Toward a Behavioral Theory of Real Options: Noisy Signals, Bias, and Learning

Abstract

We develop a behavioral theory of real options that relaxes the informational and behavioral assumptions underlying applications of financial options theory to real assets. To do so, we augment real option theory’s focus on uncertain future asset values (prospective uncertainty) with feedback learning theory that considers uncertain current asset values (contemporaneous uncertainty). This enables us to incorporate behavioral bias in the feedback learning process underlying the option execution/termination decision. The resulting computational model suggests that firms that inappropriately account for contemporaneous uncertainty and are subject to learning biases may experience substantial downside risk in undertaking real options. Moreover, contrary to the standard option result, greater uncertainty may decrease option value, making commitment to an investment path more effective than remaining flexible.

Keywords: Bayesian Learning, Competitive Advantage, Computational Model, Decision-Making Under Uncertainty, Experiential Learning, Heterogeneity, Real Option Logic, Sequential Decision-Making, Simulation, Strategic Decision-Making

JEL Classification: A10, A12, C11, C15, D80, D81, D83, G10, G11, G31, M20