Agent-based Modeling Framework Research Articles

Agent-Based Modeling of Rumor Propagation Using Expected Integrated Mean Squared Error Optimal Design

In the “Age of the Internet”, fake news and rumor-mongering have emerged as some of the most critical factors that affect our online social lives. For example, in the workplace, rumor spreading runs rampant during times when employees may be plagued with uncertainty about the nature and consequences of major changes. Positive information should be widely propagated as much as possible; however, we must limit the spread of rumors in an effort to reduce their inherently harmful effects. The purpose of this research is to explain the mechanisms for controlling rumors and suggest an approach for dispelling the rumor effect in the workplace. In this study, we will present a simple simulation framework of agent-based modeling and apply Social Impact Theory to explain rumor propagation within social networks. Based on our results, we have found that organizations can significantly reduce the spread of the rumors by improving the workplace environment and instituting counseling for those in management positions.

An Agent-Based Model for Electric Energy Policy Assessment

This paper proposes an agent-based modelling (ABM) framework to evaluate sustainable policies in large electric energy systems with considerable renewable power penetration. Policy making in electric energy systems is generally a multistage process with different characteristics including long-term dynamics, uncertainty, and multi-dimensionality. The policy evaluation thus as a major stage, examines the proposed policies before embarking to an implementation stage. Potential features of agent-based modelling are employed effectively to propose a modelling framework by taking into account complex characteristics of policy making. The proposed ABM classifies and defines clearly all major players in the energy systems with their unique rules, attributes, and interactions. It is applied to a typical large system with different electricity generation technologies including renewable to evaluate a series of policy scenarios. The policy evaluation based on the ABM reveals that non-competent or unsustainable energy policies may have non-obvious outcomes leading to long term electricity shortfall or harm the environment.

Variable Cognition in ABM Decision-Making: An Application to Livestock Vaccine Choice.

Modeling realistic human decision-making is an important feature of good policy design processes. The use of an agent-based modeling framework allows for quantitative human decision-models that assume fully rational agents. This research introduces a dynamic human decision-making sub-model. The parameterisation of human memory and “rationality” in a decision-making model represents an important extension of decision-making in ABMs. A data driven model of herd movement within a dynamic natural environment is the context for evaluating the cognitive decision-making model. The natural and human environments are linked via memory and rationality that affect herdsmen decision-making to vaccinate cattle using a once-for-life vaccine (Rift Valley fever) and an annual booster vaccine (Contagious Bovine Pleuropneumonia). The simulation model uses environmental data from Samburu county, Kenya from 2004 to 2015. The cognitive parameters of memory and “rationality” are shown to successfully differentiate between vaccination decisions that are characterized by annual and once-for-life choices. The preliminary specifications and findings from the dynamic cognition–pastoralist agent-based model (PastoralScape) indicate that the model offers much to livestock vaccination modeling among small-scale herders.

COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City.

Since its emergence in China, the COVID-19 pandemic has spread rapidly around the world. Faced with this unknown disease, public health authorities were forced to experiment, in a short period of time, with various combinations of interventions at different scales. However, as the pandemic progresses, there is an urgent need for tools and methodologies to quickly analyze the effectiveness of responses against COVID-19 in different communities and contexts. In this perspective, computer modeling appears to be an invaluable lever as it allows for the in silico exploration of a range of intervention strategies prior to the potential field implementation phase. More specifically, we argue that, in order to take into account important dimensions of policy actions, such as the heterogeneity of the individual response or the spatial aspect of containment strategies, the branch of computer modeling known as agent-based modeling is of immense interest. We present in this paper an agent-based modeling framework called COVID-19 Modeling Kit (COMOKIT), designed to be generic, scalable and thus portable in a variety of social and geographical contexts. COMOKIT combines models of person-to-person and environmental transmission, a model of individual epidemiological status evolution, an agenda-based 1-h time step model of human mobility, and an intervention model. It is designed to be modular and flexible enough to allow modelers and users to represent different strategies and study their impacts in multiple social, epidemiological or economic scenarios. Several large-scale experiments are analyzed in this paper and allow us to show the potentialities of COMOKIT in terms of analysis and comparison of the impacts of public health policies in a realistic case study.

Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability

In this paper, we assess the happiness cost of Brexit in the UK and the EU, using data from the Gallup World Poll. We implement a two-stage learning machine, using a naive Bayes classifier to extract happiness preferences of the population and then passing these onto an artificial neural network of attributes to generate dynamic happiness functions for each household, on an agent-based modelling framework. We find that there is a significant long-run cost in terms of both happiness and unemployment, which primarily affects the most vulnerable portion of the population. In addition, despite the expected instability in City's financial centre, the UK financial sector seems to be well equipped to deal with the repercussions, thus minimising the welfare costs for the country. Our findings extend the discussion of the economic costs of Brexit, by adding the welfare cost of the ensuing financial instability.

Who Is Unhappy for Brexit? A Machine-Learning, Agent-based study on Financial Instability

In this paper, we assess the happiness cost of Brexit in the UK and the EU, using data from the Gallup World Poll. We implement a two-stage learning machine, using a naive Bayes classifier to extract happiness preferences of the population and then passing these onto an artificial neural network of attributes to generate dynamic happiness functions for each household, on an agent-based modelling framework. We find that there is a significant long-run cost in terms of both happiness and unemployment, which primarily affects the most vulnerable portion of the population. In addition, despite the expected instability in City’s financial centre, the UK financial sector seems to be well equipped to deal with the repercussions, thus minimising the welfare costs for the country. Our findings extend the discussion of the economic costs of Brexit, by adding the welfare cost of the ensuing financial instability.

Agent-Based Model of a Blockchain Enabled Peer-to-Peer Energy Market: Application for a Neighborhood Trial in Perth, Australia

The transfer of market power in electric generation from utilities to end-users spurred by the diffusion of distributed energy resources necessitates a new system of settlement in the electricity business that can better manage generation assets at the grid-edge. A new concept in facilitating distributed generation is peer-to-peer energy trading, where households exchange excess power with neighbors at a price they set themselves. However, little is known about the effects of peer-to-peer energy trading on the sociotechnical dynamics of electric power systems. Further, given the novelty of the concept, there are knowledge gaps regarding the impact of alternative electricity market structures and individual decision strategies on neighborhood exchanges and market outcomes. This study develops an empirical agent-based modeling (ABM) framework to simulate peer-to-peer electricity trades in a decentralized residential energy market. The framework is applied for a case study in Perth, Western Australia, where a blockchain-enabled energy trading platform was trialed among 18 households, which acted as prosumers or consumers. The ABM is applied for a set of alternative electricity market structures. Results assess the impact of solar generation forecasting approaches, battery energy storage, and ratio of prosumers to consumers on the dynamics of peer-to-peer energy trading systems. Designing an efficient, equitable, and sustainable future energy system hinges on the recognition of trade-offs on and across, social, technological, economic, and environmental levels. Results demonstrate that the ABM can be applied to manage emerging uncertainties by facilitating the testing and development of management strategies.

Modeling, simulation and forecasting of wind power plants using agent-based approach

National economy and growth rely heavily on electricity but rapid urbanization, expeditious industrialization and increased domestic use due to population growth are among the reasons for the severe energy crisis in developing countries. The extended demand-supply gaps, depleting reservoirs of fossil fuel, and the environmental hazards altogether ignite the need for wider adoption of renewable energy resources for electricity generation. A functional assessment of the engineering design for this transition is a prerequisite before proceeding to on-ground implementation due to its high impact on system sustainability. To this end, we propose an agent-based modeling and simulation framework for the rapid prototyping of wind power plants. The proposed approach abstracts active components of wind power plants using agents and implements their dynamic behavior through agent interactions. The proposed model helps in composing different model components, design valuation, and forecasting energy generation in a cost-effective and productive manner. The proposed model is demonstrated by conceptualizing the design of the Foundation Wind Energy plant, located at Sindh, Pakistan, and the development of its agent-based model. The obtained short-term and long-term electricity generation profiles are validated with the actual data. We further compared the forecasts with the time series analysis performed on the actual data, using five different time-series forecasting models. The proposed simulation model and time series analysis model fit well on the actual data with a root mean square deviation of approximately 9 MW. The proposed framework will assist the policymakers in estimating the extent of electrical energy produced at given conditions using the wind potential available at the corridors of any country. It will further aid in the realistic analysis of the future dynamics of electricity demand and supply, hence help in effective energy planning.

