The Use of Computer Simulation in Studying Biological Evolution: Pure Possible Processes, Selective Regimes and Open-ended Evolution

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The Use of Computer Simulation in Studying
Biological Evolution Pure PossibleProcesses,
Selective Regimes and Open-ended Evolution

• Philippe HunemanIHPST (CNRS / Université PARIS I
  Sorbonne)

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• The interplay between evolutionary biology and
  computer science
• Bioinformatics, Biocomputation
• Genetic algorithms (lexicon  crossing over ,
  genes etc)
• The radical AL claim digital organisms are
  themselves living entities (rather than their
  simulations) (Adami 2002). Idea of
  non-carbon-based forms of life as the essence of
  life
• Version  2 Darwinian evolution is an
   algorithm  (Dennett, see Maynard Smith 2000)

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• What are simulations doing / teaching ?
• What is the role of natural selection in them ?
• Investigates the relations between biological
  evolution and computer simulations of evolving
  entities through natural selection.

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I. Typology of computer simulations in
evolutionary theory
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• A. Kinds of role of selection a1. formal
  selection context
• Todd and Miller (1995) on sexual selection
• (sexual selection is more an incentive for
  exploration than natural selection, since
  females, through mate choice, internalize the
  constraints of natural selection. )
• Maley on biodiversity
• (emergence of new species is more conditioned by
  geographical barriers than by adaptive potential)

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Mikel Maron 2004) Moths and industrial
melanism
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• A2. No selection context
• Boids (Reynolds)
• Chu and Adami (2000) simulation of phylogenies
  whose parameter is the mean number of same-order
  daughter families of a family
• Mc Shea (1996, 2005) increase of complexity
  with no selection

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B.Use weak and strong

• B1. Weak model is used to test one hypothesis
  on one process it simulates the behavior of the
  entities (boids Maleys biodiversity etc.). Way
  to test hypotheses about the world

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• B2 Strong entities of the model dont
  correspond to real entities the simulation is
  meant to explore the kinds of behaviors of
  digital entities themselves (Rays Tierra,
  Hollands Echo etc.). Hypotheses are made about
  the model itself
• digital organisms as defined by the sequence
  of instructions that constitute their genome, are
  not simulated they are physically present in the
  computer and live there (Adami, 2002)

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• Echo unrealistic assumptions concerning
  reproduction, absence of isolative reproduction
  for species, makes Echo a poor model of
  evolutionary biology (Crubelier 1997)

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• Langton-Sayama Loop

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II. Natural selection and pure possible processes
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• In the (weak or strong) simulations causal
  processes (i.e. counterfactual dependencies
  between classes of sets of cells, and global
  state at next step).

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• a1 a 2 .. a1P a2P ak ..
• A1 n, j A2 n, j Ak n, j ..
•  
• b1n1 b2n1 bkn1..
• Property P n at Step n (Add (on i) Disj (on j)
  Ai n, j )
• Property Pn1 at Step n1  (all the bin1 )
• If P n had not been the case, Pn1 would not
  have been the case.
• Causation as counterfactual dependence between
  steps in Cas
• Huneman Minds and machines 2008

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• -gt In  formal selection  contexts simulations
  those causal processes are actual  selective
  processes 

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• Yet the entities in the simulations can not
  exactly match biological entities
• In Echo, you dont have species easily, in Tierra
  no lineages etc.
• If one system is designed to study some level of
  biological reality, the other levels are not ipso
  facto given (whereas if you have, e.g.,
  organisms you have genes and species etc.)

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• -gt In actual biology all levels of the
  hierarchies are acting together
• So computer simulations display  pure possible
  processes  concerning the entities modelised,
  located in a target-level of the hierarchy
• (no implicit entangling between levels)

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• In the case of formal selection simulations,
   pure  selective processes occur
• Ex. of  natural selection  sensu Channon Echo
  or Hilliss coevolution between sorting problems
   natural selection  simulations. Yet in Echo,
  for ex., the class of possible actions is limited

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III. The validation problem for computer
simulations
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What do tell us such simulations ?

• They correlate pure possible processes with
  patterns of evolution
• They can not prove that some process caused some
  evolutionary result, but they provide candidate
  causal explanations   if pattern X is met,
  then process x is likely to have produced it
• And other causal processes may have been at work
  but they were not so significant regarding such
  outcome (noise ???)
• Even if we have no idea of the ecological
  context, hence of the actual selective pressures

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• Adami, Pennock, Ofria and Lenski (2003) show that
  evolution is likely to have favoured complexity
  their point is that, if there is complexity
  increasing in their sense, then deleterious
  mutations might have been selected then a
  decrease in fitness might have been involved in
  the stabilisation of more functionally complex
  genomes.

