Abstract Agent-based Models Research Articles

AbmR: An R package for agent‐based model analysis of large‐scale movements across taxa

Abstract Agent‐based modelling (ABM) shows promise for animal movement studies. However, a robust, open‐source and spatially explicit ABM coding platform is currently lacking. We present abmR, an R package for conducting continental‐scale ABM simulations across animal taxa. The package features two movement functions, each of which relies on the Ornstein–Uhlenbeck (OU) process. The theoretical background for abmR is discussed and the main functionalities are illustrated using example populations. Potential future additions to this open‐source package may include the ability to specify multiple environmental variables or to model interactions between agents. Additionally, updates may offer opportunities for disease ecology and integration with other R movement modelling packages.

The Gradient-Boosting Method for Tackling High Computing Demand in Underwater Acoustic Propagation Modeling

Agent-based models return spatiotemporal information used to process time series of specific parameters for specific individuals called “agents”. For complex, advanced and detailed models, this typically comes at the expense of high computing times and requires access to important computing resources. This paper provides an example on how machine learning and artificial intelligence can help predict an agent-based model’s output values at regular intervals without having to rely on time-consuming numerical calculations. Gradient-boosting XGBoost under GNU package’s R was used in the social-ecological agent-based model 3MTSim to interpolate, in the time domain, sound pressure levels received at the agents’ positions that were occupied by the endangered St. Lawrence Estuary and Saguenay Fjord belugas and caused by anthropomorphic noise of nearby transiting merchant vessels. A mean error of 3.23 ± 3.76(1σ) dB on received sound pressure levels was predicted when compared to ground truth values that were processed using rigorous, although time-consuming, numerical algorithms. The computing time gain was significant, i.e., it was estimated to be 10-fold higher than the ground truth simulation, whilst maintaining the original temporal resolution.

Emergency Response System Combining the Internal and External Domains (ERSCIED)

Agent-based modeling is a promising approach for developing simulation tools for natural hazards in different areas, such as during urban search and rescue (USAR) operations. The previous studies aimed to develop a dynamic agent-based simulation model in post-earthquake emergency response operations combining rescuers and victims using geospatial information systems and multi-agent systems (GISs and MASs, respectively). We use multi-agent system engineering (MaSE) to construct this model and cluster points are proposed to assemble rescuers and victims. We also proposed an approach for dynamic task allocation and for establishing collaboration among agents based on contract net protocol (CNP) and the [Formula: see text]-means clustering method. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and expert evaluation is used to sort the priorities of cluster points. The experimental background is set in the central area of Chengdu, Sichuan Province, China. 200 victims with different demands and 30 rescuers with the same rescue capability are randomly distributed in this area. We use different victim speeds to simulate different degrees of seismic damage. Compared with normal search and rescue, the ERSCIED can make some use of the capabilities of the victims for the improvement of emergency effectiveness when the victims and rescuers have a similar speed (1:1) or the victims are not much slower than the rescuers (1:2). When the victims are much slower than the rescuers (1:10), the rescuers have to wait for a long time at the cluster points and it will lead to a waste of resources. The parameters of rescuers and victims can be changed to meet different emergencies and the ERSCIED can provide effective auxiliary decision-making information for decision-makers.

Modeling the potential for vessel collision with southern resident killer whales

Agent-based models can be used to simulate the behavior of animals within a stimulus field. For example, such models are used to estimate the sound exposure of simulated animals ("animats") moving within a computed sound field. This approach has not yet been used to estimate the probability of vessel collision between marine mammals and ships. One reason is the difficulty of defining aversive behavior in response to disturbance. While many animals display aversion, models often use a simplified approach due to insufficient data. Building on the JASCO animal simulation model including noise exposure (JASMINE), a vessel collision framework was developed for southern resident killer whales (SRKWs) using modeled vessel sound fields and AIS data. Animats were programmed to increasingly avoid louder, closer, and additional vessels by changing their heading, speed, and behavioral state based on published data from SRKWs. Animats that remained within a calculated "encounter radius" despite aversive behavior were considered struck by the vessel. Parameters governing aversive behaviors were calibrated by comparing the proportion of animats struck in the simulation with the collision probability for real-world SRKWs. This model provides a starting point to model the risk of vessel collision for SRKW and other species.

