Darwin re-visited

[Galapagos]

I give up. You are now trying to correct me for things that I agree with. I must be unable to make my point.

 

But thanks, sincerely, for trying to respond.

 

Bio

 

I can't figure out what you mean when you say you agree with this article:

Evolutionary Genetics - Mutation - Learn At Scitable

 

If you agree with everything there then I'm not sure what we disagree about, so I get the feeling you probably just didn't read it...

 

More from evolgen on what a biologist means by random:

evolgen archive: Random Mutation and Natural Selection

3. The Random Nature of Genetic Mutations

Once you are comfortable with random sampling and probability as well as the nature of genetic mutations, it’s clear what biologists mean when they say, “Mutations are random.” We will start by following a single nucleotide from parent to offspring, and then move on to looking at the entire genome.

 

Let’s assume the probability of a substitution at a particular nucleotide is 10-9 (a very small number). We will only consider two possible outcomes: substitution (mutation) and no mutation. If you’ve followed me up to this point, you can see that this is analogous to the coin flipping example. We do not know if a particular nucleotide will or will not mutate in one generation, but we do know how likely a mutation event is. Whether or not this nucleotide mutates is a random process, with the probability of one in a billion (10-9) that it does mutate. One out of a billion times that nucleotide will mutate in the process of going from parent to offspring.

 

This line of thinking can be extended to an entire genome, made up of millions of nucleotides. Each nucleotide has the probability of 10-9 that it will undergo a substitution event in one generation. We can also assign probabilities to other mutational events (indels, duplications, inversions, etc) that can be estimated from natural populations or laboratory experiments. We can use these probabilities to calculate the expected number of mutations in the entire genome going from one generation to the next.

 

It’s important to understand that when biologists say the mutational process is random, we mean that it is not directed. There is nothing determining definitively that a mutation will occur at a particular nucleotide. Mutations provide the raw material on which natural selection acts. Natural selection is a deterministic process; a beneficial mutation will always reach fixation in an ideal population (i.e., natural selection will cause it to replace all the other alleles), and a deleterious mutation will always be lost. We have no way of saying for sure whether or not a particular nucleotide will mutate because mutation is a random process – we can only assign a probability that it will mutate.

[Biochemist]

If you agree with everything there then I'm not sure what we disagree about, so I get the feeling you probably just didn't read it...

Well, not really. I have worked on this post for over 2 hours, and I don't think it is particularly clear. But let me give it a shot....

evolgen archive: Random Mutation and Natural Selection

 

Let’s assume the probability of a substitution at a particular nucleotide is 10^-9 (a very small number). ...One out of a billion times that nucleotide will mutate in the process of going from parent to offspring.

 

This line of thinking can be extended to an entire genome, made up of millions of nucleotides. Each nucleotide has the probability of 10-9 that it will undergo a substitution event in one generation. ....We can use these probabilities to calculate the expected number of mutations in the entire genome going from one generation to the next.

 

Merging together data from two of your recent posts, the mutation rate attachment above demonstrated that E Coli generated resistance to the T1 phage at a rate of about 10^-10. Given that the E Coli genome is about 5 million base pairs, this means that roughly every 200th replication has a genomic alteration. Hence 199 little beggars get through unscathed for every genomic change. More intriguing, however, is that (apparently) a single nucleotide mutation results in T1 phage immunity. Small change, large effect. And this is in an E Coli for goodness sake.

 

In humans, given the larger genome (and a slightly higher mutation rate), we end up with something like 175 genomic alterations in each generation (Ref: Estimate of the Mutation Rate per Nucleotide in Humans -- Nachman and Crowell 156 (1): 297 -- Genetics). The genomic changes are heavily weighted toward CpG dinucleotides (roughly an order of magnitude more common that other loci) and the most common genomic changes are substitutions of a nucleotide (90% ish) versus an addition or deletion which would cause a frame shift. (same reference as above)

 

Overall, genomic changes are not really rare (unless you look at the 10^-9 view), they are quite common. And the genomic changes are significantly weighted toward specific loci (e.g., CpG dinucleotides), and toward changes that do not cause a frame shift (by an order of magnitude- same reference).

 

It is easy to default to a view that the polymerases have, over time, "selected" toward mutation rates that are neither too fast nor too slow, and not too damaging but just damaging enough. Too fast and you eradicate the species; too slow and nothing evolves. But this is self fulfilling prophecy. This is indeed the state of nature.

 

But entities that have found stable niches would have (rationally) selected for very low mutation rate polymerases. I don't think we have found this to be the case, although RNA virus polymerases are noteworthy for the reverse: their high polymerase infidelity (since they must adapt very rapidly to survive the immune responses of vectors). But this sort of mutation had to express itself (in the evolutionary tree) after the vectors had patent immune systems. That is, we needed a very sophisticated solution (just the right tweek on a very malleable polymerase) to a very sophisticated problem (a sophisticated immune system). Behold, there was one.

 

That is, entities that spend lots of time in the UV getting their gametes blasted are not the long term survivors. They are the road kill. Those that have tuned their polymerases are the survivors.

 

The notion that these massively complex systems survive because they (sort of accidentally) generate transcription error at just the right rate is attractive. But at the molecular level of the polymerases, we are suggesting that single-substitution nucleotide changes in the coding for polymerases will modulate polymerase activity (up or down) such that individual species "thread the needle" for the right mutation rate to survive their particular ecological niche.

 

But even the notion that transcription error rate can be modulated (versus just on or off) means that the polymerase itself had to be selected for "modulatability" not just for successful transcription.

