Congratulations to Philipp Strack who has won the 2024 John Bates Clark Medal. The John Bates Clark Medal is awarded annually each April to that American economist under the age of forty who is judged to have made the most significant contribution to economic thought and knowledge. The American Economic Association webpage has a nice review of his research. One key area of research is around the economics of information.
Strack’s approach to formalizing ideas is beautifully illustrated by his axiomatic work on informational cost functions. Arrow (1985) observed that “there are costs of information, and it’s an important and incompletely explored part of decision theory in general to formulate reasonable cost functions for information structures.” Strack (with Pomatto and Tamuz, American Economic Review 2023) provides an axiomatization of cost structures that capture the notion of a “constant marginal cost of information.” The underlying axioms for such a setting are natural: that more precise information is more costly, and that the cost of collecting n random samples is linear in n, as in Wald (1945) and Arrow, Blackwell, and Girshick (1949). With these conditions, the paper provides a cost function that is an alternative to the entropy-based cost used in the rational inattention literature (Sims 2003). Relating information costs to economic properties is essential to study information choice and for those who want to understand the burgeoning market for data.
Strack also applied economics to issues around data privacy.
His work on privacy is a second example of his approach to formalizing loosely held concepts. We would often like to be sure that certain policy or business decisions “preserve privacy,” which could mean that the collection of certain information is prohibited or that those decisions do not discriminate across individuals based on protected characteristics such as race, gender, or age, or even potentially complex combinations of such characteristics. There is now much discussion on the potential for algorithms and AI to amplify or reduce “bias” whatever that might precisely mean. Strack (with Yang, Econometrica R&R 2024) proposes and characterizes a notion of privacy-preservation for information structures, asking that certain aspects of the state of the world must be kept private, or that decisions must be taken independent of certain aspects of that state. The idea turns out to be captured by the requirement that no posterior update must occur on the protected sets. Strack characterizes information structures that comply with this requirement and shows how a decision maker can pre-process data to make it independent of protected characteristics so that any algorithm applied to this data must produce predictions free from racial or gender bias.
You can read the full AEA write-up here and his full list of publications here.