ACM EC 2026 · Rome, Italy · In-person tutorial

Algorithmic Contract Design for Teams

A tutorial on combinatorial contract design for teams, focusing on the fundamental multi-agent binary-action model [DEFK, STOC '23]. We present main algorithmic ideas and structural insights, survey subsequent developments, and highlight key open problems that define the current research frontier.

View outline EC 2026 website Materials

Overview

Contract design studies how a principal can incentivize rational agents to exert hidden effort on her behalf. In modern computational markets, classical questions of optimal incentive design must also be approached from an algorithmic perspective.

This tutorial surveys the emerging area of combinatorial contract design, with a focus on multi-agent environments, where a principal offers payments to incentivize teamwork. These environments are particularly challenging, as they combine a selection problem (e.g., choosing an effective team of agents) with equilibrium considerations (e.g., preventing free-riding).

The goal is to give a unified view of the area, covering both foundational models and recent extensions, and highlighting key techniques, structural insights, and open problems.

Designed for newcomers to the area, while also serving as a roadmap for researchers interested in multi-agent and combinatorial contract design.

Outline

Part I
45 min
Core model and foundations

We introduce the multi-agent binary-action model [DEFK '23], covering hidden actions, combinatorial reward functions, incentive constraints, equilibria, and the structure of optimal contracts.

  • Approximation algorithms for XOS and submodular reward functions
  • Core techniques: decomposition of the benchmark and finding good teams via demand queries
  • Scaling from submodular rewards to XOS
  • Tractability frontiers, hardness results, and open problems
Part II
45 min
Extensions and emerging directions

We survey recent extensions that broaden the scope of combinatorial contract design.

  • Combinatorial action spaces: tractability frontier and key insights
  • Introducing budget and fairness constraints
  • Objectives beyond the principal's utility
  • General equilibrium concepts and further multi-agent models

Target audience

The tutorial is intended for researchers and students in algorithmic game theory, economics, operation research, and computer science. Familiarity with basic algorithms and probability is assumed. Prior exposure to mechanism design or submodular optimization is helpful, but not required.

Materials

Materials will be posted here closer to the tutorial.

Selected references

Babaioff, Feldman, and Nisan. Combinatorial agency . EC 2006.

Dütting, Ezra, Feldman, and Kesselheim. Multi-agent contracts . STOC 2023.

Dütting, Feldman, and Talgam-Cohen. Algorithmic contract theory: A survey . FnT TCS, 2024.

Dütting, Ezra, Feldman, and Kesselheim. Multi-agent combinatorial contracts . SODA 2025.

Feldman. Combinatorial contract design: Recent progress and emerging frontiers . 2025.

Aharoni, Hoefer, and Talgam-Cohen. Welfare and beyond in multi-agent contracts . EC 2025.

Feldman, Gal-Tzur, Ponitka, and Schlesinger. Budget-feasible contracts . EC 2025.

Feldman, Gal-Tzur, Ponitka, and Schlesinger. Equal-pay contracts . 2026.

Dütting, Ezra, Feldman, and Kesselheim. Black-Box Lifting and Robustness Theorems for Multi-Agent Contracts . 2026.