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Unsolicited reflections on the concept of uncertainty

“These are times of great uncertainty”

The times I have said and heard this phrase cannot be counted. I usually hear it when a decision needs to be made, and we find ourselves in a complex context. But what is uncertainty? How does it affect our decisions? How significant is the impact of uncertainty on AI-based decision-making systems?

In this first article, I share some reflections on the concept of uncertainty, hoping to stimulate a useful discussion that helps me delve deeper into aspects I might not currently be considering.

Definition of Uncertainty

I've noticed that we don't always share a common understanding of what uncertainty is. According to the Treccani dictionary, uncertainty is the condition one finds oneself in when the information or data relating to a fact are uncertain or contradictory, offering insufficient or not entirely well-founded knowledge. Usually, this condition of uncertainty is determined by complex contexts in which it is not easy to determine the level of risk associated with the decision we have to make.

Uncertainty, Risk, and Complexity

The conjunction of risk and complexity often leads us to believe that uncertainty is determined by the complexity of a context and/or its riskiness. However, while risk can be assessed through statistical methods, uncertainty involves the inherent inability to recognize influential decision variables and their functional relationships. Regarding complexity, I don't think a complex system is necessarily uncertain since it is characterized by numerous parts that interact in a complex but not necessarily uncertain way.

What Causes Uncertainty

Having established that uncertainty is not linked solely to the complexity of a context or the risk associated with decisions, I consider it useful to expand its nature beyond the initial definition, which attributes it solely to data incompleteness. In my opinion, uncertainty can also stem from an inadequate understanding of the context and the undifferentiated alternatives among which we make our choices. To put it more elegantly and in line with academic discourse, the origin of uncertainty can be divided into three dimensions: informational, environmental, and intentional.

Informational Uncertainty

Informational uncertainty derives from incomplete information and is the most frequent source of uncertainty. It manifests when there is a lack of data acquisition or when one possesses an incomplete, low-quality, or insufficiently diverse data set.

Environmental Uncertainty

Environmental uncertainty is linked to the natural complexity of the context (the real world), where it is crucial to distinguish between cause-effect relationships and other phenomena that are not causally linked. If we cannot understand the context and its determining relationships, we risk being confused by the contradictory meanings conveyed by some phenomena.

Intentional Uncertainty

Intentional uncertainty arises from the fact that, in many cases, the decision-maker relies on variable criteria even when the generated choice alternatives are concrete and objective. In other words, humans make final decisions based on subjective perspectives or preferences, even in the presence of a standard decision-making process. This context with a strong subjective imprint is further accentuated by a decision-making model in which the available options are characterized by what we can define as an "undifferentiated alternative," where the choices do not present a clear distinctive factor.

Summary on the Concept of Uncertainty

A decision-making process affected by uncertainty can be characterized by three different sources of uncertainty: informational, environmental, and intentional. Environmental uncertainty arises from the unpredictability of the environment, while intentional uncertainty originates from individuals' specific preferences and needs. Informational uncertainty, although considered manageable to the extent that one can understand and describe the causal relationships between variables and acquire the most complete information possible, remains difficult to manage due to the unpredictability of some real-world events, whose nature is often determined by components linked to intentional uncertainty.

Article source: Linkedin Article by Vincenzo Gioia

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