Using Adaptive Agent-Based Simulation Models to Assist Planners in Policy Development: The Case of Rent Control
Computer simulation modeling for policy development in planning has had difficulty gaining a consistent foothold. Reasons for this include bad experiences with large-scale, comprehensive models (e.g., Forrester, 1969) and the lack of theory that one can quantify (Batty, 1994). Batty (1994) has suggested that new types of computational models, based on the tenets of complexity theory (Bernard, under revision) may prove useful. One type of complexity theory model is an "adaptive agent based model" in which the actions, interactions, and adaptations of many autonomous, heterogeneous "agents" (households, firms, etc.) produce emergent, system-wide behavior. One can examine this emergent behavior using commonly employed metrics, but one can also garner a richer, more intuitive understanding of how the individual behavior of the agents self-organize to produce the entire system. Using this type of modeling for small-scale planning problems can both inform planning theorists and improve planning practice by providing rich understanding that standard quantitative models do not. In this paper, I will present an agent-based model of rent control. Household agents (with different income levels) rented apartments from landlord agents Ð these apartments were situated on a lattice. Landlord agents continually adapted to the conditions of the marketplace (apartment demand, type of rent control in place, and so on, raising and lowering their prices as they saw fit. I varied conditions of rent decontrol and measured various metrics, such as vacancy rate, apartment quality, tenant income, and average rent paid. I found that a market with rent control typically has tenants with lower incomes than a non-rent controlled market, even substantially after the market has been suddenly decontrolled. In addition, I found that there were lower vacancy rates in regimes of rent control. As these results are not based on actual data, they are merely presented as suggestive. In fact, the point of abstract computational models such as the one presented here is not as the ultimate predictors of policy decisions, but as tools to inform and provoke discussion among policy makers. Thus, I will conclude by speculating on the use of adaptive agent-based models for assisting in policy formulation.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||Jul 1999|
|Date of revision:|
|Contact details of provider:|| Postal: 1399 Hyde Park Road, Santa Fe, New Mexico 87501|
Web page: http://www.santafe.edu/sfi/publications/working-papers.html
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Richard Arnott, 1995. "Time for Revisionism on Rent Control?," Journal of Economic Perspectives, American Economic Association, vol. 9(1), pages 99-120, Winter.
- Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-11, May.
- Gregory K. Ingram & John F. Kain & J. Royce Ginn, 1972. "The Detroit Prototype of the NBER Urban Simulation Model," NBER Books, National Bureau of Economic Research, Inc, number ingr72-1, September.
When requesting a correction, please mention this item's handle: RePEc:wop:safiwp:99-07-052. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Thomas Krichel)
If references are entirely missing, you can add them using this form.