 

Overall, the house of cards here is large and tenuous:

 

1) RNA and DNA have to be stable enough to survive in the initial pre-biologic environment to begin to auto polymerize

2) early on we standardized on our 4 bases (5 if you count Thymine in DNA) and 20 amino acids

3) Catalytic activity of RNA (the presumed early catalyst group) has to be effective, but not perfectly effective

4) Catalytic activity must rapidly focus on replication of nucleotides

5) Once nucleotide replication is generally effective, we transition to construction on proteins which, in turn build almost everything else

6) The source machinery that build the nucleotide sequences works just well enough to allow for regular errors, translating into, occasionally, very significant changes in the end morphology of the entity

7) In the end, all genetic machinery is oriented toward transcription error that is "just broken enough" to allow for improvement, including improvement in the source machinery.

 

The number of inflection points in this schema where a small change would eradicate the all further progress is large.

 

My inference is that the probabilistic sequence here is troublesome, unless you assume that this is not a rare occurrence at all. That is, it would happen again, in roughly the same fashion given similar circumstances and another 4 billion years. And my guess is that we would end up with a VERY similar evolutionary tree. This might be as reproducible as water boiling, given similar starting conditions.

 

And if I am right, it probably did occur more than once. The tree may well have started at multiple locations in nearly identical fashion, but we would not be able to tell.

 

Ergo, I think the end solution was likely at the beginning.

[Turtle]

...

Overall, the house of cards here is large and tenuous:

 

Appeal to incredulity. :naughty:

[Pyrotex]

Well, not really. I have worked on this post for over 2 hours, and I don't think it is particularly clear. But let me give it a shot....

7) In the end, all genetic machinery is oriented toward transcription error that is "just broken enough" to allow for improvement, including improvement in the source machinery....

Maybe I'm missing something, but I found this last post to be very clear. And as far as I can tell, I agree with it.

An interesting idea occurred to me with ref to your point #7.

 

The truth is, all genetic machinery is oriented toward transcription errors that are across the board, from near zero to errors in every generation. Evolution uses a form of parallel simulation where it attempts to "solve" for all error rates. Now cycle ahead a million years and you will find that only a few genetic strands survived their way through the ups and downs of ecological barriers and predators.

 

Looking backwards, it will appear that evolution was "looking for" an error rate that was neither too small nor too large -- despite the fact the evolution had no way of seeing the ecological barriers and predators ahead of time. This is an illusion. The error rate "solved for" by evolution will be the happenstance value for those genetic strands that survived.

[dabs]

Your claims about mutations are untrue and misleading:

 

Read the whole thing, I have only quoted a small portion.

 

And here, direct estimation of mutation rates:

 

I'm not sure where we are on this thread, because some of the comments are cryptic and lack context. However, the "expert" sources still leave the matter in confusion.

 

Lets quickly review the phenomenon to be explained. Biologists repeatedly make the claim "mutation is random". That is clear. Its also clear that this statement has some kind of special significance for evolutionary biologists, who often contrast the "randomness" of mutation with the non-randomness of selection. Geneticists and molecular biologists don't seem to have a burning desire to state repeatedly that mutation is "random", but evolutionary biologists do this at every opportunity.

 

So, what does "random" mean that is special for evolutionary biologists and that distinguishes mutation from selection? One interpretation offered in this thread (and elsewhere) is that "mutation is random" really means "mutation is not goal-directed; mutations do not occur because of their effects."

 

That might be what evolutionary biologists mean, but why would they say this as though it distinguishes mutation from natural selection? Scientists generally make the metaphysical assumption that the future does not cause the present, i.e., events are not caused by their consequences; processes do not take place because of their outcomes. This is fundamental to mechanistic thinking. Presumably every scientist believes that every natural process is "random" in the sense of "not goal-directed, not moving with a purpose toward a goal". Instead of saying "mutation is random", they should be saying "evolution is random" meaning "evolution is not goal-directed, not moving with a purpose toward a goal".

[Thunderbird]

It strikes me that its time to move away from some of the arcane and anthropomorphic language of biology to describe evolution. The science of complexity allows us to elevate these models by adopting a more universal language to discribe natural phenomenon by utilizing the language of self organizing systems. We tend to think and organize information in linear terms. This language is more nonlinear therefore more descriptive of natural process.

Examples....

 

 

Adaptability

The ability of an organism to learn in response to changes in its environment over the course of its lifetime. This allows it to improve its fitness over that available from its initial phenotype.

Adaptation

The ability of a species to change in response to changes in its environment over many generations. This requires changes to the genotype in a way that increases an individuals' fitness.

Agents

Individuals within an interacting population, each may have only limited freedom to react to their neighbours yet the behaviour of the whole (emergent) may be much more complex. Collections of agents are sometimes called 'swarms'. Agent-based models (ABMs) are central to complexity research.

Aggregate

A collection of parts brought together without interactions, typical of the reductionist approach which ignores emergent effects and self-organisation.

ALife

Abbreviation for Artificial Life, the study of alternative forms of life to biological (BLife), also abbreviated to AL in contrast with AI (artificial intelligence) which concentrates on the emulation of psychological behaviours.

Attractor

A point to which a system tends to move, a goal, either deliberate or constrained by system parameters (laws). The three standard attractor types are fixed point, cyclic and strange (or chaotic).

Arms Race

Two species changing in response to changes in the other, a typical predator - prey interaction. This is usually regarded as a negative-sum interaction, improvements cancel each other out.

Autocatalysis

A process that creates itself by catalytic action. A system of chemical reactions such that each reaction is aided (catalysed) by the product of another in a closed and self-perpetuating sequence.

Autonomy

A form of system that can act independently, e.g. a robot. Used in complexity to refer to active (teleological) agents rather than passive ones, i.e. agents with internal goals that can act differently in identical external circumstances.

