Agent-based Approach Research Articles

The influence of pricing interventions in food choices on Brazil: An agent-based modelling approach

This study introduces an agent-based model (ABM) to analyse beef consumption trends in Brazil from 2017 to 2020. The ABM assesses the influence of price changes, specifically a rise in beef prices, on consumption behaviours and their diverse impacts across income groups. By integrating demographic and socioeconomic factors, the model replicates beef consumption decisions within Brazil. Findings show that consumer preferences are highly responsive to beef prices, with consumption decreasing as prices increase. A 60 % price increase due to a US$ 42/tCO2e tax could potentially reduce beef consumption by 55 %, cutting livestock-related greenhouse gas emissions by 53 % in Brazil. However, this tax would disproportionately affect lower-income individuals, worsening social inequality. To address potential food insecurity and ensure adequate nutrition for all, the consequences of food price interventions must be considered. Additional measures, such as tax incentives for plant-based alternatives, should be adopted to encourage suitable dietary choices. By thoughtfully assessing pricing intervention impacts, policymakers can support climate change mitigation, promote reduced meat consumption, and ensure fair access to nutritious food. This research improves our understanding of dietary decisions, the efficacy of pricing interventions, and their implications for affordability and social equality, with relevance beyond Brazil to nations facing similar challenges.

Channel-Agnostic Training of Transmitter and Receiver for Wireless Communications.

Wireless communications systems are traditionally designed by independently optimising signal processing functions based on a mathematical model. Deep learning-enabled communications have demonstrated end-to-end design by jointly optimising all components with respect to the communications environment. In the end-to-end approach, an assumed channel model is necessary to support training of the transmitter and receiver. This limitation has motivated recent work on over-the-air training to explore disjoint training for the transmitter and receiver without an assumed channel. These methods approximate the channel through a generative adversarial model or perform gradient approximation through reinforcement learning or similar methods. However, the generative adversarial model adds complexity by requiring an additional discriminator during training, while reinforcement learning methods require multiple forward passes to approximate the gradient and are sensitive to high variance in the error signal. A third, collaborative agent-based approach relies on an echo protocol to conduct training without channel assumptions. However, the coordination between agents increases the complexity and channel usage during training. In this article, we propose a simpler approach for disjoint training in which a local receiver model approximates the remote receiver model and is used to train the local transmitter. This simplified approach performs well under several different channel conditions, has equivalent performance to end-to-end training, and is well suited to adaptation to changing channel environments.

Shaping climate change discourse: the nexus between political media landscape and recommendation systems in social networks

Recommendation systems have become ubiquitous, and they actively participate in creating our individual and collective identity. In this paper, the diffusion of climate change information has been studied based on YouTube’s recommendation system and the political media landscape. The YouTube channels of CNN, BBC News and Fox News, as the most popular channels, respectively, for Left, Center and Right parties, were explored using web scraping and social network analysis to check what kind of recommended content will pop up if a user looks for climate change videos. Using an agent-based modeling approach, the competition between Left, Center and Right media in pushing their own narrative of climate change in society was simulated. The results suggest YouTube’s recommendation algorithm is highly biased since most of the recommended content was from the same channel fitting their own political agenda. The agent-based modeling indicates the size of a network is a decisive factor in further spread of a message as Left media always dominated Center and Right media in pushing their own perspective on climate change regardless of higher weights assigned to Right media. This study shed light on how public perception on climate change can be shaped by recommendation systems and digital companies.

An in silico micro-multiphysics agent-based approach for simulating bone regeneration in a mouse femur defect model.

