This paper presents an agent-based model developed to simulate residential choice behaviour in a nonstationary housing market. The model is built around the assumption that agents have incomplete and imperfect knowledge, and thus have to base their decisions on beliefs. The aim is to illustrate how the agents deal with the uncertainty inherent in these beliefs, both at the level of a single agent, deciding among a set of successive actions, and at the level of a group of agents, negotiating over the price of a house.
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References
1.
ArentzeT ATimmermansH J P, 2005, “Modeling learning and adaptation in transportation contexts”Transportmetrica113–22
2.
BenensonIOmerIHatnaE, 2002, “Entity-based modeling of urban residential dynamics: the case of Yaffo, Tel Aviv”Environment and Planning B: Planning and Design29491–512
3.
DevischO, 2008In Search of a Complex-system Model, the Case of Residential Mobility PhD thesis, Urban Planning Group, Eindhoven University of Technology
4.
DiappiLBolchiP, 2006, “Gentrification waves in the inner-city of Milan—a multiagent/cellular automata model based on Smith's Rent Gap theory”, in Innovations in Design and Decision Support Systems in Architecture and Urban Planning Eds Van LeeuwenJ PTimmermansH J P (Springer, Berlin) pp 187–201
5.
DielemanF M, 2001, “Modelling residential mobility: a review of recent trends in research”Journal of Housing and the Built Environment16249–265
6.
EttemaDde JongKTimmermansH J PBakemaA, 2006, “PUMA: multi-agent modelling of urban systems”, paper presented at the 85th Annual Meeting of the Transport Research Board, Washington, DC; copy available from the authors
7.
GoulounovVDellaertBTimmermansH J P, 2002, “A dynamic lifetime utility model of car purchase behaviour using revealed preference consumer panel data”, paper presented at the 81st Annual Meeting of the Transport Research Board, Washington, DC; copy available from the authors
8.
JohC-HArentzeT ATimmermansH J P, 2003, “Understanding activity scheduling and rescheduling behaviour: theory and numerical simulation”, in Modelling Geographical Systems Eds BootsBOkabeAThomasR (Kluwer Academic, Dordrecht) pp 73–95
9.
MartinezF J, 1992, “The bid-choice land-use model: an integrated economic framework”Environment and Planning A24871–885
10.
NeapolitanR E, 1990Probabilistic Reasoning in Expert Systems (John Wiley, Chichester, Sussex)
SalviniPMillerE J, 2005, “ILUTE: an operational prototype of a comprehensive microsimulation model of urban systems”Network and Spatial Economics5217–234
13.
Van Der VlistA JRietveldPNijkampP, 2002, “Residential search and mobility in a housing market equilibrium model”Journal of Real Estate and Economics24277–299
14.
WaddellP, 2000, “A behavioral simulation model for metropolitan policy analysis and planning: residential location and housing market components of UbanSim”Environment and Planning B: Planning and Design27247–263
15.
WegenerMMoeckelRShurmannC, 2002, “Microsimulation of urban land use”, paper presented at the 42nd European Congress of the Regional Science Association, Dortmund, 27–31 August, http://www.ersa.org/ersaconfs/ersa02/cd-rom/papers/261.pdf
16.
ZhangJTimmermansH J PBorgersA W J, 2005, “A model of household task allocation and time use”Transportation Research B3981–95