Agent-based model: A method worthy of promotion in Library and Information Science

Agent-based model (ABM) is a branch of artificial intelligence. Its specialty is to construct a complex macro-system model by describing the perception, decision, learning and action of micro-agents. This method is widely used in many fields from natural science to social science. We discuss ABM by collecting relevant academic papers which apply to the field of Library and Information Science (LIS). This article systematically reviews how ABM is applied to the LIS field and argues that ABM can provide an exploratory tool with quantifiability, repeatability, interpretability, contingency, adaptability and other types of advantages. Finally, it is pointed out that this method is a research tool worthy of careful exploration.

Agent-based models for detecting the driving forces of biomolecular interactions

Agent-based modelling and simulation have been effectively applied to the study of complex biological systems, especially when composed of many interacting entities. Representing biomolecules as autonomous agents allows this approach to bring out the global behaviour of biochemical processes as resulting from local molecular interactions. In this paper, we leverage the capabilities of the agent paradigm to construct an in silico replica of the glycolytic pathway; the aim is to detect the role that long-range electrodynamic forces might have on the rate of glucose oxidation. Experimental evidences have shown that random encounters and short-range potentials might not be sufficient to explain the high efficiency of biochemical reactions in living cells. However, while the latest in vitro studies are limited by present-day technology, agent-based simulations provide an in silico support to the outcomes hitherto obtained and shed light on behaviours not yet well understood. Our results grasp properties hard to uncover through other computational methods, such as the effect of electromagnetic potentials on glycolytic oscillations.

Sensitivity analysis of agent-based models: a new protocol

Agent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such critiques, we propose a systematic approach to conducting sensitivity analyses of ABMs. Our approach deals with a feature that can complicate sensitivity analyses: most ABMs include important non-parametric elements, while most sensitivity analysis methods are designed for parametric elements only. The approach moves from charting out the elements of an ABM through identifying the goal of the sensitivity analysis to specifying a method for the analysis. We focus on four common goals of sensitivity analysis: determining whether results are robust, which elements have the greatest impact on outcomes, how elements interact to shape outcomes, and which direction outcomes move when elements change. For the first three goals, we suggest a combination of randomized finite change indices calculation through a factorial design. For direction of change, we propose a modification of individual conditional expectation (ICE) plots to account for the stochastic nature of the ABM response. We illustrate our approach using the Garbage Can Model, a classic ABM that examines how organizations make decisions.

PEDESTRIAN BEHAVIOR FOR DEVELOPING STRATEGY IN TOURISM AREA; AGENT-BASED SIMULATION

Agent-based modelling is an approach to develop a design strategy in socio-related studies to understand pedestrian behavior by using simulation through validation using field observation. This study area has a historic city so that having several potential advantages as destination tourists and also having urban issues. Some facilities disseminate prosperous for domestic tourist destinations, transportation hubs (land and water-based transport), and public facilities. The purpose is to develop a design strategy of pedestrian behavior in urban space to be procedure based on computational modelling. By merging the result, it helps designers to depict pedestrian movement flow, permeability, and connectivity patterns, which represent the presumptions of the origins or source of movement, destinations, generators, and attractors of movement. This simulation examines and valuates spatial behavior models allowing to route preferences of each pedestrian in order to be used in the strategy of design process for architect, urban planner, or other designer stakeholders. The result will imply a walkable pedestrian-way design, where this approach of a pedestrian experience might be an effective tool in city planning.

On the Exploratory Function of Agent-Based Modeling

Abstract Agent-based models (ABM) derive the behavior of artificial socio-economic entities computationally from the actions of a large number of agents. One objection is that highly idealized ABMs fail to represent the real world in any reasonable sense. Another objection is that they at best show how observed patterns may have come about, because simulations are easy to produce and there is no evidence that this is really what happens. Moreover, different models may well yield the same result. I will rebut these objections by focusing on an often neglected, but crucial function of ABMs. Building on Gelfert’s (2016) account of the exploratory uses of scientific models I show that, in the absence of an accepted underlying theory, successful ABMs lend inductive support to assumptions concerning certain structural feutures of the behavioral rules employed. One core step towards this goal is what I call multiple-model robustness analysis.

