We trade corporate data. We also restructure corporate data assets. To this end, we use formalisms to extract and curate sensitive open-source information, and diagnose its security-related characteristics. Our current applications are derived from the Arrow information, Grossman-Stiglitz, and Newcomb’s paradoxes.
Foundational challenges
Our foundation stewards and nurtures the study of formalisms. We source formal paradoxes, fallacies, and puzzles from across formal languages. We endorse, curate, and fund their formal statements, as long as their decidability fuses together entirely different aspects of formal reasoning or rigorously introduces entirely new formalisms.
How to profile your data sources?
How would you diagnose inefficiency from a data record? How would you distinguish between trading and governing inefficiencies from that record? How would you profile failed state of data commons from that record? How would you infer if the record discloses existence of a secret? Can you characterize the secret? Let us let you know!
Who should provide data commons?
Concentrated data markets warrant privacy regulation. Externalities warrant incentivizing traded data assets. Data commons should be provided as commons typically are. However, data is an anti-commons. Why wouldn’t we provide it ourselves if all the reasons to do so are already in place?
State of common data resource
Semantic flaws fragment data commons. Failed provision and incentivization of data commons or inefficiently regulated privacy all delegitimize the commons. Vulnerable data commons expose privacy regulation to threats, as well as corruption. This is how the state of your data commons leaks your agenda.
When does data governance fail?
Governed data is not necessarily more robust to failure than traded data is. Common data can be inefficiently utilized, incentivized, regulated, or provided. Because of these inefficiencies, fusing and integrating common data resource can fail. Whenever this happens, data governance fails, too.
Traded and governed data inefficiencies
Whereas markets succeed or fail for different reasons, markets for data assets succeed or fail because of information asymmetry. Regulation and other interventions can address market failure. However, such interventions can fail, too. What privacy regulation can secure efficiency of traded data assets?
Data equity
Markets can price data assets. However, if a specific market is characterized by weak efficiency then that market cannot price proprietary information. Alternatively, data equity is a call option on real data assets with a strike price that is the price of debt associated with the assets. Should you leverage your data assets? How?
What is an efficiently priced data asset?
Efficient pricing causes you to be indifferent between data asset sale and acquisition. In fact, there may be no data asset to trade, because too efficient a market trumps your going concern for proprietary information. Alternatively, the market may fail you, because the trade is too costly. How then to put a price tag on your data asset?
The riddle of data pricing
Imagine a riddle. If you solve it, the price of your data is the cost of expended computing resources. If you can’t solve it, this price is the amount you would pay to learn the solution. This price can also be calculated as the present value of your discounted future earnings, caused by legitimately accessing the solution. With efficiency, the three values coincide.