AI-designed contract

Decoding Contracts: Can AI Help Us Make Better Deals?

Theo Raines in Tech & Innovation December 2025 • 5 min read.

"Explore how algorithms are revolutionizing contract design, making them learnable and approximately optimal for everyone."

Contracts are the invisible threads that hold our world together, from business agreements to employment terms. Traditionally, crafting these agreements has been a complex and often opaque process, relying heavily on negotiation and expert knowledge. But what if artificial intelligence could step in to make contract design more accessible, efficient, and fair? Recent research is exploring exactly that, focusing on the learnability and optimality of contracts in various scenarios.

The central idea revolves around the 'principal-agent problem,' where one party (the principal) incentivizes another (the agent) to act in their best interest through a contract. This model is fundamental in economics and applies to countless real-world situations. However, the challenge lies in the fact that the principal often has incomplete information about the agent's actions and their impact. This information gap makes it difficult to design contracts that truly align incentives and achieve the best possible outcome.

New research investigates whether contracts with clear limits on payments ('bounded' contracts) can be effectively learned and optimized using algorithms. This is a crucial question, because while unbounded contracts might theoretically offer the best solutions, they are often impractical and difficult to implement. The focus on bounded contracts brings the algorithmic approach closer to real-world applicability, offering the potential for AI to genuinely transform how we create and manage agreements.

Breaking Down Bounded Contracts: What Are They and Why Do They Matter?

A bounded contract is simply an agreement that places limits on the payments made to the agent, which makes them more practical and easier to manage. Bounded contracts are common in settings where the agent's potential earnings are capped due to budget constraints. The study's algorithms aim to find the nearly optimal bounded contract using a polynomial number of queries. These queries are designed to gather information about the agent's actions and the potential outcomes, even when complete information is not available upfront.

The study makes two key assumptions to ensure feasibility. First, it is assumed that a costlier action by the agent leads to a better outcome for the principal. This assumption, known as First-Order Stochastic Dominance (FOSD), captures the idea that increased effort generally yields better results. Second, the study assumes that the agent's cost/effort has diminishing returns. This assumption, known as Concavity of Distribution Function Property (CDFP), reflects the idea that the more effort an agent exerts, the smaller the incremental benefit.

First-Order Stochastic Dominance (FOSD): Assumes that greater effort from the agent leads to better outcomes for the principal.
Concavity of Distribution Function Property (CDFP): Reflects diminishing returns on the agent's effort, meaning each additional unit of effort yields progressively smaller benefits.

These assumptions allow researchers to design algorithms that can learn effective contracts without needing exhaustive data. The algorithms operate within two primary models: the action query model and the contract query model. In the action query model, the algorithm can sample from any action's outcome distribution, which corresponds to historical data where agent actions and outcomes are observable. In the contract query model, the algorithm specifies a contract and observes an outcome sampled from the agent's best-response action, which is similar to real-world scenarios where you can test contracts and observe their impact.

The Future of Algorithmic Contract Design

This research opens up exciting possibilities for the future of contract design. By demonstrating that bounded contracts can be effectively learned and optimized, it paves the way for AI-driven tools that could assist in negotiations, ensure fairness, and improve outcomes for all parties involved. While challenges remain, the potential benefits of using algorithms to design contracts are enormous. As AI continues to evolve, we can expect to see even more sophisticated tools that transform how we create and manage agreements in all aspects of our lives.

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This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2402.14486,

Title: Are Bounded Contracts Learnable And Approximately Optimal?

Subject: cs.gt cs.ai cs.lg econ.th

Authors: Yurong Chen, Zhaohua Chen, Xiaotie Deng, Zhiyi Huang

Published: 22-02-2024

Everything You Need To Know

What is the 'principal-agent problem' and how does it relate to contract design?

The 'principal-agent problem' is a fundamental concept in economics and contract design, describing a situation where one party (the principal) hires another (the agent) to act on their behalf. The principal's challenge lies in ensuring the agent acts in their best interest, particularly when the principal has incomplete information about the agent's actions and their impact. This information gap complicates designing contracts that align incentives effectively. In the context of contract design, understanding the 'principal-agent problem' is crucial for creating agreements that encourage the agent to act in a way that benefits the principal, leading to the desired outcomes and optimal results.

What are 'bounded contracts' and why are they important in the context of AI-driven contract design?

A 'bounded contract' is an agreement that puts a limit on the payments made to the agent. This is particularly important because it makes them more practical and easier to implement in real-world scenarios. Unbounded contracts might offer the best solutions theoretically, but they are often impractical because of the potential for unlimited payouts. The focus on 'bounded contracts' in AI research brings the algorithmic approach closer to real-world applicability. The algorithms used in the study seek to find the nearly optimal 'bounded contract' by using a polynomial number of queries to gather information about the agent's actions and the potential outcomes.

How do 'First-Order Stochastic Dominance (FOSD)' and 'Concavity of Distribution Function Property (CDFP)' contribute to the development of AI algorithms for contract design?

Both 'First-Order Stochastic Dominance (FOSD)' and 'Concavity of Distribution Function Property (CDFP)' are key assumptions that enable the design of effective AI algorithms for contract design. 'FOSD' assumes that greater effort from the agent leads to better outcomes for the principal, aligning the agent's incentives with the desired results. 'CDFP' assumes that there are diminishing returns on the agent's effort. These assumptions allow researchers to develop algorithms that can learn effective contracts without requiring exhaustive data. Specifically, these assumptions help the algorithms model the relationship between the agent's effort, the outcomes, and the principal's payoff more efficiently.

Can you explain the 'action query model' and 'contract query model' in the context of algorithmic contract design?

In algorithmic contract design, the 'action query model' and the 'contract query model' are two primary methods used by the algorithms to gather data and learn effective contracts. In the 'action query model', the algorithm can sample from any action's outcome distribution, which corresponds to historical data where agent actions and outcomes are observable. This allows the algorithm to understand the relationship between different actions and their resulting outcomes. The 'contract query model' works by the algorithm specifying a contract and then observing an outcome sampled from the agent's best-response action. This is similar to real-world scenarios where one can test different contracts and observe their impacts. These models help the algorithms learn about agent behavior and the effects of different contract terms, allowing them to optimize contract design for better outcomes.

What are the potential benefits of using AI to design contracts, and what challenges remain?

The use of AI to design contracts holds enormous potential benefits, including increased efficiency, improved fairness, and better outcomes for all parties involved. AI-driven tools could assist in negotiations by providing data-driven insights and suggesting optimal terms. They could also help ensure fairness by identifying and mitigating biases in contract language. However, challenges remain. Developing algorithms that can accurately model complex real-world scenarios and address potential ethical concerns, such as data privacy and algorithmic bias, is an important consideration. Despite these challenges, the potential for AI to transform contract design is substantial, paving the way for more accessible, efficient, and equitable agreements in various aspects of life.