G-Computation and Agent-Based Modeling for Social Epidemiology: Can Population Interventions Prevent Posttraumatic Stress Disorder?

Agent-based modeling and g-computation can both be used to estimate impacts of intervening on complex systems. We explored each modeling approach within an applied example: interventions to reduce posttraumatic stress disorder (PTSD). We used data from a cohort of 2,282 adults representative of the adult population of the New York City metropolitan area from 2002-2006, of whom 16.3% developed PTSD over their lifetimes. We built 4 models: g-computation, an agent-based model (ABM) with no between-agent interactions, an ABM with violent-interaction dynamics, and an ABM with neighborhood dynamics. Three interventions were tested: 1) reducing violent victimization by 37.2% (real-world reduction); 2) reducing violent victimization by100%; and 3) supplementing the income of 20% of lower-income participants. The g-computation model estimated population-level PTSD risk reductions of 0.12% (95% confidence interval (CI): -0.16, 0.29), 0.28% (95% CI: -0.30, 0.70), and 1.55% (95% CI: 0.40, 2.12), respectively. The ABM with no interactions replicated the findings from g-computation. Introduction of interaction dynamics modestly decreased estimated intervention effects (income-supplement risk reduction dropped to 1.47%), whereas introduction of neighborhood dynamics modestly increased effectiveness (income-supplement risk reduction increased to 1.58%). Compared with g-computation, agent-based modeling permitted deeper exploration of complex systems dynamics at the cost of further assumptions.

Statistical analysis of tipping pathways in agent-based models

Agent-based models are a natural choice for modeling complex social systems. In such models simple stochastic interaction rules for a large population of individuals on the microscopic scale can lead to emergent dynamics on the macroscopic scale, for instance a sudden shift of majority opinion or behavior. Here we are introducing a methodology for studying noise-induced tipping between relevant subsets of the agent state space representing characteristic configurations. Due to a large number of interacting individuals, agent-based models are high-dimensional, though usually a lower-dimensional structure of the emerging collective behaviour exists. We therefore apply Diffusion Maps, a non-linear dimension reduction technique, to reveal the intrinsic low-dimensional structure. We characterize the tipping behaviour by means of Transition Path Theory, which helps gaining a statistical understanding of the tipping paths such as their distribution, flux and rate. By systematically studying two agent-based models that exhibit a multitude of tipping pathways and cascading effects, we illustrate the practicability of our approach.

Uncertainties of Sub-Scaled Supply and Demand in Agent-Based Mobility Simulations with Queuing Traffic Model

Agent-based models for dynamic traffic assignment simulate the behaviour of individual, or group of, agents, and then the simulation outcomes are observed on the scale of the system. As large-scale simulations require substantial computational power and have long run times, most often a sample of the full population and downscaled road capacities are used as simulation inputs, and then the simulation outcomes are scaled up. Using a massively parallelized mobility model on a large-scale test case of the whole of Switzerland, which includes 3.5 million private vehicles and 1.7 million users of public transit, we have systematically quantified, from 6 105 simulations of a weekday, the impacts of scaled input data on simulation outputs. We show, from simulations with population samples ranging from 1% to 100% of the full population and corresponding scaling of the traffic network, that the simulated traffic dynamics are driven primarily by the flow capacity, rather than the spatial properties, of the traffic network. Using a new measure of traffic similarity, that is based on the chi-squared test statistic, it is shown that the dynamics of the vehicular traffic and the occupancy of the public transit are adversely impacted when population samples less than 30% of the full population are used. Moreover, we present evidence that the adverse impact of population sampling is determined mostly by the patterns of the agents’ behaviour rather than by the traffic model.

