The Research Division of the Swiss Institute of Artificial Intelligence (SIAI) serves as the intellectual core of the institution — a bridge between theoretical inquiry, applied modeling, and institutional practice. While SIAI’s Executive programs cultivate leadership capacity, the Research Division is dedicated to developing the frameworks, datasets, and algorithms that underpin intelligent decision-making across organizations and societies.

At its essence, SIAI Research explores how data, models, and institutions co-evolve. The division studies not only how AI learns from data, but how institutions learn from AI — shaping policy, governance, and collective behavior in response to algorithmic systems.

Mission

Our mission is to build the science of institutional intelligence — a cross-disciplinary field uniting data science, economics, and systems design.
SIAI Research aims to:

1. Develop new analytic frameworks for interpreting organizational and economic behavior through AI-driven modeling.
2. Ensure intellectual independence and reproducibility through transparent data validation and peer review mechanisms.
3. Integrate ethical reasoning and strategic foresight into the study of automation, governance, and institutional adaptation.

The division operates under the principle that true intelligence — whether artificial or institutional — arises from structured reflection, not reactive optimization.

Research Domains

SIAI Research is organized around several key domains that reflect both technical depth and institutional relevance:

• Algorithmic Governance and Policy Systems
  Investigating how AI systems influence decision-making processes in governments, regulatory bodies, and public institutions.
• Computational Finance and Economic Systems
  Studying the intersection of quantitative finance, machine learning, and macroeconomic structure — where models drive markets and vice versa.
• Ethics, Bias, and Institutional Behavior
  Examining how social norms, incentives, and hierarchies affect data collection, model interpretation, and organizational fairness.
• AI Strategy and Organizational Learning
  Developing frameworks for how companies, universities, and public agencies can evolve from data users to intelligent institutions.
• Human–Machine Collaboration
  Exploring models of shared cognition and adaptive decision support between human analysts and autonomous systems.

Structure and Oversight

SIAI Research operates within the Gordon Institute of Artificial Intelligence (GIAI) ecosystem and is supervised by the Mathematical Data Science Association (MDSA) in Zurich.
MDSA’s oversight ensures academic neutrality, methodological integrity, and alignment with European research standards.

The division works closely with The Economy Research (TER) and The Economy Secret, enabling secure channels for both public dissemination and confidential institutional analysis.
This layered approach — open knowledge above, protected intelligence below — allows SIAI to balance transparency with strategic discretion.

Methodology

Our research philosophy emphasizes methodological pluralism:

• Quantitative rigor through data-driven modeling and simulation.
• Qualitative interpretation through institutional analysis and historical context.
• Design experimentation through AI systems that reveal structural dynamics rather than conceal them.

All research projects at SIAI aim to create not just academic output, but institutional prototypes — frameworks, policies, and systems that can be implemented within real organizations.

Vision

SIAI Research envisions a future in which AI and institutions evolve together — not in competition, but in mutual learning.
We aim to define what it means for an organization, a market, or even a nation to be “intelligent” — not simply automated, but self-aware in its data and design.