In most conversations about evolution, the words “random” and “stochastic” are used interchangeably. They are entirely different.
“Random” means absence of pattern and purpose.
The word stochastic in English was originally used as an adjective with the definition “pertaining to conjecturing”, and stemming from a Greek word meaning “to aim at a mark, guess”, and the Oxford English Dictionary gives the year 1662 as its earliest occurrence. (Wikipedia)
This conveys the flavor that “stochastic” carries in engineering, where there’s an entire field called Stochastic Control Systems.
A Southwest Airlines 737 flying from Boston to Baltimore is a stochastic control system. The wind is a random variable and the flight path is the goal.
The control system adjusts in response to random variables (wind) in order to land in Baltimore.
The plane’s control system aims at a mark, makes a guess, and corrects as it goes.
Random vs. Stochastic is not arcane quibbling about semantics. It is essential to accurately model evolution.
In Darwinian evolution, mutations were always traditionally assumed to be random; the only correction, or aim, is supplied by natural selection.
When my brother confronted me with this question in 2004, I thought, “In engineering I have never seen a system that is optimized only by replication, variation and selection. It always has some controlling or correcting mechanism.”
Was I wrong? Did the biologists know something I didn’t know? I guessed I might harbor all manner of erroneous notions. I was entirely willing to turn my worldview upside down if this was really true.
I discovered a bevy of error correction, editing, and adaptive systems employed by cells. Evolution is not driven by copying errors or “randomness” in the usual sense. Cells evolve because the cell is a stochastic control system that modifies its own genome in pursuit of its goals.
The real question is: Just how purposeful is this behavior? Denis Noble raised this question in his paper “Was the Watchmaker Blind? Or Was She One-Eyed?”
Noble doesn’t attempt an answer… but he does cite many examples of organisms adapting to the needs of threatening situations. In real time.
We don’t know how purposeful or directional evolution is. What do know is: In systems we do understand, like drones, computers, prosthetic arms, thermostats and guided missiles, “replication + random mutation + selection” are never sufficient to evolve any technology.
If replication + mutation + selection evolved technology, Genetic Algorithms would be all the rage in Silicon Valley. They are occasionally useful.
In his Algorithm Design Manual, Steven Skiena warns against genetic algorithms:
[I]t is quite unnatural to model applications in terms of genetic operators like mutation and crossover on bit strings. The pseudobiology adds another level of complexity between you and your problem. Second, genetic algorithms take a very long time on nontrivial problems. […] [T]he analogy with evolution—where significant progress requires millions of years—can be quite appropriate.
I have never encountered any problem where genetic algorithms seemed to me the right way to attack it. Further, I have never seen any computational results reported using genetic algorithms that have favorably impressed me. Stick to simulated annealing for your heuristic search voodoo needs.
— Steven Skiena
30 years of engineering are more than enough to convince me that evolutionary theorists are missing something very big (huge – massive – as big as Einstein’s theories) when they toss around words like random… and then refuse to define what they mean.
Eyes and ears and wings don’t emerge because chunks of DNA get randomly shuffled like a deck of cards. Something vastly more sophisticated is going on… right under our nose. Intelligent Design theorists are missing the same landmark discovery when they abdicate to “God did it.” Sure, I believe in God… but the true science has been bulldozed by both sides.
In her 1984 Nobel Prize paper, Barbara McClintock asked: What does a cell know about itself? This is one of the most profound and provocative questions in all of science. Even fragmentary answers promise great breakthroughs in medicine and technology.
We won’t get answers until we use precise language to describe evolution. It’s time to separate the signal from the noise.
By Anthony92931 [CC BY-SA 3.0 (//creativecommons.org/licenses/by-sa/3.0)], from Wikimedia Commons
By Abmcdonald (talk) (Uploads) – Own work, CC BY 3.0, //en.wikipedia.org/w/index.php?curid=22507293