Research at the intersection of economics, artificial intelligence, and quantum-inspired mathematical formalism.
Price is not a fixed value.
Before a transaction occurs, market prices exist as a probability distribution.
A trade acts as a measurement collapsing uncertainty into an observed price.
Applying quantum probability to asset pricing and portfolio theory. We develop non-Kolmogorovian probability models and path-integral methods to capture interference effects in market dynamics and option pricing under non-Gaussian distributions.
Using artificial intelligence to model economic systems under uncertainty. We develop data-driven methods for inference, prediction, and signal extraction.
Extracting structural signals and behavioral patterns from on-chain transaction data. Focus on network topology analysis, transaction flow modeling, and data-driven insights for decentralized ecosystems.
Modeling liquidity, mechanism design, and risk in protocol-governed financial systems. Research spans automated market makers, lending protocols, and tokenomics.
Bayesian and variational inference engines for real-time estimation of latent economic states from heterogeneous data streams.
Transformer and state-space architectures trained on multi-decade macro-financial panel data for regime classification and forecasting.
Large-scale semantic extraction from central bank communications, earnings calls, and economic news to quantify textual policy signals.
Structural causal models and counterfactual inference methods for policy evaluation and intervention analysis.
Multi-agent RL environments simulating market interaction, enabling discovery of emergent equilibria under bounded rationality.
Network-based learning over financial interconnectedness graphs for systemic risk propagation modeling.
We develop a quantum-inspired framework for asset valuation, where prices are modeled as probabilistic states rather than fixed quantities. Transactions act as measurements that collapse these states into observed market prices, providing a new perspective on valuation under uncertainty.
A novel price-to-utility ratio grounded in blockchain accounting effectively predicts long-term Bitcoin returns and supports an automated trading strategy that outperforms buy-and-hold.
EIP-1559 improves user experience by mitigating intrablock gas price differences and reducing waiting times, with limited measurable impact on consensus security.
Novel approach to understanding Bitcoin transaction patterns and user behavior through advanced cohort analysis techniques applied to blockchain data.
A comparative study of core-periphery network structure across four decentralized banks — Liquity, Aave, MakerDao, and Compound — finding that MakerDao and Compound exhibit greater decentralization in transactions, while all four protocols show concentration around primary external addresses such as Huobi, Coinbase, and Binance.
We collaborate with institutions and technical teams working at the intersection of economics, artificial intelligence, and financial systems.
Our work spans theoretical modeling, data-driven research, and applied financial analysis.
Get in TouchWe work with students interested in economic modeling, artificial intelligence, and financial systems.
We primarily work with students based in Zurich, while also supporting a limited number of students globally each year. Outstanding students may continue working with us as research fellows.
Our research environment is closely connected to the Zurich academic ecosystem.
Students who have worked with us, in collaboration with SciEcon, have gone on to:
We also support external PhD researchers working on related topics.