Agent-based Simulation Research Articles

Bridging theory and data: A computational workflow for cultural evolution

Cultural evolution applies evolutionary concepts and tools to explain the change of culture over time. Despite advances in both theoretical and empirical methods, the connections between cultural evolutionary theory and evidence are often vague, limiting progress. Theoretical models influence empirical research but rarely guide data collection and analysis in logical and transparent ways. Theoretical models themselves are often too abstract to apply to specific empirical contexts and guide statistical inference. To help bridge this gap, we outline a quality-assurance computational workflow that starts from generative models of empirical phenomena and logically connects statistical estimates to both theory and real-world explanatory goals. We emphasize and demonstrate validation of the workflow using synthetic data. Using the interplay between conformity, migration, and cultural diversity as a case study, we present coded and repeatable examples of directed acyclic graphs, tailored agent-based simulations, a probabilistic transmission model for longitudinal data, and an approximate Bayesian computation model for cross-sectional data. We discuss the assumptions, opportunities, and pitfalls of different approaches to generative modeling and show how each can be used to improve data analysis depending on the structure of available data and the depth of theoretical understanding. Throughout, we highlight the significance of ethnography and of collecting basic cultural and demographic information about study populations and call for more emphasis on logical and theory-driven workflows as part of science reform.

Evolving to find optimizations humans miss: using evolutionary computation to improve GPU code for bioinformatics applications

GPUs are used in many settings to accelerate large-scale scientific computation, including simulation, computational biology, and molecular dynamics. However, optimizing codes to run efficiently on GPUs requires developers to have both detailed understanding of the application logic and significant knowledge of parallel programming and GPU architectures. This paper shows that an automated GPU program optimization tool, GEVO, can leverage evolutionary computation to find code edits that reduce the runtime of three important applications, multiple sequence alignment, agent-based simulation and molecular dynamics codes, by 28.9%, 29%, and 17.8% respectively. The paper presents an in-depth analysis of the discovered optimizations, revealing that (1) several of the most important optimizations involve significant epistasis, (2) the primary sources of improvement are application-specific, and (3) many of the optimizations generalize across GPU architectures. In general, the discovered optimizations are not straightforward even for a GPU human expert, showcasing the potential of automated program optimization tools to both reduce the optimization burden for human domain experts and provide new insights for GPU experts.

Modelling indoor airborne transmission combining architectural design and people movement using the VIRIS simulator and web app.

A Viral Infection Risk Indoor Simulator (VIRIS) has been developed to quickly assess and compare mitigations for airborne disease spread. This agent-based simulator combines people movement in an indoor space, viral transmission modelling and detailed architectural design, and it is powered by topologicpy, an open-source Python library. VIRIS generates very fast predictions of the viral concentration and the spatiotemporal infection risk for individuals as they move through a given space. The simulator is validated with data from a courtroom superspreader event. A sensitivity study for unknown parameter values is also performed. We compare several non-pharmaceutical interventions (NPIs) issued in UK government guidance, for two indoor settings: a care home and a supermarket. Additionally, we have developed the user-friendly VIRIS web app that allows quick exploration of diverse scenarios of interest and visualisation, allowing policymakers, architects and space managers to easily design or assess infection risk in an indoor space.

Co-designing transport models as a heuristic planning tool.

Recently the transportation sector has witnessed several new technologically driven disruptions that have amplified the complexity of city planning and policymaking. Traditional well-established processes of decision-making in urban planning and transportation are proving insufficient to deal with this degree of complexity and uncertainty. This paper proposes an alternative approach, combining qualitative and normative urban design, with quantitative and predictive transport modelling. This requires urban designers and transport modellers to co-create goal-driven and agile transport models that act as a heuristic tool to guide planning decisions in early design stages. Heuristic modelling is informed by design optioning and vice versa in an iterative loop. A case study is presented to demonstrate how this approach is operationalized to study the impacts of automated vehicles on urban planning. Design workshops are used as a method to elicit responses from stakeholders, which are used to co-create the simulation models. This collaborative process grounds the research in real-world practice and enhances the communication of design proposals and research findings across disciplines. By integrating design thinking methods with agent-based transport simulations, this approach provides a better understanding of emergent effects in complex urban systems and improves stakeholder engagement in the planning process.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

