Commentary---Relevancy Is Robust Prediction, Not Alleged Realism

• Authors: Steven M. Shugan
• Venue: Marketing Science
• Year: 2009

Remember James Boswell, ninth Laird of Auchinleck, author of the famous maxim that the road to hell is paved with good intentions? Trying to build realistic theories differs dramatically from having correct explanatory theories tested on objective criteria, e.g., verifiable prediction. Evaluating theories on whether assumptions are realistic is potentially subjective, biased, and arbitrary. A theory's domain determines whether its assumptions are sufficiently realistic and when assumptions must hold and to what degree, so testing assumptions in isolation puts an unnecessary burden on the assumptions (i.e., they must hold everywhere). For theories explaining cooperation and information exchange, predictions reveal that the prisoner's dilemma assumptions (only two prisoners, four possible outcomes, two possible actions, etc.) are sufficiently realistic. For theories explaining prisoner sentencing guidelines and probation policy, predictions might suggest otherwise. Scientific methods allow the evaluation of theories on criteria such predictive accuracy, reliability, validity, and robustness---not based on realism. When multiple explanatory theories survive initial testing, one derives conflicting predictions. For example, a theory that people are broccoli produces correct predictions (people are mortal) and incorrect predictions (people are biennial). Tragic consequences can occur when theory adoption depends on whether assumptions are disliked, unpopular, or exclude a favorite variable. Denounce journals that reject models with insightful new implications because the assumptions are too simple or merely disliked. The term “unrealistic” sometimes means personally disliked.