Reinforcing feedback loop

Understand the force behind exponential changes.

Reinforcing feedback loops are found whenever behaviours or events inside the loop reinforce one another. These loops amplify the effect of the process.

That's a mouthful, but you can find real-world examples all around you. The compound interest is a very common one. The more money you have in the bank, the more you earn on interest. That money is added to your balance and so you earn on interest even more.

Effects of reinforcing feedback loops are exponential, not linear. They lead to exponential increases or decreases, whereas balancing feedback loops lead to plateaus – their goal is stability. Often, these two loops exist together within a system.

How reinforcing feedback loops work

The basic characteristic of all feedback loops is that the output of one cycle is the input for the next cycle. In the case of reinforcing loops, this input amplifies the next output.

Inside the loop, there are at least two variables. These variables reinforce each other. However, there can be also external variables that influence the loop. It's best to illustrate this with a simple example.


The compound interest is a very common example of a reinforcing feedback loop. The more money you have in a bank account, the more you earn on interest. That money is added to your balance and so you earn on interest even more later on. The cycle repeats.

In this example, you have two variables inside the loop: the balance on the account and the interest earnings. An external variable influences this loop – the interest rate. This variable will influence the output of the feedback loop but doesn't change the core mechanism of the loop.


Reinforcing feedback loop is a key tool for understanding systems because it's everywhere. It explains exponential changes.

In reality, systems are often made up of some combination of reinforcing feedback loops and balancing feedback loops, so it's useful to learn about both.


More systems thinking tools

Balancing feedback loop


Mechanism that pushes back against a change to create stability.

Iceberg Model


Uncover root causes of events by looking at hidden levels of abstractions.