Bone defects represent a challenging clinical problem as they can lead to non-union. In silico models are well suited to study bone regeneration under varying conditions by linking both cellular and systems scales. This paper presents an in silico micro-multiphysics agent-based (micro-MPA) model for bone regeneration following an osteotomy. The model includes vasculature, bone, and immune cells, as well as their interaction with the local environment. The model was calibrated by time-lapsed micro-computed tomography data of femoral osteotomies in C57Bl/6J mice, and the differences between predicted bone volume fractions and the longitudinal in vivo measurements were quantitatively evaluated using root mean square error (RMSE). The model performed well in simulating bone regeneration across the osteotomy gap, with no difference (5.5% RMSE, p = 0.68) between the in silico and in vivo groups for the 5-week healing period - from the inflammatory phase to the remodelling phase - in the volume spanning the osteotomy gap. Overall, the proposed micro-MPA model was able to simulate the influence of the local mechanical environment on bone regeneration, and both this environment and cytokine concentrations were found to be key factors in promoting bone regeneration. Further, the validated model matched clinical observations that larger gap sizes correlate with worse healing outcomes and ultimately simulated non-union. This model could help design and guide future experimental studies in bone repair, by identifying which are the most critical in vivo experiments to perform.

Looking at self-control development in adolescence through dynamic systems concepts: An agent-based modeling approach

The significance of self-control for essential life outcomes necessitates the study of its developmental pathways. In other words, other than discussions of the factors associated with self-control in different developmental periods, investigating the processes that lead to self-control development is also essential. To address this need, in this article, we present a conceptualization of trait self-control and its development using dynamic systems concepts and explore the aspects of this conceptualization by systematic simulation experiments of an agent-based model. Moreover, our focus in this article is on the adolescence period, which despite being a period of lasting changes, has received less attention on the subject of self-control development. Using an agent-based model of the parent-adolescent dyad, we show how moderate rule-setting can enhance self-control development. By simulating the processes that shape the adolescent’s trait self-control in the context of family, we provide a framework that could guide future theoretical and empirical studies of this critical ability.

Service and inventory model for maintenance workshop in the short cycle operation region: Agent-based simulation approach

Tools used in the North Sea region has a high cycle and come very often to maintenance workshop to be checked, repaired if needed and prepared for the next operations. The availability of spare parts at the workshop plays a significant role to keep the flow time as short as possible and meet such high-cycle operations. Supplying spare parts from the best-cost countries to the North Sea region with about one-year lead time makes the situation more critical and the economic order quantity and reorder point need to be found. However, having inventory at the workshop and ordering a batch of spare parts increases operational expenditures. Moreover, frequent supplies increase the environmental impacts of shipping CO2 and spare scrap rates, whereas repairing the used spare parts and reusing them again can offer a more sustainable solution. Therefore, the purpose of this paper is to develop a simulation model that can quantify the cost and benefits of reusing repaired spares compared to supplying newly built spares from the best-cost countries. To achieve this purpose, a case study has been implemented on a specific maintenance workshop within the North Sea region and the entire tool repair and spare part supply operations are conceptualized and modelled with the help of the simulation modelling approach. Two scenarios have been simulated: (1) the maintenance workshop fully depends on supplying newly built spares from the best-cost countries with no inventory stock or spare reuse, and (2) the Maintenance workshop is primarily dependent on repaired spares with an optimal level of spares stock. The simulation results, from the studied case, support the second scenario where a repair path cycle is introduced to the maintenance workshop, as a 78% reduction in lead times, a 116% improvement in worker utilization, a 73% reduction in crowding levels, a 52% reduction in scrap rate, and a potential profit increase of roughly three million NOK (20%). Therefore, it can be concluded that a local repair service is required to keep maintenance workshops in high-cycle regions at high-performance levels.

Language dynamics within adaptive networks: an agent-based approach of nodes and links coevolution

Motivated by the dramatic disappearance of endangered languages observed in recent years, a great deal of attention has been given to the modeling of language competition in order to understand the factors that promote the disappearance of a language and its unfolding dynamics. With this in mind, we build on existing network models of language competition in bilingual societies. These models deal with the interplay between the usage of a language (link state) and the preference or attitude of the speakers towards the language (node state). In this work, we allow for the case where agents have the freedom to adapt their local interactions in accordance with their language preference. This is modeled by introducing a local rewiring mechanism triggered by the dissatisfaction of an agent with its usage of a given language. Our numerical simulations show that permitting this freedom to agents likely results in linguistically segregated communities for small network sizes. However, for networks of sufficiently large size, the extinction of one of the languages is the most probable scenario. Furthermore, we analyze how the fraction of minority speakers changes with the system size and we find that this fraction grows as the total population increases, which is consistent with existing data. Overall, the results of this work help us understand the impact of speakers’ preferences and choices in the complex language landscape of bilingual societies.

