1. Requiem for Large Scale Models Douglass Lee, 1973

2. The Topic Large Scale Urban Models (LSUM’s) –Urban Simulations on a broad scale Lee thought that (at the time) LSUM’s had several fundamental flaws Despite some of the successes LSUM’s had during this time period, Lee thought that, “like dinosaurs, [LSUMS] collapsed rather than evolved.” Must consider the time period…

3. About Lee Professor at UC Berkeley –Department of City and Regional Planning Dissertation dealt with LSUMS Wrote this article early on in his career (just 5 years after his dissertation was approved)

4. Lee’s Preliminary Conclusions None of the goals for large scale models have been achieved and there is little reason the expect anything different in the future For each objective refered to as reason for building a model: –there is either a better way of achieving the objective or a better objective Methods for long-range planning (specifically LSUMS) have to change if they are to have any influence in the long run

5. The Seven Sins of LSUMs Lee introduces seven aspects of LSUMS that he argues are proof of his three conclusions –Mostly, he wants to conclude that there are far better ways of “achieving objectives” than Large Scale Urban Models

6. Hypercomprehensiveness Planning Methods of the time emphasized extremely comprehensive planning Result: the models were designed to replicate too complex a system in a single shot They were expected to serve too many purposes at a time

7. Hungriness The amount of data that is needed for LSUMs of the time was staggering –Example: San Francisco’s housing market simulation needed 15,000 items of data for a single run for a population of only 700,000

8. Grossness Given model hungriness, the models often sank under the weight of excessive data Too many goals for the models with too much data involved to be computationally tractable Lee: ironically, even though datasets were huge, models of the time did not provide enough detail to be useful to planners

9. Wrong-headedness The deviation between claimed model behavior and the equations or statements that actually govern the model Relationship between variables and equations in the models is often difficult to perceive Does not necessarily mimic reality (or what is being modeled)

10. Complicatedness Too many variables, equations, and interactions –City behavior at the microscopic level is largely unknown –We have to rely on aggregate relationships and emergent properties Can’t count on being able to model extremely detailed phenomena

11. Mechanicalness All models has to ultimately be run on computers De-bugging models was a serious time commitment Solutions were iterative, not necessarily scientific (trial and error) At the time, computers were prone to numerical and rounding error

12. Expensiveness At the time, Lee suggests a rule of thumb is $500,000.00 for a full-scale land use model Also computationally expensive

13. Breakthroughs! Lee wanted to address some of the “breakthroughs” that many people in the field have perceived He argues that these breakthroughs were not actually realized

14. Breakthroughs Monocentric Breakthrough –Focus on one specific problem Systems Breakthrough –Better handling of information in computer systems Computer Capacity Breakthrough Dynamic Breakthrough –Not really sure what this means? –More usable over time?

15. Theory in Models? Lee says models (of the time) lacked theory, which is one of the main problems with the LSUMs of the 1970s “The amount of theory available is nowhere sufficient to support a large scale urban model and the choice of theory necessarily limits the use to which the model can be put…” Discussion???