Theses and Dissertations

Advisor

Jaradat, Raed

Committee Member

Bian, Linkan

Committee Member

Ma, Junfeng

Committee Member

Keating, Charles

Date of Degree

12-10-2021

Document Type

Dissertation - Open Access

Major

Industrial and Systems Engineering

Degree Name

Doctor of Philosophy (Ph.D)

College

James Worth Bagley College of Engineering

Department

Department of Industrial and Systems Engineering

Abstract

The idea of multi-criteria decision making has been around for quite a while. All judgement tasks are potential points of bias introduction. Each judgement task was assessed to identify common biases introduced through an extensive literature review for each task and bias. In several other studies, the distinction is made between cognitive and motivational biases. Cognitive biases are widely studied and well known with mitigations that have been validated. Motivational biases are judgements influenced by the decision maker’s desire for a specific outcome, also referred to as intentional bias, that are hard to correct and received very little testing and exploration. This study tested the techniques that are identified for reducing motivational bias and tested an instrument to identify characteristics within a decision maker that would increase the likelihood that they would be motivationally biased. The results of this study provide a methodology for assessing the susceptibility to motivational biases of the decision makers and provides a framework for reducing the motivational bias within the multi-criteria decision making (MCDM) process using the general steps applicable to all multi-criteria decision analyses. Given that the general steps are used, this methodology is generalizable to any MCDM problem or domain and was found to be reliable and consistent with previous instruments and tools. A summary of the future research to further the explore the methodology and additional techniques for reducing motivational bias is proposed.

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