I’m reading some papers about mechanism design on pricing of information, following up on this data trading reading session

  • Bergemann et al. (AER 2018): The Design and Price of Information.
  • Bergemann et al. (EC 2022): Is Selling Completely Information (Approximately) Optimal?
  • Bergemann et al. (EC 2015): Selling Cookies.

Hopefully, I’ll finish them within this week. I have a strong feeling that they will be immensely interesting.

Before our discussion, a short notice: a review of numerous news posts and literature in econ-CS reveals a significant discrepancy between academic research on data trading in algorithmic game theory (AGT) and Econ-CS communities, and the evolving realities of the actual data trading market. It’s possible that there exists another body of literature I’m yet to discover; however, the current economic literature, particularly on information design, lacks a clear outline of a real-world data trading market. Moreover, with the rise of generative AI, the valuation of data, such as labeled data coveted by tech giants for exclusivity, needs reevaluation to align with AI training standards.

Now, about data trading… here’s all the problems:

Following our dip into the industry of data trading yesterday and some thinkings about that data trading markets are far less nuanced yet, here’s a summary of my readings. Today, all about shits happening in this industry:

A notable challenge is the reluctance and bureaucratic hurdles associated with data trading. The absence of a widely accessible public data market platform is not coincidental. Operating such a platform openly involves extensive legal documentation, intricate in details and terms of use, which perpetuates a preference for private, ad hoc transactions.

People do not know how much their data are worth, nor do they really want to deal with the hassle of managing them.

Data would not be the only important resource which is not widely traded; witness radio spectrum and water rights. But for data this is likely to create inefficiencies, argues Mr Weyl. If digital information lacks a price, valuable data may never be generated.

Another critical issue is the digital hand to be wary of. Data equates to power. When tech giants control vast amounts of data, they dominate the market, capable of preemptively identifying and absorbing emerging startups, thereby monopolizing innovation. The solution seems straightforward: enforce stricter anti-trust laws or, more radically:

Another idea is to promote alternatives to centralised piles of data. Governments could give away more of the data they collect, creating opportunities for smaller firms.

The complex landscape of data science and AI technology complicates the regulation of personal data use. Though managing data through a unified account per individual sounds feasible, practical implementation remains elusive. The rapid advancement of technology outpaces regulatory efforts, adding layers of complexity when intertwining privacy and competition concerns:

And there is another tension between tighter data protection and more competition: not only have big companies greater means to comply with pricey privacy regulation, it also allows them to control data more tightly.

The myriad of challenges and emerging perspectives makes it difficult to envision a future devoid of digital technology and data-driven insights. Imagine a future where our data trades are as common as swapping baseball cards in the schoolyard: “I’ll trade you three insights on consumer behavior for your latest trend analysis on tech startups”. And in the spirit of keeping things interesting, perhaps we should introduce trading stickers for data scientists, complete with holographic charts and sparkly algorithms. After all, if we’re destined to surf the waves of data, we might as well make it a beach party.