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Active experiment design in crowdsourced experiments

Jordan W. Suchow
Stevens Institute of Technology ~ Stevens Institute of Technology

As experimentation in the behavioral and social sciences moves from brick-and-mortar laboratories to the web, new opportunities arise in the design of experiments. By taking advantage of the new medium, experimenters can write complex computationally mediated adaptive procedures for gathering data: algorithms. Here, we explore the consequences of adopting an algorithmic approach to experiment design. We review several active experiment designs, describing their interpretation as algorithms. We then discuss software platforms for the efficient execution of these algorithms with people. Finally, we consider how machine learning can optimize crowdsourced experiments and form the foundation of next-generation experiment design.



Experiment design


Cognitive Modeling
Bayesian Modeling
Probabilistic Models
paper available? Last updated 3 years ago

I'm interested in learning more, do you have any papers available about this research? Thanks!

Dr. Edward Cranford 0 comments
crowd sizes? Last updated 3 years ago

really like these approaches. does this help reduce the size of the crowd needed for the experiments? or just reduces the amount of work each crowd members needs to do to contribute?

Dr. Leslie Blaha 0 comments
Cite this as:

Suchow, J. (2020, July). Active experiment design in crowdsourced experiments. Paper presented at Virtual MathPsych/ICCM 2020. Via