A Complex Systems Approach to Understanding and Combating the Obesity Epidemic

Obesity has become an important public health crisis worldwide. The obesity epidemic urgently requires well-crafted policy interventions but also represents an especially challenging problem for study and for policy design due to its complexity. Many of its features-breadth of scale, diversity in actors, and multiplicity of mechanisms-are hallmarks of a complex adaptive system. The lessons and tools of complexity science can help to understand and to combat the obesity epidemic, but only if complexity is taken seriously and the problem of obesity is approached from a systems viewpoint. Agent-based computational modeling (ABM) is an especially promising avenue for future research and for policy exploration. This is a powerful and relatively new approach in which complex dynamics are modeled by constructing "artificial societies" on computers. In an ABM, every individual actor (or "agent") in the system is explicitly represented in computer code. These agents are placed in a spatial context with specified starting conditions and are given a set of adaptive rules for interaction with each other and with their environment. The agents' decision processes and their interactions produce the output for agents themselves and for the system as a whole. In this way, the computer simulation "grows" macro-level patterns and trends from the bottom up.