Abstract

A number of important decision domains, including decisions about hiring, global warming, and weather hazards, are characterized by a global–local incompatibility. These domains involve variables that cannot be observed by a single decision maker (DM) and require the integration of observations from locally available information cues. This paper presents a new bifocal lens model that describes how the structure of the environment can lead to a unique form of overconfidence when generalizing the reliability of the local environment to a global scale. When the local environment does not reliably reflect the global environment, they are incompatible. While local perspectives vary across DMs, global–local incompatibility can be understood using the structure of classical test theory as the difference between (a) perceived estimates of the reliability derived from the local environment and (b) the true reliability of the local environment. I model global–local incompatibility as the difference between the true and estimated reliability when the assumptions of classical test theory are violated. Using a series of case studies and an empirical study, I demonstrate the widespread utility of this framework, and I conclude by discussing implications for cognitive–ecological theory, risk communication, and overconfidence.