Lion Schulz

Hi! I work as Head of Machine Learning at Bertelsmann, Europe's largest media company. I lead research and development work across Bertelsmann's portfolio, ranging from books (Penguin Random House) to TV, film and journalism (RTL and Fremantle) and music (BMG).

I am particularly interested in how to use artificial intelligence to empower creatives and the products they create. I both build products and research how the media industry and wider society get shaped by novel technologies.

I previously completed a PhD at the Max Planck Institute For Biological Cybernetics where I was part of Peter Dayan's group and worked on reinforcement learning and trust.

Before my PhD, I obtained an MSc in Cognitive and Decision Sciences from University College London where I worked with Steve Fleming. During my PhD, I was a Fulbright Scholar at MIT, doing research with Rahul Bhui, and did machine learning for Limbic.

Feel free to reach out (best on LinkedIn these days).

CV

EXPERIENCE

since 2025: Head of Machine Learning, AI Hub| Bertelsmann

2023 - 2025: Global Data Science and AI Management Fast-Track Programme| Bertelsmann

2022 - 2023: Machine Learning Scientist (2023) / Intern (2022)| Limbic

2019 - 2023: Doctoral Researcher | Max Planck Institute for Biological Cybernetics (Peter Dayan)

2022 - 2023: Fulbright Scholar | Massachusetts Institute of Technology (Rahul Bhui)

2018 - 2019: Graduate Researcher | University College London (Steve Fleming)

EDUCATION

2019 - 2024: PhD in Computational Neuroscience | Max Planck Institute for Biological Cybernetics / Univ. of Tübingen

2018 - 2019: MSc in Cognitive and Decision Sciences |University College London

2015 - 2018: BSc in Psychology | Technische Universität Dresden

PubLICATIONS

[Google Scholar]

2025

Playing repeated games with large language models
Akata, E., Schulz, L., Coda-Forno, J., Oh, S.J., Bethge, M., & Schulz, E.
Nature Human Behaviour | [link]

Mechanisms of Mistrust: A Bayesian Account of Misinformation Learning
Schulz, L., Streicher, Y., Schulz, E., Bhui, R., & Dayan, P.
PLOS Computational Biology | [link]

2024

Narrative Information Theory
Schulz, L., Patrício, M., & Odijk, D.
NeurIPS 2024 Workshop on Creativity & Generative AI

(Mal) adaptive Mentalizing in the Cognitive Hierarchy, and Its Link to Paranoia
N Alon, L Schulz, V Bell, M Moutoussis, P Dayan, JM Barnby
Compuational Psychiatry | [link]

Political Reinforcement Learners
Schulz, L., & Bhui, R.
Trends in Cognitive Sciences | [link]

Detecting and Discouraging Deception via Anomaly Detection
Alon, N., Schulz, L., Barnby, J.M., Rosenschein, J.S. & Dayan, P.
AAAI Workshop on Collaborative AI and Modeling of Humans | [link]

Detecting and Deterring Manipulation in a Cognitive Hierarchy
Alon, N., Schulz, L., Barnby, J., Rosenschein, J.S., & Dayan, P.
arXiv | [link]

2023

Between prudence and paranoia: Theory of Mind gone right, and wrong
Alon, N.,* Schulz, L.*, Dayan, P., & Barnby, J.
ICML 2023 Workshop on Theory of Mind in Communicating Agents | [link]

Emergent deception and skepticism via theory of mind
Schulz, L.*, Alon, N.,* Rosenschein, J.S., & Dayan, P.
ICML 2023 Workshop on Theory of Mind in Communicating Agents | [link]

A (Dis-)information Theory of Revealed and Unrevealed Preferences:
Emerging Deception and Skepticism via Theory of Mind
Alon, N.,* Schulz, L.*, Rosenschein, J.S., & Dayan, P.
OpenMind | [link]

Metacognitive Computations for Information Search: Confidence in Control
Schulz, L., Fleming, S. M., & Dayan, P.
Psychological Review | [link]

2022

A (dis-) information theory of revealed and unrevealed preferences
Alon, N.,* Schulz, L.*, Rosenschein, J.S., & Dayan, P.
NeurIPS 2022 Workshop on Information-Theoretic Principles in Cognitive Systems | [link]

2020

Dogmatism manifests in lowered information search under uncertainty
Schulz, L., Rollwage M., Dolan, R.J., & Fleming, S.M.
PNAS | [link]

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