On Capital & Skills

This post is the third in our series of introductory blog posts to help readers understand the fundamentals of social labs. Here we’ll look at ROI and start looking at the skill requirements for labs. 

Social Labs – Return on Investment

One of the challenges of social labs is how to evaluate impacts. We have found that the idea of multiple “capitals” provide a way of assessing the impact of labs.

If we invest in social labs, what results do we get?

One way of understanding complex social challenges is that they are collective action problems where some form of capital is being depleted.

Examples abound. With environmental challenges characterized by the “tragedy of the commons” we are rapidly depleting natural capital available in the commons to the point of risking ecosystem collapse. With challenges such as poverty alleviation we are looking at a decline in multiple capitals, for example one set of skill becoming redundant, or the lack of financial capital to support entrepreneurship and so on.

Successful social labs generate capital – and in particular social labs can be used to re-generate different forms of capital in order to address specific challenges. A mature “next generation” social lab is therefore an asset in a society because it is the source of much needed capital.

The 5 Stages of Skill Acquisition

To become competent you must feel bad. – Hubert Dreyfus

Social labs require a broad range of skills sets. In running multiple labs we’ve seen that in practice these skills are acquired in stages.Consider one model of how we acquire skills, the Dreyfus Model, which takes us through five stages of skills acquisition.

Contrasting the the “novice” with the “expert”:

The beginner is then given rules for determining actions on the basis of these features, just like a computer following a program.”

Versus

The expert not only sees what needs to be achieved; thanks to his or her vast repertoire of situational discriminations, he or she also sees immediately how to achieve this goal. Thus, the ability to make more subtle and refined discriminations is what distinguishes the expert from the proficient performer.”

Dreyfus goes on to say that the expert does not actually perform a situational analysis but is largely operating by intuition.

“Thus, the ability to make more subtle and refined discriminations is what distinguishes the expert from the proficient performer. Among many situations, all seen as similar with respect to plan or perspective, the expert has learned to distinguish those situations requiring one reaction from those demanding another.

That is, with enough experience in a variety of situations, all seen from the same perspective but requiring different tactical decisions, the brain of the expert gradually decomposes this class of situations into subclasses, each of which requires a specific response. This allows the immediate intuitive situational response that is characteristic of expertise.”

The implication of all this is that “toolkits” – 2-d documents that present a series of “tools” – are really useful for “novices” and “advanced beginners.” This is partly because toolkits – and field books – are largely decontextualized. They do not have much to say about the specific situation you find yourself in.

A good “toolkit” however can help accelerate the learning process, as we’ll see below.

While the nature of all expertise is situational, this is perhaps even more true with social labs. This is because we are trying to deal with complex social challenges where the “complex” frequently involves people and their behavior.

While every context is different, so for example, a community in Kenya dealing with a mega-project is different from a government department in Canada dealing with a healthcare issue – we learn to recognize classes of situations. We learn to recognize situations where certain approaches will work. Developing this discernment is what makes a practitioner good at what they do.

And the only way of developing this discernment is experience.

Short-Circuiting the 10,000 Hour Rule

“Managers tend to pick a strategy that is the least likely to fail, rather then to pick a strategy that is most efficient,” Said Palmer. ” The pain of looking bad is worse than the gain of making the best move.”

― Michael Lewis, Moneyball: The Art of Winning an Unfair Game

According to the 10,000 hour “rule” (also known as the “10 year rule”) in order to become “expert” at something we need to practice it for 10,000 hours. While not strictly a “rule” it is a useful “rule-0f-thumb” in terms of thinking about what it means to be world class at something.

The good news is that studies on skills acquisition, for example in the world of professional sports, however, have shown that the “rule” is not strictly true. In fact some people can become world class with a lot less and some don’t become world- class with twice as much practice. One way of thinking about this is that getting world-class at something comes from a combination of “software” – that is, training and practice, and “hardware” – things like genes and neurons.

The skills required to successfully run social labs are extremely broad. They range from group facilitation skills to storytelling skills. It is virtually impossible for any one person to be world-class at all of them. Another way of understanding this is that the range of skills required in order to deal with complex challenges required a group characterized by a diverse range of skills.

In many ways the strategy for building a team that is world-class at running successful social labs is no different from strategies for putting together world-class football teams. You have to acquire talent.

One of the biggest challenges to running social labs at the moment is that it is not seen as a full-time, professional activity. At best it’s a full-time professional activity and at worst it’s a volunteer role, done on top of “real world” work. This is a little like the days when barbers were also doctors. As the field matures, we will see the rise of full-time, professional teams.

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