The power of social networks and social media’s filter bubble in shaping polarisation: an agent-based model

AbstractTthe role social media platforms play on the emergence of polarisation is an ongoing debate in the political communication literature. Social media’s filter bubbles and online echo chambers shape people’s opinions by curating the information they have available. However, the extent to which this is the case remains unclear. Social simulation scholars have provided valuable insights into the subject through opinion dynamics models and agent-based modelling approaches. This article proposes a social simulation approach to the topic of opinion dynamics from a political communication perspective to understand how social network configurations and the media environment contribute to the emergence of national identity polarisation. We built an agent-based simulation model of national identity dynamics with a multilayer multiplex network of interacting agents in a hybrid media environment of both, traditional media and social media platforms. We use the Catalan secessionist movement to ground, contextualise and empirically inform parts of our model. We found that the initial social network setup conditions had a large impact on the emergence of polarisation amongst agents. In particular, homophily-based social networks composed of a majority of like-minded individuals produced greater polarisation compared to random networks. This was aggravated in the presence of social media filtering algorithms, selectively exposing agents to supportive information. These results emphasise the importance of both the selective exposure by social media filtering algorithms and one’s social networks (echo chambers) for polarisation to emerge. This interaction reinforces the influence of social media platforms and social networks have on the emergence of polarisation.

Exploring the importance of stochasticity to Hybrid Equilibria in a Discrete Signaling Game.

Communication via evolved signals is ubiquitous (both within and between species) in the natural world. However, how honest we should expect signals to be remains an open question. Hybrid equilibria are a form of equilibria predicted by discrete signaling games in which signalers are sometimes dishonest and signals do not completely reliably convey information on signaler quality. While these equilibria have been theoretically demonstrated in several signaling games, their dynamics in a stochastic simulation of evolutionary trajectories (that include representation of the inherent noise expected in evolution in the natural world) have not previously been studied. In this paper, we present an agent-based simulation of a discrete signaling game which exhibits hybrid equilibria. We find that while hybrid equilibria are evolutionarily attractive where they exist, populations exhibit variable and often drastic oscillating behavior around the predicted equilibrium values. We discuss how these dynamics might offer valuable opportunity for detecting hybrid equilibria in natural populations.

Sustainable Synchronization of Truck Arrival and Yard Crane Scheduling in Container Terminals: An Agent-Based Simulation of Centralized and Decentralized Approaches Considering Carbon Emissions

Background: Container terminal congestion is often measured by the average turnaround time for external trucks. Reducing the average turnaround time can be resolved by controlling the yard crane operation and the arrival times of external trucks (truck appointment system). Because the truck appointment system and yard crane scheduling problem are closely interconnected, this research investigates synchronization between the approaches used in truck appointment systems and yard crane scheduling strategies. Rubber-tired gantry (RTG) operators for yard crane scheduling operations strive to reduce RTG movement time as part of the container retrieval service. However, there is a conflict between individual agent goals. While seeking to minimize truck turnaround time, RTGs may travel long distances, ultimately slowing down the RTG service. Methods: We address a method that balances individual agent goals while also considering the collective objective, thereby minimizing turnaround time. An agent-based simulation is proposed to simulate scenarios for yard crane scheduling strategies and truck appointment system approaches, which are centralized and decentralized. This study explores the combined effects of different yard scheduling strategies and truck appointment procedures on performance indicators. Various configurations of the truck appointment system and yard scheduling strategies are modeled to investigate how those factors affect the average turnaround time, yard crane utilization, and CO2 emissions. Results: At all levels of truck arrival rates, the nearest-truck-first-served (NTFS) scenario tends to provide lower external truck turnaround times than the first-come-first-served (FCFS) and nearest-truck longest-waiting-time first-served (NLFS) scenario. Conclusions: The decentralized truck appointment system (DTAS) generally shows slightly higher efficiency in emission reduction compared with centralized truck appointment system (CTAS), especially at moderate to high truck arrival rates. The decentralized approach of the truck appointment system should be accompanied by the yard scheduling strategy to obtain better performance indicators.

