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Clarity comments on Open Thread, Dec. 28 - Jan. 3, 2016 - Less Wrong Discussion
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I'm interested in talking to people knowledgeable in decision theory/bayesian statistics about a startup that aims to disrupt the $240,000,000,000 management consulting market. it's based on the idea of prediction polls, but done on the blockchain(the same thing bitcoin uses) in a completely decentralized way.
I'm particularly interested in people who can help me out with understanding/choosing alternative scoring rules besides Brier scoring.
I can't pay you for your time, but I can virtually order you a pizza or buy you a beer :).
edit: Here's the (still very rough) elevator pitch:
For a long time companies relied on a pretty fuzzy metric: People who seemed to be better at making good decisions got to make them. This worked out decently well, but led to one undesirable result: People who were good at making excuses about their decisions ALSO got to make decisions.
The thing was, we didn't really have a better way to do it. That is, until the data revolution. Suddenly, companies had access to tons of data that they could use to ACTUALLY make better decisions. The problem was, they weren't politically set up to make use of this data, because all the people in power were those who could make good excuses.
This is were management consulting companies came in. For really big decisions, the management consulting companies would come in as outsiders, charge a bunch of money, and use their clout to use the data to make big decisions (like how many people to fire). This industry rapidly grew to the 240 billion dollar industry it is today.
But there's a huge problem with the industry - there's no objective way to tell which companies are actually good at making decisions. This leads to a case where the only way to tell which companies are good is their name and reputation - which means a monopolistic signalling market where the very few who got in early and made a name for themselves get to overcharge for their name, and new cheaper players find it very hard to enter the market.
The solution: An objective metric(bayesian scoring rule) that shows how good an organization or individual is at predicting the future. The entire history of how the company got this score is available on the blockchain, so you avoid the signaling problem by making everything auditable and therefore not having to put your trust in any one brand or company.
Not only can this allow us to take over all the big problems that management consulting currently handles, but it opens up a whole class of smaller decisions that were simply cost prohibitive in the management consulting model, and creates a new paradigm for management as a result.
Edit 2: If you're effectively altruist minded, it may be of interest to know that the reason I'm interested in doing this is to drastically reduce the cost of impact assessments.
GiveWell already uses expert advice for expedient impact assessments. Albeit on a small scale, without using academic- know how and with suboptimal choice and choice architecture of their experts. Hope you can improve on it :)
You've picked the wrong problem domain for the scoring rules. Briar comes from probability assessment, there are already more sophisticated approaches to this problem several levels removed from the mathematical theory and synthesising several theoreums.
The most proximate implementations of what you are suggesting are either delphi groups (risk analysis) or prediction markets (rationalist subculture mainly, but also academic). You probably already know how prediction markets work and you can look up 'expert elicitation' or 'eliciting expert judgement' and similar terms if you're interested. Happy to answer any tougher questions you can't get answered.
There are structured approaches to delphi groups which incorporate bayes rules and insights around the psychology of eliciting and structuring expert judgement that you could mimic. There is at least one major corporate consultancy focused on this already, however. AFAIK there are no implementations of this kind in the blockchain. Whether that is a worthwhile competitive advantage is another question.
You have a strategic mindset, I like it. If I've interpreted your question accurately, the reason other's in the know may not have responded is the xy problem.
Yes, the technology I'm using (prediction polls) are essentially this. It's Delphi groups weighted by Brier scores. The paper I link to above compares them to a prediction market with the same questions - with proper extremizing algorithms, the prediction poll actually does better (especially early on).
The reason I came up with this solution is that I wanted to use prediction markets for a specific class of impact assesments, but they weren't suited for the task. Prediction markets require either a group of interested suckers to take the bad bets, or a market maker who is sufficiently interested in the outcome to be willing to take the bad side on ALL the sucker bets. My solution complements prediction markets by being much better in those cases by avoiding the zero sum game, and instead just directly paying experts for their expertise.