Analysis of crime patterns through the integration of an agent-based model and a population microsimulation

In recent years, criminologists have become interested in understanding crime variations at progressively finer spatial scales, right down to individual streets or even houses. To model at these fine spatial scales, and to better account for the dynamics of the crime system, agent-based models of crime are emerging. Generally, these have been more successful in representing the behaviour of criminals than their victims. In this paper it is suggested that individual representations of criminal behaviour can be enhanced by combining them with models of the criminal environment which are specified at a similar scale. In the case of burglary this means the identification of individual households as targets. We will show how this can be achieved using the complementary technique of microsimulation. The work is significant because it allows agent-based models of crime to be refined geographically (to allow, for example, individual households with varying wealth or occupancy measures) and leads to the identification of the characteristics of individual victims.     

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.     

A PROPOSAL FOR A KNOWLEDGE MARKET BASED ON QUANTITY AND QUALITY OF KNOWLEDGE

Autonomous market environments have been proposed in the literature as the future of electronic markets. The ability to delegate complex negotiation processes and obtain similar or better results than their human counterparts has generated a great interest in agent-based markets. More recently, such a paradigm has been applied in the field of knowledge management and, more specifically, to knowledge sharing and exchange; however, most of the knowledge market proposals in the literature fail to give details on a key component of their models: knowledge quality. This article presents a new proposal for an agent-based market environment that aims at filling the previously mentioned gap in research. The main contribution of our research is the integration of formal mechanisms for knowledge quality and quantity measurement and the use of these values to set a price for knowledge and select the most suitable agent for negotiation.     

Lessons learned on development and application of agent-based models of complex dynamical systems

The field of agent-based modelling (ABM) has gained a significant following in recent years, and it is often marketed as an excellent introduction to modelling for the novice modeller or non-programmer. The typical objective of developing an agent-based model is to either increase our mechanistic understanding of a real-world system, or to predict how the dynamics of the real-world system are likely to be affected by changes to internal or external factors. Although there are some excellent ABMs that have been used in a predictive capacity across a number of domains, we believe that the promotion of ABM as an ‘accessible to all’ approach, could potentially lead to models being published that are flawed and therefore generate inaccurate predictions of real-world systems. The purpose of this article is to use our experiences in modelling complex dynamical systems, to reinforce the view that agent-based models can be useful for answering questions of the real-world domain through predictive modelling, but also to emphasise that all modellers, expert and novice alike, must make a concerted effort to adopt robust methods and techniques for constructing, validating and analysing their models, if the result is to be meaningful and grounded in the system of interest.     

Data-driven agent-based modeling,with application to rooftop solar adoption

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among agents. However, agent-based models are often not developed explicitly for prediction, and are generally not validated as such. We therefore present a novel data-driven agent-based modeling framework, in which individual behavior model is learned by machine learning techniques, deployed in multi-agent systems and validated using a holdout sequence of collective adoption decisions. We apply the framework to forecasting individual and aggregate residential rooftop solar adoption in San Diego county and demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. Meanwhile, we construct a second agent-based model, with its parameters calibrated based on mean square error of its fitted aggregate adoption to the ground truth. Our result suggests that our data-driven agent-based approach based on maximum likelihood estimation substantially outperforms the calibrated agent-based model. Seeing advantage over the state-of-the-art modeling methodology, we utilize our agent-based model to aid search for potentially better incentive structures aimed at spurring more solar adoption. Although the impact of solar subsidies is rather limited in our case, our study still reveals that a simple heuristic search algorithm can lead to more effective incentive plans than the current solar subsidies in San Diego County and a previously explored structure. Finally, we examine an exclusive class of policies that gives away free systems to low-income households, which are shown significantly more efficacious than any incentive-based policies we have analyzed to date.     

A model of friendship networks based on social network analysis

It is important for teachers to understand the characteristics of forming and changing of friendship networks to manage their classes effectively. Many studies have analyzed friendship networks using agent-based models. Most of the models used were designed for capturing basic phenomena in a micro-viewpoint. The purpose of our study is to simulate actual friendship networks. In this paper, we present an agent-based friendship-network model based on our analysis of friendship networks among university students. We focused on Hiramatsu’s model based on a cost-benefit analysis and modified the model with the aim of closing to the actual friendship. With our modified model, the benefit function was based on their cluster structure. The results with the modified model better matched the actual friendship networks. The clique size in the modified model approached that in the actual friendship networks, and the values of several network properties approached those of the actual friendship networks.     