Combining sterile insect releases with refuge areas to delay the evolution of resistance to Bt sugarcane: an agent-based modeling approach.

The strategic use of refuge areas is a well-known method for delaying the development of pest resistance to Bacillus thuringiensis (Bt) crop. A lesser-known method to control against resistance development is sterile insect releases. In this article, an agent-based simulation model is used to test the effectiveness of combining the use of Bt sugarcane, refuge areas, and sterile insect releases as an integrated strategy against Eldana saccharina Walker (Lepidoptera: Pyralidae) infestation and resistance development. Individual insects are modeled with their own genetic traits on a simulated sugarcane field that represents either Bt or refuge area. The model is applied to 2 hypothetical case studies. In the first experiment, resistance development and infestation dynamics in Bt sugarcane without refuge areas are considered using various sterile:wild sterile release ratios, and different release distributions. In the second experiment, the inclusion of a refuge area in Bt sugarcane is considered, using various sterile:wild releases ratios and different release distributions. A trade-off between sterile insect releases and the use of the refuge area was observed, and could, in some cases, work against each other.

Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing

Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooked the relationship between traffic volume and the degree of traffic congestion and emissions. To address these gaps, this study develops an open-source agent-based simulation platform and a heuristic algorithm to quantify the benefits of high-capacity ride-sharing with significantly lower computational costs. The simulation platform integrates a traffic emission model and a speed-density traffic flow model to characterise the interactions between traffic congestion levels and emissions. The experiment results demonstrate that ride-sharing with vehicle capacities of 2, 4, and 6 passengers can alleviate total traffic congestion by approximately 3%, 4%, and 5%, and reduce traffic emissions of a ride-sourcing system by approximately 30%, 45%, and 50%, respectively. This study can guide transportation network companies in designing and managing more efficient and environment-friendly mobility systems.

On the impact of zealots in a population of susceptible agents in a best-of-n problem within a heterogeneous network

Both humans and social animals live in groups and are frequently forced to choose between options with different qualities. When there are no leader agents controlling the group decision, consensus can be achieved through repeated interactions among group members. Various studies on collective decision-making illustrate how the dynamics of the opinions are determined by the structure of the social network and the methods that individuals use to share and update their opinion upon a social interaction. In this paper, we are interested in further exploring how cognitive, social, and environmental factors interactively contribute to determining the outcome of a collective best-of-n decision process involving asymmetric options, i.e., different costs and/or benefits for each option. We propose and study a novel model capturing those different factors, i) the cognitive load in processing social information, ii) the number of zealots (i.e., asocial agents who never change their opinion), iii) the option qualities, iv) the social connectivity structure, and v) the degree centrality of the asocial agents (i.e., the number of neighbours). By using the heterogeneous mean-field approach, we study the impact of the above-mentioned factors in the decision dynamics. Our findings indicate that when susceptible agents, i.e., individuals who change their opinion to conform with others, use the voter model as a mechanism to update their opinion, both the number and the degree of connectivity of the zealots can lead the population to converge towards the lowest quality option. Instead, when susceptible agents use methods demanding a larger cognitive cost (e.g., the majority rule), the group is marginally impacted by the presence of zealots. The results of the analytical model are complemented and extended by agent-based simulations. Our analysis also shows that the network topology can modulate the influence of zealots on group dynamics. In fact, in homogeneous networks where all nodes have the same degree, any location of the zealots has similar impact on the group dynamics. Instead, when the network is heterogeneous, our simulations confirm the model predictions showing that placing the zealots in the network hubs (nodes with several neighbours) has a much larger impact than placing them in lower-degree nodes.