Agent-based model for post-earthquake housing recovery

A framework of agent-based models for housing recovery is presented and used to investigate post-earthquake recovery in the City of Vancouver, Canada. Housing recovery is modeled for a portfolio of buildings, contrasting with the practice of assessing the reconstruction of buildings in isolation. Thus, the presented approach better captures the effect of competition for resources, infrastructure disruptions, and socioeconomic factors on recovery. The analyses include models for damage, inspection, financing, power infrastructure, and labor/materials for repairs. The presented approach is applied to simulate the recovery of 114,832 residential buildings in 22 neighborhoods in Vancouver. Results indicate that recovery after a strong earthquake will take more than three years. The density of old and rented buildings, and the income and immigration status of the homeowners are shown to be good predictors of the speed of recovery for a neighborhood. Mitigation measures are compared and it is shown that retrofitting the most physically vulnerable buildings or doubling the available workforce are effective at reducing housing recovery times. It is demonstrated that the equity in recovery between low and high socioeconomic status homeowners is improved if mitigation measures are implemented. The results presented in this article can inform disaster recovery plans and mitigation actions in Vancouver and similar communities.

Agent-based simulation of workers’ behaviors, productivity, and safety around construction obstacles

Construction sites involve many hazardous areas and fixed and movable objects around which the workers interact. Unguided site policies or workers’ behaviors can lead to productivity loss and (or) accidents. In this paper, an agent-based modeling and simulation framework has been developed to: (1) provide a detailed site model with temporary facilities and obstacles (danger zones and productivity-hindering spots); (2) model the workers’ natural behaviors towards safety while moving around the site; and (3) quantify the consequent site productivity and accident potential of any site configuration. The paper discusses the site model and the behavioral rules that autonomous agents follow to simulate the movement of aggressive and avoider workers around hazard areas. The results of two case studies and sensitivity analyses show that different simulations with different site improvement measures can quantify the effectiveness of, and optimize, site operational measures and establish rewards for positive worker’s behaviors that improve productivity and safety.

Characterisation of the impacts of autonomous driving on highway capacity in a mixed traffic environment: an agent‐based approach

The current planning and operational analysis tools utilised by researchers and practitioners for transportation facilities evaluation purposes are limited in their relevance to evaluate the future impact of connected and automated vehicles (CAVs). Since CAV technologies are becoming more prevalent on the market, the adaptation of the conventional methodologies to estimate highway performance is becoming essential to accommodate these technologies. This study tries to fill up this gap in the literature by formulating capacity analysis model estimate the potential impacts of CAVs on the capacity of highways at different market penetration (MP) levels of CAVs. The developed analytical model would present itself as a guideline to transportation agencies while developing CAV-based transportation infrastructure. Later, this study proposes an agent-based modelling and simulation framework to address the heterogeneity of drivers behaviour and the potential impact of CAVs on highway traffic performance under different MP levels. Results showed that deploying CAVs in the traffic stream has the potential to double the capacity of the highway corridor at high MP levels. The results show CAV operation and implementation readiness for both public agencies as well as state Departments of Transportation or private industries in the future.