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• Chu and Adami (2000) investigation of the
  patterns of abundance of taxa if the
  distribution of taxa resemble a certain power-law
  scheme X, it is likely that the parameter m (mean
  number of same order daughter families of a
  family) has been in nature close to the value of
  m involved in X (i.e. m1).

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The validation problem

• Epstein (1999) the case of Anasazi settlements

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• That does not prove that the rules ascribed to
  individuals are the accurate ones
• see also Reynold flocking boids it excludes a
  centered-controlled social organisation (but we
  need other assumptions to make this plausible)

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• Even more the case of Arakawas simulations in
  meteorology
• Analysis by Kuippers Lehnard 2001, Lehnard 2007
  drop  realism  in order to achieve efficiency

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• How are simulations to be validated in biology ?
• Mc Shea on complexity.
• Challenges Bonner (1988) explanation of the
  increase of complexity through selected incerase
  of size in various lineages
• Mc Shea (2005) suggests that complexity increase
  can be produced with no natural selection, only
  variation (complexity defined by diversity)
• models also produce patterns of complexity
  increase with patterns produced under various
  constraints (driven trend vs passive trends, with
  no selection).
• The pattern found in the fossil records may be
  produced by such process but we need to have an
  idea about the processes likely to have actually
  occurred

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• A minimal characterisation of computer
  simulations in evolutionary biology they
  provide candidate explanations (pure possible
  processes) and null hypotheses for evolutionary
  patterns
• For the same reason (they dont accept impure
  processes which are the ones really occurring)
  they cant prove anything by themselves

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• An example worth to investigate Hubbells
  ecological neutral theory (2001)
• It skips the level of individual selection
  generate the same outcome as what we see about
  succession, stability and persistance in
  communities

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IV. application discontinuities in evolution
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• 4.1. The longstanding problem with innovations
• Darwinism is gradualist (small mutations selected
  etc.)
• Cumulative selection accounts for adaptations

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• Novelties,
• Innovations (qualitative, eg morphological,
  differences)
• Key innovations trigger adaptive radiation, and
  new phylogenetic patterns (avian wing, fish
  gills, language) id est, phylogenetic and
  ecological causal patterns

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• Pattern and processes the role of  punctuated
  equilibria theory  (Eldredge and Gould 1976)

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An issue with discontinuity

• Problem the fitness value of half a novelty ?
  (half a wing !)
• -gt Solutions
• Find a benefit for each stage in various species
  (Darwin on the eye)
• Conceive of it as an exaptation (ex. feathers)
  (Gould and Vrba 1981)
• Developmental processes (Gould, 1977 Muller and
  Newman, 2005, etc.) variation is not  minor ,
  its a rearrangement of structures through
  shuffling of developmental modules/time (as such
  the pucntuated quilibria pattern dont require a
  specific process)

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4.2. Exploring discontinuity

• Compositional evolution (Watson 2005)
  evolutionary processes involving the combination
  of systems and susbsystems of semi-independently
  preadapted genetic material (p.3).
• consideration of building blocks obeying some new
  rules that are inspired by the biological
  phenomena of sex and of symbiosis proves that in
  those processes non gradual emergence of
  novelties is possible.

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• 1. A system with weak interdepencies between
  parts can undergo linear evolution increases in
  complexity are linear functions of the values of
  the variables describing the system. Algorithms
  looking for optimal solutions in this way are
  called hill-climbers they are paradigmatically
  gradual. They easily evolve systems more complex
  in a quantitative way, but they cant reach
  systems that would display innovations..

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• 2. If you have arbitrary strong complexities
  between the parts, then evolving a new complex
  system will take exponential time (time increases
  as an exponential function of the number of
  variables). Here, the best algorithm to find
  optimal solutions is the random search.
• 3. But if you have modular interdependencies
  (encapsulated parts, etc.) between parts, then
  evolving new complex systems is a polynomial
  function of the variables. (Watson 2005,
  68-70)

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• Algorithms of the class divide-and conquer are
  dividing in subparts the optimisation issue, and
  divide in its turn each subpart in other subparts
  the initial exponential complexity of the
  optimisation problem approached through random
  search is thereby divided each time that the
  general system is divided so that in the end
  the problem has polynomial complexity.
• Those algorithms illustrate how to evolve systems
  that are not gradual or linear improvements of
  extant systems but as polynomial functions of
  the variables, they are feasible in finite time,
  unlike random search processes.