Autopoiesis

Self-production or self-maintenance. The ability to maintain a bounded form despite a flow of material occurring. A non-equilibrium system, typically life or similar processes but in a wider sense also including natural phenomena like Jupiter's Red Spot. See also sympoietic.

Aware Systems

Systems that can respond to their environment in an autonomous way, detecting external conditions and reacting appropriately (a teleological drive). Systems that can plan ahead, also called anticipatory systems.

Axiology

The study of values and their types. These can be of four types, systemic, extrinsic, intrinsic and holarchic. Complex systems are of the latter two types.

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Basin of Attraction

The set of initial states which are drawn to an attractor of the system.

Bifurcation

A point at which a system splits into two alternative behaviours, either being possible, the one actually followed often being indeterminate (unpredictable). Related to catastrophes in Catastrophe Theory.

Boolean Network

A combination of interconnected logic gates often used to model complex phenomena and demonstrating the emergence of multiple attractors in simple systems.

Butterfly Effect

The possibility that a large change can occur from a minor shift in initial conditions. A butterfly flapping its wings in the Amazon leading to changes in the location of a typhoon elsewhere in the world. Sensitivity to initial conditions, a chaotic system.

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Canalization

The restriction of state space exploration by constraints imposed upon the system either from outside or self-generated, i.e. unavailable possibilities. This helps to preserve stability or the 'status-quo' but may also prevent better optima from being reached.

Catalysis

A reaction taking place due to the presence of an enabling agent, one that is not changed in the process. An essential part of autocatalytic processes.

Cellular Automata (CA)

Simple agents that have a limited number of states, arranged in a grid formation. The state occupied is solely determined by the agent states together with those of their immediate surroundings. The cells in the 'Game of Life' are of this type.

Chaos

A system whose long term behaviour is unpredictable, tiny changes in the accuracy of the starting value rapidly diverge to anywhere in its possible state space. There can however be a finite number of available states, so statistical prediction can still be useful.

Circular Causation

The formation of closed loops of cause and effect within the system, such that it is not possible to abstract a linear chain of explanation in the conventional manner. A feature of all complex systems, which typically incorporate many such loops and exhibit multiple interconnected causes and effects.

Classifier

A set of production rules used to match environmental data and suggest an action to be taken, usually incorporates a genetic algorithm. Each rule covers part of state space.

Coevolution

Evolution of species, not only with respect to their environment, but also as to how they relate to other species. This is a more potent form of evolution to that normally considered, changing the shape of the fitness landscape dynamically.

Competition

The idea that to survive agents must fight each other and that only one of them can be successful. This assumes that resources are limited (insufficient for both) and is often a negative-sum strategy, i.e. 'win-lose' or 'lose-lose'.

Complex Adaptive System

A form of system containing many autonomous agents who self-organize in a coevolutionary way to optimise their separate values.

Complexity

The interaction of many parts, giving rise to difficulties in linear or reductionist analysis due to the nonlinearity of the inherent circular causation and feedback effects.

Complexity Philosophy

A set of organic axioms or assumptions more appropriate to nonlinear and interacting complex systems.

Complexity Science

The study of the rules governing emergence, the constraints affecting self-organisation and general system dynamics in nonlinear adaptive interacting systems. The study of the collective behaviour of macroscopic collections of interacting units that are endowed with the potential to evolve in time.

Complex System

One not describable by a single rule. Structure exists on many scales whose characteristics are not reducible to only one level of description. Systems that exhibit unexpected features not contained within their specification. Systems with multiple objectives.

Complexity Theory

The study of how critically interacting components self-organize to form potentially evolving structures exhibiting a hierarchy of emergent system properties.

Connectionist System

A system characterised by explicit connections between the components resulting in a distributed data structure (as used in neural networks).

Connectivity

The relation of an agent to its neighbours, it can be sparsely connected (only affected by a few neighbours), fully connected (interfacing with every other agent in the system) or some intermediate arrangement. This parameter critically affects the dynamics of the system.

Constraint

A force of some sort restricting the movement of a system. See selection. In Life studies the variations of form do not allow infinite variation, something constrains the options available. Complexity studies seek the laws that apply, if any, in these cases and similar areas.

Constructivism

The idea that we construct our reality mentally rather than seeing directly an objective world. This idea is validated by research in neuropsychology and relates also to general semantics.

Cooperation

The idea that two agents can increase both their fitnesses by mutual help rather than by competition. This assumes that resources adequate for both exist, or are created by the interaction, and relates to synergy (synergic coevolution) and 'compositional evolution'.

Crossover

Sexual mating between two genotypes in which a portion of the genes of one is joined to part of the genes of the other, to create a hybrid creature. This recombination allows rapid searching of the possible phase space.

Cybernetics

The study of control or homeostasis within a system, typically using combinations of feedback loops. This can be within machines or living structures. First order cybernetics relates to closed systems, second order includes the observer perspective and third order looks to how these coevolve.

Cyclic

A system occupying a sequence of states in turn. A closed trajectory in state space.

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Discrete

Non-continuous. A step by step (countable) approach. Digital systems operate this way, with time steps being the controlling factor. A sufficiently large number of such steps can approximate to any continuous (analogue) system.

Dissipative

Using a resource flow to constantly achieve a task, which may be work (e.g. movement) or more usually to maintain the system in a steady state (e.g. a living organism). Dissipative systems operate far-from-equilibrium.

Diversity

The range of features or niches available. This could be variation within a species, or the totality of different species in an ecosystem.

Downward Causation

Effect of higher level emergent properties on the lower level part behaviour. Constraints on the area of state space available.

Dualism

The idea that issues can always be divided into either/or states, e.g. mind/matter, fact/value, right/wrong. A throwback to pre-complexity viewpoints and earlier bivalent logic and systemic valuation, replaced mostly in complex systems approaches by non-dualist (continuum) modes of thought that take into account the wider connectivity issues and the need to balance multiple objectives.

