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Active experiment design in crowdsourced experiments

Authors
Prof. Jordan W. Suchow
Stevens Institute of Technology ~ Stevens Institute of Technology
Abstract

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.

Tags

Keywords

Experiment design
algorithms
crowdsourcing

Topics

Cognitive Modeling
Bayesian Modeling
Probabilistic Models
Discussion
New
paper available? Last updated 2 months ago

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

Dr. Edward Andrew Cranford 0 comments
crowd sizes? Last updated 2 months 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