Haoying Han

Decision Network: A Planning Tool for Making Multiple, Linked Decisions

Few techniques exist specifically for planning analysis. Commonly used decision techniques focus on different, partial aspects of coordinating decisions. The garbage-can model focuses on the context in which decisions emerge to explain descriptively how organizational choices are made; the strategic choice approach focuses on the relationship between decisions from which rational actions can be taken; and the decision tree focuses on the causal sequence of decisions from which the optimal path of a plan can be derived. Drawing on the theoretical foundation of these three commonly used techniques, we introduce the conceptual framework of a tool for planning analysis, namely Decision Network, that addresses context, relationship, and sequence of decisions, with a numerical example demonstrating how the decision problem can be formulated and solved. Decision Network can be used by decision makers who are faced with more than one decision in order to make plans. Much can be built on this tool to address spatial issues.

On failure of zoning

Zoning is designed to delineate land uses in order to prevent urban development from incurring negative externalities. Morphologically speaking, zoning should result in spatial patterns that are closer to Euclidean (or regular) geometry than fractal (or irregular, self-similar) geometry. Previous computer simulations showed that zoning did not affect the fractal geometry of the urban spatial patterns of the Taipei metropolitan area. In this paper, we explore into the reasons why zoning fails in most Asian cities in general, and in Taipei in particular, through theoretical expositions. We argue that the preference for mixed uses is the main reason why zoning fails to achieve its goals. Furthermore, even a limited extent of mixed uses would result in spatial patterns characterised by fractal geometry rather than Euclidean geometry. We argue for a hybrid land regulation through the zoning and permit systems that take advantage of both the artificial and natural aspects of cities.

Are cities dissipative structures

The new emergent paradigm of urban development theory that is based on complexity sciences allows us to understand and analyse cities in a new way. Theoretically, complexity sciences enable us to depict the fundamental characteristics of urban development, including nonlinearity, self-organization, and emergence. Empirically, the agent-based modelling (ABM) approach can help us to conduct simulations of complex systems, including cities, in an effective way. In the present paper, we demonstrate a computer simulation of urban growth based on the spatial garbage can model represented in an ABM framework. In the simulation, we treated the city as an open system in that the fundamental elements of the system flow in and out of the system over time. We then computed over time the levels of entropy as a measurement of the degree of structural order of the systems, namely, decision and spatial structures. The results showed that these entropies decreased over time, indicating that the city self-organizes itself reminiscent of a dissipative structure.