Agent-based Research Articles

Effects of Intervention Timing on Health-Related Fake News: Simulation Study.

Fake health-related news has spread rapidly through the internet, causing harm to individuals and society. Despite interventions, a fenbendazole scandal recently spread among patients with lung cancer in South Korea. It is crucial to intervene appropriately to prevent the spread of fake news. This study investigated the appropriate timing of interventions to minimize the side effects of fake news. A simulation was conducted using the susceptible-infected-recovered (SIR) model, which is a representative model of the virus spread mechanism. We applied this model to the fake news spread mechanism. The parameters were set similarly to those in the digital environment, where the fenbendazole scandal occurred. NetLogo, an agent-based model, was used as the analytical tool. Fake news lasted 278 days in the absence of interventions. As a result of adjusting and analyzing the timing of the intervention in response to the fenbendazole scandal, we found that faster intervention leads to a shorter duration of fake news (intervention at 54 days = fake news that lasted for 210 days; intervention at 16 days = fake news that lasted for 187 days; and intervention at 10 days = fake news that lasted for 157 days). However, no significant differences were observed when the intervention was performed within 10 days. Interventions implemented within 10 days were effective in reducing the duration of the spread of fake news. Our findings suggest that timely intervention is critical for preventing the spread of fake news in the digital environment. Additionally, a monitoring system that can detect fake news should be developed for a rapid response.

Complexity in Systemic Cognition: Theoretical Explorations with Agent-Based Modeling

This paper presents a systemic view of human cognition that suggests complexityis an essential feature of such a system. It draws on the embodied, distributed, and extended cognition paradigms to outline the elements and the mechanisms that define cognition. In doing so, it uses an agent-based computational model (the TS 1.0.5Model) with a focus on learning mechanisms as they reflect on individual competence to gain insights on how cognition works. Results indicate that cognitive dynamics do not depend solely on macro structural elements, nor do they depend uniquely on individual characteristics. Instead, more insights and understanding are available through the consideration of all elements together as they co-evolve and interact over time. This perspective illustrates the essential role of how we define the meso domain and constitutes a clear indication that cognitive systems are indeed complex.

Agent-based simulation of citizens’ satisfaction in smart cities

Although the citizens’ satisfaction in smart cities (CSSC) has become a vital criterion for smart cities, which symbolizes the people-centric concept, the previous research on simulating CSSC only considered the technological innovation and policy changes at the macro level, resulting in the neglect of the micro emotional contagion between citizens. To address this gap, a simulation approach is proposed to consider the influence caused by emotional contagion through the agent-based model (ABM). Supported by the expectation confirmation model (ECM) and the emotional contagion theory (ECT), the ABM simulation is implemented through the Anylogic platform, based on survey data from 19 smart cities in China. The results of various scenarios show that citizens’ expectations are the key factor affecting the CSSC, and the influence of emotional contagion cannot be ignored, especially in the acquaintance society. As social networks become more intimate, the impact of emotional contagion will turn the citizen group into an “antithetical society,” which means a situation with severe polarization of CSSC. This study also discusses the possible threshold of this transition process. Based on the analysis of multiple scenarios, it is implied that CSSC should be evaluated by considering the emotional contagion, and smart cities prioritize quality over quantity.

Buffering effects of nonspecifically DNA-bound RNA polymerases in bacteria

RNA polymerase (RNAP) is the workhorse of bacterial gene expression, transcribing rRNA and mRNA. Experiments found that a significant fraction of RNAPs in bacteria are nonspecifically bound to DNA, which is puzzling as these idle RNAPs could have produced more RNAs. Whether nonspecifically DNA-bound RNAPs have any function or are merely a consequence of passive interaction between RNAP and DNA is unclear. In this work, we propose that nonspecifically DNA-bound RNAPs buffer the free RNAP concentration and mitigate the crosstalk between rRNA and mRNA transcription. We verify our theory using mean-field models and an agent-based model of transcription, showing that the buffering effects are robust against the interaction between RNAPs and sigma factors and the spatial fluctuation and temporal noise of RNAP concentration. We analyze the relevant parameters of and find that the buffering effects are significant across different growth rates at a low cost, suggesting that nonspecifically DNA-bound RNAPs are evolutionarily advantageous. Published by the American Physical Society 2024

