Decisions can be categorized based on the amount and nature of information available and decision scientists have developed somewhat standard definitions of the terms “risk”, “uncertainty”, and “certainty” to help with this distinction. Under “certainty”, the decision-maker knows with absolute clarity the outcomes associated with each decision, with no probabilities involved. An example is the choice between a new car and a trip to Hawaii, or between three types of jam available in the supermarket. In contrast, decisions under “risk” involve known probabilities of potential outcomes, such as when we gamble and know the odds. Decisions under “uncertainty” involve unknown probabilities that must be subjectively estimated if they are taken into account. Alternatively, uncertainty is sometimes used to refer more broadly to other aspects of decision problems that are often uncertain outside the lab, such as uncertainty about the available choices, the possible outcomes from those choices, and what values or utilities may be associated with those outcomes.
See this essay in the Casino Cognition Substack for a more detailed consideration of the distinction between certain, risky, and uncertain decisions. As the Substack will attempt to make clear, even in the relatively well-constrained environment of casinos, gambling decisions fit the more radical uncertainty just described far more than might first be expected given how seemingly well-constrained casino games tend to be.