Evaluating Path Signage Visibility for Equitable Design through BIM-enabled Agent-Based Simulation

Evaluating Path Signage Visibility for Equitable Design through BIM-enabled Agent-Based Simulation

Exploring the Role of Genetic and Environmental Features in Colorectal Cancer Development: An Agent-Based Approach

The complexity of issues in cancer research has led to the introduction of powerful computational tools to help experimental in vivo and in vitro methods. These tools, which typically focus on studying cell behavior and dynamic cell populations, range from systems of differential equations that are solved numerically to lattice models and agent-based simulations. In particular, agent-based models (ABMs) are increasingly used due to their ability to incorporate multi-scale features, ranging from the individual to the population level. This approach allows for the combination of statistically aggregated assumptions with individual heterogeneity. In this work, we present an ABM that simulates tumor progression in a colonic crypt, to provide an experimental in silico environment for testing results achieved in traditional laboratory research and developing alternative scenarios of tumor development. The model also allows some speculations about causal relationships in biologically inspired systems.

From agent-based simulations of disease dynamics to expert support on public health interventions

Abstract Agent-based epidemiological simulators have long been used to predict the dynamics of infectious diseases. They allow to combine compartment approaches with representing contact networks derived from real or synthetic populations. Often, scenario techniques are applied to model the impact of public health interventions. During the COVID-19 pandemics the agent-based simulator COVASIM has been established for modelling the spreading of SARS-CoV-2. It includes four different contact networks related to homes, schools, workplaces and environment. During the pandemic it became obvious that the current simulation techniques were not sufficient to cover important needs in public health administrations and society. While most simulations were based on scenarios predicting expected numbers for the infected, hospitalized or critically ill, these studies could not provide easily strategic options on a variety of equivalent public health interventions, which are all suitable to keep disease dynamics within acceptable bounds, set e.g. by hospital capacity. Knowing about a variety of equivalent options, however, is of crucial importance to enable a fair burden sharing throughout all groups of society. Therefore, we applied machine learning techniques to create an inverse model for epidemiological simulations based on COVASIM. For given transmission rates it outputs a range of quantitative interventions which are equivalently suitable to keep epidemiological dynamics within predefined bounds. Currently, it covers interventions like school and workplace closings and other mobility restrictions. Future work will be directed to include other interventions, like vaccination campaigns, and we will work to make the approach more generalizable for other infectious diseases. The tool may turn out helpful fur public health administrations and stimulate the public debate on the best way a society may take in a pandemic event or other situations where public health action has to be imposed. Key messages • Machine Learning allows to invert agent based simulators. • Instead of disease dynamics simulations a set of equivalent public health interventions consistent with pre-defined constraints can be calculated.

Understanding the evolution of engagement with brand posts on social media using agent-based models

Purpose This paper aims to delineate the nature of the costs associated with the diffusion of brand posts. Since cost is an essential component of customer lifetime value, this research also aims to identify factors that impact consumer engagement (CE) with brand posts. Design/methodology/approach This study conceptualize marginal cost of CE as the ratio of a unit change in cost for a unit change in CE – this ratio is conceptualized for a paid brand post compared to an organic brand post. The authors present a theoretically grounded simulation method that analyzes the relative impact of several factors that influence CE with brand posts. By using an agent-based simulation model, the method presented in this paper helps explain the relative impact of four factors on CE with brand posts on social media. Findings The authors explain four factors that influence the diffusion of CE with a brand post – promotion-related (i.e. whether it is paid vs organic), network-related, user-related and active periods. The authors also show that the effectiveness of paid posts vs organic posts attributed to a better audience assembled by the seeding algorithms dissipates after the initial stages of diffusion. These findings indicate that paid posts are effective vs organic posts in large part due to higher exposure than due to the efficiency of the algorithms to assemble nodes that are likely to engage with the post. Research limitations/implications An agent-based model details the impact of several factors that influence the effectiveness of paid and organic posts and presents methods to analyze the impact of these factors that have implications for theory. Practical implications The authors address the difficulty in ascertaining the effectiveness of paid posts by proposing a metric – marginal cost of engagement, defined as the incremental cost incurred by the marketer in earning one additional unit of CE. Since the costs incurred on paid and organic posts differ only on variable components, the marginal cost of engagement presents a way to benchmark paid posts against organic posts, which is very useful for managers in ascertaining the amount to spend on paid posts on social media. Originality/value There is little prior research on the costs involved in the diffusion of brand posts on social media. Identifying specific factors that impact costs is valuable for theory and practice.

