Agent-based Modeling Framework Research Articles

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.

Analysis of Flood Evacuation Process in Vulnerable Community with Mutual Aid Mechanism: An Agent-Based Simulation Framework

Timely and secure evacuation of residents during flood disasters or other emergency events is an important issue in urban community flood risk management, especially in vulnerable communities. An agent-based modeling framework was proposed in order to indicate how the community properties (e.g., community density and percentage of vulnerable residents), residents’ psychological attributes (e.g., flood risk tolerance threshold) and mutual aid mechanism affect the flood evacuation process. Results indicated that: (1) The community density negatively affected the flood evacuation efficiency. The greater the density of the community, the longer the evacuation time. (2) There was a negative correlation between the flood risk tolerance threshold of residents and evacuation efficiency. (3) The proportion of vulnerable resident agents had opposite effects on the evacuation efficiency of different types of communities, which was to negatively affect low-density communities and positively affect high-density communities. (4) Mutual aid mechanism can reduce evacuation time in low-density communities, and the effect was more pronounced with a higher proportion of vulnerable resident agents in the community. These findings can help managers to develop better emergency evacuation management for urban communities.

Size matters: Sample size assessments for chronic wasting disease surveillance using an agent-based modeling framework

Epidemiological surveillance for many important wildlife diseases relies on samples obtained from hunter-harvested animals. Statistical methods used to calculate sample size requirements assume that the target population is randomly sampled, and therefore the samples are representative of the population. But hunter-harvested samples may not be representative of the population due to disease distribution heterogeneities (e.g. spatial clustering of infected individuals), and harvest-related non-random processes like regulations, hunter selectivity, variable land access, and uneven hunter distribution. Consequently, sample sizes necessary for detection of disease are underestimated and disease detection probabilities are overestimated, resulting in erroneous inferences about disease presence and distribution.We have developed a modeling framework to support the design of efficient disease surveillance programs for wildlife populations. The constituent agent-based models can incorporate real-world heterogeneities associated with disease distribution, harvest, and harvest-based sampling, and can be used to determine population-specific sample sizes necessary for prompt detection of important wildlife diseases like chronic wasting disease and bovine tuberculosis. The modeling framework and its application has been described in detail by Belsare et al. [1]. Here we describe how model scenarios were developed and implemented, and how model outputs were analyzed. The main objectives of this methods paper are to provide users the opportunity to a) assess the reproducibility of the published model results, b) gain an in-depth understanding of model analysis, and c) facilitate adaptation of this modeling framework to other regions and other wildlife disease systems.•The two agent-based models, MOOvPOP and MOOvPOPsurveillance, incorporate real-world heterogeneities underpinned by host characteristics, disease spread dynamics, and sampling biases in hunter-harvested deer.•The modeling framework facilitates iterative analysis of locally relevant disease surveillance scenarios, thereby facilitating sample size calculations for prompt and reliable detection of important wildlife diseases.•Insights gained from modeling studies can be used to inform the design of effective wildlife disease surveillance strategies.

Challenging human driver taxis with shared autonomous vehicles: a case study of Chicago

ABSTRACT When autonomous vehicles (AVs) are fully functional, they will compete with existing mainstays in the transportation system. Sharing AVs can mitigate the inefficient use of privately-owned AVs (e.g., empty-vehicle miles), and to improve the productivity of AVs (i.e., less time parked than personal vehicles). Further, sharing AVs will enable people to travel without vehicle ownership costs and responsibilities. In this regard, taxi travelers will likely be the first users of shared autonomous vehicles (SAVs). This study investigates the trips made by taxi users in Chicago and develops an agent-based modeling framework to simulate a fleet of SAVs operating in the network, competing with human driver taxis. Results show that SAVs may attract more users than conventional taxis because of reduced driver-associated costs (driver not required in SAV services). The results provide insights to both private and public sectors who seek to implement SAVs to challenge or eventually replace existing taxis.

