| I like to think about whether a problem is inherently antagonistic when deciding whether data/models/etc. can automate it well ("eat it" in the words of the article). Not antagonistic -- predicting whether someone will get lung cancer...they're probably not going to falsify the data from their physical, etc. Antagonistic -- detecting network intrusions or predicting phishing attacks. These folks fight back, i.e. disguise, adapt, hide In the latter case, a human component to a decision task (detection, labeling, etc.) will always remain. So then when it comes to investment decisions, let's say data plus supervised ML becomes the name of the game. Start-ups will learn the model features of a business that gets investment dollars. They'll design themselves specifically to those features and in a sense dupe models for a while until the models are adapted. In this way, the human component can't be left out. Of course, it doesn't take a model to get duped. A lot of companies might hire a bunch of phds and data scientists right before being acquired, because each hire bumps up the value of the company, etc. These moves are taken to increase a valuation rather than actual value and that's antagonistic in much the same way hiding spam is. |
I mean let's say startups try to game the VC game and they learn they need to demonstrate product market fit before seeking investment. Is this a bad thing? We'll try to be as transparent about our insights as possible so startups know what we think drives/indicates their success the best.
I think you are right in that final decision is certainly not going to be reduced to a supervised ML algorithm classifying investment opportunities. But I don't think this fundamentally has to do anything with antagonistic and non-antagonistic - you just need different types of methods