In today's world driven by technological developments and digital transformation, the implementation of customized decision-making systems with properly designed strategical objectives has become inevitable. A good decision which yields high outcome benefits makes effective use of the information available to the decision-maker. The important questions at this point are how the information can be used correctly and effectively with the limitations of information processing time and ability and subsequently how new information is integrated with the current beliefs in our decision process. The answers can be, as a normative model, the Bayesian reasoning approach. However, this may not always help us reach the right decision due to cognitive biases. Business leaders can also be adversely affected by uncertain future environments and abundant information. This chapter describes how Bayesian reasoning can mitigate biases in strategic decision-making and also how Bayesian reasoning errors can be eliminated by debiasing methodology in both strategic and medical decision making.
“The maxim, ‘managing means looking ahead,’ gives some idea of the importance attached to planning in the business world, and it is true that if foresight is not the whole of management at least it is an essential part of it.” (Fayol & Storrs, 1949, as cited in Kapoor & Wilde, 2023)
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The study of decision making under uncertainty has been dominated by the closely related the theories of expected utility (EU) and subjective expected utility (SEU) formulated and axiomatized by von Neumann and Morgenstern (1944) and Savage (1954) (Einhorn and Hogart 1986) which have been accepted as the normative theories to be followed to achieve goals as maximizing utilities in judgement and decision-making processes (Baron, 2006).
On the other hand, a wide range of anomalies or paradoxes departed from the normative models have been identified in the many studies (Kahneman & Tversky, 1972; Kahneman & Tversky, 1984; Lindley et al.,1979; Lyon & Slovic, 1976; Griffin & Tversky, 1992; Tversky & Kahneman, 1983; Birnbaum and Mellers, 1983; Koehler, 1996; Bar-Hillel, 1983; Ginosar & Trope, 1987; Epley and Gilovich, 2016) in the literature. These systematic deviations from the norms are called cognitive biases defined as “cases in which human cognition reliably produces representations that are systematically distorted compared to some aspect of objective reality” (Haselton et al., 2015).
When we eliminate an impossible option, even the most improbable among the remaining options has a chance of being true as Sherlock Holmes tells Watson in her conversation with Watson in one of the novels (Feduzi & Runde, 2014). Natural disasters like China floods (1931), Indian Ocean earthquake (2004), and accidents like Chernobyl nuclear explosion and also financial crises are sudden and deeply impacted events with major collateral damage and loss of thousands lives. However, if asked right before these big events happened, it would have been said that it was impossible, but it was probable and it happened. These unpredictable or unforeseen events are called in the literature Black Swan or unknown unknowns (Feduzi & Runde, 2014). Taleb (2007) in his best seller book identifies three attributes of a Black Swan event. First it is an outlier beyond our expectation and has deep impact that we cannot cover instantly and last, we will most likely find a very valid explanation after the event takes place, and some will even say that they have already predicted it in hindsight.
Strategic decisions also require being able to foresee such events and knowing what we have now. Therefore, it is necessary to prepare to reduce or eliminate the effects of these defects. Business leaders as being human use shortcuts or rules of thumb called heuristics in order to ease to make judgements and these heuristics can also lead to the cognitive biases as well. Bayesian reasoning can be a solution to such problems by approaching them from an integrated perspective, considering many biases.