Project description
Researching dynamic SPAC frameworks and theories
The principal-agent framework, which seeks to avoid conflicts arising when autonomous agents’ incentives and information diverge, uses a principal who coordinates agents so that their rational choices align with the principal’s objectives. In recent years, the rise of AI has sparked increased interest in this field from computer science. Unfortunately, despite the rise of many influential subfields, most of the literature on the subject has focused on static, one-time models that don’t capture real-world complexities. The ERC-funded ASPAC project aims to research the algorithmic and computational foundations of stochastic principal-agent coordination to enable the development of theories of approximability, tractability, and learnability, all of which are crucial to dynamic models.
Objective
When making decisions, autonomous agents rely on their own incentives and private information. When these factors diverge, conflicts can arise, leading to inefficiencies or suboptimal outcomes. Economists have studied this phenomenon under the principal-agent framework, where the principal seeks to coordinate the agents so that the agents' rational choices align with the principal's objectives. This framework is gaining increasing attention, especially in the era of AI, where automated agents become more capable of making decisions autonomously.
In computer science, algorithmic approaches to principal-agent coordination strategies have become an area of growing interest, giving rise to influential subfields, such as algorithmic information design, contract design, and automated mechanism design. Despite this progress, much of the literature focuses on static, one-shot models, which fall short of capturing the complexities of real-world problems occurring in stochastic environments. These problems are inherently dynamic and involve sequential decision-making. Solving them requires addressing fundamentally new computational challenges arising from the interplay between the complexities of the principal-agent model and the dynamics and stochasticity of the environment.
This project aims to tackle these challenges by investigating the computational and algorithmic foundations of stochastic principal-agent coordination (SPAC). The goal is to introduce a useful framework for formalising SPAC and develop algorithms and computational theories regarding the tractability, approximability, and learnability of problems within the framework. By exploring these fundamental aspects, we expect the project to substantially advance the theoretical literature while contributing algorithmic solutions to real-world applications of SPAC.
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Keywords
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-STG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
OX1 2JD Oxford
United Kingdom
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