Intra-Organizational Logistics Management Through Multi-Agent Systems

Compared to inter-organizational logistics management, the goal of intra-organizational logistics management is to maximize the profits of the whole company sometimes at the cost of its individual units' profits. While many agent-based models have been proposed for logistics management, most of these models use an auction approach. Thus, they are not suitable for intra-organizational logistics management. In this paper, we first formulate logistics management as a distributed resource allocation problem. Then, we present our ongoing work on developing a multi-agent model for intra-organizational logistics management and using Lagrangian relaxation to decompose the problem into a set of subproblems. Our initial experimental results are very promising. We provide a detailed analysis of our results.     

Agent-based computational modeling of the stock price-volume relation

From the perspective of the agent-based model of stock markets, this paper examines the possible explanations for the presence of the causal relation between stock returns and trading volume. Using the agent-based approach, we find that the explanation for the presence of the stock price-volume relation may be more fundamental. Conventional devices such as information asymmetry, reaction asymmetry, noise traders or tax motives are not explicitly required. In fact, our simulation results show that the stock price-volume relation may be regarded as a generic property of a financial market, when it is correctly represented as an evolving decentralized system of autonomous interacting agents. One striking feature of agent-based models is the rich profile of agents' behavior. This paper makes use of the advantage and investigates the micro-macro relations within the market. In particular, we trace the evolution of agents' beliefs and examine their consistency with the observed aggregate market behavior. We argue that a full understanding of the price-volume relation cannot be accomplished unless the feedback relation between individual behavior at the bottom and aggregate phenomena at the top is well understood.     

Selecting among five common modelling approaches for integrated environmental assessment and management

The design and implementation of effective environmental policies need to be informed by a holistic understanding of the system processes (biophysical, social and economic), their complex interactions, and how they respond to various changes. Models, integrating different system processes into a unified framework, are seen as useful tools to help analyse alternatives with stakeholders, assess their outcomes, and communicate results in a transparent way. This paper reviews five common approaches or model types that have the capacity to integrate knowledge by developing models that can accommodate multiple issues, values, scales and uncertainty considerations, as well as facilitate stakeholder engagement. The approaches considered are: systems dynamics, Bayesian networks, coupled component models, agent-based models and knowledge-based models (also referred to as expert systems). We start by discussing several considerations in model development, such as the purpose of model building, the availability of qualitative versus quantitative data for model specification, the level of spatio-temporal detail required, and treatment of uncertainty. These considerations and a review of applications are then used to develop a framework that aims to assist modellers and model users in the choice of an appropriate modelling approach for their integrated assessment applications and that enables more effective learning in interdisciplinary settings.     

An agent-based learning framework for modeling microbial growth

The overall idea of this paper is to study the intelligent behavior of microbes in a binary substrate environment with agent-based learning models. Study of microbial growth enables understanding of industrially relevant processes such as fermentation, biodegradation of pollutants, antibody production using hybridoma cells, etc. Artificial intelligence techniques such as genetic algorithms and agent-based learning methodologies have been used to study microbial growth. Specifically, the objective is to (1) qualitatively model the intelligent growth characteristics of the microbes using a minimal set of generic rules as against algebraic/differential mathematical relationships and (2) propose a suitable hypothesis that explains the origin of intelligence through learning in the microbes. A microbial cell has been modeled as a collection of agents characterized by a set of resources and an objective to survive and grow. The actions of the agents are governed by generic rules such as survival, growth and division as is common for any individual in a resource-limited competitive environment. The interaction of the agents with the environment and other fellow agents enables them to “learn” and “adapt” to the changes in the environment and thus defines the dynamics of the system. The origin of intelligence in the microbes has been studied by both a simple learning rule of imitation and rule discovery studies.     

建筑物火灾中的人群疏散研究

建筑物火灾是我国频发的安全事故,所以应研究建筑物火灾人群安全疏散问题。由于在建筑物火灾中,人群疏散时出现拥堵,存在不安全因素,造成人员伤亡。针对在现有的研究中未考虑人员行为的影响,提出了智能体(Agent)的人群行为建模技术在建筑物火灾中的人群疏散仿真中的应用方法。仿真结果显示基于Agent的行为模型可以仿真出人员特性及决策过程对人群疏散的影响,弥补现有的人群疏散模型的不足。仿真结果证明,Agent的行为建模技术具有仿真火灾全过程中人员疏散行为的功能,适用于建筑物火灾中的人群优化疏散策略。     

FROM DISCRETE-TIME MODELS TO CONTINUOUS-TIME, ASYNCHRONOUS MODELING OF FINANCIAL MARKETS

Most agent-based simulation models of financial markets are discrete-time in nature. In this paper, we investigate to what degree such models are extensible to continuous-time, asynchronous modeling of financial markets. We study the behavior of a learning market maker in a market with information asymmetry, and investigate the difference caused in the market dynamics between the discrete-time simulation and continuous-time, asynchronous simulation. We show that the characteristics of the market prices are different in the two cases, and observe that additional information is being revealed in the continuous-time, asynchronous models, which can be acted upon by the agents in such models. Because most financial markets are continuous and asynchronous in nature, our results indicate that explicit consideration of this fundamental characteristic of financial markets cannot be ignored in their agent-based modeling.     

