ABSTRACT
Within the current formulation of the simulations introduced in this chapter, I have shown that imposing some structure on the garbage can model did affect the decision processes and outcomes. More surprisingly, planning as I defined here did not improve the organizational performance. It resulted in more efficient decision-making activities but at the cost of reducing the organizational performance. In addition, planning brings about order. Urban spatial change can be modeled as cellular automata simulations. The effects of planning can be examined through such simulations. The simple programs of elementary cellular automata can serve as a metaphor based on which urban spatial change can be studied. The simulation results imply that an increase in planning investment in terms of shortened planning intervals and widened planning scope enhances planning effectiveness, whereas the event-driven system is more effective than the time-driven system. Regardless, planning brings about order as manifested by fixed patterns and little information content in the spacetime plot. When faced with many, clustered, mutually dependent decisions, planners lack useful analytic skills in coping with such complexity. Based on the analytic framework of mathematics of decision theory, I focus on how to make sense of such complexity by conceptually developing appropriate search processes for identifying the scopes where useful plans are likely to be made. The insights into how to act in a web of interrelated decisions thus gained can support the notion that plan making matters.
