The global economy is nurturing an innovative class of polycentric urban configurations: network cities. A network city evolves when two or more previously independent cities, potentially complementary in function, strive to cooperate and achieve significant scope economies aided by fast and reliable corridors of transport and communications infrastructure. Creative network cities place a higher priority on knowledge-based activities like research, education and the creative arts. The cooperative mechanisms may resemble those of inter-firm networks in the sense that each urban player stands to benefit from the synergies of interactive growth via reciprocity, knowledge exchange and unexpected creativity. Two case studies are discussed briefly—Randstad Holland and Kansai, Japan. Since much of their future dynamism may rely upon transnational human resources, it is foreseen that more network cities will transcend national borders during the next millennium.

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Keywords:coevolutionary learning companion modeling complex systems science complexity eco-efficiency industrial ecology (IE) SummaryThe sciences of industrial ecology, complex systems, and adaptive management are intimately related, since they deal with flows and dynamic interdependencies between system elements of various kinds. As such, the tool kit of complex systems science could enrich our understanding of how industrial ecosystems might evolve over time. In this article, I illustrate how an important tool of complex systems science-agentbased simulation-can help to identify those potential elements of an industrial ecosystem that could work together to achieve more eco-efficient outcomes. For example, I show how agent-based simulation can generate cost-efficient energy futures in which groups of firms behave more ecoefficiently by introducing strategically located clusters of renewable, low-emissions, distributed generation. I then explain how role-playing games and participatory modeling can build trust and reduce conflict about the sharing of common-pool resources such as water and energy among small clusters of evolving agents. Collective learning can encourage potential industrial partners to gradually cooperate by exchanging byproducts and/or sharing common infrastructure by dint of their close proximity. This kind of coevolutionary learning, aided by participatory modeling, could help to bring about industrial symbiosis.

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