Developing a System for Testing Computational Social Models using Amazon Mechanical Turk
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- University of Georgia, Athens, GA (United States)
The US faces persistent, distributed threats from malevolent individuals, groups and organizations around the world. Computational Social Models (CSMs) help anticipate the dynamics and behaviors of these actors by modeling the behavior and interactions of individuals, groups and organizations. For strategic planners to trust the results of CSMs, they must have confidence in the validity of the models. Establishing validity before model use will enhance confidence and reduce the risk of error. One problem with validation is designing an appropriate controlled test of the model, similar to the testing of physical models. Lab experiments can do this, but are often limited to small numbers of subjects, with low subject diversity and are often in a contrived environment. Natural studies attempt to test models by gathering large-scale observational data (e.g., social media) however this loses the controlled aspect. We propose a new approach to run large-scale, controlled online experiments on diverse populations. Using Amazon Mechanical Turk, a crowdsourcing tool, we will draw large populations into controlled experiments in a manner that was not possible just a few years ago.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1494340
- Report Number(s):
- SAND--2015-10432; 672292
- Country of Publication:
- United States
- Language:
- English
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