Irreversible evolution with rigor

We have now seen that man is variable in body and mind; and that the variations are induced, either directly or indreictly, by the same general causes, and obey the same general laws, as with the lower animals.

— First line read on a randomly chosen page of Darwin’s The Descent of Man, in the Chapter “Development of Man from some Lower Form”. But this post isn’t about natural selection at all, so that quote is suitably random.

The intuition of my previous post can be summarized in a relatively inaccurate but simple figure:

In this figure, the number of systems is plotted against the number of components. As the number of components increase from 1 to 2, the number of possible systems greatly increase, due to large size of the space of all components (\mathbf{C}). The number of viable systems also increase, since I have yet to introduce a bias against complexity. In the figure, blue are the viable systems, while dashed lines for the 1-systems represent the space of unviable 1-systems.

If we begin at the yellow dot, an addition operation would move it to the lowest red dot. Through a few mutations — movement through the 2-system space — the process will move to the topmost red dot. At this red dot, losing a component is impossible, since losing a component would make it unviable. To lose a component, it would have to back mutate to the bottommost red dot, an event that, although not impossible, is exceedingly unlikely if \mathbf{C} is sufficiently large. This way, the number of components will keep increasing.

The number of components won’t increase without bound, however, as I said in my last post, once 1-(1-p_e)^n is large, there is enough arrows emanating from the top red dot (instead of the one arrow in the previous figure) that one of them is likely to hit the viable blues in the 1-systems. At that point, this particular form of increase in complexity will cease.

I’d like to sharpen this model with a bit more rigor. First, however, I want to show a naive approach that doesn’t quite work, at least according to the way that I sold it.

Consider a space of systems \mathbf{S} made up linearly arranged components drawn from \mathbf{C}. Among \mathbf{S}  there are viable systems that are uniformly randomly distributed throughout \mathbf{S}; any S\in\mathbf{S} has a tiny probability p_v of being viable. There is no correlation among viable systems, p_v is the only probability we consider. There are three operations possible on a system S: addition, mutation, and deletion. Addition adds a randomly chosen component from \mathbf{C} to the last spot in S (we will see that the spot is unimportant). Deletion removes a random component from S. Mutation mutates one component of S to another component in \mathbf{C} with uniformly equal probability (that is, any component can mutate to any other component with \dfrac{1}{|\mathbf{C}|-1} probability). Each operation resets S and the result of any operation has p_v of being viable.

Time proceeds in discrete timesteps, at each timstep, the probability of addition, mutation, and deletion are p_a, p_m and p_d=1-p_a-p_m respectively. Let the system at time t be S_t. At each timestep, some operating is performed on S_t, resulting in a new system, call it R_t. If R_t is viable, then there is a probability p_n that S_{t+1}=R_t, else S_{t+1}=S_t. Since the only role that p_n plays is to slow down the process, for now we will consider p_n=1.

Thus, if S=C_1C_2...C_n:

Removal of C_i results in C_1C_2...C_{i-1}C_{i+1}...C_n,

Addition of a component B results in C_1C_2...C_nB

Mutation of a component C_i to another component B results in C_1C_2...C_{i-1}BC_{i+1}...C_n

Let the initial S be S_0=C_v, where C_v is viable.

Let p_v be small, but \dfrac{1}{p_v}<|\mathbf{C}|.

The process begins on C_v, additions and mutations are possible. If no additions happen, then in approximately \dfrac{1}{p_m\cdot p_v} time, C_v mutates to another viable component, B_v. Let’s say this happens at time t. Since p_n=1, S_{t+1}=B_v. However, since this changes nothing complexity-wise, we shall not consider it for now.

A successful addition takes approximates \dfrac{1}{p_a\cdot p_v} time. Let this happen at t_1. Then at t=t_1+1, we have S_{t_1+1}=C_vC_2.

At this point, let us consider three possible events. The system can lose C_v, lose C_2, or mutate C_v. Losing C_2 results in a viable C_v, and the system restarts. This happens in approximately \dfrac{2}{p_d} time. This will be the most common event, since the chance of resulting in a viable C_2 or going through mutation to become a viable C_3C_2 are both very low. In fact, C_vC_2 must spend \dfrac{2}{p_mp_v} time as itself before it is likely to discover a viable C_3C_2 through mutation, or \dfrac{2}{ p_dp_v} before it discovers a viable C_2. The last event isn’t too interesting, since it’s like resetting, but with a viable C_2 instead of C_v, which changes nothing (this lower bound is also where Gould’s insight comes from). Finding C_3C_2 is interesting, however, since this is potentially the beginning of irreversibility.

