If you are in the project management (read: risk management) business, one of the best books that describes the philosophy and foundation for modern risk management is Peter L. Bernstein's "Against the Gods: the remarkable story of risk".
Against the Gods is historical, somewhat philosophical, and void of math!
It's a book for "thinkers"
Between the covers of this "must read" we learn this bit:
The essence of risk management lies in maximizing the areas where we have some control over the outcome while minimizing the areas where we have absolutely no control over the outcome and the linkage between effect and cause is hidden from us.
Peter Bernstein
"Against the Gods: The Remarkable Story of Risk"
Knowledge and controlDealing with risk necessarily breaks down into that in which more knowledge will help us understand deal with risk (climate change), and that in which effects are truly random and no amount of additional knowledge is going to help (rolling dice).
Bernstein goes on to develop one of the key themes of the book which is the idea that probability theory and statistical analysis have revolutionized our ability to understand and manage risk.
Picking apart Bernstein's "essence" separates matters into control and knowledge:
- We know about it, and can fashion controls for it
- We know about it, and we can't do much about it, even if we understand cause and effect
- We know about it, but we don't understand the elements of cause and effect, and so we're pretty much at a loss.
- We don't know about it, or we don't know enough about it, and more knowledge would help.
Of course, Donald Rumsfeld, in 2002, may have put it more famously:
" ....... because as we know, there are known knowns; there are things we know we
know. We also know there are known unknowns; that is to say we know
there are some things we do not know. But there are also unknown
unknowns—the ones we don't know we don't know."
No luck
So there is an ah-hah moment here: if all things have a cause and effect, even if they are hidden, there is no such thing as luck. (Newtonian physics to the rescue once again)
Thus, as a risk management regimen, we don't have to be concerned with managing luck! That's probably a good thing (Ooops, as luck may have it, if our project is about the subatomic level, then the randomness of quantum physics is in charge. Thus: luck?)
Indeed, our good friend Laplace, a French mathematician of some renown, said this:
Present events are connected with preceding ones by a tie based upon the evident principle that a thing cannot occur without a cause that produces it. . . .
All events, even those which on account of their insignificance do not seem to follow the great laws of nature, are a result of it just as necessarily as the revolutions of the sun.
Bernstein or Bayes' (with help from ChatGPT)
Following up on the idea of the knowledge-control linkage to risk management, Bayes' Theorem comes to mind. Bayes' is all about forming a hypothesis, testing it with real observations, and using those outcomes to refine the hypothesis, eventually arriving at a probabilistic description of the risk.
LaPlace, mentioned above, is one of the architects of the probability theory that underlay Bayes'. Thus, one of the most interesting discussions in the book centers on Bayes' theorem, which Bernstein describes as "one of the most powerful tools of statistical analysis ever invented."
Bayes' theorem is a manner of reasoning about random and unknown effects and a mathematical formula that allows us to update our beliefs about the probability of an event occurring based on new evidence. It is a powerful tool for making predictions and decisions based on incomplete information, and it has applications in fields ranging from medicine to finance to engineering.
Bernstein's discussion of Bayes' theorem in "Against the Gods" is particularly interesting because he highlights the fact that Bayesian reasoning is often at odds with our intuition. Humans have a tendency to overestimate the likelihood of rare events and underestimate the probability of more common events. Bayes' theorem provides a framework for overcoming these biases and making more accurate predictions.
Cognitive Bias in risk management
Bernstein talks a lot about cognitive biases and their impact on decision-making under uncertainty.
According to Bernstein, cognitive biases are mental shortcuts that people use to simplify complex decisions. These shortcuts can lead to errors in judgment and decision-making. Cognitive biases can be influenced by a number of factors, including emotions, personal experience, and cultural values.
Some examples of cognitive biases that Bernstein discusses in the book include the availability bias, which is the tendency to overestimate the likelihood of events that are more easily recalled from memory; and the confirmation bias, which is the tendency to look for information that confirms our existing beliefs and to ignore information that contradicts them.
One key point Bernstein makes is that humans have a natural tendency to be overconfident in their abilities to predict and control events. This is known as the "illusion of control" bias. People often believe they have more control over events than they actually do, leading them to take on more risk than is rational.
Another common cognitive bias is the "confirmation bias," in which people seek out information that confirms their preexisting beliefs, while ignoring or dismissing information that contradicts those beliefs. This can lead to a lack of objectivity in decision-making.
Bernstein also discusses the "hindsight bias," in which people tend to believe that an event was more predictable after it has already occurred. This bias can lead to overconfidence in future predictions, as people may believe that they could have predicted the outcome of an event that has already occurred.
Overall, Bernstein suggests that understanding and being aware of cognitive biases is essential to making better decisions and managing risk effectively. By recognizing these biases, individuals can take steps to mitigate their impact on their decision-making processes.
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