Agent-based computational economics: modeling economies as complex adaptive systems

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization to economics of the basic complex adaptive systems paradigm. This paper outlines the main objectives and defining characteristics of the ACE methodology, and discusses several active research areas.     

Self-organization of complex adaptive systems as a society of rational agents

In this paper, we aim at providing a general theoretical framework for designing complex adaptive systems as a society of rational agents. We term entities with their own interest agents. They are also rational in the sense that they only do what they want to do and what they think is in their own best interest. We formulate the dynamic interaction among those rational agents as competitive and cooperative problems. We obtain the equilibrium behavior in the long-run, and characterize the collective behavior of these rational agents. We show how complex collective behavior can emerge from the locally optimal behavior of each agent. We also describe why and how they organize themselves into a multilevel hierarchical organization with nesting structures. This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1997     

Modeling complex systems with adaptive networks

Adaptive networks are a novel class of dynamical networks whose topologies and states coevolve. Many real-world complex systems can be modeled as adaptive networks, including social networks, transportation networks, neural networks and biological networks. In this paper, we introduce fundamental concepts and unique properties of adaptive networks through a brief, non-comprehensive review of recent literature on mathematical/computational modeling and analysis of such networks. We also report our recent work on several applications of computational adaptive network modeling and analysis to real-world problems, including temporal development of search and rescue operational networks, automated rule discovery from empirical network evolution data, and cultural integration in corporate merger.     

The web from a complex adaptive systems perspective

The web continues to grow at a phenomenal rate, and the amount of information on the web is overwhelming. Finding the relevant information remains a big challenge. Due to its wide distribution, its openness and high dynamics, the web is a complex system, for which we have to imagine mechanisms of content maintaining, filtering and organizing that are able to deal with its evolving dynamics and distribution. Integrating mechanisms of self-organization of the web content is an attractive perspective, to match with these requirements. Self-organized complex systems can be programmed using situated multi-agent systems with a coupling between the agents' social organization and spatial organization. This paper explores the web from a complex adaptive system (CAS) perspective. It reviews some characteristic behaviors of CASs and shows how the web exhibits similar behaviors. We propose a model and a prototype of a system that addresses the dynamic web content organization, adopting the CAS vision and using the multi-agent paradigm.     

A new information processing measure for adaptive complex systems

This paper presents an implementation-independent measure of the amount of information processing performed by (part of) an adaptive system which depends on the goal to be performed by the overall system. This new measure gives rise to a theoretical framework under which several classical supervised and unsupervised learning algorithms fall and, additionally, new efficient learning algorithms can be derived. In the context of neural networks, the framework of information theory strives to design neurally inspired structures from which complex functionality should emerge. Yet, classical measures of information have not taken an explicit account of some of the fundamental concepts in brain theory and neural computation, namely that optimal coding depends on the specific task(s) to be solved by the system and that goal orientedness also depends on extracting relevant information from the environment to be able to affect it in the desired way. We present a new information processing measure that takes into account both the extraction of relevant information and the reduction of spurious information for the task to be solved by the system. This measure is implementation-independent and therefore can be used to analyze and design different adaptive systems. Specifically, we show its application for learning perceptrons, decision trees and linear autoencoders.     

一类复杂控制系统分散自适应鲁棒控制器设计

基于模糊逻辑系统具有充分利用语言信息和逼近连续函数性质的思想,分析研究了一类非线性不确定复杂系统的自适应控制问题.利用系统的数学模型和模糊逻辑系统对不确定性的输出信息,设计出了复杂系统的分散自适应鲁棒控制器和模糊逻辑系统参数估计的自适应律,在较弱的假设条件下,证明了这种控制器使被控系统的状态及参数估计误差一致终极有界.仿真实例表明,所提出的方法是有效的.     