Agent-based simulation model of micro-mobility trips in heterogeneous and perceived unsafe road environments

Safety concerns expressed by micro-mobility users seem to practically constrain the use and flexibility of these new modes. This paper introduces a new, parametric agent-based modeling approach for simulating micro-mobility, using MATSim as a platform. Perceived safety is introduced as a factor, which affects travel behavior of micro-mobility modes in a car-dominated, heterogeneous, and perceived unsafe road network. To meet the research objectives, different scenarios are simulated, using the city of Athens, Greece as a test bed, to examine the model performance and applicability. A universal scoring function based on travel time, cost, and safety, is proposed. This study also discusses the concept of unsafe discontinuities. Simulation results show that an unsafe road segment of 500 m long is sufficient to induce evident behavioral variations and hinder the practical use of e-bikes and e-scooters in the base scenario. The establishment of a cycle-friendly road network leads to significant changes in the chosen routes and decreases the average trip distance for micro-mobility users. In this case, the simulation process is guided by perceived safety, primarily focusing on formulating safer paths for micro-mobility mode users. Nevertheless, the routing behavior of car users is not modified due to the spatial uniformity of safety perceptions in a car-dominated road environment.

Early hominins: Successful hunters, catchers, or scavengers? An agent-based model about hunting strategies in tropical grasslands

We can see an increasing consumption of meat together with the corresponding behavioral adaptations in early hominins, such as Homo erectus. This new development was driven by one or more behavioral adaptations, such as a shift to a higher-quality diet, increased social interactions and/or changes in the life history strategies. The methods by which these hominins obtained meat—through scavenging the carcasses of large herbivores or hunting themselves—remain a topic of debate. They seem to have thrived in expanding grasslands, which offered few resources except for herds of large, gregarious mammals. In our study, we developed an agent-based model that simulates the behavior of a group of hunter-gatherers foraging in a reconstructed tropical grassland environment. The environmental parameters, including plant availability and prey population densities, are derived from the Serengeti National Park. In this model, agents gather or hunt various species either alone or as a group, using strategies early hominins may already have access to. The basic behavior and the implemented hunting strategies are based on data from recent hunter-gatherer societies living in tropical grasslands. Our model demonstrates how foragers may have thrived in tropical grasslands by either adopting fast hunting strategies, which often require access to sophisticated hunting tools, or by cooperating extensively, which would rely on an enhanced social structure to promote cooperative behavior. Our model can be used to study other scenarios by offering the option to change the environmental conditions and aspects of the agent behavior.

A WEAP-assisted agent-based modeling approach for watershed planning in a coupled human and nature system

ABSTRACT Coupled human and nature systems exhibit dynamic water demands due to diverse consumer socio-economic characteristics, resulting in inherent complexities. Modeling such systems effectively entails adopting an agent-based methodology. To enhance comprehension of water resource management in the Zarrineh-Roud Basin, northwest of Iran, a comprehensive hydrological-behavioral model has been utilized. This model consists of two primary components: An agent-based model (ABM) for simulating farmers’ decision-making processes and a water evaluation and planning (WEAP) model for simulating catchment area, cultivated area, and agricultural water consumption. Validation outcomes of the simulation model demonstrate precise estimation, particularly in the runoff estimation of the Nezam Abad hydrometric station, with statistically favorable correlation coefficient (R2) and Nash–Sutcliffe index (NS) values of 0.78 and 0.75, respectively. Analysis of different management scenarios indicates that with ample government funds, simultaneous implementation of ‘Education and increasing environmental awareness’ and ‘Alternative employment’ policies can significantly improve water resources conditions and income. In scenarios with limited financial resources, a combined implementation of ‘Water pricing,’ ‘Alternative employment,’ and ‘Education and raising environmental awareness’ emerges as the most effective strategy for managing water resources, albeit with a slight reduction in farmers’ income.

