Design Interventions
Interventions in a complex world
In a complex world, there is no reliable correlation between interventions and effect. Rather, an intervention is an experiment: the effect may or may not be the expected one.
A prerequisite for dealing with it productively is
- system thinking: connections are not linear, but follow feedback cycles
- interventions are experiments
In a world in flux, it is necessary to keep pace with change; if possible, to be faster and to drive change. Therefore: experiments.
Why experiments
What makes a good experiment?
Experiments provide quick feedback, cost little or nothing, no one has to agree to them, and they are easy to do.
Before starting an experiment, it is helpful to answer these nine questions:
- Which factors determine the current situation?
- Which factors can be controlled or influenced?
- Why should this experiment be done, and what question(s) can it answer?
- What can be observed about the current situation?
- How can you tell if the experiment is moving the situation in the desired direction or causing pleasing side effects?
- How can you tell if the experiment is moving in an unwanted direction with unwanted effects?
- What is the natural time frame for this experiment? When will you see results?
- If the experiment makes things worse, how can you resolve the situation?
- If the situation improves, how could you expand the experiment?
Source
adapted from the concept in: Derby, Esther. March 2017. ‘Change Artist Super Powers: Experimentation – Esther Derby Associates, Inc.’ Accessed 22 January 2019. . Presentation adapted.
The Paltchinsky Principles for Experiments
The Russian engineer Pjotr Ioakimowitsch Paltschinsky played a major role in the introduction of Tayloristic principles into Russian industry after the February Revolution of 1917.
He condensed three simple rules for designing experiments:
- Variation: try out new ideas and things
- survivability: experiments should be on a scale where mistakes can be survived.
(This is also the reason why stories should be small. Also to be able to complete them within an iteration – but mainly because they can also be wrong.) - selection: seek feedback and learn from mistakes.
Source
Discovered and adopted from Harford, Tim. 2012. Adapt: Why Success Always Starts with Failure. Farrar, Straus and Giroux. Supplemented with information on Palchinsky from the English Wikipedia entry.
Source
Decision-making process to maximize options
or: the last responsible moment
Identify the options available for each decision.
Identify the latest possible time at which a decision can be made, i.e. the conditions that must be met to enter into an obligation.
Decision time = deadline – option implementation time.
The first decision is made before the first option expires.
Until that moment, you continue to look for new options and refine or expand existing options.
Identify option(s) for each condition and be clear in advance which option should be exercised based on a specific condition.
Try to delay the decision time.
Most of the time this costs nothing or little.
To do this, we need to be able to implement the option as soon as possible. Try to speed up the process in the buffer time.
Understand that cost optimization is not the same as revenue optimization or risk reduction. Sometimes it pays to invest in more than one option, even if it costs a little more. Options have a value.
Wait with the decisions… and wait… and wait… until the conditions are met.
When you have to act… then as fast as possible. You can be sure because you know that you will have made the best possible decision.
