You seem to be conflating market mechanisms with political stances.
That is possible, but the existing market has been under the reins of many a political stance and has basically obeyed the same general rules of economics, regardless of the political rules that have tried to be imposed.
In theory a market can be used to solve any computational problem, provided one finds the right rules—this is the domain of computational mechanism design, an important branch of game theory.
The rules seem to be the weakest point of the system because they parallel the restrictions that political stances have caused to be placed on existing markets. If a computational market is coupled to the external world then it is probably possible to money-pump it against the spirit of the rules.
One way that a computational market could be unintentionally (and probably unavoidably) coupled to the external market is via status and signalling. Just like gold farmers in online games can sell virtual items to people with dollars, entities within the computational market could sell reputation or other results for real money in the external market. The U.S. FDA is an example of a rudimentary research market with rules that try to develop affordable, effective drugs. Pharmaceutical companies spend their money on advertising and patent wars instead of research. When the results of the computational market have economic effects in the wider market there will almost always be ways of gaming the system to win in the real world at the expense of optimizing the computation. In the worst case, the rule-makers themselves are subverted.
I am interested in concrete proposals to avoid those issues, but to me the problem sounds a lot like the longstanding problem of market regulation. How, specifically, will computational mechanism design succeed where years of social/economic/political trial and error have failed? I’m not particularly worried about coming up with game rules in which rational economic agents would solve a hard problem; I’m worried about embedding those game rules in a functioning micro-economy subject to interference from the outside world.
Just like gold farmers in online games can sell virtual items to people with dollars, entities within the computational market could sell reputation or other results for real money in the external market.
Oh—when I use the term “computational market”, I do not mean a market using fake money. I mean an algorithmic market using real money. Current financial markets are already somewhat computational, but they also have rather arbitrary restrictions and limitations that preclude much of the interesting computational space (such as generalized bet contracts ala prediction markets).
Pharmaceutical companies spend their money on advertising and patent wars instead of research.
There is nothing inherently wrong with this or even obviously suboptimal about these behaviours. Advertising can be good and necessary when you have information which has high positive impact only when promoted—consider the case of smoking and cancer.
The general problem—as I discussed in the OP—is that the current market structure does not incentivize big pharma to solve health.
I am interested in concrete proposals to avoid those issues, but to me the problem sounds a lot like the longstanding problem of market regulation.
Well … yes.
How, specifically, will computational mechanism design succeed where years of social/economic/political trial and error have failed?
Current political and economic structures are all essentially pre-information age technologies. There are many things which can only be done with big computers and the internet.
Also, I don’t see the years of trial and error so far as outright failures—it’s more of a mixed bag.
Now I realize that doesn’t specifically answer your question, but a really specific answer would involve a whole post or more.
But here’s a simple summary. It’s easier to start with the public single payer version of the idea rather than the private payer version.
The gov sets aside a budget—say 10 billion a year or so—for a health prediction market. They collect data from all the hospitals, clinics, etc and then aggregate and anonymize that data (with opt-in incentives for those who don’t care about anonymity). Anybody can download subsets of the data to train predictive models. There is an ongoing public competition—a market contest—where entrants attempt to predict various subsets of the new data before it is released (every month, week, day, whatever).
The best winning models are then used to predict the effect of possible interventions: what if demographic B3 was put on 2000 IU vit D? What if demographic Z2 stopped using coffee? What if demographic Y3 was put on drug ZB4? etc etc.
This allows the market to solve the hard prediction problems—by properly incentivizing the correct resource flow into individuals/companies that actually know what they are doing and have actual predictive ability. The gov then just mainly needs to decide roughly how much money these questions are worth.
The best winning models are then used to predict the effect of possible interventions: what if demographic B3 was put on 2000 IU vit D? What if demographic Z2 stopped using coffee? What if demographic Y3 was put on drug ZB4? etc etc.
What about predictions of the form “highly expensive and rare treatment F2 has marginal benefit at treating the common cold” that can drive a side market in selling F2 just to produce data for the competition? Especially if there are advertisements saying “Look at all these important/rich people betting that F2 helps to cure your cold” in which case the placebo affect will tend to bear out the prediction. What if tiny demographic G given treatment H2 is shorted against life expectancy by the doctors/nurses who are secretly administering H2.cyanide instead? There is already market pressure to distort reporting of drug prescriptions/administration and nonfavorable outcomes, not to mention outright insurance fraud. Adding more money will reinforce that behavior.
And how is the null prediction problem handled? I can predict pretty accurately that cohort X given sugar pills will have results very similar to the placebo affect. I can repeat that for sugar pill cohort X2, X3, …, XN and look like a really great predictor. It seems like judging the efficacy of tentative treatments is a prerequisite for judging the efficacy of predictors. Is there a theorem that shows it’s possible to distinguish useful predictors from useless predictors in most scenarios? Especially when allowing predictions over subsets of the data? I suppose one could not reward predictors who make vacuous predictions ex post facto, but that might have a chilling effect on predictors who would otherwise bet on homeopathy looking like a placebo.
