An agent-based platform for simulating complex human–aquifer interactions in managed groundwater systems

This paper presents and illustrates FlowLogo, an interactive modelling environment for developing coupled agent-based groundwater models (GW-ABMs). It allows users to simulate complex socio-environmental couplings in groundwater systems, and to explore how desirable patterns of groundwater and social development can emerge from agent behaviours and interactions. GW-ABMs can be developed using a single piece of software, addressing common issues around data transfer and model analyses that arise when linking ABMs to existing groundwater codes. FlowLogo is based on a 2D finite-difference solution of the governing groundwater flow equations and a set of procedures to represent the most common types of stresses and boundary conditions of regional aquifer flow. The platform is illustrated using a synthetic example of an expanding agricultural region that depends on groundwater for irrigation. The implementation and analysis of scenarios from this example highlight the possibility to: (i) deploy agents at multiple scales of decision-making (farmers, waterworks, institutions), (ii) model feedbacks between agent behaviours and groundwater dynamics, and (iii) perform sensitivity and multi-realisation analyses on social and physical factors. The FlowLogo interface allows interactively changing parameters using ‘tuneable’ dials, which can adjust agent decisions and policy levers during simulations. This flexibility allows for live interaction with audiences (role-plays), in participatory workshops, public meetings, and as part of learning activities in classrooms. FlowLogo's interactive features and ease of use aim to facilitate the wider dissemination and independent validation of GW-ABMs.     

High performance computing for three-dimensional agent-based molecular models

Agent-based simulations are increasingly popular in exploring and understanding cellular systems, but the natural complexity of these systems and the desire to grasp different modelling levels demand cost-effective simulation strategies and tools.In this context, the present paper introduces novel sequential and distributed approaches for the three-dimensional agent-based simulation of individual molecules in cellular events. These approaches are able to describe the dimensions and position of the molecules with high accuracy and thus, study the critical effect of spatial distribution on cellular events. Moreover, two of the approaches allow multi-thread high performance simulations, distributing the three-dimensional model in a platform independent and computationally efficient way.Evaluation addressed the reproduction of molecular scenarios and different scalability aspects of agent creation and agent interaction. The three approaches simulate common biophysical and biochemical laws faithfully. The distributed approaches show improved performance when dealing with large agent populations while the sequential approach is better suited for small to medium size agent populations.Overall, the main new contribution of the approaches is the ability to simulate three-dimensional agent-based models at the molecular level with reduced implementation effort and moderate-level computational capacity. Since these approaches have a generic design, they have the major potential of being used in any event-driven agent-based tool.     

Modelling socio-ecological systems with MAIA: A biogas infrastructure simulation

Similar to other renewable energy technologies, the development of a biogas infrastructure in the Netherlands is going through social, institutional and ecological evolution. To study this complex evolutionary process, we built a comprehensive agent-based model of this infrastructure. We used an agent-based modelling framework called MAIA to build this model with the initial motivation that it facilitates modelling complex institutional structures. The modelling experience however proved that MAIA can also act as an integrated solution to address other major modelling challenges identified in the literature for modelling evolving socio-ecological systems. Building on comprehensive reviews, we reflect on our modelling experience and address four key challenges of modelling evolving socio-ecological systems using agents: (1) design and parameterization of models of agent behaviour and decision-making, (2) system representation in the social and spatial dimension, (3) integration of socio-demographic, ecological, and biophysical models, (4) verification, validation and sensitivity analysis of such ABMs.     

Care HPS: A high performance simulation tool for parallel and distributed agent-based modeling

Parallel and distributed simulation is a powerful tool for developing complex agent-based simulation. Complex simulations require parallel and distributed high performance computing solutions. It is necessary because their sequential solutions are not able to give answers in a feasible total execution time. Therefore, for the advance of computing science, it is important that High Performance Computing (HPC) techniques and solutions be proposed and studied. In literature, we can find some agent-based modeling and simulation tools that use HPC. However, none of these tools are designed to enable the HPC expert to be able to propose new techniques and solutions without great effort. In this paper, we introduce Care High Performance Simulation (HPS), which is a scientific instrument that enables researchers to: (1) develop techniques and solutions of high performance distributed simulations for agent-based models; and, (2) study, design and implement complex agent-based models that require HPC solutions. Care HPS was designed to easily and quickly develop new agent-based models. It was also designed to extend and implement new solutions for the main issues of parallel and distributed solutions such as: synchronization, communication, load and computing balancing, and partitioning algorithms. We conducted some experiments with the aim of showing the completeness and functionality of Care HPS. As a result, we show that Care HPS can be used as a scientific instrument for the advance of the agent-based parallel and distributed simulations field.     