Dynamical Systems Theory

The mathematical study of the behaviour through time of systems. This studies the attractor structure, bifurcation behaviour and phase portraits of the system.

Dynamics

The behaviour of a system in time. Changes with time are the essence of complexity, a static system is merely a snapshot within an evolutionary continuum, however interesting it may be in its own right.

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Ecosystem

The relatively stable balance of different species within a particular area. A food chain, usually cyclic and self-sustaining.

Edge of Chaos (EOC)

The tendency of dynamic systems to self-organise to a state roughly midway between globally static (unchanging) and chaotic (random) states. This can also be regarded as the liquid phase, half way between solid (static) and gas (random) natural states. In information theory this is the state containing the maximum information.

Emergence

System properties that are not evident from those of the parts. A higher level phenomena, that cannot be reduced to that of the simpler constituents and needs new concepts to be introduced. This property is neither simply an aggregate one, nor epiphenomenal, but often exhibits 'downward causation'. Modelling emergent dynamical hierarchies is central to future complexity research.

Entropy

The tendency of systems to lose energy and order and to settle to more homogenous (similar) states. Often referred to as 'Heat Death' or the 2nd Law of Thermodynamics.

Environment

The surroundings of the system, including other systems and natural features. This context affects the direction of coevolution.

Epistasis

The effect of one variable on another, an interdependence between components rather than an independence.

Equilibrium

The tendency of a system to settle down to a steady state that isn't easily disturbed, an attractor. Traditionally, equilibrium systems in physics have no energy input and maximise entropy, usually involving an ergodic attractor, but dissipative systems maintain steady states far-from-equilibrium (also non-equilibrium).

Ergodic

Visits every point in phase space with equal probability. The basis of entropy and the opposite to the behaviour of complex systems.

ESS

Evolutionary Stable Strategy, a system that resists disturbance, a stable balance between the various interacting agents in an ecosystem.

Evolution

This is a universal idea, generalised as 'general selection theory' to be the process of 'variation, selection, retention' underlying all systemic improvement over time (including 'trial and error' learning). The term is often specifically applied however to genetic evolution where some changes, by being more efficient in functional ways, are preferred by natural selection.

Evolutionary Theory

The study of evolution based upon neo-Darwinian ideas. Modern complexity science adds additional self-organizational concepts to this theory to better explain organizational emergence.

Evolutionary Computation

A set of techniques, using ideas from natural selection, within computer science. Includes genetic algorithms, genetic programming, classifiers, evolutionary programming and evolutionary strategies.

Evolutionary Psychology

The study of how biological evolution and genetic development affects mind function and social behaviour.

Extrinsic Value

A form of judgement that allows a continuum of possibilities, i.e. a measurement of goodness or presence. This corresponds to fuzzy logic operations.

Extropy

A term used to denote the tendency of systems to grow more organised, in opposition to the entropy expectation. Also called 'ectropy', 'enformy', 'negentropy' or 'syntropy' (or more generally 'self-organization'). The reasons for this are partly the motivation behind Complexity Theory.

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Feedback

A linking of the output of a system back to the input. Traditionally this can be negative, tending to return the system to a wanted state, or positive tending to diverge from that state. Life employs both methods.

Finite State Machine

A machine with a fixed number of internal options or possibilities. These could be as few as 2 (Yes/No) or any number of separate possibilities, each determined by some combination of input parameters.

Fitness

The ability of an organism to survive and flourish in its current environmental conditions, relative to the other creatures also there. A measure of 'quality of life'.

Fitness Landscape

The number of separate niches available within an organism's phase space, often regarded as peaks on a landscape. The higher the peak, the better the option, the steeper the slope the greater the selection pressure.

Flocking

The phenomenon of bird flocking can be explained by simple rules telling an agent to stay a fixed distance from a neighbour. The apparently intelligent behaviour of a flock navigating an obstacle follows directly from the mindless application of these rules.

Flows

The movement of resources from a place of high concentration to a low (e.g. energy goes from hot to cold). By utilising such flows systems can perform work (including self-organization). When flows in opposite directions balance, the system can arrive at the steady state (dynamic equilibrium) that characterises dissipative systems.

Fractal

A System having similar detail at all scales, leading to intricate patterns and unexpected features. Fractal geometry explores systems with non-integer dimensions.

Fuzzy Logic

A way of dealing with uncertain information and variables that do not permit simple yes/no categorisations (e.g. colour). Can also be used to make decisions where uncertainty occurs (fuzzy control). This is a form of non-Aristotelian logic (see general semantics).

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Game Theory

The study of interactions between intelligent agents, concentrating on whether outcomes are zero, positive or negative sum.

General Semantics

The study of how the way we use language constrains our thought patterns. It especially emphasises the need to adopt a non-Aristotelian viewpoint if we are to escape the errors of dualism. This relates to the new paradigm thinking behind complexity science and stresses that our 'maps' of reality are not equal to the 'territory' but are always only restricted viewpoints. See constructivism also.

General Systems Theory (GST)

The interdisciplinary idea that systems of any type and in any specialism can all be described by a common set of ideas related to the holistic interaction of the components. This nonlinear theory rejects the idea that system descriptions can be reduced to linear properties of disjoint parts.

Genetic Algorithm (GA)

The use of evolutionary techniques to diversify, combine and select options in order to improve performance, following the methods of natural selection by coding options as genes.

Genetic Programming

A form of variable length GA that uses directly acting program instructions as the genes.

Genotype

The combination of genes that make up an organism. This has no form itself but directs the creation of the phenotype following the interaction of system, dynamics and environment. Usually regarded as comprising a number of alleles or bits (systems having two states, 0 or 1, off or on).

