Bayesian decision theory and inference have left a deep and indelible mark on the literature on management decision-making. There is however an important issue that the machinery of classical Bayesianism is ill equipped to deal with, that of "unknown unknowns" or, in the cases in which they are actualised, what are sometimes called "Black Swans". This issue is closely related to the problems of constructing an appropriate state space under conditions of deficient foresight about what the future might hold, and our aim is to develop a theory and some of the practicalities of state space elaboration that addresses these problems. Building on ideas originally put forward by Francis Bacon (1620), we show how our approach can be used to build and explore the state space, how it may reduce the extent to which organizations are blindsided by Black Swans, and how it ameliorates various well-known cognitive biases.
Tony Lawson's work on probability and uncertainty is both an important contribution to the heterodox canon as well as a notable early strand of his ongoing enquiry into the nature of social reality. In keeping with most mainstream and heterodox discussions of uncertainty in economics, however, Lawson focuses on situations in which the objects of uncertainty are imagined and can be stated in a way that, potentially at least, allows them to be the subject of probability judgments. This focus results in a relative neglect of the kind of uncertainties that flow from the existence of possibilities that do not even enter the imagination and which are therefore ruled out as the subject of probability judgments. This paper explores uncertainties of the latter kind, starting with and building on Donald Rumsfeld's famous observations about known unknowns and unknown unknowns. Various connections are developed, first with Nassim Taleb's Black Swan, and then with Lawson's Keynes-inspired interpretation of uncertainty.
Recent studies on the construction and use of "small world representations" in strategic decisionmaking under Knightian uncertainty say little about how such representations should be updated over the implementation phase. This paper draws on the psychology of reasoning to take a step towards answering this question. We begin by theorizing small world representations and how the scenario spaces they contain are constructed and may be updated over time. We then introduce two well-known heuristic methods of inquiry, disconfirmation and counterfactual reasoning, translate them into practical procedures for updating scenario spaces, and compare the relative performance of these procedures in strategic situations of Knightian uncertainty. Our principal findings are that the procedure based on counterfactual reasoning is superior to the one based on disconfirmation with respect to (1) counteracting the confirmation bias, (2) promoting the exploration of the set of imaginable scenarios, and (3) facilitating action to mitigate or exploit the consequences of what would otherwise have been Black Swans. We close with some broader implications for the study of strategic decision-making under Knightian uncertainty.It is widely recognized that decision-makers operating in situations of Knightian uncertainty
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