A computer program that simulates proxemic behavior in a group of individuals is presented. This software implements a migration model [1, 3] based in cellular automata theory. We make a distinction between a micro level of agent behavior and macro level w ith macro effects. Our objective is to describe and understand the principles of proxemic behavior, group interaction and social dynamics by investigating micro level features. Of course, empirical data will be necessary in the future to validate our model. Agents or individuals are located in a two-dimensional space or lattice, usually a rectangle-shaped one, their initial positions being either random or fixed. Our program uses integer units to represent both the time and space dimensions. Thus, every agent can move one space unit at each discrete time unit or iteration within its Moore neighborhood [2]. A set of valences for each pair of agents is specified per simulation. Valences are represented by desired (or ideal) distances in a non-symmetrical matrix. Positive and negative valences correspond to distances that are short or large relative to lattice size, respectively, while neutral valences are represented by non specification of an ideal distance. Agents move according to an optimization criterion, which consists in minimizing an objective function of the discrepancy between possible future distances (as computed from available positions in the agent's Moore neighborhood) and ideal distances among agents. At each iteration, agents will move to a cell in a current neighborhood for which the function is minimized. Depending on the specific values in the non-symmetrical matrix, different kinds of social dynamics or proxemic behavior can be obtained. Users can control the dynamics of the process by specifying either constant or variable valences. Valences can be made to vary as a function of time or as a function of proximity.

The P-SPACE program (Proxemic Space) was developed in C language and runs in MS-DOS. An external file is used to specify simulation parameters: Number of agents and objects, lattice dimension, initial agent positions, fixed object positions, number of iterations, valences, etc. The program generates graphical and ASCII output. The former permits visual inspection of proxemic dynamics and the latter allows numerical information to be read by standard statistical packages or spreadsheets.

References

1. Epstein, J.M.; Axtell, R. (1996). Growing Artificial Societies. Social Science from the Bottom Up. Cambridge: MIT Press.
2. Hegselmann, R. (1996). Understanding social dynamics: the cellular automata approach. In K.G. Troitzsch, U. Mueller, G.N. Gilbert and J.E. Doran (Eds.), Social Science Microsimulation, pp. 282-306.
3. Sakoda, J.M. (1971). The checkerboard model of social interactions. Journal of Mathematical Sociology, 1, 119-132.