Appropriation of space with agriculture: a study of associative learning through a simulation model based on agents

Abstract

The forms of appropriation of space follow conditioned learning patterns. The way these patterns follow is held to be biased by learning acquired in successive trials. In this sense, we have tested a process of appropriation of the space of a donation area that in the past was appropriated with garbage, with rubble, with pruning remains and with eventual fires to burn the garbage. The appropriation has been with agricultural activity, with fruit trees, with shade trees and with children’s games in the Lomas de Las Hilamas neighborhood of the municipality of León, Guanajuato, Mexico. In this document, we explore the construction of a model based on agents that account for the appropriation of space as a conditioned learning process, for which the Rescorla-Wagner associative learning model is taken up again. We propose that if the donors perceive the donation area as a foreign space through a process of successive tests of exposure to an appropriate agent of the space, it can be achieved that other neighbors also appropriate the space. After this process, the residents have been seen to perceive the space as their own, and the appropriation with garbage has been reduced. The results of the agent-based simulation model use a learning rate of 0.84 and we have obtained that on average 2 agents appropriate the space 121 times in a discrete time of one year. These results are similar to those specified in the field work.