>Feedback occurs when outputs of a system are routed back as inputs as part of a [chain](https://en.wikipedia.org/wiki/Signal_chain_(signal_processing_chain) "Signal chain (signal processing chain)") of [cause-and-effect](https://en.wikipedia.org/wiki/Causality "Causality") that forms a circuit or loop. The system can then be said to _feed back_ into itself. The notion of cause-and-effect has to be handled carefully when applied to feedback systems:
>
> Simple causal reasoning about a feedback system is difficult because the first system influences the second and second system influences the first, leading to a circular argument. This makes reasoning based upon cause and effect tricky, and it is necessary to analyze the system as a whole.
>
> — Karl Johan Åström and Richard M.Murray, _Feedback Systems: An Introduction for Scientists and Engineers_
> [Wikipedia](https://en.wikipedia.org/wiki/Feedback)
## The Foundational Feedback Loop
```mermaid
flowchart TD
a[beliefs]-->b[behavior]
b-->c[outcomes]
c-->sg1
sg1-->sg2
a--updates----sg2
subgraph sg1 [Fielding Outcomes]
direction LR
d[Luck]
e[Skill]
d-- or ---e
end
subgraph sg2 [Learning]
direction LR
f[experience]
g[emotions]
h[judgments]
i[biases]
f---g
g---h
h---i
i---f
end
```
>We have the opportunity to learn from the way the future unfolds to improve our beliefs and decisions going forward. The more evidence we get from experience, the less [[uncertainty]] we have about our beliefs and choices. Actively using outcomes to examine our beliefs and bets closes the feedback loop, reducing uncertainty. This is the heavy lifting of how we learn.
>
>Ideally, our beliefs and our bets improve with time as we learn from experience.
>*[[Thinking in Bets]], p.80*
Explore Further: [[Fielding Outcomes]] | [James Clear on Feedback Loops](https://jamesclear.com/feedback-loops) | [[Learning]]
Tags: #buddings
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