Augmented reality as an interface to adaptive hypermedia systems

Adaptive Hypermedia has sought to tackle the problems of dealing with complex, heavily structured information and the presentation of views of that structure to users. Increasingly, adaptive content is achieved through different forms of context. Using two case-study applications, we will reflect on how Augmented Reality may present solutions to a number of Adaptive Hypermedia presentation problems. Each case study describes a different physical interaction metaphor for exposing the complex adaptation of hypermedia content in an intuitive way. The preliminary findings of our early evaluations are discussed. Finally, conclusions are drawn as to how Augmented Reality applications could use the modelling techniques of the Adaptive Hypermedia community to deal more easily with complex information.     

Simulation of carbon-based model for virtual plants as complex adaptive system

This research presented a teleonomic-based simulation approach to virtual plants integrating the technology of intelligent agent as well as the knowledge of plant physiology and morphology. Plant is represented as the individual metamers and root agents with both functional and geometrical structure. The development of plant is achieved by the flush growth of metamer and root agents controlled by their internal physiological status and external environment. The eggplant based simulation results show that simple rules and actions (internal carbon allocation among organs, dynamic carbon reserve/mobilization, carbon transport in parallel using a discrete pressure-flow paradigm and child agent position choosing for maximum light interception, etc.) executed by agents can cause the complex adaptive behaviors on the whole plant level: carbon partitioning among metamers and roots, carbon reserve dynamics, architecture and biomass adaptation to environmental heterogeneity and the phototropism, etc. This phenomenon manifest that the virtual plant simulated in presented approach can be viewed as a complex adaptive system.     

A hierarchical composite framework of parallel discrete event simulation for modelling complex adaptive systems

As complex adaptive systems(CAS) continue to grow in scale and complexity, and the need for system adaptability increases, systems modelling has become an essential concern. Parallel discrete event simulation became a preferred choice as logical process world view, which bridges complex system modelling and high-performance computing. To resolve the shortcoming of this world view identified with respect to modularity and scalability. A hierarchical composite modelling framework was proposed, which is a three-level architecture intended to support the composition and integration of sub-models. The bottom layer is simulation model component(SMC), which is not a model but implement some simulation-specific support functionality. The middle layer is logical process model(LP), which describes an agent which can react to the current situation by executing a sequence of SMCs. The top layer is CAS system model, which defines a CAS model consist of several LPs and also the interactions between these LPs. The hierarchical composite modelling process and parallel simulation execution strategy are discussed to support the modelling and simulation of a CAS. In order to verify its effectiveness, a complex social opinion system model is proposed based on this hierarchical composite modelling framework. The experimental results confirms the viability of utilizing multi-level architecture for simulating large scale complex adaptive systems.     

iCoSim-FMS: An intelligent co-simulator for the adaptive control of complex flexible manufacturing systems

We describe an intelligent co-simulator for real time production control of a complex flexible manufacturing system (CFMS) having machine and tool flexibility. The manufacturing processes associated with the CFMS are complicated with each operation being possibly done by several machining centers. The co-simulator design approach is built upon the theory of dynamic meta-model based supervisory control with the cooperation of its own embedded intelligent blocks. The system is implemented by coupling of the centralized simulation controller (CSC) and real-time simulator for enforcing dynamic strategies of shop floor control. The posteriori adaptive co-simulator is equipped with a concurrent bilateral mechanism for simulation optimization based on appropriate control rules enhancing performance criteria simulation efficiency. A working intelligent adaptive controller prototype (iCoSim-FMS) has been developed to validate the proposed approach and compare its performance with well known FMS heuristic methods.     