Global Optimum

The very best possible fitness over the entirety of state space.

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Heterarchical

A weblike branching structure where multiple owners are possible and loops may form. A N:M structure.

Hierarchical

A treelike branching structure where each component has only one owner or higher level component. A 1:N structure.

Hill Climbing

The ability of mutation to increase the fitness of a agent, such that it climbs to a higher position on the fitness landscape.

Holarchic Value

A form of judgement that takes into account all the values present within all the entities that form the hypersystem, plus their interactions, a 'whole systems' valuation or fitness measurement of the multi-level whole.

Homeostasis

Resistance to change. The ability of a system to self-regulate and maintain a particular state.

Hyperstructure

A set of systems interconnected and evolving together. The dynamic term we use for this is hypersystem.

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IFS

Iterated Function System. A mathematical method of applying affine transformations to a seed to obtain a fractal image. Fractal compression works in reverse to derive an appropriate seed and transformation from the original image.

Intrinsic Value

A form of judgement that takes into account all the values present within the system, an holistic valuation or fitness measurement of the whole.

Iteration

A loop that uses the current value of a system to derive its future value by re-inserting it into the equations controlling the system dynamics. Feedback. The linking of effect back to cause.

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Julia Set

The inverse of the Mandelbrot set, using a single point from that set to generate a new unique set.

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L-System

Lindenmayer systems allow simple rules to serve as a way of generating complex images by iteration. This can create extremely natural forms, flowers, trees etc.

LIFE

A game invented by John Conway, it uses cellular automata to evolve lifelike patterns. It is also a universal computer and can in theory execute any program imaginable, given a large enough pattern.

Local Interaction

A property of agents that restricts them to reacting only to those other agents immediately adjacent. Most agents in alife systems behave this way.

Local Optimum

An easily found optimum in state space, but not guaranteed to be the global optimum.

Lotka-Volterra System

Equations that model population cycling in co-evolutionary systems. Forms cover predator-prey, war games and epidemics.

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Mandelbrot Set

The mapping of the behaviour of a specific complex formula across space by colour coding the result of each starting point as convergent or divergent, generating a fractal boundary.

Mapping

Transforming a input to an output by following a rule or look-up table. Also the selective study of 'reality'.

Memory

Storage of information or resource in such a way as to allow it to be reused at a later date.

Meta-

A prefix used to denote a higher level of thought about the subject, e.g. metascience (where we consider how we approach science), meta-ethics where we consider how we define normative behaviour. Each level in a complex system can be considered as a meta-viewpoint upon the previous level of emergence. Relates to category or type theory and higher-order logic.

Multiobjective

The need to take into account many conflicting variables in order to obtain an optimum fitness. This is a problem due to epistasis.

Mutation

The random change of any part of the genotype, typically by reversing the state of one bit. Natural systems often mutate by the action of radiation, cosmic rays or carcinogenic agents.

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Nanotechnology

The manufacture of systems of molecular size that emulate the behaviour of larger systems. Any alife system is potentially creatable in these dimensions, using standard biological or even inorganic components.

Natural Selection

The three stage process of variation, selection, reproduction (or persistance) that underlies evolution in all areas (in biology the synthesis of Medelian genetics with natural selection is called neo-Darwinism). It is combined within complex systems thinking with self-organization.

Negative Sum

The idea, from game theory, that agents combine in such a way that both lose or that the total change is a reduction in overall fitness, sometimes called dysergy or 'lose-lose'. Related to competition, where if the interactions repeat then we have escalating trajectories of fitness losses.

Networks

Connected systems, the properties of which do not entirely depend on the actual units involved but on the dynamics of the interconnections.

Neural Network

A simplified emulation of the connections of the human brain, used for investigating learning and self-organisation within an artificial environment.

Neutrosophic Logic

A new form of logic that goes beyond fuzzy logic by adding an axis for indeterminacy and thus takes into account not only what is measured but also what is not, a more whole systems or intrinsic logic better suited to complex systems.

Neutrosophy

A form of philosophy that emphasises paradox and the complementary and contextual nature of truth. This fits in with the idea of balance, emphasised within complex systems in the notion of 'edge-of-chaos'.

Niche

A peak in the ecological fitness landscape occupied by one variety of creature, often unopposed. Niches, in coevolutionary thought, are created by the organism interactions, do not exist in isolation and are a way of maximising group fitness by minimising competition (see synergy).

Non-Equilibrium

A system driven by energy flows away from a steady state of maximum entropy. Also called far-from-equilibrium.

Non-Linear

Systems that behave in an unexpected way, not changing proportionally to a change in input. Sometimes going down when you expect them to go up, doing nothing instead, or changing drastically with only minor changes to the input. Nonlinear systems fail the mathematical principle of 'superposition'.

Non-Standard

Having a non-homogeneous (uneven) distribution in space and/or time (exhibits patterns).

Non-Uniform

Having parts that are not the same in function or behaviour (varied rules or laws).

Non-Zero Sum

A situation in which it is possible for all participants to win or lose simultaneously, so that the fitness scores may total to a positive or negative sum overall.

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Open Systems

Allowing resources (e.g. material or information) to enter or leave the system, sucking in resources from outside or giving out more than they take in.

Optimum

A state that is the best fit for the current situation, the top of the local fitness landscape. All minor changes make the solution worst.

Optimization

The search for the global optimum, or best overall compromise within a (typically) multivalued system. Where interactions occur many optima are typically present (the fitness landscape is 'rugged') and this situation has no analytical solution, generally requiring adaptive solutions.

Orbit

The path taken by a cyclic attractor. A regular sequence that once entered cannot be exited without perturbation.