Of criminals and cancer: The importance of social bonds and innate morality on cellular societies

The current dogma in cancer biology contends that cancer is an identity problem: mutations in a cell's DNA cause it to “go rogue” and proliferate out of control. However, this largely ignores the role of cell-cell interaction and fails to explain phenomena such as cancer reversion, the existence of cancers without mutations, and foreign-body carcinogenesis. In this proof-of-concept paper, we draw on criminology to propose that cancer may alternatively be conceptualized as a relational problem: Although a cell's genetics is essential, the influence of its interaction with other cells is equally important in determining its phenotype. We create a simple agent-based network model of interactions among normal and cancer cells to demonstrate this idea. We find that both high mutation rates and low levels of connectivity among cells can promote oncogenesis. Viewing cancer as a breakdown in communication networks among cells in a tissue complements the gene-centric paradigm nicely and provides a novel perspective for understanding and treating cancer.

Agent-Based New Product Diffusion Model and Detection of Sales Decline Phenomenon with CaR

Agent-Based New Product Diffusion Model and Detection of Sales Decline Phenomenon with CaR

Experiment and path optimization on Large-Space Multi-Area crowd evacuation

Abstract In large spaces where there are multiple evacuation zones, the flow patterns during evacuation are intricate, leading to congestion in emergency scenarios, and affecting evacuation efficiency. To address this, we proposed an optimization method for crowd evacuation paths in large-space multi-area environment settings under fire scenarios by utilizing multi-agent simulation. Using Pathfinder software, disorganized evacuation simulations are conducted within expansive multi-zonal settings to pinpoint areas with inadequate evacuation capabilities. Subsequent evacuation experiments are performed for verification, facilitating adjustments to agent-based model parameters for evacuees in fire scenarios, and carrying out organized evacuation simulations. Through meticulous simulation analysis, we aim to optimize the most efficient evacuation paths under organized responses in large-space multi-area settings. Results indicate that in emergency conditions due to fire, the evacuation speeds reach 1.8 m/s, and significant reductions in total evacuation time are achieved through simulation optimization, with the time required for the slowest individuals to evacuate decreasing from 240.32 s to 209.32 s. Furthermore, the flow rate per second at congested exits diminishes from 2.01 persons/s to 1.39 persons/s, highlighting the efficacy of the proposed path optimization method.

Interplay of network structure and talent configuration on wealth dynamics.

The economic success of individuals is often determined by a combination of talent, luck, and assistance from others. We introduce an agent-based model that simultaneously considers talent, luck, and social interaction. This model allows us to explore how network structure (how agents interact) and talent distribution among agents affect the dynamics of capital accumulation through analytical and numerical methods. We identify a phenomenon that we call the "talent configuration effect," which refers to the influence of how talent is allocated to individuals (nodes) in the network. We analyze this effect through two key properties: talent assortativity (TA) and talent-degree correlation (TD). In particular, we focus ons three economic indicators: growth rate (n_{rate}), Gini coefficient (inequality: n_{Gini}), and meritocratic fairness (n_{LT}). This investigation helps us understand the interplay between talent configuration and network structure on capital dynamics. We find that, in the short term, positive correlations exist between TA and TD for all three economic indicators. Furthermore, the dominant factor influencing capital dynamics depends on the network topology. In scale-free networks, TD has a stronger influence on the economic indices than TA. Conversely, in lattice-like networks, TA plays a more significant role. Our findings address that high socioeconomic homophily can create a dilemma between growth and equality and that hub monopolization by a few highly talented agents makes economic growth strongly dependent on their performances.

Growth-induced collective bending and kinetic trapping of cytoskeletal filaments.

Growth and turnover of actin filaments play a crucial role in the construction and maintenance of actin networks within cells. Actin filament growth occurs within limited space and finite subunit resources in the actin cortex. To understand how filament growth shapes the emergent architecture of actin networks, we developed a minimal agent-based model coupling filament mechanics and growth in a limiting subunit pool. We find that rapid filament growth induces kinetic trapping of highly bent actin filaments. Such collective bending patterns are long-lived, organized around nematic defects, and arise from competition between filament polymerization and bending elasticity. The stability of nematic defects and the extent of kinetic trapping are amplified by an increase in the abundance of the actin pool and by crosslinking the network. These findings suggest that kinetic trapping is a robust consequence of growth in crowded environments, providing a route to program shape memory in actin networks.

