ChristianKl comments on Fixing Moral Hazards In Business Science - Less Wrong

33 Post author: DavidLS 18 October 2014 09:10PM

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Comment author: ChristianKl 20 October 2014 12:02:43AM 0 points [-]

Looking closer, I actually think their existing Fulfillment APIs would just work for this (ie the webapp controls an Amazon fulfillment account, the person seeking a test ships two pallets of physical product there, the webapp says where to send them).

You are right, if we already have hosted our webapp at trust-place we should be able to use the existing Amazon API.

If the company whose product is tested simply ships additional copies to the Amazon warehouse, those copies could by achieved by the trusted organisation. If anybody doubts that the products are real the trusted organisation has copies that they can analyse. If the whole things scales the trusted organisation also can randomly inspect products to see if they contain what they should contain.

Agree. I think having a trusted third party handle the shipments is cleaner at the moment. I'm still curious what blogospheroid's thread comes to. It seems like the paranoia of cryptoland is helping us see some more holes in modern experiment design

Yes cryptoparanoia is always fun ;) The web app could regularly publish hashes of the data of specific studies to a public block chain. That way any tempering that happens afterwards can be detected and you only need to trust that the web app is temper proof the moment the data gets transmitted.

Comment author: DavidLS 20 October 2014 02:12:24AM 0 points [-]

If anybody doubts that the products are real the trusted organisation has copies that they can analyse.

This is a great point. Maybe community members could bet karma on the outcome of a tox screening? This could create a prioritized list.

One problem with my earlier suggestion is that some companies will want narrowly selected participant pools. These will necessarily differ from the population at large, and might create data that looks like a poison placebo is being used. I see two possible solutions to this problem:

  1. Log baseline data before the treatment is used. If people do worse on the placebo, that would be very suspicious.
  2. Include an additional group of testers that do something different not related to the placebo/product. "Eat an apple every day for the next week". If the placebo group did worse than the apple group, that would be very suspicious.

I feel like #2 from above is unsatisfying though, if we think it works then why are we using normal placebos?

The web app could regularly publish hashes of the data of specific studies to a public block chain. That way any tempering that happens afterwards can be detected and you only need to trust that the web app is temper proof the moment the data gets transmitted.

This would actually be really easy to implement. (Not the block chain portion, the per-study rolling checksums).

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