A Decision Analysis Perspective on Multiple Response Robust Optimization

Decisions in which multiple objectives must be optimized simultaneously occur frequently in government, military, and industrial settings. One method a decision maker may use to assist in such decisions is the application of a desirability function. An informed specification of the desirability function\u27s parameters is essential to accurately describe the decision maker\u27s value trade-offs and risk preference. This thesis uses utility transversality to analyze the implicit trade-off and risk attitude assumptions attendant to the desirability function. The desirability function does not explicitly account for response variability. A robust solution takes not only the expected response into account, but also its variance. Assessing a utility function over desirability as a means to describe the decision maker\u27s risk attitude produces a robust operating solution consistent with those preferences. This thesis examines robustness as it applies to the desirability function in a manufacturing experiment example. Different levels of diplomatic, informational, military, and economic (DIME) instruments of national policy are investigated to examine their effect on the political, military, economic, social, infrastructure, and information (PMESII) systems of a nation. AFRL\u27s National Operational Environment Model (NOEM) serves as a basis for identifying a robust national policy in a scenario involving the Democratic Republic of Congo

A Multisite Preregistered Paradigmatic Test of the Ego-Depletion Effect

We conducted a preregistered multilaboratory project (k = 36; N = 3,531) to assess the size and robustness of ego-depletion effects using a novel replication method, termed the paradigmatic replication approach. Each laboratory implemented one of two procedures that was intended to manipulate self-control and tested performance on a subsequent measure of self-control. Confirmatory tests found a nonsignificant result (d = 0.06). Confirmatory Bayesian meta-analyses using an informed-prior hypothesis (δ = 0.30, SD = 0.15) found that the data were 4 times more likely under the null than the alternative hypothesis. Hence, preregistered analyses did not find evidence for a depletion effect. Exploratory analyses on the full sample (i.e., ignoring exclusion criteria) found a statistically significant effect (d = 0.08); Bayesian analyses showed that the data were about equally likely under the null and informed-prior hypotheses. Exploratory moderator tests suggested that the depletion effect was larger for participants who reported more fatigue but was not moderated by trait self-control, willpower beliefs, or action orientation.</p