Max R. P. Grossmann

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.

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.