Integrating demand forecasts into the operational strategies of shared automated vehicle mobility services: spatial resolution impacts

ABSTRACT This study aims to evaluate and quantify the impact of demand forecast spatial resolution on the operational performance of a shared-use automated vehicle (AV) mobility service (SAMS) fleet. To perform the evaluation, this study employs an agent-based modeling framework that includes user requests, AVs, and an SAMS fleet controller. In the simulation, an SAMS fleet controller dynamically assigns AVs to on-demand user requests and repositions empty AVs throughout the service region to serve expected future demand requests. The fleet controller uses an offline demand forecast model and an online optimization model that jointly assigns AVs to users and repositioning trips. Results indicate that despite demand forecast quality decreasing at higher spatial resolutions, the operational efficiency of the SAMS fleet increases with higher spatial resolution forecasts (i.e. smaller subareas). Results also indicate that there is a significant operational value associated with improving short-term demand forecasts at high spatial resolutions.

Using deep reinforcement learning to speed up collective cell migration

BackgroundCollective cell migration is a significant and complex phenomenon that affects many basic biological processes. The coordination between leader cell and follower cell affects the rate of collective cell migration. However, there are still very few papers on the impacts of the stimulus signal released by the leader on the follower. Tracking cell movement using 3D time-lapse microscopy images provides an unprecedented opportunity to systematically study and analyze collective cell migration.ResultsRecently, deep reinforcement learning algorithms have become very popular. In our paper, we also use this method to train the number of cells and control signals. By experimenting with single-follower cell and multi-follower cells, it is concluded that the number of stimulation signals is proportional to the rate of collective movement of the cells. Such research provides a more diverse approach and approach to studying biological problems.ConclusionTraditional research methods are always based on real-life scenarios, but as the number of cells grows exponentially, the research process is too time consuming. Agent-based modeling is a robust framework that approximates cells to isotropic, elastic, and sticky objects. In this paper, an agent-based modeling framework is used to establish a simulation platform for simulating collective cell migration. The goal of the platform is to build a biomimetic environment to demonstrate the importance of stimuli between the leading and following cells.

An agent-based model framework for understanding the decisions of households and exploring bottom-up effects on housing sustainability

Housing plays a crucial role in the transition towards sustainability. Zero-energy or low-carbon housing solutions are key, but their success depends on their acceptance by the users. We propose a conceptual and an operational framework for understanding households’ residential choices as a base for an agent-based model, using the tenant as basic unit to populate it. We firstly conceptualise the decision-making process of tenants as a system. Secondly, we cluster the synthetic agents populating the model into typologies of dwellings and tenants. Thirdly, we display their actions in the action arena, and set-up the test runs. Lastly, we introduce the next steps to follow for the simulation of the bottom-up effects on housing sustainability.

Hierarchical Agent-Based Modeling for Improved Traffic Routing

Agent-based model (ABM) simulation is a bottom–up approach that can describe the phenomena generated from actions and interactions within a multiagent system. An ABM is an improvement over model simulations which only describe the global behavior of a system. Therefore, it is an appropriate technology to analyze emergent phenomena in social sciences and complex adaptive systems such as vehicular traffic and pedestrian crowds. In this paper, a hybrid agent-based modeling framework designed to automate decision-making processes during traffic congestion is proposed. The model provides drivers with real-time alternative routes, computed via a decentralized multi-agent model, that tries to achieve a system-optimal traffic distribution within an entire system, thus reducing the total travel time of all the drivers. The presented work explores a decentralized ABM technique on an autonomous microgrid that is represented through cellular automata (CA). The proposed model was applied to high-density traffic congestion events such as car accidents or lane closures, and its effectiveness was analyzed. The experimental results confirm the efficiency of the proposed model in not only accurately simulating the driver behaviors and improving vehicular traffic flows during congestion but also by suggesting changes to traffic dynamics during the simulations, such as avoiding obstacles and high-density areas and then selecting the best alternative routes. The simulation results validate the ability of the proposed model and the included decision-making sub-models to both predict and improve the behaviors and intended actions of the agents.

