We use all the elements of intelligence to help us understand what our choices really are, drawing on the limited data available to us as well as the mental models we have developed or acquired. This mental task can be thought of in probabilistic terms. At every step, we try to make sense of the probability distribution of different outcomes.

The same is true of learning. Some learning is algorithmic, some is experimental, and much is sequential—what you can learn depends on what you have already learned.

The mark of any intelligent creature, institution, or system is that it is able to learn. It may make mistakes, but it won’t generally repeat them. That requires an ability to organize intelligence into a series of loops, which have a logical and hierarchical relationship to each other.”

First-loop learning is what we recognize as everyday thought. It involves the application of thinking methods to definable questions, as we try to analyze, deconstruct, calculate, and process using heuristics or frameworks”

Second-loop learning becomes relevant when the models no longer work or there are too many surprises. It may be necessary to generate new categories because the old ones don’t work (imagine a group that has moved from a desert environment to a temperate mountain zone), and it may be necessary to generate a new model, for example to understand how the stars move. This second loop also involves the ability to reflect on goals and means.”

Third-loop learning involves the ability to reflect on and change how we think—our underlying ontologies, epistemologies, and types of logic. At its grandest, this may involve the creation of a system of science, or something like the growth of independent media or spread of predictive analytics”

Big Mind: How Collective Intelligence Can Change Our World, Geoff Mulgan

January 20, 2019