Basically any sort of self-fulfilling prophesy looks like a way to steal money away from solving the health care problem.
That is possible, but the existing market has been under the reins of many a political stance and has basically obeyed the same general rules of economics, regardless of the political rules that have tried to be imposed.
The rules seem to be the weakest point of the system because they parallel the restrictions that political stances have caused to be placed on existing markets. If a computational market is coupled to the external world then it is probably possible to money-pump it against the spirit of the rules.
One way that a computational market could be unintentionally (and probably unavoidably) coupled to the external market is via status and signalling. Just like gold farmers in online games can sell virtual items to people with dollars, entities within the computational market could sell reputation or other results for real money in the external market. The U.S. FDA is an example of a rudimentary research market with rules that try to develop affordable, effective drugs. Pharmaceutical companies spend their money on advertising and patent wars instead of research. When the results of the computational market have economic effects in the wider market there will almost always be ways of gaming the system to win in the real world at the expense of optimizing the computation. In the worst case, the rule-makers themselves are subverted.
I am interested in concrete proposals to avoid those issues, but to me the problem sounds a lot like the longstanding problem of market regulation. How, specifically, will computational mechanism design succeed where years of social/economic/political trial and error have failed? I’m not particularly worried about coming up with game rules in which rational economic agents would solve a hard problem; I’m worried about embedding those game rules in a functioning micro-economy subject to interference from the outside world.
Oh—when I use the term “computational market”, I do not mean a market using fake money. I mean an algorithmic market using real money. Current financial markets are already somewhat computational, but they also have rather arbitrary restrictions and limitations that preclude much of the interesting computational space (such as generalized bet contracts ala prediction markets).
There is nothing inherently wrong with this or even obviously suboptimal about these behaviours. Advertising can be good and necessary when you have information which has high positive impact only when promoted—consider the case of smoking and cancer.
The general problem—as I discussed in the OP—is that the current market structure does not incentivize big pharma to solve health.
Well … yes.
Current political and economic structures are all essentially pre-information age technologies. There are many things which can only be done with big computers and the internet.
Also, I don’t see the years of trial and error so far as outright failures—it’s more of a mixed bag.
Now I realize that doesn’t specifically answer your question, but a really specific answer would involve a whole post or more.
But here’s a simple summary. It’s easier to start with the public single payer version of the idea rather than the private payer version.
The gov sets aside a budget—say 10 billion a year or so—for a health prediction market. They collect data from all the hospitals, clinics, etc and then aggregate and anonymize that data (with opt-in incentives for those who don’t care about anonymity). Anybody can download subsets of the data to train predictive models. There is an ongoing public competition—a market contest—where entrants attempt to predict various subsets of the new data before it is released (every month, week, day, whatever).
The best winning models are then used to predict the effect of possible interventions: what if demographic B3 was put on 2000 IU vit D? What if demographic Z2 stopped using coffee? What if demographic Y3 was put on drug ZB4? etc etc.
This allows the market to solve the hard prediction problems—by properly incentivizing the correct resource flow into individuals/companies that actually know what they are doing and have actual predictive ability. The gov then just mainly needs to decide roughly how much money these questions are worth.
What about predictions of the form “highly expensive and rare treatment F2 has marginal benefit at treating the common cold” that can drive a side market in selling F2 just to produce data for the competition? Especially if there are advertisements saying “Look at all these important/rich people betting that F2 helps to cure your cold” in which case the placebo affect will tend to bear out the prediction. What if tiny demographic G given treatment H2 is shorted against life expectancy by the doctors/nurses who are secretly administering H2.cyanide instead? There is already market pressure to distort reporting of drug prescriptions/administration and nonfavorable outcomes, not to mention outright insurance fraud. Adding more money will reinforce that behavior.
And how is the null prediction problem handled? I can predict pretty accurately that cohort X given sugar pills will have results very similar to the placebo affect. I can repeat that for sugar pill cohort X2, X3, …, XN and look like a really great predictor. It seems like judging the efficacy of tentative treatments is a prerequisite for judging the efficacy of predictors. Is there a theorem that shows it’s possible to distinguish useful predictors from useless predictors in most scenarios? Especially when allowing predictions over subsets of the data? I suppose one could not reward predictors who make vacuous predictions ex post facto, but that might have a chilling effect on predictors who would otherwise bet on homeopathy looking like a placebo.
Basically any sort of self-fulfilling prophesy looks like a way to steal money away from solving the health care problem.