I'm totally reformatting the "theory" above into very plain language in an attempt to make it more accessible to people who haven't had the mathematical and bilogical background I have. As follows is a presentation of it that should be written in very plain terms without jargon. You should be able to use this post and understand the comments I've made prior in the thread if you haven't been able to yet.
Natural selection is fundamental. Basically, it says that organisms that are less capable of passing their essence to the next generation will be replaced by ones that are. Natural selection is well observed and supported. But it has a very area where it is useful. Natural selection is difficult to apply in its direct form to understanding the nature of politics, economies, ecological forces, and other "macro", or large scale, phenomena, even though undoubtedly it is at the heart of these systems.
An interesting idea in the mechanics of natural selection came with Dawkin's
selfish gene "framework". I use the term framework instead of theory for a very important reason. My first premise is that saying natural selection works on individuals trying to pass their genes to the next generation, and saying that genes are acting to propogate themselves are equivelant statements. They're simply different ways of viewing the same phenomenon, and can be used interchangably depending on which one makes the most sense for the given question at hand.
An interesting consequence of this is kin selection, that is, performing benevolent acts towards another member of your family. Since the other member of your family shares some of your genes, helping them survive and propogate also helps you spread your genes indirectly.
All organisms alive today are thought to have descended from a single ancestor. If you were to follow this first ancestor, its kids, their kids, on and on until the present day, you could construct a
phylogenic tree, also called a family tree. All organisms alive today are related at least in some degree.
Game theory is a mathematical model that allows theorists to examine situations in which multiple
agents, which can also be understood as "players" in a "game", interact strategically to maximize their wins, or alternatively, to minimize their losses. I will be using the terms "game", which means a set of rules with consequences, and "agents", meaning players of a "game". In this simple context, biological competition can be seen as a game, as can wars, economies, and just about everything that's ever been made into any sort of video game
What I propose is a
framework for understanding the circumstances for certain observed facts in both real biology and Darwinbots evolutionary simulations. The basic framework is something like this:
agents in a game that do not manage to survive are ill represented in any slice of time. Or put more simply, things that don't survive don't exist. This is a generalization of the central tenant of natural selection to
all processes, and forms the central tenant to my framework which I'll call
abstract natural selection. The premise is almost a tautology; it should be easily accepted. The world around us is made of
things that are good at existing.
Things that aren't good at existing aren't very numerous. Unstable crystals don't persist, unstable isotopes radioactively decay into stabler ones.
This should bring up a fundamental question in your mind. How exactly am I defining "good at existing"? A radioactive isotope with a half life of 100 years might seem stable from a human perspective but unstable from a geological time frame. Is the isotope stable or not? This is the second premise of the framework for abstract natural selection:
the ability for an agent to survive in a game depends on the time frame your considering. Or put another way, the ability for something to exist is a relative, qualitative term, and depends on the context in which you're using it.
Reality is made up of a hierarchy of different time frames you can observe the universe from. If you view the universe in cosmological time, on the scale of the death and birth of stars, nothing is really all that permanent. Stars are constantly birthing and dying. Even black holes slowly evaporate into nothing through electron tunneling. The next step up is geologic time, involving the development of crystals and minerals in the crust of the Earth. In this time frame, the universe seems stabler, with stars lasting long enough for planets to form and their crusts to harden, etc. Above geologic time is simple life, like bacteria. Above that is the more complex life forms and their evolution. Above that is ecology and the succession of grasses to trees in an area, and on up until you get to many human constructs such as economies and nations that have a cycle lifespan measured in centuries or decades or even just a few years.
Each time frame has specific rules unique to that time frame. The goal for agents that play in these time frames is to exist. Those that do are represented and exist. Those that don't aren't. Again, it's almost a tautology. The universe is populated predominantly by very old, slow burning stars because the fast burning ones don't exist as long, and die out unrepresented, not existing. The different rules come from the fact that the a force on one time scale might be insignificant, but on another time frame could be very important. For instance, plate techtonics isn't all that important for the political life of nations, but it's had profound impacts on long term evolutionary development.
All these timeframes might have different rules, but they're also interacting. If a star dies, any life on a planet around it dies. There is constant feedback between the timeframes' games.
The universe can be seen as a large hierarchy of games being played in different time frames with different rules. The outcome of games on one level of the heirarchy can effect the outcome of games on anotherNow, back to our idea of a phylogenic tree. Imagine grouping all the individuals that are related by being either parents or children of each other. Groups will overlap, but you'll be able to do it. Imagine treating these groups as new entities in a new phylogenic tree. Imagine doing it over and over, creating larger and larger groups. Eventually you'll have all life ever developed on Earth in a single group. Now imagine the
time frame that each group represents. Singe generations are considerably shorter lived than, say, the entire group of 300 generations to which it belongs.
Each of these time frame's groups can be seen as agents in that time frame's game. This means that agents that might be in direct competition in one time frame's game might be
the same agent in another. This is the fundamental lever point for my idea. Depending on how you group life forms, you can change the rules by which those life forms are interacting, and even wether they're competing or cooperating. Life can cooperate on one level and compete on another
at the same time.
Note that not all agents in a time frame's game are necessarily competing. Not all games are "zero-sum", where the winners winning comes at the expense of a loser. In general if agents
are competing, I'll say they are competing for a specific
niche, where niche is another generalized term and simply means a limited resource.
