This project is more on the academic side -- disclaimer, I was involved in it, but it's led by Justin Harris at Microsoft Research: "Sharing Updatable Models on Blockchain" https://github.com/microsoft/0xDeCA10B
The idea is that the smart contract is a learning algorithm, and people can donate data to a public repository stored on a blockchain. The learned model is publicly available for everyone. People can also receive incentives for donating data in some implementations.
That's one big problem for sure. Another is when the information is costly to gather, and there is a Schelling point for people without the information. For example, we all get in the habit of saying "10 degrees" whenever anyone asks the temperature of any city.
Similar, there are cases like "What is the capital of the state of New York?" A Schelling-point answer might easily be New York City, even though the correct answer is Albany. There's research on this stuff in the "peer prediction" a.k.a. "information elicitation without verification" literature, e.g. Prelec, Seung, McCoy 2017: https://marketing.wharton.upenn.edu/wp-content/uploads/2017/...
Prediction markets also exist for non-binary events, whether a finite set of outcomes, countably infinite, continuous, or beyond.
Instead, the problem is they still require an oracle for the actual event outcome. That is, everyone is betting on some event, but once it happens, an oracle is needed to tell the market what the actual outcome was. So they don't solve the oracle problem. They're another application that can make use of oracles.
I like it. This is a well written argumentation on why not all webpages need to be Webapps. I'll save this for later.
One little nitpick or question, the page isn't very readable on my mobile (iOS), it's too wide and tiny text.
A quick check tells me you have not set a meta-viewport. I've found adding <Pre><meta name="viewport" content="width=device-width, initial-scale=1"></Pre> makes things work better for mobile most of the time.
Last year I was part of a working group on a data dividend proposal for the state of California, which you can read about at https://www.datadividends.org/ .
Our conclusions led us much closer to the spirit of this EFF article than the term "data dividends" might suggest. We recommended against any kind of personalized payments. We considered a small universal basic income funded by a "data tax", but because of the small amount (as mentioned by the EFF), we focused on use of a data tax to fund public projects and initiatives aimed at redistributing the benefits of data more equitably.
We mostly stayed away from recommending privacy or data ownership regulation because (a) California had recently passed the CCPA and (b) this question seemed outside of our mandate, but I agree that the two should be considered together.
That puts the state in the position of benefiting from the sale of its citizens' private data. At that point, what incentive would CA have to oppose further encroachment on privacy?
Thanks for the reply - interesting concern. By analogy, I don't think putting taxes on cigarettes means the state will try to get more people to smoke. I also think it depends on how the money is allocated. Your concern would be strongest, I think, if the money went into some general-purpose fund. But if it funds open-source projects or a data-relations board, then I don't see much cause for concern.
> By analogy, I don't think putting taxes on cigarettes means the state will try to get more people to smoke.
To be clear: I don't mean that the state would try to encourage companies to collect and monetize more of their users' information. What I'm suggesting is that they would have a weaker incentive to protect the privacy of their citizens if part of their revenue depended on it being infringed.
Great point! We ultimately felt (and this is above my paygrade) that it was outside the scope of what we were asked to explore/recommend. So we avoided suggesting taxes that drastically change corporate incentives and behavior around private data. Instead we focused on more equitably distributing the value that's currently generated from data.
We did discuss this and felt that a very deep dive would be needed. (A) It's not an objective no-brainer exactly what behavior you want to disincentivize. (B) The economic impacts/consequences could be huge, so the tradeoff needs to be carefully considered.
One example is Google Maps. Right now it has privacy drawbacks, but it also generates a lot of utility for a lot of people and is relatively freely accessible for individuals. You'd want to be careful about screwing that up. For example, incentivizing Google to switch to a paid access model.
I guess part of what this EFF article is saying is: these decisions are better treated as part of privacy law / human rights, rather than economics...I'd be interested to hear your thoughts!
"You'd want to be careful about screwing that up."
Google will some how manage.
Government's job is to define the rules of the game, make things predictable. There's no need to second guess yourself by anticipating how the belligerents, err, players will react, and preemptively conceding the initiative.
You act. They react. Lather, rinse, repeat.
I understand the realpolitik, constraints, bargaining.
I'm just encouraging you to become clear about what you want, ultimately. Then find a path to get there.
Some day you'll have an opportunity to do great things. Are you ready? What will you do?
That's how the other side works. They spend $100m annually war gaming and coming up with plans. Then when the next inevitable crisis hits, they rush the field.
Hi all, we are a couple CS researchers and made this game purely for people to have fun.
It's designed to be accessible to anyone who has never heard of a prediction market. In fact, I would love to hear if you think this is a good prediction-market tutorial.
