Propositions To Guide Evidence-Based Decision-Making

Abstract

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">The methods by which managers make decisions in the face of inaccurate data, changing environments and other pervasive uncertainties, have been studied by researchers and business practitioners for many decades.<span style="mso-spacerun: yes;">  </span>Much attention is given to highly quantitative decision-theoretic techniques; such methods generally deal with these inherent impediments to rational decisions using mathematical concepts from the theories of probability, stochastic processes, estimation and optimization, fuzzy sets, etc.<span style="mso-spacerun: yes;">  </span>The very act of quantification itself has great implications for managers’ cognitive processes, the impact upon various groups within the organization, and the final outcomes.<span style="mso-spacerun: yes;">  </span>Indeed, use of formal methods and actual reliance on hard numbers improves decision quality and speed by providing efficient cognitive simplifications and convergent expectations.<span style="mso-spacerun: yes;">  </span>This paper synthesizes findings from several relevant streams of literature and proposes several simple propositions for further discussion and future research. It applies these ideas to two illustrative examples of major complex business decisions.</span></span></p>