AI-driven adaptive analysis for finding emergent behavior in military capability design

This article introduces and demonstrates a new methodology for searching for emergent behavior in simulation-based analysis as rare, extreme events using adaptive techniques enabled by recent advances in Bayesian machine learning (ML). The new methodology, Low-cost Adaptive exploratioN to Track down Extreme, Rare events using Numerical optimization (LANTERN), supports analysis activities in defense planning that iteratively build up the understanding of new technology and concept alternatives in complex military scenarios. Central to this process is emergent behavior—hard-to-predict but highly important behaviors that present problems or opportunities. These unexpected behaviors can be generated in complex military scenarios and are crucial to decision-making, but are often difficult to find and work with due to the expense and cost of the existing approaches for working with high-fidelity military simulation. To address this challenge, LANTERN is formulated to accelerate the discovery of emergent behavior as rare, extreme events by combining human expert understanding with new artificial intelligence (AI)-driven adaptive experimentation techniques in iterative analysis. A demonstration of the methodology is presented using military agent-based simulation scenarios developed in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM). The demonstration highlights how analysis can focus directly on searching for emergent behavior and shows substantial improvements over brute-force Monte Carlo approaches.

Agent-Based Geosimulation of Yayoi Period Population Dynamics in Kyushu, Japan

Current scholarship in Japanese archaeology recognizes the people and culture of the Yayoi period as being descended from both continental migrants (Toraijin) and the native inhabitants (Jomon people). However, the number of migrants that came to Japan and the degree to which they were directly responsible for the development of Yayoi culture is a topic of debate. Using northern Kyushu as a case study, the author employs an agent-based simulation based on geospatial data to model the spread of population, rice agriculture, and continental genetics during the Early Yayoi period. Multiple simulations are performed with varying parameters related to migrant population size, marriage customs, and the ratio of men to women amongst migrants to test their effect on the demographics of the Yayoi period. Simulation results were compared to population estimates and the distribution of mitochondrial DNA in order to determine their validity. Experiment 1 showed that the average number of continental migrants was likely in the order of magnitude of 10s of people per year (approx. 40 to 50) during the first 500 years of the Yayoi period. Experiment 2 demonstrated that polygyny can lead to increased population growth and aids in the spread of continental genetics, but without more rigorous evaluation criteria it is difficult to conclude whether it was practiced during the period. The methods used in this study demonstrate how agent-based simulations can model realistically sized populations moving through geographic space by basing simulation inputs on geospatial data and population estimates.

Coordination mechanisms applied to logistical systems for local disaster preparedness: a Latin American case

