Experiments with oTree

I develop experiments with the oTree framework. Below you can find a description of some of my experiments and a link to try them out. If you are interested in using some of them in your own research, feel free to contact me.

DOSE risk aversion task
The Dynamically Optimized Sequential Experimentation (DOSE) task (Camerer, Chapman, Snowberg & Wang, 2018) is a method to elicit preferences. The researcher defines a functional form for the preferences, a prior distribution of parameters and a list of questions. The procedure then selects the question that is most informative about the subject’s preferences and updates the believe about the parameters based on the subject’s answer. This allows precise estimation of preference parameters with less questions than other procedures. The procedure is also robust to subjects making mistakes.

Forecasting Task
In this task, subjects see a graph of a statistical process and forecast future realisations by clicking on the graph. The code supports subjects making forecasts for up to 5 periods ahead.

Dynamic Asset Selling Task
In this task subjects see the price of an asset visualised in a chart. The asset price develops over time and subjects need to decide at which point to sell the asset. The code supports 3 selling strategies: Selling at any time, selling by choosing an upper and lower bound at which they want to sell and selling at a pre specified time.

Experiment Demo
Here you can find a demo of the experiments mentioned above. to access them, click on the link, choose your experiment and then click on the link below "Session-wide link".