Agent-based evolving network modeling: a new simulation method for modeling low prevalence infectious diseases

Agent-based network modeling (ABNM) simulates each person at the individual-level as agents of the simulation, and uses network generation algorithms to generate the network of contacts between individuals. ABNM are suitable for simulating individual-level dynamics of infectious diseases, especially for diseases such as HIV that spread through close contacts within intricate contact networks. However, as ABNM simulates a scaled-version of the full population, consisting of all infected and susceptible persons, they are computationally infeasible for studying certain questions in low prevalence diseases such as HIV. We present a new simulation technique, agent-based evolving network modeling (ABENM), which includes a new network generation algorithm, Evolving Contact Network Algorithm (ECNA), for generating scale-free networks. ABENM simulates only infected persons and their immediate contacts at the individual-level as agents of the simulation, and uses the ECNA for generating the contact structures between these individuals. All other susceptible persons are modeled using a compartmental modeling structure. Thus, ABENM has a hybrid agent-based and compartmental modeling structure. The ECNA uses concepts from graph theory for generating scale-free networks. Multiple social networks, including sexual partnership networks and needle sharing networks among injecting drug-users, are known to follow a scale-free network structure. Numerical results comparing ABENM with ABNM estimations for disease trajectories of hypothetical diseases transmitted on scale-free contact networks are promising for application to low prevalence diseases.

Adaptive Sampling Simulated Annealing for the Synthesis of Disaggregate Mobility Data from Origin–Destination Matrices

Agent-based modelling has been suggested as a highly suitable approach for the tackling of future mobility challenges. However, the application of disaggregate models is often hindered by the high granularity of the required input. Recent research has suggested a combinatorial optimization-based framework to enable the conversion of typical origin–destination matrices (ODs) to suitable input for agent-based modelling (e.g., trip-chains, tours, or activity-schedules). Nonetheless, the combinatorial nature of the approach requires very efficient and scalable optimization processes to handle large-scale ODs. This study suggests an advanced optimization technique, coined as the adaptive sampling simulated annealing (ASSA) algorithm, able to exploit high-level calibration information (in the form of a joint distribution) for the efficient addressing of large-scale combinatorial problems. The proposed optimization algorithm was evaluated using high-level information about the departure profile, the types of activities, and the travel time of the expected output and a set of large-scale trip-purpose- and time-period-segmented OD matrices of 253,000 trips. The obtained results showcase the ability of the methodology to accurately and efficiently convert large-scale ODs into disaggregate mobility traces since the inputted ODs were converted into thousands of travel-demand equivalent, disaggregate mobility traces with an accuracy exceeding 90%. The implications are significant since the abundance of travel-demand information in ODs can be now exploited for the preparation of disaggregate mobility traces, suitable for sophisticated agent-based transport modelling.

Statistical Challenges in Agent-Based Modeling

Agent-based models (ABMs) are popular in many research communities, but few statisticians have contributed to their theoretical development. They are models like any other models we study, but in general, we are still learning how to fit ABMs to data and how to make quantified statements of uncertainty about the outputs of an ABM. ABM validation is also an underdeveloped area that is ripe for new statistical developments. In what follows, we lay out the research space and encourage statisticians to address the many research issues in the ABM ambit.

Remarks on the Behavior of an Agent-Based Model of Spatial Distribution of Species

Agent-based models have gained considerable notoriety in ecological modeling as well as in several other fields yearning for the ability to capture the emergent behavior of a complex system in which individuals interact with each other and with their environment. These models are implemented by applying a bottom-up approach, where the entire behavior of the system emerges from the local interaction between their components (agents or individuals). Usually, these interactions between individuals and their enclosing environment are modeled by very simple local rules. From the conceptual point of view, another appealing characteristic of this simulation approach is that it is well aligned with the reality whenever the system is composed of a multitude of individuals (behavioral units) that can be flexibly combined and placed in the environment. Due to their inherent flexibility, and despite of their simplicity, it is necessary to pay attention to the adjustments in their parameters which may result in unforeseen changes on the overall behavior of these models. In this paper we study the behavior of an agent-based model of spatial distribution of species, by analyzing the effects of the model parameters and the implications of the environment variables (that compose the environment where the species lives) on the models’ output. The presented experiments show that the behavior of the model depends mainly on the conditions of the environment where the species live, and the main parameters presented in life cycle of the species.