A mixed-method approach to determining contact matrices in the Cox's Bazar refugee settlement.

Contact matrices are an important ingredient in age-structured epidemic models to inform the simulated spread of the disease between subgroups of the population. These matrices are generally derived using resource-intensive diary-based surveys and few exist in the Global South or tailored to vulnerable populations. In particular, no contact matrices exist for refugee settlements-locations under-served by epidemic models in general. In this paper, we present a novel, mixed-method approach for deriving contact matrices in populations, which combines a lightweight, rapidly deployable survey with an agent-based model of the population informed by census and behavioural data. We use this method to derive the first set of contact matrices for the Cox's Bazar refugee settlement in Bangladesh. To validate our approach, we apply it to the UK population and compare our derived matrices with well-known contact matrices collected using traditional methods. Our findings demonstrate that our mixed-method approach successfully addresses some of the challenges faced by traditional and agent-based approaches to deriving contact matrices. It also shows potential for implementation in resource-constrained environments. This work therefore contributes to a broader aim of developing new methods and mechanisms of data collection for modelling disease spread in refugee and internally displaced person (IDP) settlements and better serving these vulnerable communities.

AN AGENT BASED MODEL TO IMPROVE TRAFFIC FLOW AND ASSESS THE IMPACT OF SMART TRAFFIC LIGHT

The Traffic in developing country undergoes severs perturbations due to either a misunderstanding of the physical phenomenon or in bad calibration of equipments such as traffic light. Due to this high complexity, mathematical models fail to well represent different scenario. Those issues are mostly due to the high amount of parameter which should be taken into consideration when dealing with some mathematical model and the drivers behaviors. Since agent-based model is a computational simulation that represents individual entities (called agents) and their interactions within a defined environment. It is a powerful tools used to investigate human behavior impact in a mathematical model. Therefore, this paper proposed a novel agent-based approach framework for effectively making traffic light timing dynamic according to traffic density. Moreover, based on different rules such as lane changing, stop and goat the traffic light, we develop an Agent based model to represent the relationships between the traffic changing and its environment. The use of agent-based models for traffic light time optimization offers a more granular, adaptive, and integrative approach that better capture the complexities of real-world traffic scenarios. Finally, the experimental result is applied on synthetic data and compared to other results in almost the same context for both static and dynamic traffic light. The results show that the travel time distribution is less for dynamic traffic light.

The Emergence of the Contractor’s Innovation Capability at Project Level: An Agent-Based Modeling Approach

Contractors play a crucial role in ensuring efficient innovation within construction projects but limited research has focused on the innovation capability of contractors. This study utilizes a multi-method approach, including case studies, surveys, and interviews to collect data for analysis. Based on this, a conceptual model is proposed and a simulation mode which utilizes agent-based modeling (ABM) is constructed. ABM is the microscopic bottom-up approach which can describe and follow the agents and interactions. This study proposes a novel conceptual model to examine the emergence of contractor innovation capability at the project level, from a knowledge flow perspective. It fills the research gap regarding innovation capability in temporary cross-organizational projects. Additionally, an ABM simulation model is developed considering project and participant characteristics, providing insights into the formation rule and development mechanisms of contractors’ innovation capability at the project level. The conclusions are as follows: (1) The demand for innovation drives the innovative behavior of different entities within the project. (2) Knowledge availability in public domains and other entities’ knowledge creation capability provide critical support for contractor innovation. (3) Contractors’ capability to absorb and integrate knowledge serves as the foundation for achieving innovation. (4) When contractors possess strong capabilities, effective synergy among organizations facilitates the generation of innovative outcomes.

