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0 pointsbraza10mo ago0 comments

I don't think this is adequately addressed by the "complicated vs. complex" framing—especially not when the distinction is made using reductive examples like taxes (structured, bureaucratic, highly formalized) versus climate change (broad, urgent, signaling-heavy).

That doesn’t feel right.

Let me bring a non-trivial, concrete example—something mundane: “ePOD,” which refers to Electronic Proof of Delivery.

ePOD, in terms of technical implementation, can be complex to design for all logistics companies out there like Flexport, Amazon, DHL, UPS, and so on.

The implementation itself—e.g., the box with a signature open-drawing field and a "confirm" button—can be as complex as they want from a pure technical perspective.

Now comes, for me at least, the complex part: in some logistics companies, the ePOD adoption rate is circa 46%. In other words, in 54% of all deliveries, you do not have a real-time (not before 36–48 hours) way to know and track whether the person received the goods or not. Unsurprisingly, most of those are still done on paper. And we have:

- Truck drivers are often independent contractors.

- Rural or low-tech regions lack infrastructure.

- Incentive structures don’t align.

- Digitization workflows involve physical paper handoffs, WhatsApp messages, or third-party scans.

So the real complexity isn't only "technical implementation of ePOD" but; "having the ePOD, how to maximize it's adoption/coverage with a lot uncertainty, fragmentation, and human unpredictability on the ground?".

That’s not just complicated, it’s complex 'cause we have: - Socio-technical constraints,

- Behavioral incentives,

- Operational logistics,

- Fragmented accountability,

- And incomplete or delayed data.

We went off the highly controlled scenario (arbitrarily bounded technical implementation) that could be considered complicated (if we want to be reductionist, as the OP has done), and now we’re navigating uncertainty and N amount of issues that can go wrong.

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tuyiown10mo ago

I was very centered on the software part of the problem. A complex problem can be solved with a complicated chain of small technical solution. At implementation level, its complicated, not complex, e.g. you mostly need knowledge of the general problem to understand the solution, and many added things make things complicated.

My take is that if your complex problem is only solvable by complex software (e.g. not a combination of simple small parts), and _cannot_ be reduced to simpler things, you are in the complex space.

Maybe it's too reductive, it's just my opinion, but it's a good way for me to determine predictability on ability to solve a problem with many unknown, at the engineering level. The dangerous blockers are in complex space, identifying them early is critical. Complicated stuff can be worked around and solved later.

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