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Honestly, the title threw me off at first—I used to work in supply chain optimization which very much follows a predict → optimize pattern, and I think there ar
by tikhonj 4y ago
Honestly, the title threw me off at first—I used to work in supply chain optimization which very much follows a predict → optimize pattern, and I think there are interesting directions[1] for preferring a different approach, but the abstract and title made it pretty clear that the work focuses on a fundamentally different area. Supply chain forecasting is qualitatively different from the predictions in this essay because it applies to aggregate behavior, not to individuals.
[1]: In particular, what we did (and what most people do?) is predict first, then optimize. Prediction and optimization were done by different systems, built and maintained by different teams, optimizing for different performance metrics. But, over time, I became increasingly convinced that this separation was not effective and that we could get substantially better performance by jointly optimizing the prediction and decision-making models. I saw a paper[2] demonstrating this idea mathematically, but now I think some variant of this would make sense from an organizational and systems-design point of view as well. If I ever get the chance to build a supply chain optimization system from scratch, I'd want to start with the forecasting and optimization components much closer together technically and organizationally, even if we don't jump into trying joint optimization from the beginning.
[2]: "Smart Predict then Optimize", https://arxiv.org/abs/1710.08005 https://arxiv.org/abs/1710.08005
- YeGoblynQueenne 4y agoI'm completely clueless about "supply chain optimisation" but I wonder whether it involves more automated processes, than human beings? I'm asking, because the article does focus on tools used to predict human behaviour (as I think you note as you say it focuses on a different area). Predicting human behaviour is hard. I imagine that it's far easier to predict the behaviour of an artificial system, or a process, whose rules are (... more or less ... ) known. For example, I imagine that predicting, say, the rate of defects of a certain product coming out of some factory's production line, is not that hard to do -given enough data etc. And then, most of the time there's probably many fewer ethical and social issues to consider in such cases. I'm saying probably! Since I don't know the subject at all. But it's clear that there are social issues in, e.g., predicting recidivism or making automated decisions about taking children into foster care.
- tikhonj 4y agoI was working in retail, which means we were trying to forecast human behavior—how much of a given item people would want to buy at some time—but we only cared about that behavior in aggregate, not at the level of individual people. That definitely makes it qualitatively different to the sorts of prediction and optimization that the paper talks about.
- YeGoblynQueenne 4y agoThanks. I guess I get a black hole for a faulty assumption (a star for a right guess?).