Nearly a century ago, Frank Knight famously distinguished between risk and uncertainty with respect to the nature of decisions made in a business enterprise. He associated generating economic profit with making entrepreneurial decisions in the face of fundamental uncertainties. This uncertainty is complex because it cannot be reliably hedged unless it is reducible to risk. In making sense of uncertainty, the mathematics of probability that is used for risk calculations may lose relevance. Fast-and-frugal heuristics, on the other hand, provide robust strategies that can perform well under uncertainty. The present paper describes the structure and nature of such heuristics and provides conditions under which each class of heuristics performs successfully. Dealing with uncertainty requires knowledge but not necessarily an exhaustive use of information. In many business situations, effective heuristic decision-making deliberately ignores information and hence uses fewer resources. In an uncertain world, less often proves to be more.

We review various statistical performance metrics that have been used with prospective surveillance schemes. We consider scenarios under which the metrics are most useful and discuss some of their advantages and disadvantages. A contrast is made between the approaches and metrics used in industrial process monitoring and those used in public health surveillance. The in-control average time between signal events (ATBSE) and the in-control average signal event length (ASEL) are introduced as performance metrics that are useful when a monitoring procedure is not reset to its initial state after a signal.We give particular attention to the recurrence interval, defined as the fixed length of time (measured in number of time periods) for which the expected number of false alarms is one. The recurrence interval is used in public health surveillance whereas in-control time-to-signal measures are used in industrial statistical process control. We compare the recurrence interval and measures based on the time-to-signal properties for the temporal monitoring case using exponentially weighted moving average (EWMA) charts, cumulative sum (CUSUM) charts, and Markov dependent signaling processes. We recommend that measures based on the time-to-signal properties be used when possible to evaluate the performance of surveillance schemes for ongoing monitoring.

Heuristics are commonly viewed in behavioral economics as inferior strategies resulting from agents' cognitive limitations. Uncertainty is generally reduced to a form of risk, quantifiable in some probabilistic format. We challenge both conceptualizations and connect heuristics and uncertainty in a functional way: When uncertainty does not lend itself to risk calculations, heuristics can fare better than complex, optimization-based strategies if they satisfy the criteria for being ecological rational. This insight emerges from merging Knightian uncertainty with the study of fast-and-frugal heuristics. For many decision theorists, uncertainty is an undesirable characteristic of a situation, yet in the world of business it is considered a necessary condition for profit. In this article, we argue for complementing the study of decision making under risk using probability theory with a systematic study of decision making under uncertainty using formal models of heuristics. In doing so, we can better understand decision making in the real world and why and when simple heuristics are successful.