The Fortune magazine has a special issue on Decision-Making. There are plenty of useful remarks for decision makers, but there is also some food for thought for Machine Learning scientists!
In a way, making decision is the ultimate goal of artificial intelligence, and the learning aspect is a key element in this process. There is a deep connection between Machine Learning and decision theory.
Vladimir Vapnik, one of the founders of statistical learning theory likes to use this quote
Subtle is the Lord, but malicious he is not
The way he interprets this in the context of learning from data is:
the reality might be hard or even impossible to model (and understand) accurately, but there still can be ways to make the good decisions.
One consequence of this idea is that, when one infers a model from data, one should judge the quality of the model not from the point of view of how it fits the "true model", but from the point of view of how good are the decisions one makes based on this model, and the goodness of a decision is not measured as how different it is from an hypothetical "ideal decision", but should be measured by how much return one gets, compared to the best possible return.
Going back to the Fortune articles, here is an interesting quote that shows that directly relates to the well-known overfitting phenomenon in machine learning (you think you got a pattern but you just got a coincidence):
People are clinically overoptimistic, for
instance, assigning zero probability to events that are unlikely but
not impossible (such as a massive iceberg in path of a really big
ship). We see "patterns" in the random movements of stocks, just as
people once saw bears and swordsmen in the scatterplot of the nighttime
sky. We make choices that justify our past choices and then look for
data to support them. We not only make these errors; we make them
Another thing of interest is the interview of Jim Collins
about decision-making. In particular he says the following
World is uncertain : great decisions stem from saying "I don't know"
This emphasizes one critical aspect of decision making: uncertainty handling. When making a decision, one must not only estimate the odds of the possible outcomes but also assess the uncertainty in this estimate.
He also talks about how important it is to talk to people and to provoke debates:
Decisions are not about strategy and consensus, they are about people and discussions. People because only them can use their experience to adapt to real-life situations and constant changes. Discussions, because great leaders are good at igniting the dialog and debate among teams with various experiences, using Socratic questions.
Translated into Machine Learning/Process Intelligence terms, this means that you should use user's knowledge as much as possible, and integrate opinions and experiences into your decision-making procedure. The goal being to extract information and thus reduce uncertainty. An ideal decision support machine would thus ask the right questions to the right persons, relay information between people (in order for them to compare with their own experience) so that they can react, and finally integrate all the information into quantitative assessments of the possible consequences of the various decisions that can be made.
There are surely other ideas to be taken from "human decision-making" in order to build better decision support systems. The key being to avoid eliminating humans from the process, but rather using them as much as possible for what they are good for.