If we want to have any hope in controlling our destiny we have to understand our environment. In one sense, if we treat our environment as a control system, capable of responding to a stimulus, we need to understand not only its behavior, but how it will respond to the stimulus. One can ask: will it collapse in response to dwindling resources? Or will it rebound and stay resilient? For that we require a good model of the system. And of course, the simpler the model to describe, the better.

The system thinker and cyberneticist Ross Ashby summed it up with two seminal ideas. His simplicity criteria, the "Law of Requisite variety" states, "Variety absorbs variety, defines the minimum number of states necessary for a controller to control a system of a given number of states." Our capabilities thus become limited by the amount of information available to us. The second, the "Good Regulator theorem" goes "every good regulator of a system must be a model of that system". In other words, to regulate any causal system, we should require a model of how the system will behave normally and how it will react to a disturbance. The two relate directly to the classical view of control theory, that of controllability and observability. That may sound a tad idealistic, but that's how an engineer would respond to a problem statement.

The seeming diverse complexity of a system such as the Amazon rain forest, remains in many ways simple to describe. Interestingly, we can actually understand how that system evolves and adapts, given that we have a simple-enough model to work with. To compare and contrast, we can also consider a model of oil resources and how we can understand a seemingly random distribution of reserves.

Further, to have any chance of controlling the behavior we need good observability via good measurements. A human mobility metric is just one example of this, one simple to model, which gives us a good understanding, and one that we can monitor in the future.

For these three cases, (1) biodiversity, (2) oil abundance, and (3) human mobility, I will describe a few simple models based on entropy principles (maximum entropy dispersion, the "entroplet") and working with barest and most minimal information available to us. We will see how far that can take us. The discipline of complex and resilient systems remains wide-open for discussion.