Digital Library of the European Council for Modelling and Simulation

Title:	Investigation Of Cognitive Neighborhoodsize By Agent-Based Simulation
Authors:	Jens Steinhoefel, Frauke Anders, Dominik Kalisch, Hermann Koehler, Reinhard Koenig
Published in:	(2012).ECMS 2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann. European Council for Modeling and Simulation. doi:10.7148/2012 ISBN: 978-0-9564944-4-3 26^th European Conference on Modelling and Simulation, Shaping reality through simulation Koblenz, Germany, May 29 – June 1 2012
Citation format:	Steinhoefel, J., Anders, F., Kalisch, D., Koehler, H., & Koenig, R. (2012). Investigation Of Cognitive Neighborhoodsize By Agent-Based Simulation. ECMS 2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann (pp. 669-675). European Council for Modeling and Simulation. doi:10.7148/2012-0669-0675
DOI:	http://dx.doi.org/10.7148/2012-0669-0675
Abstract:	Different social groups tend to settle in different parts of cities leading over time to social segregation. Neighborhood obviously plays an important role in this process – and what constitutes neighborhood is a cognitive notion. In segregation analysis neighborhood borders are often drawn arbitrarily or simple assumptions are used to weight neighbor influences. Some authors have developed ideas to overcome such approaches by more detailed models. In this work we investigate the size of a cognitive neighborhood on the base of a continuous, geographically unlimited definition of neighborhood, using a distance-dependent function as such neighborhood “size” definition. We use agent-based simulation of the choice of residence as our primary investigation tool. Tobler’s first law of geography tells us that close things are more related than far ones. Extrapolating this thought and applying it to the question discussed here one could expect that closer neighbors have – on their own and in sum – more influence than those living further apart. The “sum” in the last sentence would lead to a neighborhood weighting of less than the inverse square of distance. The results of this investigation confirm that this is the case.
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