Organic System

A form of system that is autonomous and adaptive, based upon biological ideas rather than mechanical ones.

Organization

A non-random arrangement of parts, generally serving a purpose or function. The restriction of the system to a small area of its state space.

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Parallelism

Several agents acting at the same time independently, simultaneous computation similar to that which happens within living systems.

Pareto-optimal

A set of equivalent optimised solutions that all have the same global fitness but embody different compromises or niches between the objectives

Perturbation

A forced change to a system. This can result in a sudden shift to a new state, an immediate return to the old state or a long transient resulting in one or the other.

Phase Space

All the possibilities available to the system in theory. The sum total of possible states the system can occupy. In complex systems only a very small proportion of such states are found - the system is said to occupy only a minute proportion of state or phase space.

Phase Transition

A movement between static, ordered or chaotic states or back again. Usually used in connection with a change of state in physics from solid, to liquid, to gas or the reverse, but of general applicability in complexity theory.

Phenotype

The form of the organism. A result of the combined influences of the genotype and the environment on the self-organizing internal processes during development.

Positive Sum

The idea, from game theory, that when agents interact they can both benefit, the whole being greater than the sum of the parts, also called synergy or 'win-win'. When the interactions repeat we have escalating trajectories of positive fitness effects.

Prisoners Dilemma

A problem whereby a prisoner gets freedom by giving evidence against a fellow villain, but only if the fellow prisoner does not do the same. If both keep quiet a better overall result will obtain than either if both confess, or if just one confesses; yet for an individual the best result is still to confess. An example of a non-zero sum game, where cooperation pays both parties.

Probability

The chance of obtaining a particular result, e.g. if a 10 sided die is thrown it will be 10%. For complex problems there can be many outcomes, some of which do not seem to be ever realised, even if they appear to be equally probable.

Process

A change taking place in time, such that an input is transformed to an output. This can be cyclic if the sequence of changes is such that the output recreates the input (such as autocatalysis).

Process Thought

The treatment of reality as the evolution of processes rather than the behaviour of objects. In this methodology we recognise that 'things' are simply standing waves (attractors) in a continuous dynamical process and have no inherent absolute properties.

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Redundancy

The ability of a system to suffer degradation without altering its state. The ability to withstand perturbation without damage.

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Selection

A choice between available options based on consideration of fitness within the current environmental context. A bias on movement in state space. See evolution.

Self-Organized Criticality (SOC)

The ability of a system to reach edge-of-chaos by self-organization.

Self-Organisation

Ability to create structure without any external pressures, an emergent property of the system. Related to extropy or negentropy. Internal constraints.

Self-Organizing Systems (SOS)

Systems that generate their form by a process of self-organisation, either wholly or in part.

Self-similarity

Appearing the same at all magnifications. Fractal boundaries have this feature.

Separatrix

The unstable boundary between two attractors.

Simulation

Modelling a system by implementing in a computer some relevant features. If all features are operational then the system is real not a simulation. Alife is sometimes said to be real life under this definition, unlike say a model of a volcano which cannot melt the computer - a feature of real volcanic lava, which is not included in the model.

Stability

Unchanging with time. This can be a static state (nothing changes) or a steady state (resource flows occur). In complex non-equilibrium systems we have multistable states, i.e. many semi-stable positions possible within a single system.

State Space

The total theoretical possibilities available to the system, by combinations of the parts. Also called phase space.

Strange Attractor

An attractor whose variables never exactly repeat their values but always are found within a restricted range, a small area of state space.

Stochastic

Random or unpredictable effects, often associated with probabilistic or statistical treatments.

Sympoietic

A more open form of self-maintenance than autopoietic, more appropriate for social and ecological forms of organization. Exhibits more diffuse structures and fuzzy boundaries.

Synergetics

The use of geometric ideas within a systems view to describe and understand reality. Closely associated with Buckminster Fuller who applied it also to human behaviour.

Synergy

The idea that combined parts have properties that are more or less that the sum of the parts (positive-sum or negative-sum rather than zero-sum). Related to emergence but much wider. The negative-sum version is sometimes called dysergy, leaving synergy to mean only beneficial effects also studied as symbiosis, 'holistic darwinism', 'synergistic selection', 'synergic evolution', 'cooperative coevolution' or 'compositional evolution' and many combinations thereof.

Synthetic

Made up of parts. Assembled. More than the sum of the components. Opposite of analytic (taking apart).

System

A collection of interacting parts that forms an integrated and consistent whole, isolatable from its surroundings. The concept of dynamics or change over time is central to our treatment of complex systems.

System Dynamics

The study of how systems actually behave, using models to simulate the assumptions and rules being followed. Often the behaviour seen is very different than the behaviour people expect.

Systemic Value

A form of judgement that allows only two possibilities, good or bad (present or absent, in or out). This corresponds to Boolean operations (based upon Aristotelian logic).

Systems Thinking

The systems approach relates to considering wholes rather than parts, taking all the interactions into account, see also General Systems Theory. It considers processes rather than things to be primary.

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Trajectory

The path through state space taken by a system. It is the sequence of states or path plotted against time. Two general forms affect fitness, positive-sum and negative-sum.

Transient

A short term phenomena seen on the way towards, and before reaching, a steady state.

Transient Attractor

An temporary attractor formed within the transient behaviour of a system. This is a state (like a glider in the Game of Life) that only persists for a short time before dissipating with new perturbations (e.g. a smoke ring). Most attractors in evolving complex systems are of this type, due to the presence of continual perturbations.

Turing Machine

A form of universal computer, assumed to take its instructions from an infinite paper punched tape and output results to the same medium before stopping upon completion of the program.

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Universal Computer

A computer able to perform any task if suitable programmed. Most personal computers are of this type (at least for a small range of tasks). Any system with sufficient flexibility of interaction may perform this function, for example some automata or neural networks.