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.     

A general framework for analyzing techno-behavioural dynamics on networks

A general framework for assessing future impacts of technology on society and environment is presented. The dynamics between human activity and technological systems impact upon many processes in society and nature. This involves non-linear dynamics requiring an understanding of how technology and human behaviour influence each other and co-evolve. Conventionally, technological and behavioural systems are analyzed as separate entities. We develop an integrated theoretical and methodological approach termed techno-behavioural dynamics focussing on networked interactions between technology and behaviour across multiple system states. We find that positive feedback between technology learning, evolving preferences and network effects can lead to tipping points in complex sociotechnical systems. We also demonstrate how mean-field and agent-based models are complimentary for capturing a hierarchy of analytical resolutions in a common problem domain. Assessing and predicting co-evolutionary dynamics between technology and human behaviour can help avoid systems lock-in and inform a range of adaptive responses to environmental and societal risk.     

An agent-based architecture for multimodal interaction

In this paper, an executable generic process model is proposed for combined verbal and non-verbal communication processes and their interaction. The agent-based architecture can be used to create multimodal interaction. The generic process model has been designed, implemented and used to simulate different types of interaction between verbal and non-verbal communication processes: a non-verbal communication process can add and modify content to a verbal communication process, but can also provide a protocol for the (control of the) verbal communication process. With respect to the communication protocol both stimulus–response behaviour and deliberative behaviour have been modelled and simulated. The semantics of the model has been formalized by three-levelled partial temporal models, covering both the material and mental proceses and their relations.     

Parallelizing evolutionary computation: A mobile agent-based approach

Parallel computation models have been widely used to enhance the performance of traditional evolutionary algorithms, and they have been implemented on parallel computers to speed up the computation. Instead of using expensive parallel computing facilities, we propose to implement parallel evolutionary computation models on easily available networked PCs, and present a multi-agent framework to support parallelism. With the unique characteristics of agent autonomy and mobility, mobile agents can carry the EC-code and migrate from machine to machine to complete the computation dynamically. To evaluate the proposed approach we have developed a prototype system on a middleware platform JADE to solve a time-consuming task. Different kinds of experiments have been conducted to assess the developed system and the preliminary results show the promise and efficiency of our mobile agent-based approach.     

A COMPARISON OF SOME FUZZY RELATION-BASED LINGUISTIC PREFERENCE MODELS FOR MULTIPLE-FACTOR PROJECT ASSESSMENT

An agent-based simulation model representing a theory of the dynamic processes involved in innovation in modern knowledge-based industries is described. The agent-based approach allows the representation of heterogenous agents that have individual and varying stocks of knowledge. The simulation is able to model uncertainty, historical change, effect of failure on the agent population, and agent learning from experience, from individual research and from partners and collaborators. The aim of the simulation exercises is to show that the artificial innovation networks show certain characteristics they share with innovation networks in knowledge intensive industries and which are difficult to be integrated in traditional models of industrial economics.     

Agent-based analysis and simulation of meta-reasoning processes in strategic naval planning

This paper presents analysis and simulation of meta-reasoning processes based on an agent-based meta-level architecture for strategic reasoning in naval planning. The architecture was designed as a generic agent model and instantiated with decision knowledge acquired from naval domain experts and was specified as an executable agent-based model which has been used to perform a number of simulations. To evaluate the simulation results, relevant properties for the planning decision were identified and formalized. These properties have been validated for the simulation traces.     

Process models for agent-based development

A great deal of research in the area of agent-oriented software engineering (AOSE) focuses on proposing methodologies for agent systems, i.e., on identifying the guidelines to drive the various phases of agent-based software development and the abstractions to be exploited in these phases. However, very little attention has been paid so far to the engineering process subjacent to the development activity, disciplining the execution of the different phases involved in the software development. In this paper, we focus on process models for software development and put these in relation with current researches in AOSE. First, we introduce the key concepts and issues related to software processes and present the various software process models currently adopted in mainstream software engineering. Then, we survey the characteristics of a number of agent-oriented methodologies, as they pertain to software processes. In particular, for each methodology, we analyze which software process model it (often implicitly) underlies and which phases of the process are covered by it, thus enabling us to identify some key limitations of currently methodology-centered researches. On this basis, we eventually identify and analyze several open issues in the area of software process models for agent-based development, calling for further researches and experiences.     

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.