Author: Quantifiable

Our foundation champions the exploration of formalisms. We seek out paradoxes, fallacies, and puzzles from across formal languages, curating and funding their decidable statements. Our focus is on those that merge distinct aspects of formal reasoning or introduce entirely new, transformative formalisms.

How do you diagnose inefficiency in a data record? How do you distinguish between trading and governing inefficiencies? Can you identify a failed data commons from the record? How would you infer if the record discloses a secret—and can you characterize it? Let us know!

Concentrated data markets warrant privacy regulation, while externalities necessitate incentivizing traded data assets. Data commons should be provided in the traditional manner, but data is an anti-commons. If all the reasons to provide it ourselves are already in place, why wouldn’t we?

Semantic flaws fragment data commons. The failed provision and inefficient incentivization of data commons, along with poorly regulated privacy, can delegitimize the commons entirely. Vulnerable data commons expose privacy regulation to threats and corruption. This is how the state of your data commons can inadvertently leak your agenda.

Governed data is not inherently more robust to failure than traded data. Common data can be inefficiently utilized, incentivized, regulated, or provided. These inefficiencies can lead to failures in fusing and integrating common data resources. And whenever this occurs, data governance fails as well.

Markets succeed or fail for many reasons, but markets for data assets primarily succeed or fail because of information asymmetry. While regulation and other interventions can help address market failures, they themselves are not infallible. What privacy regulation can ensure the efficiency of traded data assets?

Markets can price data assets. However, in a market characterized by weak efficiency, proprietary information cannot be accurately priced. Alternatively, data equity can be seen as a call option on real data assets, with a strike price equal to the price of debt associated with those assets. Should you leverage your data assets? And if so, how?

Efficient pricing makes you indifferent between selling and acquiring a data asset. But in such a market, there may be no incentive to trade—perfect efficiency erodes the proprietary value that sustains your business. Alternatively, the market may fail entirely if the cost of transacting outweighs the benefit. So how do you put a price tag on your data asset?

Imagine a riddle. If you solve it, the price of your data is the cost of the computing resources you expend. If you can’t, the price is what you’d be willing to pay to learn the solution. Alternatively, it can be calculated as the present value of your discounted future earnings—earnings enabled by legitimate access to the solution. With perfect efficiency, all three valuations converge.

Our capability is grounded in the relationship between counterfactuals and data assets. Counterfactuals introduce trade-offs among causality, strategy, and confidentiality, which shape and constrain the informational value of data assets. This interplay enables us to efficiently transform, restructure, regulate, and secure data assets with precision.