UrbanSim simulates the development of urban areas, including land use, transportation, and environmental impacts, over periods of twenty or more years. Its purpose is to aid urban planners, residents, and elected officials in evaluating the long-term results of alternate plans, particularly as they relate to such things as housing, business and economic development, sprawl, open space, traffic congestion, and resource consumption. From a software perspective, it is a large, complex, system, with heavy demands for excellent space efficiency and support for software evolution. It consists of a collection of models that represent different urban actors and processes, an object store that holds the state of the simulated urban environment, a model coordinator that schedules models to run and notifies them when data of interest has changed, and a translation and aggregation layer that performs a range of data conversions to mediate between the object store and the models. The paper concludes with a discussion of the lessons learned regarding implicit invocation, object storage, and automatic code generation that yield acceptable space and time efficiency, as well as support for software evolution, within this architectural framework.

A watershed in systems engineering is represented by the advent of swarm-based systems that accomplish missions through cooperative action by a (large) group of autonomous individuals each having simple capabilities and no global knowledge of the group’s objective. Such systems, with individuals capable of surviving in hostile environments, pose unprecedented challenges to system developers. Design and testing and verification at much higher levels will be required, together with the corresponding tools, to bring such systems to fruition. Concepts for possible future NASA space exploration missions include autonomous, autonomic swarms. Engineering swarm-based missions begins with understanding autonomy and autonomicity and how to design, test, and verify systems that have those properties and, simultaneously, the capability to accomplish prescribed mission goals. Formal methods-based technologies, both projected and in development, are described in terms of their potential utility to swarm-based system developers. 1.

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics

This paper proposes a new framework that integrates simulated and physical agents to provide an efficient way for companies to form supply chains dynamically. In this integrated framework, physical agents coordinate with inter-organizational physical agents to conduct business processes whereas simulated agents model and analyze business processes to support physical agents in making rational decisions under uncertain situation and with incomplete information. This paper surveys different techniques used for dynamic process coordination and explains how the proposed integrated framework can be used by companies to reach a commonly accepted goal in dynamic supply chains. This paper also elaborates the efficient supply chain formation using a business process example of the mold industry, and finally discusses the development issues of this framework and future research directions.

In this paper, we explore the way in which virtual reality (VR) systems are being broadened to encompass a wide array of virtual worlds, many of which have immediate applicability to understanding urban issues through geocomputation. We sketch distinctions between immersive, semi-immersive and remote environments in which single and multiple users interact in a variety of ways. We show how such environments might be modelled in terms of ways of navigating within, processes of decision-making which link users to one another, analytic functions that users have to make sense of the environment, and functions through which users can manipulate, change, or design their world. We illustrate these ideas using four exemplars that we have under construction: a multi-user internet GIS for London with extensive links to 3-d, video, text and related media, an exploration of optimal retail location using a semi-immersive visualisation in which experts can explore such problems, a virtual urban world in which remote users as avatars can manipulate urban designs, and an approach to simulating such virtual worlds through morphological modelling based on the digital record of the entire decision-making process through which such worlds are built.

“Customerized ” innovation through the emergence of a mutually adaptive and learning environment SASKIA J.M. HARKEMA * AND WALTER BAETS Abstract. – This paper describes an experimental approach to model an innovation process on the basis of principles and concepts of complexity theory and its possible implications on the outcome of the innovation process. It is part of ongoing research carried out at the University of Nyenrode in the Netherlands, at the Nyenrode Institute of Virtual Education and Knowledge Management. In this paper the intricate and complex relation underlying the process of new product development and customer response, is the focus of attention. This relation is primarily defined as a process of knowledge management and mutual learning (Baets, 1998). In addition innovation is defined as a process of “manageable chaos ” (adapted from Quinn, 1985). This means that innovation is conceptualized as a process of interaction and subsequent knowledge flows between people that are organized in a network and form a complex system. In the sixties Simon, one of the founders of complexity theory, defined a complex system as one made up of many parts that have many intricate interactions. An alternative will be brought forward to model innovation processes. Instead of defining innovation success in terms of organizational characteristics or factors linked to the success rate of a product innovation; the latter will be modeled as the outcome of interaction among a variety of agents that pursue strategies in a co-evolutionary process with each other. Classification Codes: M21. 1.