Since we need \dfrac{2}{p_mp_v} time as C_vC_2 to discover C_3C_2, but each time we discover C_vC_2, it stays that way on average only \dfrac{2}{p_d} time, we must discover C_vC_2 \dfrac{p_d}{p_mp_v} times before we have a good chance of discovering a viable C_3C_2. Since it takes \dfrac{1}{p_a\cdot p_v} for each discovery of a viable C_vC_2, in total it will take approximately

\dfrac{1}{p_a p_v}\cdot\dfrac{p_d}{p_mp_v}=\dfrac{p_d}{p_ap_mp_v^2}

timsteps before we successfully discover C_3C_2. Phew. For small p_v, we see that it takes an awfully long time before any irreversibility kicks in.

Once we discover a viable C_3C_2, there is 1-(1-p_v)^2 probability that at least one of C_3 and C_2 are viable by themselves, in which case a loss can immediately kick in to restart the system again at a single component. The number of timesteps before we discover a viable C_3C_2 in which neither are viable by themselves is:

\dfrac{p_d}{p_ap_mp_v^2(1-(1-p_v)^2)} .

Unfortunatly this isn’t quite irreversibility. Now I will show that the time it takes for C_3C_2 to reduce down to a viable single component is on the same order as what it takes to find viable C_3C_4C_5 or C_4C_2C_5,  in which all single deletions (for C_3C_4C_5, the single deletions are: C_4C_5, C_3C_5, and C_3C_4) are all unviable.

We know that C_3 and C_2 are unviable on their own. Thus, to lose a component viably, C_3C_2 must mutate to C_3C_v (or C_vC_2), such that C_3C_v (or C_vC_2) is viable and C_v is also independently viable. To reach a mutant of C_3C_2 that is viable takes takes \dfrac{1}{p_mp_v} time. The chance the mutated component will itself be independently viable is p_v. Thus, the approximate time to find one of the viable systems C_3C_v or C_vC_2 is \dfrac{1}{p_mp_v^2}. To reach C_v from there takes \dfrac{2}{p_d} time, for a total of


time. It’s quite easy to see that to go from C_3C_2 to a three component system (either C_3C_4C_5 or C_4C_2C_5) such that a loss of a component renders the 3-system unviable, is also on the order of \dfrac{1}{p_v^2} time. It takes \dfrac{1}{p_ap_v} to discover the viable 3-system C_3C_2C_5, it then takes \dfrac{2}{3\cdot p_mp_v} time to reach one of C_3C_4C_5 or C_4C_2C_5 (two thirds of all mutations will hit either C_3 or C_3, of these mutation, p_v are viable). Each time a viable 3-system is discovered, the system tends to stay there \dfrac{3}{p_d} time. We must therefore discover viable 3-systems \dfrac{2p_d}{3\cdot 9p_mp_v} times before we have a good chance of discovering a viable 3-system that is locked-in and cannot quickly lose a component, yet remain viable. In total, we need


time. Since p_m, p_a, p_d are all relatively large numbers (at least compared to p_v), there is no “force” for the evolution of increased complexity, except the random walk force.

In the next post, I will back up statements with simulations and see how this type of processes allows us to define different types of structure, some of which increases in complexity.

2 Responses to Irreversible evolution with rigor

  1. You really should find a way to describe this clearly and rigorously with pictures (don’t try to draw graphs of the type you sketch on whiteboards, but instead try a graph theory kind of graph with directed edges labeled by probabilities). I think this will make it easier for you to explain (and maybe even understand) your thoughts more clearly. You’ve presented this to me in person before, and yet I still couldn’t really follow the description in this post. If you are going to throw in random math-jargon, try to be complete with inequalities and such whenever you say something is smaller. Also avoid words like “much larger” or “very very small” unless you are making an approximation in that step (for instance, if you are making a first order approximation of something, you might say p >> p^2 or some such to carry through your argument. Otherwise the words are just distracting.

    The most important comment I can make is to figure out where to be general and where not to be general. Could the gist of this had been described with binary strings WLOG? Or where could you have made obvious WLOG assumptions? I think I can see a few places. The best way to do this, I think, is to apply your ideas to concrete models instead of trying to talk about them abstractly.

  2. Pingback: An update | Theory, Evolution, and Games Group

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