Purpose The study aims to present an agent-based simulation model (ABM) for exploring interorganizational coordination scenarios in local disaster preparedness. This approach includes local actors and logistical processes as agents to compare various strategic coordination mechanisms. Design/methodology/approach The ABM model, developed in the Latin American context, specifically focuses on a case study of Colombia. Three coordination mechanisms (centralized, decentralized and cluster-type) have been evaluated using three performance indicators: effectiveness, efficiency and flexibility. Findings Simulation results show that the decentralized scenario outperforms in terms of efficiency and flexibility. On the contrary, the centralized and cluster-type scenarios demonstrate higher effectiveness, achieving a greater percentage of requirements coverage during the disaster preparedness stage. The ABM approach effectively evaluates strategical coordination mechanisms based on the analyzed performance indicators. Research limitations/implications This study has limitations due to the application of results to a single real case. In addition, the focus of the study is primarily on a specific type of disaster, specifically hydrometeorological events such as flash floods, torrential rains and landslides. Moreover, the scope of decision-making is restricted to key actors involved in local-level disaster management within a municipality. Originality/value The proposed ABM model has the potential as a decision-making tool for policies and local coordination schemes for future disasters. The simulation tool could also explore diverse geographical scenarios and disaster types, demonstrating its versatility and broader applicability for further insights and recommendations.

EOS: Impact Evaluation of Electric Vehicle Adoption on Peak Load Shaving Using Agent-Based Modeling

The increasing adoption of electric vehicles (EVs) by the general population creates an opportunity to deploy the energy storage capability of EVs for performing peak energy shaving in their households and ultimately in their neighborhood grid during surging demand. However, the impact of the adoption rate in a neighborhood might be counterbalanced by the energy demand of EVs during off-peak hours. Therefore, achieving optimal peak energy shaving is a product of a sensitive balancing process that depends on the EV adoption rate. In this paper, we propose EOS, an agent-based simulation model, to represent independent household energy usage and estimate the real-time neighborhood energy consumption and peak shaving energy amount of a neighborhood. This study uses Residential Energy Consumption Survey (RECS) and the American Time Use Survey (ATUS) data to model realistic real-time household energy use. We evaluate the impact of the EV adoption rates of a neighborhood on performing energy peak shaving during sudden energy surges. Our findings reveal these trade-offs and, specifically, a reduction of up to 30% of the peak neighborhood energy usage for the optimal neighborhood EV adoption rate in a 1089 household neighborhood.

Estimation of Domestic Load Profile for Effective Demand-Side Management

In this article domestic base and shift based load profile are estimated for effective Demand Side Management (DSM). Domestic electricity consumption in Pakistan accounts for approximately 40% of total electricity sales. Effective DSM can reduce electricity consumption. Power generation companies are increasingly recognising the importance of analysing their customers' consumption patterns. This article proposes an agent-based modelling approach for forecasting household electricity consumption profile. AnyLogic software is used for establishing the model that uses variables and functions to estimate base and shiftable load of three hundred households. This article also estimates the weekly and 24- hour load profile for effective DSM operations by smart grid infrastructure. The results show that the agent-based simulation closely approximates real-time data, with a difference of only 2.4%.

Mapping secondary data gaps for social simulation modelling: A case study of the journeys of Syrian asylum seekers to Europe

Simulation models of social processes may require data that are not readily available, have low accuracy, are incomplete or biased. The paper presents a formal process for collating, assessing, selecting, and using secondary data as part of creating, validating, and documenting an agent-based simulation model of a complex social process, in this case, asylum seekers’ journeys to Europe. The process starts by creating an inventory of data sources, and the associated metadata, followed by assessing different aspects of data quality according to pre-defined criteria. As a result, based on the typology of available data, we are able to produce a thematic map of the area under study, and assess the uncertainty of key data sources, at least qualitatively. We illustrate the process by looking at the data on Syrian migration to Europe in 2011–21. In parallel, successive stages of the development of a simulation model allow for identifying key types of information which are needed as input into empirically grounded modelling analysis. Juxtaposing the available evidence and model requirements allows for identifying knowledge gaps that need filling, preferably by collecting additional primary data, or, failing that, by carrying out a sensitivity analysis for the assumptions made. By doing so, we offer a way of formalising the data collection process in the context of model-building endeavours, while allowing the modelling to be predominantly question-driven rather than purely data-driven. The paper concludes with recommendations with respect to data and evidence, both for modellers, as well as model users in practice-oriented applications.