基于Agent的复杂系统分布仿真建模方法的研究

基于Agent的分布仿真是研究大型复杂系统的一种有效的、重要的方法。为了减小复杂系统仿真的复杂度,增加仿真模型的重用和可维护性,需要研究基于Agent分布仿真的建模方法。首先对复杂系统及其特性进行了分析,对基于Agent的仿真进行了全面的论述,然后对基于Agent的复杂系统仿真中的复杂系统建模分析、Agent建模分析以及Agent的分布进行了分析,给出了基于Agent的复杂系统分布仿真的建模步骤,最后给出了在此建模思想指导下的金融证券市场的建模过程。     

Incorporation of BIM-based probabilistic non-structural damage assessment into agent-based post-earthquake evacuation simulation

Earthquakes can cause severe damage to structural and non-structural elements of buildings; consequently, they pose high risks to human lives. To mitigate such risks, attention has been paid to enhancing the indoor environment for increased building safety. Yet little effort has been made to assess a building occupants' evacuation behaviors in response to damage to the indoor environment. This paper addresses this issue with a novel simulation framework that couples human behaviors with changes to the indoor building environment during post-earthquake evacuation. In particular, we present a building information modelling (BIM)-based prototype that simulates seismic damage to the non-structural indoor elements and visualizes its impacts on evacuation using a color-coded heat map. The simulated damage is then used as input to an agent-based model for post-earthquake evacuation. Using a probabilistic method to assess the non-structural elements' damage states, we are able to evaluate the impact of indoor damage on the evacuation process. We performed a trial of our prototype for a hypothetical earthquake in an educational building. The results revealed how the average evacuation time would increase as the earthquake intensity increases (from 38.6 s for the no-damage scenario to 122.9 for the highest-damage scenario). The proposed prototype has the potential to be joined with other tools, such as finite-element-based simulation, to incorporate structural analysis as well. Planners and designers can explicitly use our model's output to analyze the post-earthquake evacuation with the indoor non-structural damage to assess different building design geometries that increase the chances of a suitable evacuation process.     

An extensible simulation environment and movement metrics for testing walking behavior in agent-based models

Human movement is a significant ingredient of many social, environmental, and technical systems, yet the importance of movement is often discounted in considering systems’ complexity. Movement is commonly abstracted in agent-based modeling (which is perhaps the methodological vehicle for modeling complex systems), despite the influence of movement upon information exchange and adaptation in a system. In particular, agent-based models of urban pedestrians often treat movement in proxy form at the expense of faithfully treating movement behavior with realistic agency. There exists little consensus about which method is appropriate for representing movement in agent-based schemes. In this paper, we examine popularly-used methods to drive movement in agent-based models, first by introducing a methodology that can flexibly handle many representations of movement at many different scales and second, introducing a suite of tools to benchmark agent movement between models and against real-world trajectory data. We find that most popular movement schemes do a relatively poor job of representing movement, but that some schemes may well be “good enough” for some applications. We also discuss potential avenues for improving the representation of movement in agent-based frameworks.     

Agent-Based Simulation of Animal Behaviour

In the biological literature on animal behaviour, in addition to real experiments and field studies, also simulation experiments are a useful source of progress. Often specific mathematical modelling techniques are adopted and directly implemented in a programming language. Modelling more complex agent behaviours is less adequate using the usually adopted mathematical modelling techniques. The literature on AI and Agent Technology offers more specific methods to design and implement (also more complex) intelligent agents and agent societies on a conceptual level. One of these methods is the compositional multi-agent system design method DESIRE. In this paper it is shown how (depending on the complexity of the required behaviour) a simulation model for animal behaviour can be designed at a conceptual level in an agent-based manner. Different models are shown for different types of behaviour, varying from purely reactive behaviour to pro-active, social and adaptive behaviour. The compositional design method for multi-agent systems DESIRE and its software environment supports the conceptual and detailed design, and execution of these models. A number of experiments reported in the literature on animal behaviour have been simulated for different agent models.     

