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I like Rich Hickey's definitions Simple (roots: sim, plex) – one fold or braid Complex – braided or folded together Easy – near to our capabilities These ar

I like Rich Hickey's definitions Simple (roots: sim, plex) – one fold or braid Complex – braided or folded together Easy – near to our capabilities These are really articulate definitions that helped me as a programmer. Emergent complexity from simple systems – it seems like there might be an insight here. Avdi says we can build complex systems out of simple components. He says complex systems are hard to understand. He says it's not feasible to understand the system or predict it. So they need to be measured and observed. That's interesting. I don't know if it's true that a system built out of simple components cannot be understood. How precise is the model? How many layers deep?

uoaei 7y ago
Complexity as defined in most scientific disciplines today refers to systems which are "greater than the sum of their parts," i.e., where the behavior of the whole system cannot be predicted by looking at each component in isolation, but only can be understood by the action and interaction between components and how that evolves over time. Most systems of interest are too big to enumerate all the possible n-ary interactions between constituent parts, so we must study them by other means: probe plausible simulations, define and compute statistics from observable data, or fit models which capture plausible correlations. To complicate this further, it usually takes just one cycle or feedback loop in the network / system to introduce enough complexity that most traditional analytical mathematical tools break down. I studied complex (adaptive) systems as my Master's education and can confirm most techniques reduce to "linearize around the important bits and extrapolate from there". Examples of complex systems include: social networks (IRL and online), economies, biological systems. Machine learning is one of the most promising tools for probing these because if we can fit an appropriate model, we can hopefully capture enough of the behavior that we can reliably extrapolate to unseen data / states.

uoaei 7y ago

Complexity as defined in most scientific disciplines today refers to systems which are "greater than the sum of their parts," i.e., where the behavior of the whole system cannot be predicted by looking at each component in isolation, but only can be understood by the action and interaction between components and how that evolves over time. Most systems of interest are too big to enumerate all the possible n-ary interactions between constituent parts, so we must study them by other means: probe plausible simulations, define and compute statistics from observable data, or fit models which capture plausible correlations. To complicate this further, it usually takes just one cycle or feedback loop in the network / system to introduce enough complexity that most traditional analytical mathematical tools break down. I studied complex (adaptive) systems as my Master's education and can confirm most techniques reduce to "linearize around the important bits and extrapolate from there". Examples of complex systems include: social networks (IRL and online), economies, biological systems. Machine learning is one of the most promising tools for probing these because if we can fit an appropriate model, we can hopefully capture enough of the behavior that we can reliably extrapolate to unseen data / states.