АГЕНТ-ОРИЕНТИРОВАННОЕ МОДЕЛИРОВАНИЕ КАК ОСНОВА УСТОЙЧИВОГО РАЗВИТИЯ СОВРЕМЕННОЙ ЭКОНОМИКИ

Agent-based (simulation) modeling is one of the methods of studying complex systems, processes, and phenomena in various spheres of human activity: business, service, manufacturing, science, medicine, transport services, and logistics. In this study, a model is built – an approximation to a real object (system, process), especially in relation to the most important economic properties for the study. By experimenting with the model, you can extract indicators that are of interest for the functioning of the real enterprise system. Having obtained sufficiently stable statistics from these data, you can use it in the future for economic planning or forecasting, which is an essential tool for sustainable development in general.

Good Decisions or Bad Outcomes? A Model for Group Deliberation on Value-Laden Topics

ABSTRACT Agent-based models present an ideal tool for interrogating the dynamics of communication and exchange. Such models allow individual aspects of human interaction to be isolated and controlled in a way that sheds new insight into complex behavioral phenomena. This approach is particularly valuable in settings beset by confounding factors and mixed empirical evidence. The political communication setting of deliberation is one such salient setting: in business, politics, and everyday life, individuals with varying opinions, experience, and information attempt to collaborate and make decisions. Empirical evidence suggests that such collaborative reasoning can lead to good decisions, yet there are numerous deliberative failures which may frequently cause groups to reach bad outcomes. Using the substantive setting of deliberation, this paper presents an agent-based model aimed at disambiguating the individual factors which influence decision-making conversations. We model this communicative process as a deliberative game of “giving and asking for reasons.” Agents share beliefs around possible policy initiatives and attempt to enact “good” policies through a process of mutual exchange and consideration. The model considers an interconnected policy landscape in which implementing or not implementing a policy mediates the value of other policies. Within this framework, the paper considers the impacts of three canonical failures of deliberation: limited cognitive capacity, group factions, and tendencies to make poor judgments when accepting or rejecting others’ views. We find that cognitive capacity can significantly decrease the ability of a group to reach a good decision. However, this effect appears to be mitigated for groups of opposing factions. Indeed, polarized groups do surprisingly well at identifying optimal policy solutions, suggesting that heterogeneous agents can achieve good outcomes if they are willing to talk and learn from each other.

The FLAME-accelerated signalling tool (FaST) for facile parallelisation of flexible agent-based models of cell signalling

Agent-based modelling is particularly adept at modelling complex features of cell signalling pathways, where heterogeneity, stochastic and spatial effects are important, thus increasing our understanding of decision processes in biology in such scenarios. However, agent-based modelling often is computationally prohibitive to implement. Parallel computing, either on central processing units (CPUs) or graphical processing units (GPUs), can provide a means to improve computational feasibility of agent-based applications but generally requires specialist coding knowledge and extensive optimisation. In this paper, we address these challenges through the development and implementation of the FLAME-accelerated signalling tool (FaST), a software that permits easy creation and parallelisation of agent-based models of cell signalling, on CPUs or GPUs. FaST incorporates validated new agent-based methods, for accurate modelling of reaction kinetics and, as proof of concept, successfully converted an ordinary differential equation (ODE) model of apoptosis execution into an agent-based model. We finally parallelised this model through FaST on CPUs and GPUs resulting in an increase in performance of 5.8× (16 CPUs) and 53.9×, respectively. The FaST takes advantage of the communicating X-machine approach used by FLAME and FLAME GPU to allow easy alteration or addition of functionality to parallel applications, but still includes inherent parallelisation optimisation. The FaST, therefore, represents a new and innovative tool to easily create and parallelise bespoke, robust, agent-based models of cell signalling.

Paradigm shifts

Abstract The author studies the evolution of the number of coexisting beliefs in a financial market. Crucially, he undertakes to do so in a framework where the paradigms, beliefs, and models driving agents behavior are left totally unspecified; i.e., the author does not make any parametric or non-parametric model assumptions. The overreaching aim of this exercise is to characterise the dynamic of the variety of beliefs in an auction-based financial market independently of any assumptions on agents behaviors. The resulting framework may be seen as an abstract agent-based model. In a computer experiment the authors exhibits a cycle between two states, so that either all agents act according to the same belief, or there is no leading belief; i.e., there is one dominating belief, or none. Further, the author finds that the frequency of this cycle is positively linked to the quality of the information available to the agents.