Abstract:

In this work we study how changes in the social environment can influence individual tendency to obesity. In particular, we investigate whether the social environment and induced interactions can lead to clusters of healthy or unhealthy individuals as well as the potential of external interventions, like healthy diet, physical activity and reverse psychology, as local corrective measures with global positive effects.

We use cellular automata for modelling the social interdependence with regards to obesity. Cellular automata provide a means for capturing complex phenomena and observing their evolution over time via simulation. A cellular automaton is an abstract model composed of a rectangular grid of cells. At each time step, each cell of the grid is at a certain state. The way the state of a cell changes from one time step to the next time step is determined, in some sense "automatically", according to the current state of the cell itself and the current states of its neighboring cells.

For the purpose of our study, we assume that each cell hosts either an individual or a small, socially-strongly-connected group of individuals. At each time step, each cell can be in one of four possible states, ranging from healthy to obese. However, social interaction and homophily, i.e., the principle that we tend to be similar to our friends, can alter cell states as time evolves.

We focus on how to exploit the underlying social structure so that local physical (i.e., healthy diet and physical activity) and psychological (i.e., reverse psychology) enhancements result in global improvements.

Our simulation results indicate that underlying social mechanisms, like homophily, assisted by reverse psychology can certainly limit obesity and result in global improvement.

Keywords:

Cellular automaτα, obesity, social interdependence mechanisms, homophily, reverse psychology.