In a market experiment, we analyze the propensity of participants to delegate their pricing decisions to an algorithm. The optional algorithm is the result of extensive (offline) Q-learning simulations. It is capable of tacit collusion and, when playing against itself, is more collusive than...

Large Language Models (LLMs) have the potential to profoundly transform and enrich experimental economic research. We propose a new software framework, "alter_ego", which makes it easy to design experiments between LLMs and to integrate LLMs into oTreebased experiments with human subjects. Our...

Large Language Models (LLMs) have the potential to profoundly transform and enrich experimental economic research. We propose a new software framework, "alter_ego", which makes it easy to design experiments between LLMs and to integrate LLMs into oTreebased experiments with human subjects. Our...

The application of machine learning promises great growth potential for industrial production. The development process of a machine learning solution for industrial use cases requires multi-layered, sophisticated decision-making processes along the pipeline that can only be accomplished by...

This paper investigates the collusive and competitive effects of algorithmic price recommendations on market outcomes. These recommendations are often non-binding and common in many markets, especially on online platforms. We develop a theoretical framework and derive two algorithms that...

The application of machine learning promises great growth potential for industrial production. The development process of a machine learning solution for industrial use cases requires multi-layered, sophisticated decision-making processes along the pipeline that can only be accomplished by...