A hierarchical multi-resolution agent-based modeling and simulation framework for household electricity demand profile

Analyzing demand behavior of end consumers is pivotal in long term energy planning. Various models exist for simulating household load profiles to cater different purposes. A macroscopic viewpoint necessitates modeling of a large-scale population at an aggregate level, whereas a microscopic perspective requires measuring loads at a granular level, pertinent to the individual devices of a household. Both aspects have lucrative benefits, instigating the need to combine them into a modeling framework which allows model scalability and flexibility, and to analyze domestic electricity consumption at different resolutions. In this applied research, we propose a multi-resolution agent-based modeling and simulation (ABMS) framework for estimating domestic electricity consumption. Our proposed framework simulates per minute electricity consumption by combining large neighborhoods, the behavior of household individuals, their interactions with the electrical appliances, their sociological habits and the effects of exogenous conditions such as weather and seasons. In comparison with the existing energy models, our framework uniquely provides a hierarchical, multi-scale, multi-resolution implementation using a multi-layer architecture. This allows the modelers flexibility in order to model large-scale neighborhoods at one end, without any loss of expressiveness in modeling microscopic details of individuals’ activities at house level, and energy consumption at the appliance level, at the other end. The validity of our framework is demonstrated using a case study of 264 houses. A validated ABMS framework will support: (a) Effective energy planning; (b) Estimation of the future energy demand; (c) and the analysis of the complex dynamic behavior of the consumers.

Innovation in African-American high-tech enterprises: a multi-agent approach

African-American-owned high-tech enterprises and innovations are underrepresented in industry in comparison to non-African-American-owned ones. Various complex and intertwined socio-economic factors hinder the innovation capability of African-American-owned high-tech enterprises leading to underrepresentation of these businesses. Understanding the causal relationship between firm’s interactions with internal and external entities and its ability to innovate can foster the efforts of a high-tech enterprise in increasing and sustaining innovation capabilities. Agent-based modeling (ABM) emerges as one of the popular approaches to the study of complex socio-technological systems. Characterizing the organizational behavior of African-American-owned high-tech enterprises through the ABM perspective may provide a better understanding of the drivers, processes, and outcomes of this industry segment. By analyzing interview data among African-American entrepreneurs, this study proposes an ABM framework to represent and analyze the innovation capabilities of African-American-owned technology enterprises in comparison to other types of ownership. The ABM model illustrates the key involved agents, their attributes, actions, and the complex interactions amongst them. Simulation results indicate that African American population is underrepresented in the high-tech industry due to two significant factors of social and economic standings implying that the simulation trajectory is in the right direction. Model calibration, verification using real data and implementation plans related to policy development discussions and factors impacting African-American enterprises are also discussed in the study.

From self-consumption to decentralized distribution among prosumers: A model including technological, operational and spatial issues

The power sector is undergoing a fundamental transition from centralized fossil-fuelled to distributed production systems. Usually, the decentralization of the energy distribution is treated by considering the interactions between prosumers and main grid, neglecting to include the rising chance for prosumers to directly exchange the produced energy, thus bypassing the grid. In this direction, this paper proposes a prosumer-centred dynamical model able to take the energy interactions among prosumers into account, along with the usual interactions established with the grid. The modelling approach takes inspiration from the methodological framework of agent-based models, particularly appropriate to deal with problems characterized by a significant number of interactions among the parts. Specifically, the developed model has been formulated with the main scope of investigating to what extent energy exchanges among prosumers reduce the supply from traditional plants. The electricity demand and production of each prosumer, the distance among buildings, operational and technological features (usage of the links, installed capacity of the energy production systems have been included in the analysis. Hourly-based simulations run on NetLogo performing a real urban area in Southern Italy as a case study for two scenarios evaluating the distribution from photovoltaic panels and small-scale cogeneration units. In addition, an a posteriori environmental analysis to evaluate the reduction of carbon emissions due to the distribution among prosumers has been conducted. Results reveal that short-range interactions among prosumers are preferred when planning to reduce the electricity supply from the main grid. In addition, the spatial configuration of the buildings within the area as well as the capacity of the installed energy production systems significantly affect the distribution. Finally, simulations highlight the noticeable impact of seasonality on both the distribution and the emissions’ reduction.

