Practical Reasoning: Decision making in Rainforest innovation ecosystemsPosted: March 14, 2015
“If scientific reasoning were limited to the logical processes of arithmetic, we should not get very far in our understanding of the physical world. One might as well attempt to grasp the game of poker entirely by the use of the mathematics of probability.” Vannevar Bush, Founder of the US National Science Foundation.
We ended our February blog with the question “So, what can we do if we must make decisions regarding wicked problems but cannot use deduction?” Wicked problems are intractable, interconnected, problems with unclear cause and effect connections – see January’s blog for details of how wicked problems are defined. In this blog I will suggest that not being able to use deduction does not mean we cannot use reason and deliberation.
We can measure factors or variables as the basis for decision making because complex adaptive systems have ‘basins of stability’ – as introduced in our August 2014 blog – which are steady state systems maintained by the feeding in of external energy. For corporations and innovation ecosystems this equilibrium is a kind of order. In the language of complexity these steady states, regions of quasi-stability or system-level order, are known as ‘attractors.’ Empirical research has shown that in large complex systems such as communities and corporations, these attractors maintain conditions required for emergent self-organization, adaptive capability – and measurement.
The Rainforest Scorecard: A Practical Framework for Growing Innovation Potential process and scoring model introduced in our January bonus-blog Measuring Culture, Performance, and Innovation seeks to describe an ideal organizational ‘system state’—the aggregate set of conditions or features of systems that are generally present in innovative organizations. This idealized model is in turn used as a gauge against which organizations can measure and evaluate their own state of innovation.
Buridan’s donkey, named after the 14th century French philosopher Jean Buridan, had a problem in trying to make a rational decision using deduction. This hungry donkey standing midway between two equally nourishing looking piles of hay is unable to make a rational decision to choose one pile over the other and consequently dies of hunger.
Another philosopher, David Milligan, in his book Reasoning and the Explanations of Actions, written in 1980 but still fresh, explained that a good deliberative reasoner is “not someone who simply obeys the rules of logic,” but someone who is also a sound judge and can defend his or her decisions about how to act by pointing to reasons which supports them.
“The deductivist [a person using deduction] tries to reduce the elements of sound judgment and correct evaluation either to the application of logic or to a kind of subjective response.” Milligan does not talk about complex systems or linearity as such, although everything he discusses applies (another example, as as we have seen before, and will see again, of the significance of philosophy in understanding complexity). Rather than downgrading the importance of logic, the author is trying to show that “reason is far wider and has a far more important role in action than might appear from the deductive list account.” Milligan’s work launches us into the necessary search for non-deductive ways of reasoning and decision making where there is an abundance of wicked problems – which is almost everywhere.
Another feature to take into account is that decision making will always be ‘bounded’ – that is we cannot know all the factors which possibly should be taken into account when making a decision, and thus we cannot reach an optimal solution. This concept was first proposed by the economist Herbert Simon as an alternative basis for the mathematical modeling of decision making; we will have to be satisfied with a less than optimal solution. The decision-maker is thus sometimes referred to as a ‘satisficer’ – someone who is satisfied with a good enough solution. For readers of this series the advantages of sub-optimum solutions will sound familiar (e.g. Imperfect Works Feb 2013 blog in this series. More about satisficers and maximizers in future blogs.
We should point out that being satisfied with boundedness and sub-optimality does not imply accepting insufficient depth of knowledge of those factors we do know about.
All this may be sounding a bit abstract, so in April’s blog we will apply these ideas of non-deductive reasoning to trying to choose one of two options for the solution to a wicked problem.