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Semi-Centralized Platforms for Steering Online Multi-Learner Environments

Project description

Designing better platforms for systems of learning agents

The project focuses on scenarios in which multiple machine learning algorithms interact on a shared platform. This is the case, for example, in many financial exchanges and online advertising platforms. These platforms usually take a passive role in the interactions, providing the shared infrastructure but keeping individual learning algorithms fully decentralised. The ERC-funded PLA-STEER project aims at establishing the theoretical foundations for new platforms that can take an active role in the interactions among its users. By leveraging online learning and computational game theory, the project will explore when and how these platforms can guide learning agents toward beneficial outcomes, ultimately enhancing fairness and accountability in decision-making processes as machine learning becomes increasingly prevalent.

Objective

In scenarios like online advertising markets and financial exchanges, autonomous, self-interested learning agents engage in strategic interactions via a shared platform. Platforms typically opt for a passive role, providing the shared infrastructure necessary for the operation of the multi-learner environment, while keeping learning procedures decentralized. Fully centralized systems can potentially yield superior outcomes by optimizing shared objectives like social welfare, but they are seldom chosen.
The goal of this project is to bridge the gap between these two extremes by establishing the theoretical foundations of semi-centralized platforms (SCP). SCPs aim to combine the best attributes of both centralized and decentralized systems, enabling next-generation platforms to operate efficiently at scale with the flexibility of decentralized learning, while also being able to steer learning agents towards desirable objectives. Using tools from online learning and computational game theory, we will develop innovative techniques to determine when and how platforms should actively influence the actions of learning agents. In this endeavor, we will i) develop a better understanding of the learning dynamics of traditional platforms; ii) explore methods to overcome well-known computational challenges that hinder the convergence of multi-learner systems towards shared objectives; and iii) extend fundamental game-theoretic models to realistic settings.
This research will pave the way for practical applications on real-world platforms and address pressing concerns related to fairness and accountability in their outcomes, which are expected to become even more significant as machine-learning algorithms gain wider adoption.

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Topic(s)

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2024-STG

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Host institution

UNIVERSITA COMMERCIALE LUIGI BOCCONI
Net EU contribution

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.

€ 1 316 544,00
Address
VIA SARFATTI 25
20136 Milano
Italy

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Region
Nord-Ovest Lombardia Milano
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 316 544,00

Beneficiaries (1)

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