A while back, Bruce Skarin asked for an explanation of the bifurcations in a nuclear core model. I can’t explain that model well enough to be meaningful, but I thought it might be useful to explain the concept of bifurcations more generally.
A bifurcation is a change in the structure of a model that brings about a qualitative change in behavior. Qualitative doesn’t just mean big; it means different. So, a change in interest rates that bankrupts a country in a week instead of a century is not a bifurcation, because the behavior is exponential growth either way. A qualitative change in behavior is what we often talk about in system dynamics as a change in behavior mode, e.g. a change from exponential decay to oscillation.
This is closely related to differences in topology. In topology, the earth and a marble are qualitatively the same, because they’re both spheres. Scale doesn’t matter. A rugby ball and a basketball are also topologically the same, because you can deform one into the other without tearing.
On the other hand, you can’t deform a ball into a donut, because there’s no way to get the hole. So, a bifurcation on a ball is akin to pinching it until the sides meet, tearing out the middle, and stitching together the resulting edges. That’s qualitative.
Just as we can distinguish a ball from a donut from a pretzel by the arrangement of holes, we can recognize bifurcations by their effect on the arrangement of fixed points or other invariant sets in the state space of a system. Fixed points are just locations in state space at which the behavior of a system maps a point to itself – that is, they’re equilbria. More generally, an invariant set might be a an orbit (a limit cycle in two dimensions) or a chaotic attractor (in three).
A lot of parameter changes in a system will just move the fixed points around a bit, or deform them, without changing their number, type or relationship to each other. This changes the quantitative outcome, possibly by a lot, but it doesn’t change the qualitative behavior mode.
In a bifurcation, the population of fixed points and invariant sets actually changes. Fixed points can split into multiple points, change in stability, collide and annihilate one another, spawn orbits, and so on. Of course, for many of these things to exist or coexist, the system has to be nonlinear.
My favorite example is the supercritical pitchfork bifurcation. As a bifurcation parameter varies, a single stable fixed point (the handle of the pitchfork) abruptly splits into three (the tines): a pair of stable points, with an unstable point in the middle. This creates a tipping point: around the unstable fixed point, small changes in initial conditions cause the system to shoot off to one or the other stable fixed points.
Similarly, a Hopf bifurcation emerges when a fixed point changes in stability and a periodic orbit emerges around it. Periodic orbits often experience period doubling, in which the system takes two orbits to return to its initial state, and repeated period doubling is a route to chaos.
I’ve posted some model illustrating these and others here.
A bifurcation typically arises from a parameter change. You’ll often see diagrams that illustrate behavior or the location of fixed points with respect to some bifurcation parameter, which is just a model constant that’s varied over some range to reveal the qualitative changes. Some bifurcations need multiple coordinated changes to occur.
Of course, a constant parameter in one conception of a model might be an endogenous state in another – on a longer time horizon, for example. You can also think of a structure change (adding a feedback loop) as a parameter change, where the parameter is 0 (loop is off) or 1 (loop is on).
Bifurcations provide one intuitive explanation for the old SD contention that structure is more important than parameters. The structure of a system will often have a more significant effect on the kinds of fixed points or sets that can exist than the details of the parameters. (Of course, this is tricky, because it’s true, except when it’s not. Sensitive parameters may exist, and in nonlinear systems, hard-to-find sensitive combinations may exist. Also, sensitivity may exist for reasons other than bifurcation.)
Why does this matter? For decision makers, it’s important because it’s easy to get comfortable with stable operation of a system in one regime, and then to be surprised when the rules suddenly change in response to some unnoticed or unmanaged change of state or parameters. For the nuclear reactor operator, stability is paramount, and it would be more than a little disturbing for limit cycles to emerge following a Hopf bifurcation induced by some change in operating parameters.