An Introduction to Optimal Experimental Design
Progress in science depends on well-designed experiments, yet when it comes to testing computational models, good design can be elusive because model similarities and differences are difficult to assess. Further, observations can be expensive and time-consuming to acquire (e.g., fMRI scans, children, clinical populations). There has been a growing interest by researchers in the design of adaptive experiments that lead to rapid accumulation of information about the phenomenon under study with the fewest possible measurements. In addressing this challenge, statisticians have developed optimal experimental design (OED) methods that combine the power of statistical computing techniques with the predictive precision of the formal models, yielding experiments that are highly efficient and maximally informative with respect to a given experimental objective. This presentation provides an overview of OED with an emphasis on recent developments and applications in the behavioral sciences.
Thank you for your clear talk! I was wondering whether there are any constraints on the kinds of computational models that can be used with this method, given the necessity for Bayesian optimization.
Could you provide a reference where you assess the global utility of the power and exponential functions with ADO using human data? I'd be curious to see which of the two functions is considered the "most optimal" one according to ADO.