Outcome Feedback Effects on Risk Propensity in an MCPLP Task

In this experimental analysis, the effects of outcome feedback on risk propensity were assessed within the multiple-cue-probability-learning-paradigm (MCPLP). The individual decision maker in this task received outcome feedback on a decision-by-decision basis. It was hypothesized that information on his/her success or lack of success (outcome feedback) on each decision would influence the decision to risk (commit) resources. Hierarchical regression results revealed that after all other performance effects had been partialled out, current outcome feedback explained much of the commitment decision.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Identifying preferred solutions to multi-objective binary optimisation problems, with an application to the multi-objective knapsack problem

In this paper we present a new framework for identifying preferred solutions to multi-objective binary optimisation problems. We develop the necessary theory which leads to new formulations that integrate the decision space with the space of criterion weights. The advantage of this is that it allows for incorporating preferences directly within a unique binary optimisation problem which identifies efficient solutions and associated weights simultaneously. We discuss how preferences can be incorporated within the formulations and also describe how to accommodate the selection of weights when the identification of a unique solution is required. Our results can be used for designing interactive procedures for the solution of multi-objective binary optimisation problems. We describe one such procedure for the multi-objective multi-dimensional binary knapsack formulation of the portfolio selection problem