An Illustration of a Modified-Machlup (mM) Assessment for Resolving When Experts Disagree (WED) Issues as Applied to Three Archetypal Examples for Model Validation : Yule-Simpson Paradox (YSP), Fair Employee Compensation (FEC), and Put-Call Parity (PCP)

This paper (1) introduces a modified-Machlup (mM) assessment, as well as DQA and DQE concerns and a DQXY Thesis that tie the mM assessment to the Duhem-Quine (DQ) Thesis and theories of model validation (TMV), and then (2) applies the mM and DQA/DQE concepts to three archetypal models (i.e., YSP, FEC, and PCP) that (2a) represent both (observational) empirical and (theoretical) analytical models and (2b) illustrate “when experts disagree” (WED) issues, including (2c) issues that a Board might face as part of a corporate governance of model validation (CGMV) concern, including for corporate finance concerns (and, hence, CGMVCF), and (2d) issues that can be generalized to more-complex models that a Board also likely faces (e.g., assessing performance measures and valuing financial derivatives) in its role as a monitor of corporate activity. Since the Board may (3) not be technically trained in mathematics and statistics, (3a) a “novice-expert problem” (NEP) is also identified, for which (3b) an Evaluator-Decider-Advisor-Choice (EDAC) solution concept is introduced. The EDAC solution concept can utilize the mM and DQA/DQE attention-directing tools to help solve or resolve the NEP-WED issue for the Board. (4) An archetypal micro-CGMV story is introduced, and its application to the three archetypal models leads to two broad conclusions that a less-technically-trained Board would likely be interested in. First, regression equation coefficient estimates cannot necessarily identify unique factor sensitivity. Second, arbitrage pricing models cannot identify valuation for stand-alone financial instruments (including financial derivatives), especially those held for risk bearing; accordingly, any use of arbitrage pricing techniques may misrepresent corporate risk exposure and valuation. The key to both conclusions is understanding the conditionalities involved in the various models being utilized for argument purposes, especially as rote calculations, with such conditionalities fundamentally relating to the DQ Thesis. Accordingly, the mM and DQA/DQE assessments (1) help lead to the two conclusions and identify the DQ Thesis concerns involved, (2) help identify Bad Creativity (i.e., creativity that misleads) arising from misuse of mathematical technology, (3) help identify the Missing Premises in flawed enthymemic rhetorical argumentative structures and, consequently, (4) help Boards make better decisions