Replacing Rules by Neural Networks A Framework for Agent-Based Modelling

Agent-based modelling is a successful technique in many different fields of science. As a bottom-up method, it is able to simulate complex behaviour based on simple rules and show results at both micro and macro scales. However, developing agent-based models is not always straightforward. The most difficult step is defining the rules for the agent behaviour, since one often has to rely on many simplifications and assumptions in order to describe the complicated decision making processes. In this paper, we investigate the idea of building a framework for agent-based modelling that relies on an artificial neural network to depict the decision process of the agents. As a proof of principle, we use this framework to reproduce Schelling’s segregation model. We show that it is possible to use the presented framework to derive an agent-based model without the need of manually defining rules for agent behaviour. Beyond reproducing Schelling’s model, we show expansions that are possible due to the framework, such as training the agents in a different environment, which leads to different agent behaviour.

Integration of agent-based modelling of social-spatial processes in architectural parametric design

ABSTRACTA representation framework for modelling the social-spatial processes of inhabitation is proposed to extend the scope of parametric architectural design process. We introduce an agent-based modelling framework with a computational model of social-spatial dynamics at its core. Architectural parametric design is performed as a process of modelling the temporal characteristics of spatial changes required for members of a social group to reach social spatial comfort. We have developed a prototype agent-based modelling system using the Rhino-Grasshopper platform. The system employs a human behaviour model adapted from the PECS (Physical, Emotional, Cognitive, Social) reference model first proposed by Schmidt and Urban. The agent-based model and its application was evaluated by comparative modelling of two real Vietnamese dwellings: a traditional vernacular house in Hue and a contemporary house in Ho Chi Minh City. The evaluation shows that the system returns differentiated temporal characteristics of spatial modifications of the two dwellings as expected.

Understanding the Multimodal Evacuation Behavior for a Near-Field Tsunami

This paper presents an agent-based tsunami evacuation modeling (ABTEM) framework in Netlogo to analyze the impact of various multimodal evacuation behaviors on life safety for a near-field tsunami. The objective of this work is to investigate how: milling time, choice of modes (i.e., walking and automobile), and critical variables involved in an evacuation scenario (e.g., walking, driving speed), affect life safety. Using the city of Seaside, Oregon, which is one of the most vulnerable cities on the Oregon coast, as a study site, different evacuation scenarios are included in the model to assess the impact of parameters involved on the mortality rate in a tsunami evacuation event. The results show that: choice of evacuation mode strongly and non-linearly influences the expected number of casualties; use of vehicles leads to the creation of congestion and bottlenecks, and thus, higher mortality rate; the mortality rate is strongly correlated with milling time; and the mortality rate is sensitive to the variations in average walking speed of the population. The results will help emergency managers, community leaders, and city and state agencies in their decision-making process for creating effective and efficient evacuation plans to increase life safety and community resilience.

Impact of Electronic Liquidity Providers Within a High-Frequency Agent-Based Modeling Framework

The current contribution addresses the impact of high-frequency electronic liquidity provision strategies on the intraday dynamics of financial markets, by means of an artificial stock market. As novel design feature, an event-based intraday time implementation is proposed, allowing for the generation of time-stamped intraday events, which make possible both the aggregation of time series at various time frequencies, as well as the correct simulation of trading strategies that follow different temporal frequencies, e.g., low- and high-frequency. We provide new insights with respect to the determinants of extreme events, such as flash crashes. Finally, we compare the causal chains and the effectiveness of two potential regulatory policies under the same market circumstances, i.e., minimum resting time and financial-transaction taxes, not only with respect to their flash crash prevention power, but also regarding their impact on market participants and market quality, shedding new light on the policy trade-offs.

