In a scenario where data is bought and sold in a double-auction market, will the trading price and volatility of a given piece of data become inversely proportional to its accuracy over time? In other words, will "true" data tend to propagate until there is no scarcity, while falsehoods tend to trade at higher and more volatile prices?

The reason why this would be useful to know is that, if "truth is cheap", then volatility and long-term price trend can be used as an indicator of accuracy.

Definition of terms:

  • "True" data is more accurate, less noisy, more useful to its buyers. A simple example of true data would be a current and correct list of phone numbers for a set of people.
  • "False" data is less accurate, noisy, obsolete, or outright wrong, less useful and more risky for its buyers. A simple example of false data would be an old and partially incorrect list of phone numbers for a set of people.

Note that by these definitions, "true" or "false" is not a boolean -- a set of data can be partially false.

Note also that, until the buyer tests the data, they have no way of knowing whether it is true or false -- they may need to buy two conflicting sets of the same data and test both -- this generates some intermittent demand for "false" data.


I think the answer to this question may be "yes".

Sellers would tend to raise their ask prices in order to compensate for their increased risk when selling data they themselves believe to be suspect, leading to a wider bid-ask spread and lower liquidity. See the "possible implementation" section below for what the sellers' risk might be.

Likewise, buyers would tend to lower their bids for data that scores lower in their purchasing heuristics -- their scoring might include known prior data quality from the same seller, number of sellers offering the same data hash, age of data, etc. Again see the "implementation" section below.

If a seller believes a piece of data is more correct, they will believe there to be less risk, and they will move ask prices lower in order to remain competitive. This lowers the bid-ask spread as well as volatility of matched prices.


If "truth is cheap" turns out to be correct, then systems based on this principle could be useful both in low-level distributed algorithms and in high-level social systems and governance. I've been pondering this question myself for a while for an open-source project I want to launch, and I figured I'd check in here before starting significant work on it.


One specific low-level problem I want to be able to solve, for example, is in choosing the most valid block of data for a particular file or record in a distributed system. Existing distributed systems have difficulty hosting more than one economic actor on a peer-to-peer basis without explosive growth of the data itself, and a decreasing signal-to-noise ratio. If anyone can add data to a commons regardless of validity or usefulness, then the commons tends to become more noisy over time. The traditional cure for this is active curation by founders or their delegates. That doesn't work as well in truly distributed systems where founders don't own most of the nodes.

Traditionally, conflicts in distributed data systems are often prevented by locking before writing, and resolving conflicts when they do arise is traditionally handled by voting or merging, often with humans in the loop. Replication of data for safety in distributed systems is traditionally either manually specified or on a best-effort basis.

Possible Implementation:

I think market structure likely matters a great deal in whether "truth is cheap" works or not. Here are some example conditions which I think would enable this:

  1. A subset of traders have ways of testing data for accuracy. They discard inaccurate data, and offer unlimited copies of the remainder for sale.
  2. A subset of traders need accurate data for their own internal use, and have the option of applying complex algorithms to choose between competing sellers.
  3. Sellers provide a cryptographic hash of the data in their offer, but not the data itself. This allows the buyer to count how many sellers are offering the same data, and enables the buyer to choose the lowest price.
  4. Sellers digitally sign their offers.
  5. Sellers incur risk -- testers may sell information describing bad data, including the original offer and seller's signature. Another possible source of risk could be contracts with some sort of forced settlement similar to options assignment.

I'm obviously handwaving a lot of details in the interest of brevity, and am completely skipping over currency, accounting, and transparency of peer account balances. Feel free to ask if something seems missing.

  • $\begingroup$ Whoever ticked the downvote -- I'm open to feedback as to how the question might be improved. $\endgroup$
    – stevegt
    Commented May 1, 2019 at 5:00
  • 3
    $\begingroup$ Your question is very unclear to me. There are all these technical details but there is no clear indication of what I do with the data. Why on Earth would I pay for falsehoods if I can identify them by their higher price? $\endgroup$
    – Giskard
    Commented May 1, 2019 at 7:02
  • $\begingroup$ Edited to add detail, motivation, and example, moving technical implementation to the bottom; I agree that's a distraction. Let me know if there's anything else you'd like to see. $\endgroup$
    – stevegt
    Commented May 1, 2019 at 20:40
  • $\begingroup$ It's not obvious to me why this was closed. The question seems quite clear to me. That said, I think Giskard hit on the key issue. Price depends on demand as well as supply. For the hypothesis to be correct demand for high-priced data would need to be sufficiently high, despite the fact that high-priced data is known to be of a lower quality. $\endgroup$
    – Ubiquitous
    Commented May 1, 2019 at 21:11
  • $\begingroup$ I've been thinking bid-ask spreads would widen for falsehoods, resulting in a higher ask. But now that you both bring it up, I think those wide spreads would result in falsehoods showing a higher price volatility and low volume, rather than a high and stable price. I'll think about this for a bit, and unless anyone can see something else I'm missing I may answer my own question with a "no" if it comes off of hold -- this is useful, thanks. $\endgroup$
    – stevegt
    Commented May 1, 2019 at 21:25

1 Answer 1


I think that your question touches on several topics. In terms of keywords to search for articles, I would use "information" or "signals" instead of "data". Inaccurate signals are usually called "noisy signals". One of the hurdles I see is that you are trying to talk about information as cheap or expensive, there are a couple of recent articles trying to derive formally a cost function for information of different quality (see Pomatto et al, working paper). Their intention is to think of information as a commodity that can be bought and sold and what is the right way to think of their cost. However, it seems to me that your notion of cost is more related to how easily truth spreads vs noise. There are plenty of models on the communication or spread of information. Its ability to spread depends on why people share information and what kind of information can they share (is it costly to produce information?).

Under the conditions you pose, it seems that everyone wants to have and sell the most accurate information available if they can perfectly measure accuracy and costlessly discard noisy information. It seems pretty straight-forward that the supply of information will increase over time, and the price of it will go down. Not sure how the conditions posed will lead to any notion of "falsehood" being expensive, so that might be a problem.

  • $\begingroup$ Good feedback. I've added a condition about sellers incurring risk. I've been thinking of other options, but that may be one way to push prices up for noisy data. $\endgroup$
    – stevegt
    Commented May 1, 2019 at 4:59

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