Big Picture: Extinction Systems and Reliable Decision-Making

My last post was very specific. I said that the big picture wasn't quite clear yet. Well, it's not. But it's not entirely unclear so as not to be blogged about! Here's a brief outline of what I'm thinking now.

So, the first chapter studies the problem of making reliable decisions given priors and hypothetical inputs (management plans or intended outcomes) to a Bayesian model and a complex system of extinction (perhaps in general or something like frogs in particular). (The previous post details where I'm heading with respect to this chapter.)

The second and third chapters go together. We will take the decision-making process one-step further by considering the adaptive management framework in conservation biology. So, now we can make plans and decisions by considering updated priors and new hypothetical inputs. In the second chapter we will introduce adaptive management and discuss the conditions a system must meet to be amenable to adaptive management. In the third chapter we will discuss Bayesian adaptive management and some other popular framework(s) (so, might have to include these in the first chapter discussion as well) and compare them for optimal decision making in the adaptive management framework. One might be optimal for this context, the other that, etc. Whatever.

The fourth and fifth chapters go together. We will take note that the previous chapters have the beginnings of an algorithm or heuristic device for making decisions under various kinds of uncertainty (from highest level of uncertainty to less degrees of it). In the fourth chapter we will make the case that current algorithms in conservation biology are good but lacking/bad but not unsalvageable. Again, whatever. In the fifth chapter we will present our algorithm and consider extensions to other forms of uncertainty and systems relevant to extinction concerns.

Finally, in the sixth chapter we will examine how this algorithmic structure of conservation biology fits with accounts of discipline/theory structure from the philosophy of science. It's not laws, it's not syntactic, it's not semantic, it's algorithmic. Input a particular problem and system of uncertainty and output a reliable decision making protocol.

Obviously this will change (and it has changed; I just haven't shared every version). But, it's a start.