Don’t try to tame wicked problems: Part 2Posted: February 9, 2015
“Many development partner tools and business processes deal with static, simple or linear problems. There is considerable demand for new methods and principles that can help development partners better navigate the complex, dynamic realities they face on a day-to-day basis.”
From best practice to best fit: Understanding and navigating wicked problems in international development. Ben Ramalingam, Miguel Laric and John Primrose, UK Department for International Development (DfID).
Don’t try to tame wicked problems: Part 1 introduced ‘wicked problems’ though six typical characteristics of these problems.
In the last few years international development organizations seem to be discovering complex systems and wicked problems. This series of blogs are not intended to be literature reviews, but two examples out of many are:
One question being raised is whether the method of Logical Framework Analysis, also referred to as ‘logframes’ can be used, can be relied upon, when dealing with complex systems generally and with wicked problems in particular.
The Logical Framework Analysis was tested by USAID in the 1970s for evaluation of technical assistance projects, and used extensively by governments, consultants, and international aid and development organizations for project planning and evaluation ever since. Logical Framework design is not an evaluation in itself; it provides a plan of the project against which project progress can be assessed by evaluators. It was also intended to make evaluation less threatening. Furthermore, where there are clear and logical relationships between inputs and outputs this can lead to efficient task delegation.
As noted in our January blog, the behaviors of complex innovation ecosystems don’t fit well into logframes which deal with inputs and outputs and the tasks which produce the latter from the former. To illustrate what we are talking about a rather simplified logframe (it typically is a 4×4 matrix) might look something like this:
|Goal||Improve creation of spin-off companies from universities.|
|Purpose/Outcome||An effective improved company creation system is operating.|
|Output||New spin-off companies developed.
New incentives created.
Increased number of role models and mentors.
New methods in place.
|Input||Analyze problems with current methods to create spin-off companies.
Provide more early stage, start-up, funding.
Find more brokers available to help match R&D needs to sources.
Provide more incentives to researchers.
Identify role models and mentors.
Create an entrepreneurship culture.
Inventory physical and people assets.
This table suggests we can produce a certain set of outputs from a certain set of inputs to achieve the required outcome. These concepts can help us think through a project in an orderly, logical fashion assuming there is a definite cause and effect relationship between any level and the level immediately above it; in wicked problems this is not the case. Cause and effect logic is also the basis for strategy maps and best-practice balanced scorecards. Finally, an emphasis on cause and effect suggests a rational expectations hypothesis, which does not take into consideration extra-rational motives which influence behavior.
At T2 Venture Creation we just published a short book about measuring variables in innovation ecosystems, The Rainforest Scorecard: A Practical Framework for Growing Innovation Potential. The work is a measurement methodology based on complexity characteristics and does not assume linear cause and effect relationships, but does recognize that ‘emergence’ is a critical feature of complex adaptive systems. Measurement however is only the first step; decisions and actions must follow. In the decision making process this traditionally implies deduction – reasoning which links a set of premises with a logical, and necessarily true, conclusion. Probably the best known example of such reasoning is:
- All me are mortal (premise)
- Socrates is a man (premise)
- Therefore, Socrates is mortal (conclusion)
So, what can we do if we must make decisions regarding wicked problems but cannot use deduction? And, furthermore, we will have to make decisions in spaces where indicators of success may be fallible –as discussed in the April 2013 blog in this series Fallibility and the Making of Good Decisions: Solving the right problem Part 2. We shall turn our attention to this question next time.
Next time: Practical Reasoning: Decision making in Rainforest innovation ecosystems.