Development of Software Framework for Large-Scale Agent-Based Modeling of Complex Social Systems

Development of Software Framework for Large-Scale Agent-Based Modeling of Complex Social Systems

Resilience Evaluation Framework for Integrated Civil Infrastructure–Community Systems under Seismic Hazard

In this paper, an agent-based modeling framework to evaluate the seismic resilience of an integrated system comprising the community and its civil infrastructure systems is proposed. Specifically, an agent-based model of the recovery process of civil infrastructure systems is incorporated into a previously developed compositional supply/demand seismic resilience quantification framework. The proposed model represents the behavior of the operators of civil infrastructure systems as they strive to recover their functionality in the aftermath of an earthquake as well as their mutual interactions and the interactions with the community they provide services to. A case study of the seismic resilience of a virtual system comprising the electric power supply system, the transportation system and the community (EPSS-TS-Community system) is conducted using the proposed framework. A parametric investigation is carried out to examine the effect of different earthquake magnitude scenarios as well as different behaviors of the involved agents and their interaction on the seismic resilience of the virtual system. It was demonstrated that the proposed agent-based modeling approach is effective in representing the interactions among different participants in the recovery process. It was also revealed that timely and well-planned intervention in the recovery process can be effective in alleviating the post-earthquake lack of resilience resulting from the insufficient supply of civil infrastructure service to meet the community demands. Therefore, the proposed framework could be employed to formulate the recovery trajectory of the intertwined socio-technical system subjected to different earthquake scenarios. The interplay among different agents, as well as the interdependence between civil infrastructure systems is found to profoundly shape the recovery path for this integrated EPSS-TS-Community system.

Agent-Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities.

In this article, an agent-based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community-built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent-based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS-community system is quantified using direct, EPSS-related, societal, and community-related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS-community seismic resilience. The use of the agent-based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent-based EPSS-community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.

Analysis of Rooftop Photovoltaics Diffusion in Energy Community Buildings by a Novel GIS- and Agent-Based Modeling Co-Simulation Platform

The present work introduces an empirically ground agent-based modeling (ABM) framework to assess the spatial and temporal diffusion of rooftop photovoltaic (PV) systems on existing buildings of a city district. The overall ABM framework takes into account social, technical, environmental, and economic aspects to evaluate the diffusion of PV technology in the urban context. A city district that includes 18 720 households distributed over 1 290 building blocks and a surface area of 2.47 km2 is used to test the proposed ABM framework. Results show how the underlying regulatory framework (i.e., the rules of the internal electricity market) influences the pattern and intensity of adoption, thus realizing different shares of the available potential. Policies that support the establishment of `prosumers' within Condominiums (i.e., energy community buildings), and not in single-family houses only, is key to yield high diffusion rates. The installed capacity increases by 80% by switching from the one-to-one configuration to the one-to-many paradigm, i.e., from 5.90 MW of rooftop PV installed on single-family households and/or single PV owners to 10.64 MW in energy community buildings. Moreover, the possibility to spread the auto-generated solar electricity over the load profile of the entire population of Condominium results in self-consumption rates greater than 50% and self-sufficiency ratios above 20% for the majority of the simulated buildings.

A socio-technical holistic agent based model to assess cockpit supporting tools performance variability

Abstract The mesh of information that influence a human actor in its decision-making process is difficult to formalize and more difficult to validate. Stochastic approaches to represent human performance provides a fast modelling approach that can be statistically validated, however, do not contribute to a better understanding of performance variability consequences neither the design of mitigation mechanisms in critical processes. Better modelling approaches are needed to support a deeper understanding of the causes of a poor performance and the propagation mechanisms that spark emergent behavior and its effects. In this paper it is presented an agent based modeling framework to identify those factors that affects the performance of the pilot flying cockpit functionalities considering different socio-technical operational conditions. As a result, it is expected the design of new cognitive computing supporting tools to improve pilot situational awareness.

A stochastic spatial-explicit agent-based modeling framework to better understand transmission dynamics of swine diseases in the US: the example of porcine reproductive and respiratory syndrome (PRRS).

A stochastic spatial-explicit agent-based modeling framework to better understand transmission dynamics of swine diseases in the US: the example of porcine reproductive and respiratory syndrome (PRRS).