Complexity Partners

Making Sense in a Complex World

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Decision Making: Risk vs Uncertainty

The trouble with decision making (DM) as a topic is its complex character which is doesn’t lend itself well to unpack it in a linear mode. So I won’t, and even won’t follow the (seemingly linear) steps laid down as a baseline in one of the first blogs but coming back to them later. For as soon as we want to dive further into our process, we have to determine first which domain we are finding ourselves in. An ordered domain or a complex one?

To begin with, most people seem to conflate #risk with #uncertainty. Risk is mapped to the ordered/complicated domain and can be calculated. Whether that is the probability of winning or losing at the casino, or the risks of flying an airplane under normal conditions. With uncertainty, all bets are off. The outcome cannot be known or calculated. Both conditions require very different approaches to decision making. The best decision under risk is not the best decision under uncertainty.

Interesting added dimension: DM researcher @Gerd Gigerenzer states that with high uncertainty, meaning: unstable conditions, only a few data available but many variables, experienced experts do better relying on their (trained!) #intuition or simple #heuristics rather than trusting complicated algorithms and calculation models as DM support. Novices should instead train their intuition first before relying on it. And for anything that can be calculated (risk), algorithms can help. Most DM researchers argue for a good mix of the use of intuition and formal decision making support.

Both domains, risk and uncertainty harbour more layers of difficulties for decision making. Gigerenzer states that most people don’t have a natural understanding of probabilities expressed in percentages. His research shows for example, that a shockingly high number of medical doctors don’t understand their own statistical data in test results or risks. As a most infamous example he mentions a UK press conference about pharmaceutical drug safety where an announcement scared tens of thousands of women into ceasing to take the new generation anti baby pill: They stated a thrombosis risk of 100% (!) against the first generation pills. But 100% of what? The real (relative) data that wasn’t released at first stated that of every 7000 women, the first-generation pill showed 1 case of thrombosis, the 3rd generation showed 2 women out of 7000 with thrombosis. From 1 to 2 an increase of 100%; correct and misleading.

Another trap is lurking for intuitive decision makers. Usually they can’t explain their intuitions and hunches, as tacit knowledge can’t really be explained. In some areas where mistakes tend to be punished (hospitals) rather than being used to learn (aviation), most often this leads to defensive decision making. Experts tend to go against their better judgement (intuition) and choose or recommend inferior solutions that won’t get them either fired or sued. According to Gigerenzer, far over 90% of medical doctors in the USA prescribe medicines for protective reasons not for clinical reasons. Protection against the patient. With detrimental consequences for the whole industry and the people in it (#fagilistas). 

Gerd Gigerenzer (2014):“Risk Savvy: How to Make Good Decisions”. Penguin Books ; Gerd Gigerenzer (2008):“Gut Feelings: the Intelligence of the Unconscious”, Penguin Books

Photo by Carl Raw on Unsplash


Black Box: How do we make decisions?

With the amount of decisions that we make every day, it is astonishing that the process of making decisions is not well understood. So how do we make the best choice?

The very act of deciding seems a bit like the proverbial piece of soap in the bathtub: the more you want to get a grip on it the more it slips away. Much is written today about VUCA conditions, and decision making in complex adaptive spaces with highly uncertain outcomes, volatile ingredients and complex relationships are a different animal all together to deal with. We run an Adaptive Leadership training some time ago with top level leaders from the wider UNO network. We wanted to test if their complexity of thinking was matching the complexity of their jobs and run a Decision Making Assessment (LDMA; from Lectica). These leaders were presented with an ill-structured dilemma (no right or wrong solution) to which they had to come up with ways of responding and deciding and their reasoning. When asked about to portrait decision making process in a way that it could be followed or repeated by others, much to our surprise most came up with a list of action rather than some decision making process. That made us even more curious. We ventured more into this terrain.

The weird thing is that even in ‘normal’ conditions people are not aware of how they make choices. Some people pose their questions attentively, gather relevant information superbly and then “wing” it with the actual act of deciding. And then come up with a perfect explanation in hindsight.

So, starting to establish a baseline around decision making, let’s consider basic steps, drawing on the Lectical Decision Making Assessment and Russo & Schoemaker (Winning Decisions):

  1. Framing: the general goal of the decision maker including the way they think about the knowledge upon which they base their decision
  2. A realistic approach to gathering intelligence
  3. Coming to Conclusions: organising and analysing the information and a way to coordinate different perspectives (weighing)
  4. An approach to communicating and implementing the decision made
  5. Learning from Experience, including a way to measure the decision’s effectiveness so adjustments can be made

In the next blog snippet, I will elaborate a bit more on the single steps, each provides rich ground for further exploration.

Outlook: In some next blogs I intend to bring in more and more layers of decision making, exploring input from different topics, authors, influenzers  and frameworks: Dave Snowden, Gary Klein, Bonnitta Roy, Gerd Gigerenzer, Andy Clarke;; concepts/models/ methods: Framing, Cognitive Biases; Intuition; Sensemaking; Cynefin Framework, OODA Loop, Risk vs. Uncertainty, Heuristics, Constraints, Learning, Failure, Innovation, Theory of Change