Universal Constructor

A machine able to construct any other object (including a copy of itself) give the appropriate instructions.

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Values

The dimensions or objectives we choose with which to measure the system and those variables we attempt to optimise in deriving fitness. Due to neural associations, the often imagined dualism between 'fact' and 'value' is invalid, thus values (purposes) can and should form a part of our scientific worldview.

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Whole Systems

The inclusion in our definition of 'system' of all the issues involved, including all the nested levels of interconnected smaller systems and how they relate to each other and work dynamically as a whole.

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Zero Sum

The idea, from game theory and economics, that agents swap resources, so that what one loses the other gains leaving a net no-change in fitness (contrast with non-zero, positive and negative sums).

[Biochemist]

Maybe I'm missing something, but I found this last post to be very clear. And as far as I can tell, I agree with it.

An interesting idea occurred to me with ref to your point #7.

 

The truth is, all genetic machinery is oriented toward transcription errors that are across the board....

 

Looking backwards, it will appear that evolution was "looking for" an error rate that was neither too small nor too large.... This is an illusion. The error rate "solved for" by evolution will be the happenstance value for those genetic strands that survived.

Well, I have to admit I am tickled that you could decipher my post.

 

The only (potential) disagreement we have is around the character of the illusion. My suggestion is that the environment that ended up creating life was not only possible, it is likely. We look at this massive amount of biochemical complexity and think (credibly) that most of the actions are not random because earlier states minimize or bound many of the options. Ergo, given these constraints, we surmise that life developed because of an aggregation of increasingly complex states that minimized the options for sequential developments.

 

We still have a bit of divergence (I think) around the probability of the overall evolutionary scheme.

 

I am suggesting that not only was the probability of life initially high (actually near 100%) given the initial planet conditions, but that the probability of specific tree that we see was also high.

 

Further, given that we had a quasi end-point about 500 million years ago (where, apparently, phyla stopped being added), it looks like the tendency toward life (and increasing complexity) had a natural end point, and we are essentially there.

 

Hence, if we duplicated the initial conditions again, we would not only get "life", but we would get a very similar (or identical) tree, contingent only on the macroenvironmental events (asteroids, ice ages and such) that cataclysmically altered niches. With the same cataclysms, we would get the same tree.

 

Simplifying, if we put a bunch of chemicals into a glass of water and waited an hour and a frog, a bird and a man hopped out, we would be surprised. If we duplicated the experiment and it happened again, we would run experiments to determine causality (as we are doing).

 

Unfortunately, we don't have the luxury of being able to run the whole experiment again.

[Pyrotex]

Perhaps one day, maybe in the year 23,480 AE (After Edison), the University of London, England, and the University of Olympus, Mars, will band together and beseech the Office of the Galactic Overlord for an academic grant of 825 Trillion Stardollars to purchase a dozen nearly identical planets, each in the cooling phase where water oceans are forming; and to seed said planets with identical doses of amino acids, pyroxenes, flavinoids and vitamin C; and to isolate said planets from further disturbance; and to carefully track the progress of biological evolution (if any); to determine the validity of the "Repeatability Theory of Biochemist" as proposed in the ancient website of Hypography.com.

 

Good luck with that. :eek_big:

[Biochemist]

Perhaps one day, maybe in the year 23,480 AE (After Edison)........to determine the validity of the "Repeatability Theory of Biochemist" as proposed in the ancient website of Hypography.com.

I would be honored!

[Thunderbird]

Perhaps one day, maybe in the year 23,480 AE (After Edison), the University of London, England, and the University of Olympus, Mars, will band together and beseech the Office of the Galactic Overlord for an academic grant of 825 Trillion Stardollars to purchase a dozen nearly identical planets, each in the cooling phase where water oceans are forming; and to seed said planets with identical doses of amino acids, pyroxenes, flavinoids and vitamin C; and to isolate said planets from further disturbance; and to carefully track the progress of biological evolution (if any); to determine the validity of the "Repeatability Theory of Biochemist" as proposed in the ancient website of Hypography.com.

 

Good luck with that. :)

You really put the Smart in "Smart ***." :eek_big:

[Galapagos]

My internet/phone has been out the past day or so, and unfortunately I am about to go to a night class, then return to wake up for an early class tomorrow, then I'm gonna be in lab all afternoon. I will rejoin the discussion either late tonight, or tomorrow afternoon! :confused:

[Thunderbird]

Reconsidering Lamarckism

 

Published Jan 17, 2009

From the magazine issue dated Jan 26, 2009

 

 

Of course Darwin was not “wrong” but I am glad to see Lamarck is getting some well deserved reconsideration.

 

 

The existence of this parallel means of inheritance, in which something a parent experiences alters the DNA he or she passes on to children, suggests that evolution might happen much faster than the Darwinian model implies. "Darwinian evolution is quite slow," says Whitelaw. But if children can inherit DNA that bears the physical marks of their parents' experiences, they are likely to be much better adapted to the world they're born into, all in a single generation. Water fleas pop out helmets immediately if mom lived in a world of predators; by Darwin's lights, a population of helmeted fleas would take many generations to emerge through random variation and natural selection. The new Lamarckism promises to "reveal how the environment affects the genome to determine the ultimate traits of an individual," says Whitelaw.

Begley: Was Darwin Wrong About Evolution? | Newsweek Voices - Sharon Begley | Newsweek.com

[Pyrotex]

It strikes me that its time to move away from some of the arcane and anthropomorphic language of biology to describe evolution.... This language is more nonlinear therefore more descriptive of natural process...

Tbird,

this is an awfully long quote. I sure would like to know where you got it from. Seems only fair for you to give proper credit for your source.