Agent-Based Social Simulation in Markets

We show that certain desired behavioural properties of agent-based models can be deterministically induced by an appropriate mathematical structure. We also point out problems related to the handling of parameters, and of the modelling of time, in agent-based models. Our purpose is to illustrate some problems of agent-based social simulations in markets, as a first step towards the more ambitious goal of providing a methodology for such simulations.     

Behaviour and space in agent-based modelling: Poverty patterns in East Kalimantan,Indonesia

Applied models of policy interventions are increasingly expected to consider households’ responses to these interventions, which makes agent-based modelling popular in applied policy situations. Implementing an adequate level of agent heterogeneity and mapping it into a spatial environment are critical factors of such applied modelling. However, policy applications demand the characterisation and parameterisation of behavioural response functions of heterogeneous agents and the spatial distribution of heterogeneous agents, which are neither highly transparent nor greatly tested steps in implementing agent-based models. This paper describes an agent-based model of fuel price changes for a case study in East Kalimantan, Indonesia, and specifically: (a) the characterisation and parameterisation approach, (b) resulting agent types for approximating behavioural heterogeneity, and (c) emerging spatial poverty and deforestation patterns. The model highlights the spatial dynamics of poverty dynamics, indicating that the direct impact of deforestation on poverty among forest-dwelling communities is to trigger their migration into peri-urban areas. Overall, the model suggests that poverty increases in response to fuel price reductions.     

Agent-based simulation of electricity markets: a survey of tools

Agent-based simulation has been a popular technique in modeling and analyzing electricity markets in recent years. The main objective of this paper is to study existing agent-based simulation packages for electricity markets. We first provide an overview of electricity markets and briefly introduce the agent-based simulation technique. We then investigate several general-purpose agent-based simulation tools. Next, we review four popular agent-based simulation packages developed for electricity markets and several agent-based simulation models reported in the literature. We compare all the reviewed packages and models and identify their common features and design issues. Based on the study, we describe an agent-based simulation framework for electricity markets to facilitate the development of future models for electricity markets.     

基于Agent的分布仿真平台原型系统设计与实现

基于Agent的分布仿真是基于Agent的建模与仿真ABMS(Agent-Based Modeling and Simulation)研究的重要组成部分。在提出的基于Agent的分布仿真软件框架和通信系统设计与实现的基础上,利用Java语言和面向对象的方法设计实现了一个基于Agent的分布仿真平台原型系统ADSimE。介绍了该分布式仿真平台的主要实现细节,给出了全系统的主要Agent类的UML设计,重点阐述了保守策略仿真Agent类和乐观策略仿真Agent类的设计、处理流程以及KQML消息解析的实现细节。最后以闭合排队网络为例,说明了怎样在该环境下进行基于Agent的分布仿真应用的开发。     

An agent-based model of the influence of neighbourhood design on daily trip patterns

Post-war suburban neighbourhoods are often criticised for promoting automobile use and leading to problems such as traffic congestion, air pollution, automobile reliance, limited transit access and reduced social interactions. Newer designs, such as the neo-traditional and fused grid designs, aim to ameliorate these problems. But neighbourhood trip and traffic patterns are the collective outcome of individual decisions. Such phenomena often cannot be adequately explained by traditional aggregated methods. With consideration of personal characteristics, preferences and feedbacks between pedestrian and automobile traffic, an agent-based trip and traffic simulation model was developed and calibrated based on data from Ottawa, Ontario. Experiments show that the neo-traditional and fused grid designs generally provide more pedestrian benefits such as fewer crossings, shorter facility-access distance, less emission exposure and more social interaction opportunities, but these benefits also depend on the implementation such as the location of pedestrian-only routes. The influences are often complex. For example, elimination of pedestrian-only routes may lead to more social opportunities, but also much higher emission exposure. The study shows the importance of complex system based study of urban and neighbourhood designs, and the promise of a meso-level approach to urban and transportation simulation that can improve planning outcomes.     

Study of the performance of multi-behaviour agents for supply chain planning

In today's industrial context, competitiveness is closely associated to supply chain performance. Coordination between business units is essential to increase this performance, in order to produce and deliver products on time to customers, at a competitive price. While planning systems usually follow a single straightforward production planning process, this paper proposes that partners adapt together their local planning process (i.e. planning behaviours) to the different situations met in the supply chain environment. Because each partner can choose different behaviour and all behaviours will have an impact on the overall performance, it is difficult to know which is preferable for each partner to increase their performance. Using agent-based technology, simulation experiments have been undertaken to verify if multi-behaviour planning agents who can change planning behaviours to adapt to their environment can increase supply chain performance. These agents have been implemented in an agent-based planning platform, using a case study illustrating a lumber supply chain. The performance analysis shows that advanced planning systems can take advantage of using multiple planning processes, because of the dynamic context of supply chains.     