Financial Revolution through Agent-based Artificial Simulation Computational Models for Predicting Market Behavior

The fundamental theory of the Efficient market hypothesis (EMH), which states that market participants are rational, has received a lot of criticism. The complexity of behavior in the capital market is still a black box, especially when psychological biases influence aggressively on decision-making amid uncertainty. Experimental research on finance and capital markets in the form of AI using machine learning seeks to predict the results of more complex interactions. This multidisciplinary approach offers efforts to explain social phenomena from the micro level to macro descriptions which are built artificially through the computational world. The processing modeling approach is preferred because it includes the complexes that emerge from the behavior and interactions of individuals in the real world. Agent Based Model (ABM) is an AI approach in the form of computational simulation that performs a bottom-up approach by combining irrational–rational agent interactions through networks in microenvironments. Using the ABM approach through Netlogo computing, this study proves that AI can be used to analyze investor behavior in the capital market. Keywords: Agent Based Model, artificial intelligence, investor behavior

Comparative Study of SFPE and Steering Modes in Pathfinder to Optimise Evacuation Routes

This paper investigates the possibilities of using an agent-based evacuation model to analyse the formation of queues at the exits of enclosed spaces and the evacuation process of people. The aim is to investigate how the density of people in a crowd affects the safe movement of people and how the width, number, and location of exits affect evacuation time. The analysis was carried out using the Pathfinder evacuation model, which provides two modes to simulate the movement of people: steering and SFPE. An enclosed space of 20 m × 30 m was investigated. First, the differences between the modes of the evacuation model to simulate the movement of people were compared. Then, the steering mode with limited door flow was set and the effect of the width, number, and location of the exits with a total width of 4 m on the evacuation time was investigated. The results of this study highlight differences in the simulation of the movement of people according to the different modes and provide valuable information for the design of safe escape routes. Proper design of escape routes can prevent an adverse situation that could arise when evacuating a large number of people.

Testing the Feasibility of an Agent-Based Model for Hydrologic Flow Simulation

Modeling streamflow is essential for understanding flow inundation. Traditionally, this involves hydrologic and numerical models. This research introduces a framework using agent-based modeling (ABM) combined with data-driven modeling (DDM) and Artificial Intelligence (AI). An agent-driven model simulates streamflow and its interactions with river courses and surroundings, considering hydrologic phenomena related to precipitation, water level, and discharge as well as channel and basin characteristics causing increased water levels in the Medio River. A five-year dataset of hourly precipitation, water level, and discharge measurements was used to simulate streamflow. The model’s accuracy was evaluated using statistical metrics like correlation coefficient (r), coefficient of determination (R2), root mean squared error (RMSE), and percentage error in peak discharge (Qpk). The ABM’s simulated peak discharge (Qpk) was compared with the measured peak discharge across four experimental scenarios. The best simulations occurred in scenario 3, using only rainfall and streamflow data. Data management and visualization facilitated input, output, and analysis. This study’s ABM combined with DDM and AI offers a novel approach for simulating streamflow and predicting floods. Future studies could extend this framework to other river basins and incorporate advanced sensor data to enhance the accuracy and responsiveness of flood forecasting.

The role of digital social practices and technologies in the Swiss energy transition towards net-zero carbon dioxide emissions in 2050

Digitalization can be an enabler of new and less energy-intensive lifestyles. However, it can be associated with rebound effects in energy consumption. For example, home office can reduce the mobility demand but increase residential energy consumption. Here, we analyze the role of households' social practices induced by information and communication technologies (ICTs) in Switzerland's transition to net-zero carbon dioxide emissions in 2050. We couple the Swiss TIMES Energy Systems Model with a socio-technical-economic agent-based model (SEED) to perform long-term pathway analysis by accounting for the socio-economic and technical aspects of technology adoption. We find an overall net-positive contribution of digitalization to the Swiss energy transition. A society adopting more digital practices and ICTs requires 10–20% less energy in 2050 than a society with a digitalization rate frozen at 2020 levels. We show that any rebound effects in energy consumption that digital practices can induce are counterbalanced by more efficient technologies and optimized processes enabled by ICTs. While digitalization helps to meet the Swiss energy and climate targets for 2050 and reduces the energy transition costs for society, policy support is necessary to develop the population's digital skills, services' digitalization, and network infrastructures.

