This annotated reading list surveys emerging works at the intersection of information design and large language models (LLMs). While classical information design theory studies signaling in abstract mathematical models, real-world communication often occurs in natural language. We highlight papers that use LLMs to elicit and communicate information through natural-language messages, cover techniques used in "information design + LLM" research such as language-space optimization and LLM proxies, and discuss papers on LLM persuasion. We aim to illustrate the potential of LLMs to bridge the gap between the theory and practice of information design.
Algorithms increasingly mediate repeated strategic interactions in marketplaces, from automated pricing to auction bidding. When one party commits to a learning algorithm, the other party can respond strategically over time by steering the algorithm's internal state toward a favorable long-run outcome. This note surveys a line of work that studies this "learning-as-commitment" perspective via a geometric object we call a menu : the convex set of long-run outcomes an opponent can induce against a fixed learning rule. Menus provide a common language for (i) comparing learning algorithms against strategic opponents, (ii) optimizing over learning rules under uncertainty about opponent objectives, and (iii) characterizing when an opponent can manipulate learning dynamics beyond what they could achieve with a static strategy. Using this machinery, we converge upon no-swap-regret algorithms as an "optimal" commitment strategy for robust learning against a strategic opponent. We also identify principled generalizations of no-swap-regret beyond normal-form games that preserve the same strategic guarantees while remaining computationally tractable.
This is the eleventh annual collection of profiles of the junior faculty job market candidates of the SIGecom community. The forty one candidates for 2026 are listed alphabetically and indexed by research areas that define the interests of the community. The candidates can be contacted individually, or collectively via the moderated mailing list ecom-candidates2026@acm.org.
ML model evaluation often takes one of two main approaches: risk minimization , associated with "high accuracy" or calibration , meaning that predictions are "trustworthy" and can be interpreted from a probabilistic lens. There is an extensive line of work which has studied the relationship between risk minimization and calibration, mostly focusing on the binary outcome setting. Even in the binary setting, there are a variety of proposed calibration metrics which non-trivially interact. In the multiclass label setting, the choices to be made are even more complex and particularly there are different semantics for different notions. Here, we briefly present an annotated reading list reviewing some of the proposed definitions and their relationships.
Von Neumann's minimax theorem asserts that the ability to defend against any opponent strategy implies the existence of an offensive strategy that guarantees the same value. This note revisits that symmetry from a constructive, oracle-based point of view. Given access to a defense oracle that, for any opponent strategy x , returns a response y guaranteeing payoff at least v , we ask how efficiently one can compute an offense strategy y * that guarantees value at least v – ε against all x. A classical construction via no-regret learning yields such a y * after O ((1/ε) 2 ) calls to the defense oracle. In this note, I describe a different construction that uses only O (log(1/ε)) calls to the oracle (up to polynomial factors in the dimension and encoding size). I then illustrate this primitive through three applications: computing Φ-equilibria in convex and extensive-form games beyond polynomial type, computing expected solutions to variational inequalities, and computing expected fixed points of possibly discontinuous maps.
