Executive Summary

Problem Description

Present State of Knowledge

Approach and Method

Modeling and Measuring the Information City

Information and Household Mobility in Cities and Metropolitan Areas

Telecommunications, Infrastructure, and the Environment

Information and Telecommunications Technology and Inner-City Communities

Broader Impacts

Dissemination of Research

References

 

 

Research Area #1: Modeling and
Measuring the Information City

New York University's Taub Urban Research Center has been a pioneer in efforts to identify those characteristics that define the informational capacity of a city. Researchers have measured levels of Internet use and presence in major cities and metropolitan areas and compared the capacity of backbone networks in regions across the country. At the same time, computer scientists at NYU's Courant Institute of Mathematical Sciences have been actively involved in developing models that can strengthen our capacity to understand information-based activities in cities and regions.

If the information city is defined by information-based activities, then it is essential to have robust indicators that can be used in cities across the country. To date, efforts in this direction have suffered from a technologically deterministic approach, offering little of value to our understanding of the role of information in cities and metropolitan regions. Rather than attempt to force hypotheses to fit readily available data, there is a genuine need for a conceptual model and new measures of information flows that build upon recent research in computer science, economic development, environmental sciences, and urban planning.

This proposal differs from conventional approaches to studying cities and the dynamics of change in urban areas. First, we assume that the primary technological forces driving the transformation of urban areas are occurring on a much smaller scale than has been the case historically. Unlike previous upheavals which followed the advent of large-scale technological innovations like factory-based mass production and the construction of the interstate highway system, the transformation of the metropolis into an information city is being driven by the diffusion of intelligence and awareness (via technology) across the many components of urban life. The capabilities of individuals and small-scale actors are changing rapidly, increasing the flexibility of businesses and labor, public agencies, and infrastructure systems. Second, the fundamental effect of these innovations is to increase the number and sophistication of communications and interaction among units of the urban system. This study is unique in its embrace of new, more sophisticated approaches to understanding the increasingly complex dynamics of urban systems.

This part of the project will advance understanding of the dynamics of large urban systems by three measurement techniques:

  • Statistical Profiles;
  • Case Studies;
  • Mathematical Simulation Modeling.

Statistical Profiles: Recent studies by Mitchell Moss and Anthony Townsend have built upon the knowledge-sharing relationships with private firms that specialize in the collection of geographical data on telecommunications (see attached letter from Robert B. Tierney, AT&T; Telegeography Inc.; Imperative!; and Matrix and Information Demography Services; Boardwatch Magazine). Moss and Townsend will compare data across a subset of large American cities and metropolitan areas, to analyze the production and movement of information between cities and metropolitan areas and flows of information through the telecommunications infrastructure. Professor Zvia Naphtali will supervise the use of geographic information systems to map information flows over telecommunications infrastructure.

Case Studies: Case studies of selected cities and metropolitan areas will be conducted to supplement our quantitative analyses of information flows and household mobility. Information and telecommunications technology has been a central factor in the economic development of many U.S. cities. We will examine the role of telecommunications systems in the centralization of financial services and the dispersion of routine back office functions by analyzing the changing economic base of cities such as New York, Charlotte, North Carolina, and Wilmington, Delaware. Charlotte, the second largest banking center in the United States, has benefited from corporate strategies facilitated by interstate banking and advances in information systems. Wilmington, Delaware is one of the nation's leading cities for credit card processing centers, while cities such as Phoenix, Arizona and Omaha, Nebraska have emerged as centers for telephone-based reservation systems. At the same time, cities such as Hartford, Connecticut have witnessed serious economic decline as the insurance industry has been restructured, in part because of the deployment of new information technologies. A series of case studies will be directed by Mitchell Moss and conducted in consultation with Robert Warren of the University of Delaware, Professor Brian Berry of the University of Texas at Dallas, and Professor Luis Suarez-Villa, a regional economist at the University of California at Irvine.

Mathematical Simulation Modeling: Our mathematical model for an information city is based on complex adaptive systems in which a large number of intelligent agents interact with each other independently, but through limited and perhaps corrupted publicly accessible information. Based on classical game theory developed by von Neumann and Morgernstern, this model simulates the behavior of a complex system comprised of rational individuals, each seeking to maximize its available resources. However, such an idealistic mathematical abstraction fails to capture the nuances of the interactions among agents in a real-world system; that they only learn approximations of others' behavior and that publicly available information may be incomplete or inaccurate. Under such circumstances, it is possible that a system may never reach a state of equilibrium. However, such a complex multi-agent system of adaptive and intelligent agents do exhibit many interesting behaviors common to socioeconomic systems: bubbles and crashes, speculative behavior, continuous adaptation and learning, and coalition formation. In particular, this model provides an interesting clue as to how these agents will form coalitions in a hierarchical manner: communities, cities, and states and how they are governed by their own selfish needs as well as the flows of information reflecting the actions of the others.

Professor Bud Mishra at NYU's Courant Institute and his students have developed a new tool: Complex Adaptive Financial Environment (CAFE), a simulator for complex adaptive systems developed in the new platform-independent language, Java. CAFE allows one to simulate a large number of agents, often organized in a hierarchical fashion, interacting independently in complex ways. CAFE and the related CATS system (Complex Adaptive Traffic Simulator in Milan, Italy) have been used to study economies, computer networks, and traffic systems, and can be easily modified to model urban structures. By varying such factors as network capacity, constraints on usage, degree of participation, tradeoffs between the need for privacy versus the need for accurate public information, it is possible to quantify the influence of information networks and technology on the economic, social, and physical structure of the cities. In addition to increasing our understanding of the interplay of technological and socioeconomic forces, a byproduct of this study would be an improved methodology for the design of information technology systems in an urban setting.

It is noteworthy that there are several research groups in the United States investigating similar issues: The Swarm system at the Santa Fe Institute has been used to study financial systems; the Spawn system at Xerox Parc has been used in distributed control of large scale structures (e.g., air-conditioning in a large building); the 6 Degrees of Separation system at Carnegie-Mellon University in which the agents only interact with a small number of neighbors; Microsoft's Firefly system that creates virtual communities of agents (in this case real people on the Web) who share common interest; and the Walras system of University of Michigan where agents make resource allocation decisions in a distributed manner.

 

With support from the National Science Foundation, under the Urban Research Initiative
(C) 1999, 2000, 2001 Taub Urban Research Center, New York University
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