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• Compositional evolution concerns pure processes
  that embody those classes of algorithms with
  polynomial rates of complexification, and have
  genuine biological correspondents sex
  symbiosis. mechanisms that encapsulate a group
  of simple entities into a complex entity (Watson
  2005, 3), and thus proceed exactly in the way
  algorithmically proper to polynomial-time
  complexity-increasing algorithms like divide and
  conquer.

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• Watson refined the usual crossover clause in GA,
  integrating various algorithmic devices (for ex.
  messy GA, according to Goldberg, Korb and Deb
  1989) in order to account for selection on blocks
  that take into account correlation between
  distant blocks, hence creation of new blocks
  (Watson 2005, 77).

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• . This proves that processes formally structured
  like those encapsulated processes such are
  symbiosis, endosymbiosis, may be lateral gene
  transfer have been likely to provide
  evolvability towards the most complex
  innovations, the ones not reachable through
  gradual evolution

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• The bulk of the demonstration is the identity
  between algorithmic classes (hill-climbing,
  divide-and-conquer, random search) and
  evolutionary processes (gradual evolution,
  compositional evolution).
• So the solution of the gradualism issue is
  neither a quest of non-darwinian explanation
  (order for free, etc.), nor a reassertion of
  the power of cumulative selection that needs to
  be more deeply investigated (Mayr ), but the
  formal designing of new modes of selective
  processes, of the reason of their differences,
  and of the differences between their evolutionary
  potentials. In this sense, discontinuities in
  evolution appear as the explananda of a variety
  of selective processes whose proper features and
  typical evolutionary patterns are demonstrated by
  computer sciences

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Open-ended evolution

• Potential for discontinuities and novelties is
  constant or increasing
• New adaptive radiations wings for insects and
  birds, etc. as opening possibilities of for
  other novelties
• Not predictible but retrodictible

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Modelling open-ended evolution

• Question what is specific to evolution in the
  biosphere ?
• Limits in modeling open ended evolution in Alife
  (Bedau and Packard 1998)

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• Classify possible evolutionary patterns, with
  criteria that will take into account the degree
  of likeliness of discontinuities and emergences.
• Those patterns will include classes of the pure
  possible processes that are directly implemented
  within the computational devices, and appear to
  be objects of investigation in computer sciences.

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• , Bedau and Packard (1998) three kinds of
  emergence class II is bounded emergence
  Hollands (1995) GA Echo , as opposed to class
  I, no emergence, in an Echo simulation with no
  selection (what they call Echo neutral shadow),
  and class III is unbounded emergence manifest
  in the phanerozoic fossil records i.e. the
  history of Life.
• Bounded for Bedau and Packard means that the
  range of adaptations exhibited is somehow finite,
  which is not the case in class III
• Intuition no new environment to be colonized in
  digital evolution

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Channons classification (2002)

• Artificial selection in the SAGA simulation, 2.
  natural selection of program codes in Rays
  Tierra, which seems a now limited evolution, 3.
  less limited evolution by Channons natural
  selection in Geb simulation
• Is this class 3 BP class III (phanerozoic
  records)?

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Typology in terms of driving processes

• No selection. Phase transitions, etc.
• Gradual evolution. Smooth landscapes, cumulative
  selection, problem of shifting balance theory,
• Compositional / discontinuous evolution. Moving
  landscapes (not smooth) problems of facilitators
  of evolution (Wagner and Altenberg 1996
  evolvability as constraints on the
  genotype-phenotype map). No fixed optima, hence
  some open ended evolution.

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• Local patterns of evolution can be simulated,
  hence providing candidate processes
• General pattern of open-ended evolution in
  phanerozoic record is still unmatched (see Taylor
  2004 for a state of the art)
• No a priori reason for this
• But it might be that no pure possible process is
  likely to generate this

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• The possibilities provided by those models settle
  the ground for empirically deciding about the
  specificity of life as a this-worldly feature (as
  opposed to  life  by AL theorists)

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Conclusion

• Computational models are not a very general
  domain of which biology would exemplify some
  cases. (Against strong AL claim)
• On the contrary they mostly provide pure possible
  processes that might causally contribute to
  origin of traits or evolutionary patterns.
• The class of possible processes being larger than
  the real processes, obviously not all processes
  simulated are likely to be met in actual biology

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• The main difference between algorithms and
  biology might not be the chemical implementation
  of earthly life (replicators are DNA etc), but
  the fact that processes at work in biology are
  never pure in the sense that they involve all the
  levels of the hierarchy

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• Algorithmic devices only permit to single out one
  or few entities within them. In this sense they
  are only generating the pure processes involving
  solely those entities.
• This constrains the form of the validation
  problem for computer simulations in evolutionary
  biology