novel global sensitivity analysis (GSA) framework for agent-based models (ABMs) that explicitly handles their distinctive features, such as multi-level structure and temporal dynamics. The framework uses Grassmannian diffusion maps to reduce output data dimensionality and sparse polynomial chaos expansion (PCE) to compute sensitivity indices for stochastic input parameters. To demonstrate the versatility of the proposed GSA method, we applied it to a non-linear system dynamics model and epidemiological and economic ABMs, depicting different dynamics. Unlike traditional GSA approaches, the proposed method enables a more general estimation of parametric sensitivities spanning from the micro level (individual agents) to the macro level (entire population). The new framework encourages the use of manifold-based techniques in uncertainty quantification, enhances understanding of complex spatio-temporal processes, and equips ABM practitioners with robust tools for detailed model analysis. This empowers them to make more informed decisions when developing, fine-tuning, and verifying models, thereby advancing the field and improving routine practice for GSA in ABMs.

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.

Maternal care leads to the evolution of long, slow lives

Animals, and mammals in particular, vary widely in their "pace of life," with some species living long lives and reproducing infrequently (slow life histories) and others living short lives and reproducing often (fast life histories). These species also vary in the importance of maternal care in offspring fitness: In some species, offspring are fully independent of their mothers following a brief period of nutritional input, while others display a long period of continued dependence on mothers well after nutritional dependence. Here, we hypothesize that these two axes of variation are causally related to each other, such that extended dependence of offspring on maternal presence leads to the evolution of longer lives at the expense of reproduction. We use a combination of deterministic modeling and stochastic agent-based modeling to explore how empirically observed links between maternal survival and offspring fitness are likely to shape the evolution of mortality and fertility. Each of our modeling approaches leads to the same conclusion: When maternal survival has a strong impact on the survival of offspring and grandoffspring, populations evolve longer lives with less frequent reproduction. Our results suggest that the slow life histories of humans and other primates as well as other long-lived, highly social animals such as hyenas, whales, and elephants are partially the result of the strong maternal care that these animals display. We have designed our models to be readily parameterized with demographic data that are routinely collected by long-term researchers, which will facilitate more thorough testing of our hypothesis.

Understanding the Emergence of Computational Institutional Science: A Review of Computational Modeling of Institutions and Institutional Dynamics

Computational Institutional Science (CIS) is an emerging stream of social science research performing institutional analysis with computational methods. While such scholarship is garnering increased attention, it draws on many different theories and engages disparate disciplinary backgrounds. Thus, this article provides an overview of CIS articles utilizing agent-based modeling, a computer simulation experiment method, to better understand critical theoretical lenses and topics. More specifically, this article clusters and analyzes 148 articles based on commonalities and differences empirically derived from their overlapping citations (i.e., citation cluster) and abstract content (i.e., topic cluster). Based on our analysis, we discuss the trends observed across cluster types and the overlap between them to better situate CIS inquiry.

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.

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.

Bridging the gap between agent based models and continuous opinion dynamics

There is a rich literature on microscopic models for opinion dynamics; most of them fall into one of two categories – agent-based models or differential equation models – with a general understanding that the two are connected in certain scaling limits. In this paper we show rigorously this is indeed the case. In particular we show that both ordinary and stochastic differential equations can be obtained as a limit of agent-based models by simultaneously rescaling time and the extent to which an agent updates their opinion after an interaction. This approach provides a pathway to analyse much more diverse modelling paradigms, for example: the motivation behind several possible multiplicative noise terms in stochastic differential equation models; the connection between selection noise and the mollification of the discontinuous bounded confidence interaction function; and how the method for selecting interacting pairs can determine the normalisation in the corresponding differential equation. Our computational experiments confirm our findings, showing excellent agreement of solutions to the two classes of models in a variety of settings.

How can agent-based modelling provide new insights into the impact of minimum unit pricing in Scotland?