Identifying Impacts of School-Escorted Trips on Traffic Congestion and the Countermeasures in Bangkok: An Agent-Based Simulation Approach

The often-discussed issue of parental escorting of children to school and its potential disruption of traffic flow has been extensively examined in the literature. Still, the specific effects of traffic congestion remain understudied. To fill this gap, this study addresses the impacts of school trips on traffic congestion. An agent-based model is applied to simulate various scenarios and assess their impact. Our findings indicate that the traffic speed without school trips is higher speed during peak hours by around 8% and average travel time in the city is reduced significantly. We examine countermeasures: Firstly, flexible working hours can lead to a morning traffic speed improvement of approximately 1.9%. Secondly, staggered school schedules can result in a 6.9% increase in traffic speed at 7 a.m. Optimizing school loading spaces can also enhance road capacity, mitigating road lane blockages during drop-off and pick-up periods. This research sheds light on the impact of parent-escorting travel existence and offers potential solutions to alleviate traffic congestion.

How Does Facebook Retain Segregated Friendship? An Agent-Based Model Approach

Facebook, the largest social networking site in the world, has overcome the structural barriers that historically constrain individuals to reach out to different others. Through the platform, people from all walks of life and virtually any location can develop diverse friendships online. However, friendships on Facebook have been as segregated as friendships in real life. This research sought to understand why the leading social networking site intended to “bring the world closer together” retains segregated friendship. In doing so, I employed a series of agent-based simulations based on the Framework for Intergroup Relations and Multiple Affiliations Networks (FIRMAN). As demonstrated, Facebook has primarily enhanced users’ capacity to maintain a larger number of friendships (tie capacity), but it has done little to empower users’ ability to accept diversity and befriend different others (tie outreachability). Facebook must focus on the latter should they truly wish to contribute to the development of a more inclusive society. While in this study I focus on ethnically segregated friendship on Facebook, I argue that the same explanation might also hold for racially and ideologically segregated friendships on other bidirectional social networking sites.

COVID-19 transmission in a resource dependent community with heterogeneous populations: An agent-based modeling approach

Outbreaks of COVID-19 in crowded work locations led to mass infection events during the pandemic that stressed health capacity in rural communities. This led to disparate responses – either isolating and restricting workers to facilities and potentially amplifying spread between them, more intense community wide restrictions, or an acceptance of higher disease spread. An extreme case is the salmon fishery in Bristol Bay, Alaska, where fishermen, factory workers, and residents all interact during the summer fishing season. During the pandemic, policy measures were debated, including community mask mandates, restricting workers to their boats and factories, and even closing the valuable seasonal fishery. We develop an agent-based SIR model (ABM) to examine COVID-19 transmission in a resource-dependent community populated by distinct subgroups. The model includes a virus spreading within and between three heterogenous populations who interact with other members of their type in their home location, and with different types of agents when out in the community. We simulate various non-pharmaceutical interventions and vaccination rates across these groups. Results demonstrate the efficacy of non-pharmaceutical interventions and vaccinations, as well as tradeoffs between duration and intensity and tradeoffs between groups impacted by the outbreak. This ABM demonstrates the impact of public policy mechanisms on health outcomes in resource-dependent communities with distinct populations.

A multi-agent resource bidding algorithm for order acceptance and assembly job shop scheduling

This study uses an agent-based approach with a combinatorial auction mechanism to solve the joint order acceptance and assembly job shop scheduling problem. A set of jobs is offered. Each job has a revenue, ready time, due date, deadline, and consists of a set of operations with precedence relationships. Jobs that deviate from their due dates incur earliness/tardiness penalties. An operation may require several units of capacity per time unit and a resource could have multiple units of capacity. The manufacturer can reject any job to satisfy the capacity constraints and maximise the overall profit. We develop a mathematical model for the problem, then use an agent-based approach to solve it. First, the relaxed problem is decomposed into a set of job-level subproblems. Each job is optimised individually without considering the capacity constraints. Profitable jobs at the individual level submit their optimal schedules as combinatorial bids to an auctioneer to acquire combinations of resource capacity-time units. Then, the auctioneer records the profit upper bound, resolves capacity conflicts to reach a feasible solution, records the profit lower bound, and updates the dual variables. Experimental results show that the proposed methodology can solve large-sized problems in reasonable CPU times.

