NetLogo User Community Models
## WHAT IS IT?
This project is a social influence model. It models the spread of obesity in a social network, specifically amongst family and close friends.
There are two types of turtle agents in this model: healthy blue agents and overweight red agents. Each agent represents an individual that interacts and influences its family and close friends, represented by each agent's neighbours.
The simulation shows the influence of an individual's neighbours’ body mass index (BMI) on its own BMI, which is also affected by the population's inclination to eat (food intake) or exercise (physical activity).
This model works on the theory of homophily, often expressed by the expression "birds of a feather flock together", where individuals often have the tendency to associate and adapt to similar others.
## HOW IT WORKS
Each agent's BMI is generated randomly by the system, 50% with a healthy BMI and 50% with an overweight BMI. These percentages will be affected by the %food-intake slider and %physical-activity slider. Based on Singapore's guidelines, having a BMI between 18.5 and 22.9 is healthy, while a BMI between 23.0 and 27.4 is considered overweight. In this model, obese and underweight BMI were not included.
The %food-intake slider and %physical-activity slider can be adjusted to determine the population's inclination to eat and exercise respectively. Adjusting any of the slider causes a change in each agent's BMI, such that increasing the %food-intake increases BMI, but increasing the %physical-activity decreases BMI. In this model, it is assumed that food intake & physical activity will influence an individual's BMI by maximum 10%.
Lastly, an agent's BMI will be calculated using the average of its neighbouring agent's BMI. Because each agent can represent both an individual and a neighbour, an agent's BMI continuously changes until it becomes stable or until the whole population has become of the same type (overweight or healthy).
## HOW TO USE IT
setup --- setting up the healthy blue agents and overweight red agents in the population
%food-intake --- determines the percentage the population eats
%physical-activity --- determines the percentage the population exercises
go --- starts the simulation by determining each agent's BMI
## THINGS TO NOTICE
Each agent's BMI is influenced by its neighbouring agents, such that it is likely to become similar to its neighbours. If an agent is surrounded by overweight red agents, it is likely to become overweight as well. Similarly, if it is surrounded by healthy blue agents, it is likely to become healthy. In the event where there are too many healthy agents or overweight agents, the whole population is influenced into becoming healthy or overweight respectively.
## THINGS TO TRY
Adjusting the %food-intake slider > %physical-activity slider will increase the number of overweight red agents, whereas if the %food-intake slider < %physical-activity slider, the number of healthy blue agents increases.
The greater the difference between the %food-intake slider and the %physical-activity slider, the more likely the entire population will become either healthy or overweight, wiping out the other.
## NETLOGO FEATURES
random-float --- reports a number between (including) 0 and a specified number.
## PURPOSE OF PROJECT
Obesity is a major problem in today's society. Because overweight individuals run the risk of developing life-threatening illnesses or even death, this project hope to model the influence of obesity in a population so as to be able to better understand the spread of obesity and develop interventions to help prevent it.
Looking at how socially contagious obesity is, it is important to realise that not only can family and friends affect an individual's weight, but an individual can also impact his/her family and friends. Therefore, instead of blaming them, why not ask what you can do for your overweight friends and get on the path towards a healthier lifestyle.
This model narrows down the factors that influence BMI to just eating habits, physical activity and social network. In fact, there are many other factors that have been found to affect a person's weight, such as social environment, genetic factors, poverty and age. All of which can be further looked into, so as to create a more accurate, realistic model.
## CREDITS AND REFERENCES
Bahr, D. B., Browning, R. C., Wyatt, H. R., & Hill, J. O. (2012). Exploiting social networks to mitigate the obesity epidemic. Obesity, 17(4), 723-728. Retrieved from http://onlinelibrary.wiley.com/doi/10.1038/oby.2008.615/pdf
Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England journal of medicine, 357(4), 370-379. Retrieved from http://www.nejm.org/doi/pdf/10.1056/NEJMsa066082
(back to the NetLogo User Community Models)