In recent years we have gained insight into the impact of minimum unit pricing (MUP)-a legal floor price below which a given volume of alcohol cannot be sold-on population-level reductions in alcohol sales, consumption and harm. However, several questions remain unanswered including how individual-level purchasing changes impact the local economy (e.g., balance between on-licence and off-licence outlets), lead to long-term population-level trends (e.g., youth drinking) and social harms (e.g., violence). Agent-based modelling captures heterogeneity, emergence, feedback loops and adaptive and dynamic features, which provides an opportunity to understand the nuanced effects of MUP. Agent-based models (ABM) simulate heterogeneous agents (e.g., individuals, organisations) often situated in space and time that interact with other agents and/or with their environment, allowing us to identify the mechanisms underlying social phenomena. ABMs are particularly useful for theory development, and testing and simulating the impacts of policies and interventions. We illustrate how ABMs could be applied to generate novel insights and provide best estimates of social network effects, and changes in purchasing behaviour and social harms, due to the implementation of MUP. ABMs like other modelling approaches can simulate alternative implementations of MUP (e.g., policy intensity [£0.50, £0.60] or spatial scales [local, national]) but can also provide an understanding of the potential impact of MUP on different population groups (e.g., alcohol exposure of young people who are not yet drinking). Using ABMs to understand the impact of MUP would provide new insights to complement those from traditional epidemiological and other modelling methods.

Game-theoretic agent-based modelling of micro-level conflict: Evidence from the ISIS-Kurdish war.

This article delves into the dynamics of a dyadic political violence case study in Rojava, Northern Syria, focusing on the conflict between Kurdish rebels and ISIS from January 1, 2017, to December 31, 2019. We employ agent-based modelling and a formalisation of the conflict as an Iterated Prisoner's Dilemma game. The study provides a nuanced understanding of conflict dynamics in a highly volatile region, focusing on microdynamics of an intense dyadic strategic interaction between two near-equally- powered actors. The choice of using a model based on the Iterated Prisoner's Dilemma, though a classical approach, offers substantial insights due to its ability to model dyadic, equally-matched strategic interactions in conflict scenarios effectively. The investigation primarily reveals that shifts in territorial control are more critical than geographical or temporal factors in determining the conflict's course. Further, the study observes that the conflict is characterised by periods of predominantly one-sided violence. This pattern underscores that the distribution of attacks, and target choices are a more telling indicator of the conflict nature than specific behavioural patterns of the actors involved. Such a conclusion aligns with the strategic implications of the underlying model, which emphasises the outcome of interactions based on differing aggression levels. This research not only sheds light on the conflict in Rojava but also reaffirms the relevance of this type of game-theoretical approach in contemporary conflict analysis.

Theories and approaches of social psychology

Social psychology is a field that studies how social thinking, behavior, and influence shape daily lives. This paper examines various social psychology theories, including social cognition, individual identity, and social influences. The main goal is to gain insight into the human behavior and interactions by exploring social, psychological, and sociological theories. The study used a systematic review based on previous research over three months and analyzed 80 research papers, ultimately using 28 papers that mostly covered the aim of this study. The findings highlighted the importance of racial diversity in shaping contemporary race relations and emphasized the need for cohesive yet diverse communities. The analyses revealed that individuals’ tendencies to self-serve inflate their traits’ moral value, influencing others’ self-perceptions and judgments. Innovative approaches such as agent-based modeling offered new perspectives on social dynamics, while identity emerged as a fluid construct shaped by socio-historical contexts. The ecological perspective provided valuable insights into the adaptive relationship between individuals and their environments. This review illuminates the crucial role of social psychology in understanding the complex interplay of individual behaviors, societal influences, and evolving identities. Throughout the paper, the discussion concerns the connection between theories and research and the application of findings to real-life situations. Hence, social psychology provides valuable insights into individual behavior and the social environment.

Using Agent Based Modelling to Advance Evaluation Research in Radicalization and Recruitment to Terrorism: Prospects and Problems

Our paper argues that Agent Based Modelling (ABM) can play an important role in evaluating interventions for counter radicalization and recruitment. Its advantages are due to three realities of research and practice in this area of study. First, field research on radicalization and recruitment raises significant ethical and human subjects dilemmas beyond traditional criminological and social research that are not present to the same degree in ABM experiments. Second, given the lack of existing studies and especially existing evaluation studies, ABMs provide a mechanism for identifying which programs or practices should be focused upon in field experiments, thus allowing researchers to focus in on interventions which have most promise of success. Third, ABM allows researchers to examine very large populations of individuals, which can solve the base rate problem for evaluating interventions focused on rare events, for example on recruitment to terrorism or terrorist attacks. We illustrate these advantages of ABM studies for assessing programs and policies in the context of an ABM model developed in Proton (an Horizon 2020 project). We focus in particular on how ABMs can be developed in ways that provide valid outcome results. In this context, we emphasize the importance of a strong theoretical foundation for ABM studies, creating a realistic landscape with actual data, and testing the ABM model for its fit to outcomes in the real world.