Last, notice that games that occurr on shorter time frames have a faster pace. Again, this is almost a tautology. For instance, if a bacteria is killed by another bacteria, it's not really going to matter if it happens to live on a planet around a sun that's going to die in 3 billion or 8 trillion years. Dead is dead, non existance is non existance.
Games that are played in shorter time frames have more weight than games that are played in longer time frames, because their paces are faster and their effects more immediate.
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That defines the base of my idea in its entirety. Some of the basic premises might be unfounded, but alot of it is simply reorganizing existing facts to allow us to look at data in a new way. It is particularly useful for explaining some rather peculiar things I've seen that would seem to contradict Dawkins claim that selection at the level of organisms or populations almost never overrides selection on genes. For instance:
Cannibalism and the leagues - There is something of a pardox to the following fact: conspec recognition in leagues is important for success. Bots that eat their children or other members of the species will not do as well against another species that doesn't. However, in evolutionary simulations, this conspec recognition
always, except in extremely contrived situations, is selected against and disappears. What's odd is that it usually follows a reverse pattern to kin selection: when it first appears, it's almost always indiscriminate eating of everything, and is usually first directed at fellow siblings and even the parent, and only later is the bot able to find less related bots to much on. These bots also seem less able to bear young successfully, with the number of successful reproductions much lower than the unmutated forefather.
Using the Framework of Abstract Natural Selection (FANS) allows us to state and understand the phenomenon on very real terms. When a bot species is fighting another bot species, the entire battle usually is measured in the low thousands of cycles. "Cannibots" usually appear
after some time of mutations has occurred, somewhere on the order of hundreds to low millions of cycles.
Battling against other species occurs in a slightly different time frame. In this time frame, the two species are battling for a single niche (victor of the round). All bots of a single species are essentially the same agent, and it would not be wise for an agent in a zero sum game to fight against itself, which is why cannibalism doesn't work in the leagues.
In the longer term, however, the fight for the niche will long be over, and only a single bot species will survive. In this long term there is no more game to play in the shorter term, since it's already been played and won. The most significant game is now the battle between individuals to survive (not even to necessarily pass on their genes. That's a longer term, multigenerational game). At first, since they have a conspec recognition gene and all bots are essentially identical, the only way to survive better and have more kids than someone else is to be lucky enough to either stumble onto a fresh veggy patch or have a veggy spawn right next to you. If you are always crowded out of these new veggies, you will die.
Dumb luck really. And since no one is being actively hunted by anything, life span can be
very long.
However, eventually a cannibot will develop. This cannibot has a huge advantage in the game of surviving because it actively destroys other agents. It no longer needs to get lucky and find a new veggy patch or have one spawn, because it's eliminating any other potential rivals for food. And it's easy pickings because they don't fight back. What I'm saying is that cannibots develop not because it's a new food source (although that's an important plus), but because it's a trait for eliminating rivals.
In essence, cannibots don't work in the leagues because in that time frame, the species are the agents and cannibalism would be like suicide. Cannibalism works after a league is done, in longer term evosims where all the bots are basically very similar, because the time frame and thus the rules of the dominant game and the definition of an agent, change.
To test if this idea is true or not, we'd have to figure out some way to rig up a league round
with mutations enabled that would last longer than it usually takes for cannibots to develop and dominate the sim, but isn't setup in such a way that the bots co-exist. One needs to eventually win. If the cannibots still develop, spread, and belong to the species that wins the round, this would disprove this explanation. If they don't, that would be strong circumstancial evidence.
Big berthas - Big berthas are very large, very sterile bots that constantly shoot. They used to develop in evo sims before I increased the problems associated with waste. Basically, a bot would break its gene to reproduce, and would instead constantly acquire nrg and body. The body would make it stronger and basically unkillable by the smaller, reproducing bots. 5 or 6 Big berthas would eventually kill all strains of reproducing bots, causing a mass extinction event when the big berthas eventually died. What's interesting is that these 5 or 6 big berthas would all be independant mutations since the big berthas never reproduced.
Reconciling this with natural selection presented me with some problems. These bots would appear "fitter" since eventually they'd replace all the reproducing bots. But there was no hope their genes being passed on to the next generation or even surviving indefinately, since eventually costs would kill the big bertha and its genes with it. It was the very definition of dead end.
However, if you use FANS this makes sense. Big berthas were unkillable. Usually a single hit from one of its shots would kill another bot. Similar to cannibots, their survival in the contest against other individuals was all but insured. In their young life they had to contend with other bots' crowding them out for food, accidentally killing them with stray shots, and even being hunted by smaller cannibots.
However, once they were monstrous, other bots simply couldn't kill it. They had effectively won the more dominant game of surviving against other individuals. Unfortunately, the selective pressures for this game also caused them to adopt a strategy that wasn't beneficial in the less dominant game of long term survival of the genome. But these longer term selective pressures didn't get to act until the big berthas had decimated all other kinds of bots, and by then it was too late. Again, shorter term games have stronger pressures. Bots will sacrifice success in the longer term games for success in the more immediate moment, mostly because evolution isn't forward thinking, and doesn't know better.
These two examples show how FANS can be used to understand why you see evolution proceed along paths that in the long term are less effective. It can be used to explain why you see bots "devolve" in long term simulations. To understand the other half of my idea, about the "abstraction ceiling" try reading the other posts again after reading this.