Use case maps as architectural entities for complex systems

The paper presents a novel, scenario based notation called Use Case Maps (UCMs) for describing, in a high level way, how the organizational structure of a complex system and the emergent behavior of the system are intertwined. The notation is not a behavior specification technique in the ordinary sense, but a notation for helping a person to visualize, think about, and explain the big picture. UCMs are presented as “architectural entities” that help a person stand back from the details during all phases of system development. The notation has been thoroughly exercised on systems of industrial scale and complexity and the distilled essence of what has been found to work in practice is summarized. Examples are presented that confront difficult complex system issues directly: decentralized control, concurrency, failure, diversity, elusiveness and fluidity of runtime views of software, self modification of system makeup, difficulty of seeing large scale units of emergent behavior cutting across systems as coherent entities (and of seeing how such entities arise from the collective efforts of components), and large scale     

Organizations as self-organizing and sustaining systems: a complex and autopoietic systems perspective

Many alternative theories about organization exist. Despite this, or perhaps because of it, adequate explanation of the relationship between macro and micro processes of organization, and organizational dynamics remains elusive. In the recent past there has been growing interest in two areas of systems science that offer a different basis for understanding the generative and dynamic qualities of organizations. These are autopoietic theory and complex adaptive systems theory. In this paper, we outline a theory of organization built on a synthesis of these two theoretical strands. It is argued that the approach provides an improved framework for understanding the nature and dynamics of organizational phenomena, and as such a more rigorous basis upon which to base future organizational research.     

A neural network approach to adaptive protective systems problem in the complex power generating units

Practical successes have been achieved with neural network models in a variety of domains, including energy‐related industry. The large, complex design space of electrical power systems (EPS) is only minimally explored in current practice. The satisfactory results that nevertheless have been obtained testify that neural networks are a robust modeling technology; at the same time, however, the lack of a systematic design approach implies that the best neural network models generally remain undiscovered for most applications. This paper presents an approach to an adaptive protective systems problem in the complex power generating units. First, we demonstrate the complex interdependencies between various parameters of EPS protection systems. We then present an approach, based on protection and adaptation criteria, for designing a neural network based adaptive protection system. © 2000 John Wiley & Sons, Inc.     

Scenario-based run-time adaptive MPSoC systems

The ever-increasing performance demand of modern embedded applications drives the development of multi-processor system-on-chip (MPSoC) systems in the embedded domain. Today’s MPSoC-based products increasingly have to deal with multiple application execution scenarios which may change dynamically at run time. To improve the system performance, a state-of-the-art solution is to dynamically adapting the allocation of system resources at run time for each execution scenario based on pre-determined resource schemes that have been optimized at design time. However, such approaches will not work well for MPSoC systems that have a large number of execution scenarios and/or frequent run-time variations in execution scenario behavior. In this work, we therefore propose a scalable run-time self-adaptive framework for MPSoC systems that addresses these problems, thereby considerably improving the system efficiency.     

Expert system-user integrated adaptive systems

Many expert systems operate in dynamic environments where various pertinent environmental variables and conditions vary with the passage of time. These environmental variables and conditions may affect both the set of conditions applied to input variables of expert systems and the set of recommendations provided by expert systems. For this reason, expert systems developed according to dynamic structure will generate timely recommendations. To incorporate dynamic characteristics into the structure of expert systems, it is necessary to develop expert systems as adaptive systems. This paper intends to integrate concepts of learning and adaptiveness into expert system technology.

Expert systems used to assist loan officers in improving the decision-making process of commercial loans are typical examples of expert systems that operate in dynamic environments. This paper illustrates that the quality of information provided to loan officers by expert systems may be improved when expert systems are designed as adaptive expert systems.     

基于联合滤波算法的复杂时变系统在线自适应逆控制

针对一类结构和参数均具备时变特性的复杂时变系统,提出一种新的基于联合滤波算法的在线自适应逆控制方法.该方法在处理参数时变问题的同时可兼顾系统的结构时变特性,实现复杂动态系统的在线跟踪控制.同时提出新的联合Volterra核函数滤波算法,该算法克服了原Volterra滤波器计算复杂运算速度慢的缺点,实现了动态非线性系统的在线跟踪控制.通过仿真分析可以得出,对于此类线性、非线性复杂时变系统,基于新的联合滤波器的自适应逆控制方法可以快速有效的实现动态对象在线建模与控制.