[Pyrotex]

Reconsidering Lamarckism...

Don't hold your breath for Lamarckism to make a comeback.

 

If you don't understand the (admittedly complex) process of DNA genetics, and the sources of genetic change, and the mechanism for sexually transmitting changes to the next generation, then Lamarckism may have a great intellectual appeal and a certain rationale.

 

However, if you DO understand those things, then you see clearly that there is NO POSSIBLE mechanism for translating an "experience" into an appropriately corresponding genetic mutation. Not even remotely, farfetchedly, conceptually, virtually possible. There is NO path, NO mechanism, NO process, NO way to translate "I almost got eaten by a new predator" into a new sequence of DNA specifically designed to provide a complex defense against that new predator.

 

Genes do NOT code for body parts. There ARE NO genes for trunks, long necks, shells, protective "caps", an extra set of pincers, eyes, teeth, fangs, claws or sacks of foul-smelling musk.

 

Genes code for proteins and enzymes. Genes code for chemistry.

[Thunderbird]

It strikes me that its time to move away from some of the arcane and anthropomorphic language of biology to describe evolution.... This language is more nonlinear therefore more descriptive of natural process...QUOTE]Tbird,

this is an awfully long quote. I sure would like to know where you got it from. Seems only fair for you to give proper credit for your source.

 

Sorry for the long quote, I just have an affinity for these terms and how well they describe life’s system, on a multitude of dimensions. The web site was created specifically to be utilize in my work in the reentry process for ex-offenders.

When individuals are released from prison it is important to have in place a model that reflects cyclical behavior, for example the central attractor may have been drugs prior to incarceration. A complex cycle of behaviors develops around this attractor. The goal is to quickly replace this void with positive goals and activity's, such as school, work, church, family, community service etc, and sustain them long enough to create a catalytic action among all parts of the new system. The traditional system for habitual drug offenders contain two main attractors that in reality support one another. Drugs - Prison.

 

A complexity perspective on work with offenders and victims of crime

 

A Complexity Theory perspective on working with offenders and victims provides an alternative framework that may uncover new insights into the better way of working in the criminal justice area. An effective complex adaptive system has strong autonomy and efficient connectivity. If any member of a community violates the autonomy or connectivity of another, a crime is committed. Work with offenders and victims focuses on restoring the autonomy and connectivity of those involved and the whole community, better enabling the dynamics of self-organisation to re-emerge. Offenders are seen as developing schemas supported by cognitive distortions that allow them to bypass the barriers that keep most of us from offending.

Chaos Theory and Complexity Theory: A non-technical introduction to the science of Chaos and Complexity

[dabs]

If you don't understand the (admittedly complex) process of DNA genetics, and the sources of genetic change, and the mechanism for sexually transmitting changes to the next generation, then Lamarckism may have a great intellectual appeal and a certain rationale.

 

However, if you DO understand those things, then you see clearly that there is NO POSSIBLE mechanism for translating an "experience" into an appropriately corresponding genetic mutation.

 

I'm able to see several possible ways to do this (I guess this means I must not understand genetics?). For instance, UV light causes TT dimers to form in DNA (look up "pyrimidine dimers" on wikipedia to learn more). These tend to block replication, so they reduce fitness. At the same time, they are mutagenic, a form of damage, with a tendency to result in TT-to-something-else mutations. The mutation away from TT reduces the vulnerability to fitness-reducing UV damage.

 

The UV light is the external experience, and the loss of the TT dinucleotide is the mutation. If you like to think in terms of animals, the UV light is the predator that likes to take a bite out of its genomic prey, and mutation in the genomic prey removes the place where the predator likes to bite, rendering the predator less effective.

 

Note that this mutational pathway is not an evolved adaptation per se. Repair would be an adaptation and, indeed, many organisms have a photolyase enzyme that specifically reverts pyrimidine dimers.

 

I don't know whether this pattern actually occurs in nature, but that is not the point. The point is that its a biochemical possibility (as opposed to an impossibility). I can think of other possibilities in which an environmental factor that causes instability in DNA also causes mutations away from the unstable state. There is a fundamental physical principle behind this, which is the connection between thermodynamic and kinetic stability. If some state is unstable, there are kinetically favorable transformations away from it, to other states. Mutation pathways, of course, are enzyme-catalyzed, but they are composed of chemical transformations and so tend to reflect what is chemically favorable already.

[HydrogenBond]

Darwin made some good observations. But his theory alone was just the beginning. Genetic was added next. These two have given us the genetic-alpha and omega-selective advantage of evolution. There are still steps in the middle.

 

For example, selective advantage is environmental dependent. Or the environment sets the constraints of what will be an advantage. The Arctic circle and the equator will not allow the same genetic changes to lead to selective advantage. The environment narrows what will actually work in terms of selective advantage. This is part of the theory credited to Darwin's observations.

 

Let us do a simple thought analysis. We have DNA that is 10,000 bases long. We throw the dice for 10 base pair changes in these 10,000 base pairs. I am not going to do the math but there are tons of possible ways. If purely random was in affect, we should see all of these, with very few able to actually work in terms of selective advantage. Maybe with bacteria, where we have zillions, this might be possible, but with animals that breed once a year, it might take a billion years per change which is not observed.

 

Another analogy is winning the lottery, which will be analogous to the selective advantage. There are many possible number combinations since it is based on probability. The winner wins the jackpot, but the money comes from the losers. This model of evolution implies the selective advantage coming at the same time the collective floor is lowering due to all the random genetic mutations that will be duds and/or cause problems in existing systems. Observation appears to indicate the floor sort of stays put or almost everyone get free tickets. For this to happen, the changes need to be much more targeted. There can still be random but has to occur more constructively than destructively.