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While any planning occurs in a particular system, few planning theorists view planning itself as computation and the system as universal. Grounded on the presumption that planning can be viewed as computation in a system capable of universal computing, we argue in this chapter that the conditions under which making plans can yield benefits can be tested rigorously. We define intelligence as computation so they are used interchangeably here. The model based on which our argument proceeds is called one-dimensional cellular automata of two colors and nearest neighbors. We now know that such one-dimensional cellular automata are capable of universal computing in that they can emulate any system of simple programs with various levels of complexity. In a recent paper, we demonstrated how urban spatial change can be related to one-dimensional cellular automata. In particular, when viewed as a model of cellular automata, cities are complex structures based on the underlying rules showing semi-lattice characteristics. Therefore, such a model provides a useful tool for studying, at least metaphorically, urban spatial change. In this formulation, making plans is to look ahead in the planner’s spacetime trajectory in the universal system, and to specify the initial conditions based on which the cellular automaton evolves. Revision of plans can be viewed as resetting the current state, rather than the underlying rules, for the one-dimensional cellular automaton for another run. Under what conditions would such a definition of making plans yield benefits? The benefits are defined as the increase of the computational capability of the system or as the degree of self-organization of the evolved patterns. We will propose a simulation design to show that planning should yield benefits in a system in which decisions (or states of the cells in the cellular automaton) are interrelated; the states of the cells at one-time step are partially irreversible in time; actions are indivisible and thus discrete in the spacetime; and unpredictability and thus uncertainty defies perfect foresight. We also expect that making plans defined here does not affect the system’s fundamental characteristics, such as universality. Preliminary results show that the event-driven system is most effective among three regimes of planning behavior.
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Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2003). An introduction to management science. Mason, OH: South-Western.
Arthur, W. B. (2015). Complexity and the economy. Cambridge, UK: Oxford University Press.
Axelrod, R. (1997). The complexity of cooperation. Princeton, NJ: Princeton University Press.
Caruso, G., Peeters, D., Cavailhès, J., & Rounsevell, M. (2009). Space–time patterns of urban sprawl, a 1D cellular automata and microeconomic approach. Environment and Planning B: Planning and Design, 36(6), 968–988. CrossRef
Ghallab, M., Nau, D., & Traverso, P. (2004). Automated planning: Theory and practice. San Francisco, CA: Morgan Kaufmann Publishers.
Hopkins, L. D. (2001). Urban development: The logic of making plans. London: Island Press.
Lai, S.-K. (2003). On transition rules of complex structure in one-dimensional cellular automata: Some implications for urban change. Annals of Regional Science, 37, 337–352. CrossRef
Lai, S.-K. Why plans matter for cities, Cities (forthcoming).
LaValle, S. M. (2006). Planning algorithms. Cambridge, UK: Cambridge University Press. CrossRef
Langton, C. (1989). Artificial life. Reading, MA: Addison-Wesley.
Mandelbaum, S. J. (2008). Planning intelligence. Planning Theory, 7(3), 318–322. CrossRef
Schaeffer, P. V., & Hopkins, L. D. (1987). Planning behavior: The economics of information and land development. Environment and Planning A, 19, 1221–1232. CrossRef
Webster, C. J., & Wu, F. (1999a). Regulation, land-us mix, and urban performance. Part 1: Theory. Environment and Planning A, 31, 1433–1442. CrossRef
Webster, C. J., & Wu, F. (1999b). Regulation, land-use mix, and urban performance. Part 2: Simulation. Environment and Planning A, 31, 1529–1545. CrossRef
Wilson, A. G. (1970). Entropy in urban and regional modeling. London: Pion Ltd.
White, R., & Englen, G. (1993). Cellular automata and fractal urban form: A cellular modelling approach to the evolution of urban land-use pattern. Environment and Planning B: Planning and Design, 25, 1175–1199. CrossRef
Wolfram, S. (1994). Cellular automata and complexity. New York: Addison-Wesley.
Wolfram, S. (1999). The mathematica book (4th ed.). Champaign, IL: Wolfram Media Inc.
Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media Inc.
- Planning as Computational Intelligence in Complex Socio-spatial Systems
- Chapter 2