Feedback Loops

Feedback loops are places where the output of a process is fed back into the system as input, influencing future action. They influence how systems behave over time and contribute to patterns of stability or change.  It seems they are rarely thought of as places for intervention despite having the ability to amplify or suppress outcomes (see Johnston et al.).

Feedback loops can be categorized into two main types:

Reinforcing loops, also known as positive feedback loops, amplify changes within a system, leading to exponential growth or decline and causing instability if not controlled. This type of loop can result in rapid escalation or runaway behavior. In the framework image, an increase in the number of people running raises the level of panic, which in turn causes more people to run, creating a cycle that escalates rapidly. Such loops can be beneficial, like viral marketing, but they can also be detrimental like the spread of disease or panic (or viral marketing).

Balancing loops, or negative feedback loops, counteract changes, promoting stability within a system. They work to bring the system back to a desired state or set point, acting as a self-correcting mechanism. In the framework image, feeling hungry leads to eating lunch, which reduces hunger and thus decreases the drive to eat more, maintaining balance. These loops are essential for systems to remain stable, such as thermostat-regulated temperature control.

This diagram explains Feedback Loops by contrasting the Reinforcing Loop (leading to instability and exponential growth) with the Balancing Loop (producing stability and self-correction). The Reinforcing Loop is exemplified by the relationship between the number of people running and the level of panic, while the Balancing Loop shows the self-correcting action of eating lunch in response to feeling hungry.

Deeper Dive

  • Waterloo Institute for Social Innovation and Resilience.  Describing systems / Feedback Loops.
  • Meadows, D. Thinking in Systems: A Primer, 2008.
  • Johnston, LM, CL Matteson, and DT Finegood.  Systems Science and Obesity Policy: A Novel Framework for Analyzing and Rethinking Population-Level Planning. American Journal of Public Health 104(7): 1270-8, 2014.

Related Frameworks