Journal of the American Institute of Planners · 1973 · 836 citations · 32 references
EngineeringMachine LearningGlobal PlanningEnvironmental PlanningSocial SciencesOperations ResearchLarge-scale ModelsComprehensive PlanningSystems EngineeringModeling And SimulationDigital PlanningData Intensive ModelingLarge Ai ModelPlanning Support SystemDesignUrban PlanningStrategyComputer ScienceLarge ModelsAbstract AbstractPlanning TheoryAi PlanningFoundation ModelAutomationPlanning PracticePlanningData Modeling
The paper evaluates fundamental flaws in large‑scale models and the planning context that led to their collapse. The study finds that none of the goals of large‑scale models have been achieved, each objective has a better alternative or a more useful question, and long‑range planning methods must change drastically. The paper’s conclusions are summarized in three key points.
Abstract Abstract The task in this paper is to evaluate, in some detail, the fundamental flaws in attempts to construct and use large models and to examine the planning context in which the models, like dinosaurs, collapsed rather than evolved. The conclusions can be summarized in three points: 1. In general, none of the goals held out for large-scale models have been achieved, and there is little reason to expect anything different in the future. 2. For each objective offered as a reason for building a model, there is either a better way of achieving the objective (more information at less cost) or a better objective (a more socially useful question to ask). 3. Methods for long-range planning-whether they are called comprehensive planning, large-scale systems simulation, or something else-need to change drastically if planners expect to have any influence on the long run.
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The Science of "Muddling Through"
Charles E. Lindblom · Public Administration Review · 1959 · 8.1K citations
Robert Moffitt · American Economic Review · 2009 · 4.7K citations