Max R. P. Grossmann


Primary research areas

  1. Behavioral and experimental economics
  2. Philosophy & economics
  3. Law & economics
  4. Economic modeling, microfoundations, methods and tools

Publications in peer-reviewed journals

Find me on Google Scholar and Semantic Scholar.

z-Tree unleashed: A novel client-integrating architecture for conducting z-Tree experiments over the Internet
with Duch, M. L. & Lauer, T. Journal of Behavioral and Economic Finance, 2020. Read more.
We present z-Tree unleashed, a novel approach and set of scripts to aid the implementation of computerized behavioral experiments outside the laboratory. z-Tree unleashed enables subjects to join the experiment using a web portal that requires no software apart from a web browser. Experimenters are likewise enabled to administer their experiments from anywhere in the world. Except for z-Tree itself, z-Tree unleashed is entirely based on free and open-source software. In this paper we give a high-level overview of z-Tree unleashed’s features and benefits and its design. We also show how to set up the server and demonstrate the steps required for conducting an entire experiment. We subsequently explain how to leverage the security and routing features of a virtual private network with z-Tree unleashed, enabling servers to securely run behind routers.

Selected work-in-progress

Knowledge and Freedom
Job Market Paper, please check back later
We study the relationship between information and paternalism. When is autonomy granted to an individual that is less or better informed, and if no autonomy is granted, how does the intervention look like? We introduce the Estimation Game, a novel experimental design that varies the amount of ambiguity inherent in a binary lottery. Using a simple model of Bayesian learning and von Neumann-Morgenstern expected utilities, we show theoretically that learning can help decision-makers (“Choosers”) make informed decisions in the Estimation Game, and that a “Choice Architect” who seeks to actualize Chooser preference will intervene based on the full-information counterfactual. This model of utilitarian or statistical intervention reconciles J. S. Mill's early utilitarianism with his later classical liberalism by microfounding the appeal of freedom given sufficient knowledge, and it presents a normative policy standard against which actual interventions can be compared. We conduct experiments of the Estimation Game with a Choice Architect to examine the extent to which Chooser knowledge matters in terms of restrictions of the Chooser's freedom of choice. Although more knowledge leads to fewer interventions on the extensive margin, which option is imposed is a matter of personal Choice Architect preference, not utilitarian consideration. The intensive margin of interventions is strongly taste-based. We interpret these findings as a ``moral license'': the act of helping a clueless individual induces Choice Architects to then impose their own vision, even when adjusting for the false consensus bias that is prevalent in beliefs.
Paternalism in Data Sharing
with Ockenfels, A.

Inactive projects

Dynamic treatment assignment
AnonPay: Enhancing participant privacy in online experiments

Other publications

Stochastic volatility with mixed frequency data
Master’s thesis, University of Cologne. September 16, 2019.
This thesis focuses on two extensions to the stochastic volatility (SV) model: First, explanatory variables are introduced in the form of a low-frequency mixed-data sampling (MIDAS) term along with the daily autoregressive component of the SV model. Second, we consider a model in which an additional autoregressive term is introduced, but it evolves in a low-frequency manner compared to the daily term. Additional macro variables permit the identification of this supplementary term. Chapter 1 introduces the basic models and provides an overview of previous approaches. Chapter 2 presents the new models and discusses some of their properties. All SV models are estimated using the Kálmán filter; moreover, it is shown how volatility forecasts can be obtained. In chapter 3, we study the models using Monte Carlo simulations. In chapter 4, we apply the models to financial series. Finally, we summarize the results in chapter 5. Proofs, additional figures and tables, and the code of the reference implementation are provided.
Technological Transition and Price Discrimination
Bachelor’s thesis, University of Cologne. June 6, 2017. Download
Sophomore’s Dream: 1,000,000 digits
Computation (world record). 2017. Download (sig)
See here for further information. The previous world record computation (from 2013, also by me) can be downloaded here.