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The University of Southampton
Interdisciplinary Research Excellence

Optimal Experimental Designs: Squeezing Every Ounce of Information from an Experiment Event

Time:
10:00 - 11:50
Date:
23 March 2015
Venue:
Building 44, Room 1057 Highfield Campus University of Southampton

For more information regarding this event, please email spg@soton.ac.uk .

Event details

This is the first of a series of SIRDF seminars organized by colleagues in Psychology, Maths, S3RI, and Economics. These seminars aim to foster interdisciplinary collaboration in the application of computer and mathematical modelling to problems of behavior and cognition. Please pass on this advertisement to interested parties and if you would like to be notified of future dates please email either Prof. Erik Rhwshle (E.Rhwshle@soton.ac.uk) or Dr. Steven Glautier (spg@soton.ac.uk).

Accurate and efficient measurement is at the core of empirical scientific research. To ensure measurement is optimal, and thereby maximize inference, there has been a recent surge of interest among researchers in the design of experiments that lead to rapid accumulation of information about the phenomenon under study with the fewest possible measurements. Statisticians have contributed to this area by introducing methods of optimizing experimental design (OED), which is related to active learning in machine learning and to computerized adaptive testing in psychometrics. The methodology involves adapting the experimental design in real time as the experiment progresses. Specifically, in OED, an experiment is run as a sequence of stages, or mini-experiments, in which the values of design variables (e.g., stimulus properties, task parameters, testing schedule) for the next stage are chosen based on the information (e.g., responses) gathered at earlier stages, so as to be maximally informative about the question of interest (i.e., the goal of the experiment). OED has become increasing popular in recent years, largely due to the advent of fast computing, which has made it possible to solve more complex optimization problems, and as such is starting to reach everyday experimental scientists. In our lab we have developed and applied our own versions of OED. In this talk I will demonstrate the application of OED in the areas of retention memory and risky choice.

Speaker information

Jay Myung,Ohio State University,Professor

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