An agent-based model approach to assessing the role of vegetative fragments in seagrass connectivity

Understanding how populations in the marine environment exchange genetic material is vital for the implementation of suitable conservation measures. However, dispersal can be challenging to empirically assess as it can occur over huge distances. Increasingly, agent-based models (ABMs) are being used to predict the dispersal pathways of populations of many taxa, including seagrasses. Most seagrass ABMs have been based on sexual propagules (i.e. fruits, seeds, spathes), and overlook the potential role of asexual vegetative fragments in long-distance dispersal. In this study, we developed two contrasting ABMs, one for vegetative fragments and another for fruits and seeds using Halophila ovalis and Thalassia hemprichii respectively as model species, to evaluate (i) the dispersal potential of these two dispersal strategies, and (ii) the local and regional seagrass connectivity around Singapore. Our models demonstrate that vegetative fragments could be an effective mode of dispersal for H. ovalis, having similar establishment patterns to T. hemprichii fruits/seeds and establishing at greater distances on average. The models also show that most meadows are well connected locally and regionally, regardless of the mode of dispersal. The ABMs identified possible source and sink meadows that should be conserved to provide a repository of genetic material, as well as a supply of propagules for recruitment. We highlight the need to include vegetative fragments alongside sexual propagules when evaluating seagrass connectivity in future modeling efforts.

Agent-based approach for elucidating the release from collective arrest of cell motion in corneal epithelial cell sheet

Changes in cell fluidity have been observed in various cellular tissues and are strongly linked to biological phenomena such as self-organization. Recent studies suggested variety of mechanisms and factors, which are still being investigated. This study aimed to investigate changes in cell fluidity in multi-layered cell sheets, by exploring the collective arrest of cell motion and its release in cultures of corneal epithelial cells. We constructed mathematical models to simulate the behaviors of individual cells, including cell differentiation and time-dependent changes in cell-cell connections, which are defined by stochastic or kinetic rules. Changes in cell fluidity and cell sheet structures were expressed by simulating autonomous cell behaviors and interactions in tissues using an agent-based model. A single-cell level spatiotemporal analysis of cell state transition between migratable and non-migratable states revealed that the release from collective arrest of cell motion was initially triggered by a decreased ability to form cell-cell connections in the suprabasal layers, and was propagated by chain migration. Notably, the disruption of cell-cell connections and stratification occurred in the region of migratable state cells. Hence, a modeling approach that considers time-dependent changes in cell properties and behavior, and spatiotemporal analysis at the single-cell level can effectively delineate emergent phenomena arising from the complex interplay of cells.

Leveraging campus-scale Wi-Fi data for activity-based occupant modeling in urban energy applications

The widespread availability of open datasets in urban areas is transforming how urban energy systems are planned, simulated, and visualized. Urban energy models, however, require an understanding of urban dwellers, as their activities create the demands for energy in buildings. In this paper, we explore using campus-scale Wi-Fi data to identify typical occupant activity patterns as an input to an agent-based model of building occupants at the district scale. The data is taken from a Singaporean university’s Wi-Fi network at high resolution. Each record comprises a timestamp, a device identifier, the location of the device within the campus, and the access point to which it is connected. The Wi-Fi dataset contains 120 different buildings on campus and 10,300 anonymized individual devices. Activities are then assigned to each location on campus according to the building use type. In order to test the methodology, the activity plans of 27,604 undergraduate students, 8,304 graduate students, and 12,018 employees were simulated over a workweek. The results show the model’s ability to produce plausible activity plans but could be improved by implementing sampling rules and expanding the source dataset to include off-peak dates. Nevertheless, using such an agent-based modeling approach at the district scale appears to be a promising methodology to assess the impacts of different planning strategies on occupant behavior and district energy demand.

