Postdoc in rationally resolving disagreements about complex issues (EXPIRED)

The Intelligence Community Postdoctoral Research Fellowship Program (https://orise.orau.gov/icpostdoc/) has announced a postdoc on improving human reasoning on complex topics:

“Probabilistic Visualization of Complex Arguments to Resolve Analytic Disagreements”

Prof. Simon Cullen (Carnegie Mellon University) is interested in supervising this research at CMU. The postdoc will be provided with offices in the Department of Philosophy at CMU.

Award Details

*Appointments are for 24 months. The Program has the discretion to fund a third year.

* Annual stipend (which include funds for health insurance) is between $75,000 - $79,000

* Annual travel budget of $6,000

* Lab budget $5,000/year

Postdoc Eligibility

* U.S. citizenship required

* PhD. received within 5 years of the application deadline

The postdoc cannot directly conduct human subjects experimentation. But the postdoc will collaborate with others who do related human subjects experimentation.

For further information contact Prof. Simon Cullen as soon as possible. His website is https://www.simoncullen.org/

Project description: Individuals and organizations often disagree in good faith. These disagreements can arise from different evidence, different probabilities assigned to that evidence, different evaluations of the importance of that evidence in the reasoning, and even in the methods for reasoning. Previous research has shown that visualizing (mapping) an argument can deepen understanding of the issues and help people who disagree reach a correct common understanding. But that research has focused on relatively simple arguments about hypothetical situations in which the subjects are not personally vested intellectually or emotionally. This research will explore ways to use similar approaches to complex, real-life reasoning, where subjects often are personally vested. 

Research should include developing collaborative, easy-to-use software and techniques to visualize the logical structure of complex real-life arguments.  Despite the arguments’ complexity, the visualizations should be intuitive enough that people will understand them relatively easily and will find them helpful, even illuminating.  Visualizations that only a highly trained person can understand are of little interest to this program. To achieve the requisite simplicity, the techniques should help people focus on key contested assumptions, explicit or implicit.  The techniques for producing the visualizations may be automated. If not, then the process should be intuitive and easy to learn. 

Since many disagreements come from differing estimates of the initial conditions (for example, different prior probabilities), the visualizations should make clear the (subjective) probabilities of key claims and of likelihoods/likelihood ratios, and any underlying arguments for those probabilities. 

Example Approaches: 

Develop easy-to-use, collaborative argument visualization software that focuses on clarifying points of disagreement.

Develop easy-to-use collaborative software that encourages discussion and resolution of different likelihoods.

Bayesian approaches to argument mapping.

Develop techniques to identify evidence with the greatest discriminating power between the hypotheses.