An agent-based simulation model for analyzing the governance of the Brazilian Financial System

Regulation can play an important role in effectively managing systemic risk while providing accountability to all affected governments. IMF points out weak governance structures as one of the main causes for financial/economical crisis. However, research in this area is still limited. One of the reasons is the inherent complexity of the public sector governance notion. In this research, the regulatory governance of the financial sector is conceived as a complex system, in which governance is perceived as a phenomenon resulting from the interactions among all the actors that influence or are influenced by regulatory activities within the financial sector. An agent-based simulation was developed to analyze and evaluate the emergent behaviors from the governance in the Brazilian finance sector under different macroeconomics variables and different attitudes, perceptions and desires of economic and political actors. The agent-based model is combined with an econometric model, which is intended to characterize the macroeconomic environment. The regulatory environment is modeled by computational agents using BDI (beliefs–desires–intentions) architecture. The agents have beliefs about their environment and desires they want to satisfy, thus leading them to create intentions to act. The agents’ behavior was modeled using fuzzy rules built by means of content analysis of newspapers and in-depth interviews with experts from the financial area. Computational experiments demonstrate the potential of the agent-based model simulation in the study of complex environments involving regulatory governance.     

Firestorms: Modeling conflict diffusion and management strategies in online communities

This study aims to provide a better understanding of how organizations can manage public conflict and firestorms in social media spheres. We develop an agent-based simulation model of conflicts in firm-hosted online communities and find that a collaborating conflict management style characterized by high levels of cooperativeness and assertiveness helps to successfully handle conflict. However, the effectiveness of this collaborative style is highly dependent on contingency factors related to the participating individuals and the social structure within the community itself, such as the number of moderators and aggressors present in the community, their credibility, as well as the overall opinion of the community. Depending on these factors, collaborating and accommodating conflict management becomes more or less successful. Under some configurations it is even less effective than more competitive and assertive management styles. Therefore, to successfully handle conflict and restrain the escalation of a firestorm, organizations need to optimally adapt their conflict management style to varying conditions by considering individual-level and community-level characteristics.     

Approaching parallel computing to simulating population dynamics in demography

Agent-based modelling and simulation is a promising methodology that can be applied in the study of population dynamics. The main advantage of this technique is that it allows representing the particularities of the individuals that are modeled along with the interactions that take place among them and their environment. Hence, classical numerical simulation approaches are less adequate for reproducing complex dynamics. Nowadays, there is a rise of interest on using distributed computing to perform large-scale simulation of social systems. However, the inherent complexity of this type of applications is challenging and requires the study of possible solutions from the parallel computing perspective (e.g., how to deal with fine grain or irregular workload). In this paper, we discuss the particularities of simulating populating dynamics by using parallel discrete event simulation methodologies. To illustrate our approach, we present a possible solution to make transparent the use of parallel simulation for modeling demographic systems: Yades tool. In Yades, modelers can easily define models that describe different demographic processes with a web user interface and transparently run them on any computer architecture environment thanks to its demographic simulation library and code generator. Therefore, transparency is provided by two means: the provision of a web user interface where modelers and policy makers can specify their agent-based models with the tools they are familiar with, and the automatic generation of the simulation code that can be executed in any platform (cluster or supercomputer). A study is conducted to evaluate the performance of our solution in a High Performance Computing environment. The main benefit of this outline is that our findings can be generalized to problems with similar characteristics to our demographic simulation model.     

复杂系统基于Agent的建模与仿真设计模式及软件框架

提出了一种基于Agent的建模与仿真设计模式——ABMS设计模式，阐述了ABMS设计模式的主要内容。该模式的提出有利于在建模与仿真领域以及软件开发界之间建立关于ABMS的对话联系与交流。然后设计了一种包含仿真服务模型ServerAgent和仿真模型结构SimAgent的ABMS分布仿真框架，该框架将最大限度实现仿真重用，并将支持大规模的基于Agent的复杂系统的仿真，包括军事对抗以及天战系统的仿真。该框架部分借鉴了HLA的思想，其中的ServerAgent提供底层的通用仿真服务。