An innovative approach for optimising CCP default management through agent-based modelling

This paper develops a rigorous model to analyse the market impact of liquidation. Invoking innovative techniques from agent-based simulation, we construct the order book's reaction function to liquidation. A methodology is then developed for establishing the optimal close-out strategy which balances the velocity of positional liquidation with that consequential market reaction function. This model can be used in parallel to more traditional calculations of market volatility to ensure that the overall risk capitalisation of a central clearing counterparty remains robust. In the case of financial markets, agent-based models often seek to account for the so-called stylised facts of financial markets. Stylised facts are simply empirical regularities that appear to be stable across markets and over time. Such facts range from statistical concepts, such as the distribution of returns, to more abstract notions such as stock market bubbles and crashes.

Exploring the Influence of Household Agent-Based Modelling on Whole-life Decarbonisation of Residential Buildings

Abstract Besides the operational and embodied carbon associated with the physical building, how significant is the influence of the heterogenous nature of household decision making in whole-life decarbonisation of the housing sector? This paper investigates the effects of explicitly considering these factors through agent-based modelling (ABM) of households integrated in bottom-up building stock modelling considering typologies of physical houses (existing and new/future) and different households, and how these might evolve to 2050 in the State of Victoria, Australia. The state population is represented by household typologies based on socio-economic status, tenure, and decisions as influenced by financial, physical/family needs and behavioural factors. The ABM is implemented using Python MESA software and the different house typologies’ whole-life carbon are calculated using the Australian Zero Emission House (AusZEH) modelling software and the Environmental Performance in Construction (EPiC) LCA database, respectively. Considering household decisions yielded higher operational carbon reduction on average across various scenarios, however these have less impact on reducing embodied carbon. And in Victoria, with increasing trends in population and housing demand, embodied carbon dominates whole-life carbon (WLC) outcomes. The heterogeneity of household decisions cannot be ignored and should be further studied along with embodied carbon reduction strategies and a broad range of scenarios that consider the dynamic and uncertain nature of factors that drive and influence the WLC of buildings.

Methodological individualism and agent-based computational simulation: A reply to Kincaid and Zahle

This study reflects on Harold Kincaid and Jule Zahle’s view that there is no necessary association between methodological individualism and agent-based models because the analysis of social phenomena in terms of the latter cannot always be regarded as an implementation of the former. Their view remains in contention with the standpoint of several philosophers of science and social scientists, including Chen and Di Iorio. Kincaid and Zahle’s main argument against such a standpoint is that agent-based simulation is compatible with holistic explanations that are at odds with methodological individualism. The following study argues that Kincaid and Zahle’s conclusion remains untenable since it stems from inaccurate historical assumptions concerning the tradition of methodological individualism and the way the individualism-holism debate is understood within this tradition.

Tax Incentives for Economic Growth in the Russian Far East: Broad vs. Targeted Stimuli

The purpose of this study is to justify the choice of tax incentive policy instruments aimed at the economic development of the Russian Far East, which is facing acute demographic and environmental problems. To model the dynamics of the region’s real economic system, this study employed a mathematical model based on actual data from 2010 to 2021, covering economic, technological, and socio-ecological aspects. Using the versatile AnyLogic 8.0 platform for agent-based and system dynamics modeling, experiments on alternative tax incentive policy options involving both broad tax incentives and targeted economic development measures were conducted. Specifically, a 50% investment tax deduction for residents in special economic zones in the Russian Far East was implemented. The experimental results show that, despite comparable population dynamics, targeted stimulation of growth poles through public-private partnership programs outperforms broad tax incentives for economic entities in the Russian Far East. This is evident in higher economic growth rates in the region, particularly during the experimental period, except for 2040–2050, where adverse demographic trends constrain growth in both scenarios. The theoretical significance of the application of this method has shown that it allows us to obtain new significant results in the subject area of research due to the consideration of the complex interaction of factors of influence both at the micro- and macro-level, primarily behavioral factors that are fundamentally important for understanding the action of taxes. The practical implications of this study lie in defining the parameters of tax policy to target and stimulate growth poles in regions serving as hubs for generating and disseminating new technologies. The planned perspective is to encourage population growth and ensure sustained economic development in Russia’s Far East. It is advisable to explore comprehensive tax and budgetary regulations that simultaneously address economic, socio-demographic, and environmental issues in the region. Doi: 10.28991/ESJ-2024-08-03-021 Full Text: PDF