>But you point out a real problem. There is so much reinvention by people who simply don't know what was done previously. I've often wondered if and where the

>But you point out a real problem. There is so much reinvention by people who simply don't know what was done previously. I've often wondered if and where the boundaries of human knowledge will run into this very problem. Mostly we rely on the concept of reductionism and hope that will generalize and scale but it rarely does. We often have different theories at different scales for different things we to want model and explain with science in general. If it's found that these ideas don't connect and we have to forever add more and more generalized nuggets of knowledge, models, etc for specific case, even if they are reductionist in nature, we may get to a point where there's just so much information the act of discovery of prior art may be more work than simply redoing the experiment against the ultimate judge (reality) and see if it flies. I used to work with a handful of bioinformaticists in the era of the affordable sequencing boom and there was a lot of debate about what to do with all the sequence data. Do they archive it and pay the cost to manage, index, and make it searchable or does it make more sense to simply pay the couple thousand to rerun the experiment and assure yourself that no process flaws were introduced in a prior paper, that you eventually agree with the prior work, and so on. There's almost a little bit of built in reproducibility in this unintended act of duplicating prior work. Software is especially bad because it's such a relatively new domain and is rarely done rigorously so people just run through cycles over and over again, assuming something people did before was wrong or that the environment changed and that approach was just inappropriate for it's time period. Plus, people in this industry seem to be driven to do innovative new work so they have no interest in looking backwards even if they should.