Developing tools for municipalities to meet carbon targets

Carbon emissions must be cut in half by 2030 to meet the Paris Agreement’s goals, and cities and municipalities are at the forefront of the fight against climate change. In 2017, the energy use of buildings directly accounted for 51% of emissions in the City of Victoria and offered the greatest opportunity for the municipal government to act. Unfortunately, at this point, many cities and municipalities lack the tools and locally relevant data to make effective policy decisions. This research aims to develop a practical framework for analyzing and comparing the carbon impact of policies enacted by municipal governments, and is specifically focused on the energy consumption, and operating and embodied carbon related to single-family dwellings (SFDs) in the City of Victoria, which contains a heterogeneous building stock with construction dates ranging between 1860 to present day. The underlying model has been developed based on statistical modeling and agent-based behavioral responses to different policy actions. The agent-based modelling approach models stock development in terms of new construction, retrofit, and replacement by simulating individual decisions at the building level. The results can be used to identify optimal efforts to minimizing barriers or bottlenecks in achieving low-carbon ambitions while understanding or addressing related aspects such as housing affordability. Municipalities can use the dashboard to identify and prioritize climate solutions that meet their stringent obligations.

Dynamic open-source simulation engine for generic modeling of district-scale energy systems with focus on sector coupling and complex operational strategies

This paper introduces a new simulation engine for generic modeling that allows fast and efficient simulation of the energy supply of district-scale sector-coupled energy systems during their early planning stage. The engine combines rule-based control algorithms, system dynamics modeling and an agent-based approach to simulate energy systems, while providing extensive possibilities to model complex operational strategies without requiring linearisation. The simulation engine determines the energy flow and operational state of all components at each time step, providing a comprehensive understanding and evaluation of the system’s behavior. Users can select and size technologies, determine the connections between technical components, and define operational strategies individually. The underlying mathematical model is based on energy balances, with a special emphasis on the order in which the supplied, requested, and transformed energy is calculated. Additionally, the simulation engine is suitable for black box optimisation e.g. for optimal sizing of components. To demonstrate its applicability, the simulation engine is used to model an exemplary sector-coupled energy system using practice-related operational strategies, and the results show the expected behavior according to the implemented mathematical models and operational strategies. The simulation engine is released as open-source software, making it suitable for participatory development.

Evaluating the incentive for soil organic carbon sequestration from carinata production in the Southeast United States

Soil organic carbon (SOC) can be increased by cultivating bioenergy crops to produce low-carbon fuels, improving soil quality and agricultural productivity. This study evaluates the incentives for farmers to sequester SOC by adopting a bioenergy crop, carinata. Two agricultural management scenarios – business as usual (BaU) and a climate-smart (no-till) practice – were simulated using an agent-based modeling approach to account for farmers’ carinata adoption rates within their context of traditional crop rotations, the associated profitability, influences of neighboring farmers, as well as their individual attitudes. Using the state of Georgia, US, as a case study, the results show that farmers allocated 1056 × 103 acres (23.8%; 2.47 acres is equivalent to 1 ha) of farmlands by 2050 at a contract price of $6.5 per bushel of carinata seeds and with an incentive of $50 Mg−1CO2e SOC sequestered under the BaU scenario. In contrast, at the same contract price and SOC incentive rate, farmers allocated 1152 × 103 acres (25.9%) of land under the no-till scenario, while the SOC sequestration was 483.83 × 103 Mg CO2e, which is nearly four times the amount under the BaU scenario. Thus, this study demonstrated combinations of seed prices and SOC incentives that encourage farmers to adopt carinata with climate-smart practices to attain higher SOC sequestration benefits.