The Wall Street Journal ran an intriguing story last month about Netflix’s management overriding recommendations coming from the company’s algorithms.
Analysis showed that promotions for the comedy “Grace and Frankie” were more successful when they only featured one of the two stars of the show.
Apparently fearful of alienating one of their stars, Netflix’s management decided to include both in promotions—even though that would produce a sub-optimal response.
The subtitle of the article mentions
overriding the metrics
However, this isn’t how I see it.
Data science produces inputs to the decision-making process—not recommendations to be followed slavishly. Netflix’s management presumably considered all the information at their disposal and made a decision that they believed would maximise their long-term rewards.
This is as it should be…even at Netflix.
The formal analysis could have been extended to include information on the excluded star’s contract, longevity as an asset, propensity to be offended, etc.
Maybe game theory could have been applied…and some bright Netflix quant could have developed a “diva scale”. But, this would have complicated the analysis considerably and compromised its accuracy.
Looks like data and judgement might have been combined effectively in this case.
Dota 2 is a multiplayer online battle arena game (MOBA) where two teams of five players compete to occupy each other’s bases.
While the human players displayed periods of indecision, the bots, known as the OpenAI Five, were able to coordinate their actions much more efficiently.
Making and implementing decisions requires effective communication. Coordination is one of five key factors to be considered when resolving dilemmas in confrontation analysis, for example.
The OpenAI Five will play an exhibition game against the winners of the main Dota 2 tournament at the end of August—pitting AI against the newly-crowned best players in the world.
He discussed “data culture” and the tendency for data scientists to focus on the technology while losing sight of the wider context.
Davies points out that decision-makers want to know
How does working with you, your data, or your new “tool” make me faster, smarter, cooler, or richer? Isn’t that what everybody is looking for?
Data scientists will fail to add value to their businesses until they can confidently answer those questions.