Energy saving potentials of integrating personal thermal comfort models for control of building systems: Comprehensive quantification through combinatorial consideration of influential parameters

Research studies provided evidence on the energy efficiency of integrating personal thermal comfort profiles into the control loop of Heating, Ventilation, and Air-Conditioning (HVAC) systems (i.e., comfort-driven control). However, some conflicting cases with increased energy consumption were also reported. Addressing the limited and focused nature of those demonstrations, in this study, we have presented a comprehensive assessment of the energy efficiency implications of comfort-driven control to (i) understand the impact of a wide range of contextual factors and their combinatorial effect and (ii) identify the operational conditions that benefit from personal comfort integration. In doing so, we have proposed an agent-based modeling framework, coupled with EnergyPlus simulations. We considered five potentially influential parameters and their combinatorial arrangements including occupants’ thermal comfort characteristics, diverse multi-occupancy scenarios, number of occupants in thermal zones, control strategies, and climate. We identified the most influencing factor to be the variations across occupants’ thermal comfort characteristics - reflected in probabilistic models of personal thermal comfort - followed by the number of occupants that share a thermal zone, and the control strategy in driving the collective setpoint in a zone. In thermal zones, shared by fewer than six occupants, we observed potentials for average energy efficiency gain in a range between −3.5% and 21.4% from comfort-driven control. Accounting for a wide range of personal comfort profiles and number of occupants, the average (±standard deviation) energy savings for a single zone and multiple zones were in ranges of [−3.7 ± 4.8%, 5.3 ± 5.6%] and [−3.1 ± 4.9%, 9.1 ± 5.1%], respectively. Across all multi-occupancy scenarios, a range between 0.0% and 96.0% of combinations resulted in energy savings.

An agent-based modelling approach to simulate the investment decision of industrial enterprises

China is the leading ammonia producer and relies on a coal-based technology which makes the already energy intensive Haber-Bosch process, one of the most emission intensive in the world. This work is the first to propose an agent-based modelling framework to model the Chinese ammonia industry as it characterises the specific goals and barriers towards fuel switching and carbon capture and storage adoption for small, medium, and large enterprises either private or state-owned. The results show that facilitated access to capital makes investments in sustainable technologies more attractive for all firms, especially for small and medium enterprises. Without policy instruments such as carbon price, the decrease in emissions in the long-term is due to investments in natural gas-based technologies, as they typically have lower capital and operating costs, and also lower electricity consumption than coal-based production. Conversely, with policy instruments in place, a strong decrease in emissions occurs between 2060 and 2080 due to investors choosing natural gas and biomethane-based technologies, with carbon capture and storage. In the long term, natural gas and biomethane could compete, with the outcome depending on infrastructure, supply chain availability and land use constraints.

Capturing flood-risk dynamics with a coupled agent-based and hydraulic modelling framework

ABSTRACT Two-way interactions and feedback between hydrological and social processes in settled floodplains determine the complex human–flood system and change vulnerability over time. To focus on the dynamic role of individual and governmental decision making on flood-risk management, we developed and implemented a coupled agent-based and hydraulic modelling framework. Within this framework, household agents are located in a floodplain protected by a levee system. Individual behaviour is based on Protection Motivation Theory and includes the options to (1) not react to flood risk; (2) implement individual flood protection measures; or (3) file a complaint to the government. The government decides about reinforcing the levee system, compromising between a cost-benefit analysis and filed complaints from households. We found that individual decision making can significantly influence flood risk. In addition, the coupled agent-based and hydraulic modelling framework approach captures commonly observed socio-hydrological dynamics, namely levee and adaptation effects. It provides an explanatory tool for assessing spatial and temporal dynamics of flood risk in a socio-hydrological system.

An agent-based model to investigate microbial initiation of Alzheimer\u2019s via the olfactory system

