Organizational leaders set policies, intending to shape behavior. Policies will not change external forces outside the organization. People both crave and resent organizational policies. The response to policies is often unexpected (both positive and negative). History is replete with examples of failed policies and surprised leaders.
There is a lesson about setting policies in the novel Dune. Duke Leto tells his son Paul, “Give as few orders as possible. Once you’ve given orders on a subject, you must always give orders on that subject.” Bureaucrats and administrators – with sincere intentions – tend of proliferate policies.
The common trap for policymakers is forgetting that the real world, with actual people in a constellation of societies, in real economies, is a complex adaptive system. Policies which assume otherwise will shred and decay. Leaders continuing to wishfully believe that flawed assumption build policies on top of policies in desperate attempts to “fix” situations.
Let’s remind ourselves about the properties of complex adaptive systems (CAS), and then consider how best to design policies.
CAS cannot be accurately described by linear models with constant assumptions. They do not mechanistically trend to a desired equilibrium. CAS have high non-linearity. They always contain elements that drive instability. A CAS can be resilient but is not inherently predictable. CAS are like biological systems, not car engines – we don’t even understand all the parts or how they relate to one another.
Seven suggestions for thinking wisely about policies in a CAS world:
- Consider the tradeoff between efficiency on one dimension, and sustainable resilience overall. Decide which problems you prefer to have.
- Buffers, margin, and modularity can increase resilience.
- Guard against the worst downside case rather than maximize one upside dimension.
- Expect the unexpected. Anticipate people “working around” a policy. Think about leading indicators which give you time and space to adjust to an unwanted system dynamic.
- Think in terms of scenarios; there is no such thing as a single point probability forecast for a CAS.
- Change a policy rather than “stacking” an additional policy as a “solution.”
- Accept that no policy is “perfect” because a CAS demands you work in terms of tradeoffs.