Residential consumer enrollment in demand response: An agent based approach

Residential consumers play an important role in the sustainable transition of the energy system by leveraging their household loads for demand response (DR). This paper aims to analyze the enrollment rates of residential consumers within DR through an agent-based model (ABM). Both economic and noneconomic (social/behavioral) parameters that influence the consumer enrollment in DR are considered. An energy management model, a home energy management system (HEMS), is used to identify the potential economic savings of consumers enrolling in DR. Consumers are randomly assigned to different neighborhoods and have different social relationships (e.g., friends, neighbors), which, in turn, influences their decision-making in the ABM. The results of this paper highlight the indirect relationship of expected annual savings and direct relationship of the share of consumers having electric vehicles (EV), photovoltaics (PV), and battery energy storage systems (BESSs) on the DR enrollment rates. Based on the enrollment rates, the maximum energy savings were obtained in April and the minimum during the last quarter of the year. Monte Carlo analysis is employed to handle the randomness associated with different variable selections, which provides a ±10% variation of consumer enrollment rate in DR. The results of this study have practical implications for energy flexibility in the residential sector.

The importance of the incubation time distribution in compartmental epidemiological models

This study investigates the utilization of various mathematical models for comprehending and managing outbreaks of infectious diseases, with a specific focus on how different distributions of incubation times influence predictions regarding epidemics. Two methodologies are examined: a compartmental SEnIR ODE model, which represents an enhanced version of the mean-field SEIR model, and a stochastic agent-based complex network model. Our findings demonstrate that the selection of diverse incubation time distributions can result in noteworthy discrepancies in critical epidemic forecasts, highlighting the crucial role of precise modeling in shaping effective public health interventions. The research underscores the necessity of integrating authentic distribution patterns into epidemic modeling to increase its reliability and applicability.

Part feeding scheduling for mixed-model assembly lines with autonomous mobile robots: benefits of using real-time data

ABSTRACT Mixed-model assembly is increasingly widespread to meet customer requirements for customisation and short delivery times. Flexible part feeding systems are required to timely replenish assembly stations with materials, avoid station idle times, and limit inventory levels on the shop floor. Part feeding scheduling is a complex and dynamic problem, affected by processing time fluctuations, equipment failures, and variations of product mix. Although real-time data of factory processes and resources is widely available and can be exploited using a digital twin of the part feeding system, there is a lack of scientific evidence on the benefits of using real-time data in part feeding scheduling. This research addresses this gap by developing an agent-based simulation model of a part feeding system with a fleet of autonomous mobile robots (AMRs) and comparing a real-time dynamic part feeding scheduling approach with static benchmark approaches. Numerical results indicate that using real-time data improves the performance of the part feeding system and the assembly system significantly, and allows improving the trade-off between the AMR fleet size and the total storage capacity on the shop floor, resulting in lower investment costs for AMRs given a certain storage capacity or lower required storage capacity given an AMR fleet.

Norms prioritizing positive assessments are likely to maintain cooperation in private indirect reciprocity

Exploring the evolutionary mechanisms of cooperation in societies where reputational consensus cannot be assumed, as in divided societies, is important for understanding the basic principles of human behavior in modern societies. Indirect reciprocity provides a major explanatory mechanism, but most studies have focused on how donors, i.e., one who decides whether to donate (cooperate or help) to a recipient or do nothing, are assessed. It is natural to think that there is no consensus among assessors in our society, and there is no reason to update only donor assessments when updating assessments. We constructed an agent-based model that enables updating of both donors’ and recipients’ images. Our exhaustive simulations showed that the well analyzed assessment rules updating donors’ images are only second best to an assessment rule updating bad images in most likely for maintaining cooperative regimes. Such a social norm that prioritizes a positive assessment is considered tolerant, which is also consistent with previous studies arguing that tolerant evaluation is important in private-assessment schemes.