BackgroundAlzheimer’s disease (AD) is a degenerative brain disease. A novel agent-based modelling framework was developed in NetLogo 3D to provide fundamental insights into the potential mechanisms by which a microbe (eg. Chlamydia pneumoniae) may play a role in late-onset AD. The objective of our initial model is to simulate one possible spatial and temporal pathway of bacterial propagation via the olfactory system, which may then lead to AD symptoms. The model maps the bacteria infecting cells from the nasal cavity and the olfactory epithelium, through the olfactory bulb and into the olfactory cortex and hippocampus regions of the brain.ResultsBased on the set of biological rules, simulated randomized infection by the microbe led to the formation of beta-amyloid (Aβ) plaque and neurofibrillary (NF) tangles as well as caused immune responses. Our initial simulations demonstrated that breathing in C. pneumoniae can result in infection propagation and significant buildup of Aβ plaque and NF tangles in the olfactory cortex and hippocampus. Our model also indicated how mucosal and neural immunity can play a significant role in the pathway considered. Lower immunities, correlated with elderly individuals, had quicker and more Aβ plaque and NF tangle formation counts. In contrast, higher immunities, correlated with younger individuals, demonstrated little to no such formation.ConclusionThe modelling framework provides an organized visual representation of how AD progression may occur via the olfactory system to better understand disease pathogenesis. The model confirms current conclusions in available research but can be easily adjusted to match future evidence and be used by researchers for their own individual purposes. The goal of our initial model is to ultimately guide further hypothesis refinement and experimental testing to better understand the dynamic system interactions present in the etiology and pathogenesis of AD.

An agent-based framework for improving wildlife disease surveillance: A case study of chronic wasting disease in Missouri white-tailed deer

Epidemiological surveillance for important wildlife diseases often relies on samples obtained from hunter-harvested animals. A problem, however, is that although convenient and cost-effective, hunter-harvest samples are not representative of the population due to heterogeneities in disease distribution and biased sampling. We developed an agent-based modeling framework that i) simulates a deer population in a user-generated landscape, and ii) uses a snapshot of the in silico deer population to simulate disease prevalence and distribution, harvest effort and sampling as per user-specified parameters. This framework can incorporate real-world heterogeneities in disease distribution, hunter harvest and harvest-based sampling, and therefore can be useful in informing wildlife disease surveillance strategies, specifically to determine population-specific sample sizes necessary for prompt detection of disease. Application of this framework is illustrated using the example of chronic wasting disease (CWD) surveillance in Missouri’s white-tailed deer (Odocoileus virginianus) population. We show how confidence in detecting CWD is grossly overestimated under the unrealistic, but standard, assumptions that sampling effort and disease are randomly and independently distributed. We then provide adjusted sample size recommendations based on more realistic assumptions. Wildlife agencies can use these open-access models to design their CWD surveillance. Furthermore, these models can be readily adapted to other regions and other wildlife disease systems.

Experiments and Agent Based Models of Zooplankton Movement within Complex Flow Environments

The movement of plankton is often dictated by local flow patterns, particularly during storms and in environments with strong flows. Reefs, macrophyte beds, and other immersed structures can provide shelter against washout and drastically alter the distributions of plankton as these structures redirect and slow the flows through them. Advection–diffusion and agent-based models are often used to describe the movement of plankton within marine and fresh water environments and across multiple scales. Experimental validation of such models of plankton movement within complex flow environments is challenging because of the difference in both time and spatial scales. Organisms on the scale of 1 mm or less swim by beating their appendages on the order of 1 Hz and are advected meters to kilometers over days, weeks, and months. One approach to study this challenging multiscale problem is to insert actively moving agents within a background flow field. Open source tools to implement this sort of approach are, however, limited. In this paper, we combine experiments and computational fluid dynamics with a newly developed agent-based modeling platform to quantify plankton movement at the scale of tens of centimeters. We use Artemia spp., or brine shrimp, as a model organism given their availability and ease of culturing. The distribution of brine shrimp over time was recorded in a flow tank with simplified physical models of macrophytes. These simplified macrophyte models were 3D-printed arrays of cylinders of varying heights and densities. Artemia nauplii were injected within these arrays, and their distributions over time were recorded with video. The detailed three-dimensional flow fields were quantified using computational fluid dynamics and validated experimentally with particle image velocimetry. To better quantify plankton distributions, we developed an agent-based modeling framework, Planktos, to simulate the movement of plankton immersed within such flow fields. The spatially and temporally varying Artemia distributions were compared across models of varying heights and densities for both the experiments and the agent-based models. The results show that increasing the density of the macrophyte bed drastically increases the average time it takes the plankton to be swept downstream. The height of the macrophyte bed had less of an effect. These effects were easily observed in both experimental studies and in the agent-based simulations.