KNOW-NET-COMPET PROJECT

KNOW-NET-COMPET – Embedded Knowledge and Networks: Complexity in Antitrust Collusion Mitigation

FCT Project Reference: 2024.07378.IACDC – Embedded Knowledge and Networks: Complexity in Antitrust Collusion Mitigation (DOI:10.54499/2024.07378.IACDC)

The KNOW-NET-COMPET project explores the use of advanced data-driven techniques to improve the detection and analysis of collusive behaviors in markets, such as cartels and other anti-competitive practices. Funded by FCT Portugal, the project addresses the limitations of traditional antitrust tools by integrating network analysis, natural language processing (NLP), and knowledge graph methodologies.

Its core objective is to develop an interpretable decision-support tool capable of combining institutional knowledge embedded in previous decisions with external data sources. This unified knowledge structure enables the identification of potential collusive patterns, missing links between entities, and the assessment of risks in specific market segments.

The project unfolds in three key phases:

1. Data Mapping and Theoretical Foundations: identifying and cataloguing relevant data sources and conducting a comprehensive literature review on computational antitrust and AI/ML models;

2. Graph Construction and Integration: building a flexible knowledge graph architecture that integrates structured and unstructured data and allows continuous updates;

3. Applied Case Studies: testing the system through concrete use cases such as link prediction and market segmentation, demonstrating its potential to support antitrust analysis and policy.
   
   

This project contributes to the broader effort of making AI-based regulatory tools more transparent, explainable, and adaptable to the complexity of real-world market dynamics.