Must be good then, if you are already in chapter 5 & learning new things... Can't wait!

Bought this as well now & looking forward to read. Thanks for sharing!

Thank you, appreciated!
Concerning opens-source: We discussed this and would all be happy to review an implementation for openspiel.

6/6 🧵Future Work: We hope deep incentive design can serve as a general-purpose tool for people to build on. If you have an incentive design problem, plug in your loss/problem instance or feel free to reach out!

5/6 🧵Results: We validate on three tasks: multi-agent contract design, machine scheduling, and inverse equilibrium problems. For each, a single network handles the *full* distribution of problem instances across all game sizes from 2×2 to 16×16.

4/6 🧵The framework (see figure): We learn the (unique) equilibrium function with a pretrained "differentiable equilibrium block" and backpropagate through it to train our mechanism generator on the whole distribution of problems—no per-instance optimization at test time.

3/6 🧵The idea: Using max-entropy (coarse) correlated equilibria renders the bilevel problem differentiable. Thereby we unlock the whole toolkit of machine learning and gradient-based optimization to tackle this game-theoretic problem.

2/6 🧵The problem: You're a designer who (partially) controls the rules of a game and agents in response play an equilibrium. How do you set the rules so the resulting behavior aligns with your objective? This is incentive design and it shows up in contract & mechanism design, machine scheduling etc.

[1/6] 🧵Hi there! Our paper "Deep Incentive Design with Differentiable Equilibrium Blocks" is out now, born from my internship at Google DeepMind with @lukemarris.bsky.social and Georgios Piliouras.
Thread below!
Paper: arxiv.org/abs/2603.07705

If ICLR is any indication, LLMs + Game Theory / Multi-Agent is thriving. We'd love to see your research ideas at AAMAS this May in Cyprus! Submission deadline is Feb 4. More details below.

@sharky6000.bsky.social , we already did:) See here: bsky.app/profile/vtho...

Unlike board games, real-world strategic interactions are messy. Traditional game theory thus needs a boost for the age of agentic AI. Our #AAMAS2026 workshop "Strategic Engineering"(sites.google.com/view/se-aama...) in Cyprus aims to bridge the gap. Come join us to unlock truly strategic AI!

Learning to Steer Markovian Agents under Model Uncertainty Designing incentives for an adapting population is a ubiquitous problem in a wide array of economic applications and beyond. In this work, we study how to design additional rewards to steer multi-agen...

[2/2] Interested? Talk with Jiawei at ICLR or check out the full version here: arxiv.org/abs/2407.10207. The paper is joint work Zebang Shen, Heinrich Nax, and Niao He.

[1/2]Our work “Learning to steer Markovian Agents under Model Uncertainty” explores the problem of steering (unknown) learning dynamics in Markov Games towards desirable outcomes (e.g. Pareto optimal Nash).
My co-author Jiawei Huang is presenting it at ICLR on Apr 24, 3:00-5:30pm GMT+8, Poster #401.

E65: NeurIPS 2024 – Posters and Hallways 3

- Claire Bizon Monroc of Inria : WFCRL for Wind Farm Control
Andrew Wagenmaker of @ucberkeleyofficial.bsky.social : Leveraging Simulation to Bridge Sim-to-Real Gap
- @harwiltz.bsky.social of @mila-quebec.bsky.social : Multivariate Distributional RL
(cont)

Computing Perfect Bayesian Equilibria in Sequential Auctions with Verification We present an algorithm for computing pure-strategy epsilon-perfect Bayesian equilibria in sequential auctions with continuous action and value spaces. Importantly, our algorithm includes a verificati...

I'm at AAAI this week, presenting our paper “Computing Perfect Bayesian Equilibria in Sequential Auctions” (arxiv.org/abs/2312.04516) done at @eth-ai-center.bsky.social.

If interested, join the poster session (#657 on Saturday 12:30 – 14:30) or oral presentation (room 115C on Sunday 2pm).