Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Bounding separable recourse functions with limited distribution information

The recourse function in a stochastic program with recourse can be approximated by separable functions of the original random variables or linear transformations of them. The resulting bound then involves summing simple integrals. These integrals may themselves be difficult to compute or may require more information about the random variables than is available. In this paper, we show that a special class of functions has an easily computable bound that achieves the best upper bound when only first and second moment constraints are available.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44185/1/10479_2005_Article_BF02204821.pd

To Include, or Not to Include? Accuracy of Personality Judgments from Resumes with and Without Photographs

We investigate whether adding the applicant's photograph on his/her resume boosts or hampers the accurate assessment of the applicant's personality (Big 5 and intelligence). One hundred and fourteen participants rated 8 applicants (4 men and 4 women) in terms of their personality (Big 5 and intelligence). The design was a 3 (condition: resume with photograph, resume only, and photograph only) x 2 (participant gender) between-subjects design. Results show that in all conditions, personality (with the exception of agreeableness) were assessed at better than guessing level. Adding a photograph to the resume did not change the accuracy of the personality assessment significantly

Emergent power hierarchies and group performance

In newly formed groups, informal hierarchies emerge automatically and readily. In this study, we argue that emergent group hierarchies enhance group performance (Hypothesis 1) and we assume that the more the power hierarchy within a group corresponds to the task-competence differences of the individual group members, the better the group performs (Hypothesis 2). Twelve three-person groups and 28 four-person groups were investigated while solving the Winter Survival Task. Results show that emerging power hierarchies positively impact group performance but the alignment between task-competence and power hierarchy did not affect group performance. Thus, emergent power hierarchies are beneficial for group performance and although they were on average created around individual group members' competence, this correspondence was not a prerequisite for better group performance