Abstract
I investigate the relationship between adaptation, as defined in evolutionary theory through natural selection, and the concept of emergence. I argue that there is an essential correlation between the former, and “emergence” defined in the field of algorithmic simulations. I first show that the computational concept of emergence (in terms of incompressible simulation) can be correlated with a causal criterion of emergence (in terms of the specificity of the explanation of global patterns). On this ground, I argue that emergence in general involves some sort of selective processes. Finally, I show that a second criterion, concerning novel explanatory regularities following the emergence of a pattern, captures the robustness of emergence displayed by some cases of emergence (according to the first criterion). Emergent processes fulfilling both criteria are therefore exemplified in evolutionary biology by some so-called “innovations”, and mostly by the new units of fitness or new kinds of adaptations (like sexual reproduction, multicellular organisms, cells, societies) sometimes called “major transitions in evolution”, that recent research programs (Maynard-Smith and Szathmary 1995; Michod 1999) aims at explaining.
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Notes
What is properly unpredictable? Maybe not the feature itself (large brains in individuals, for example, came according to the laws of chemistry and physics implemented in molecular biology), but somehow the success it met, which generated new patterns of evolution and diversity. So the most manifest aspect of this unpredictability is that the phylogenetic pattern after a key innovation displays a global shape that is not predictable from its previous course—even if each of the consequences (new species, etc.) could be predictable locally according to a fine-grained knowledge of how selection acts in the various environments. Moreover, not every key innovation is an “evolutionary step”, since most of them are, albeit widely spread among several phyla, not generalized to all the following lineages in the same way than eukaryotes, sex, etc. are. But, before having a rigorous concept of emergence, there is no sense in choosing one series of examples instead of another as paradigm of emergence—we would rather address this issue after having made sense of emergence as such.
Korn (2005) attempts to make sense of emergence in biology by privileging the concept of information and the consideration of biological hierarchy. The present article approaches the problem differently since it first addresses emergence as a somehow formal concept in formal sciences. One should expect that the two approaches yield the same identifications of what is emergent in biology. This question is left open.
The lexicon of emergence is widespread in studies of complexity and artificial life—wherever people deal with simulations. For example, in a paper about the Evoloop (first initiated by Langton 1989), the authors write: “Evoloop mutate through direct interaction (collision) of their sheath structure, leading to a change in the gene sequence of offspring loops. This results in a uniquely emergent process of evolution, one which has been considered to generally favour small-sized loops due to their robustness and high replication rate” (Salzberg et al. 2003). Rasmussen et al. (2002) define their own project in these terms: “We discuss the detailed process of micellation using the three dimensional molecular dynamics lattice gas. Finally from these examples we can infer principles about formal dynamical hierarchies. We present an ansatz for how to generate robust, higher order emergent properties [my emphasis] in formal dynamical systems that is based on a conjecture of necessary minimal complexity within the fundamental interacting structures once a particular simulation framework is chosen”. Füchslin and Mc Caskill (2001) address the “process of emergence” (9198) of a genetic code by accounting in their simulation for the evolution of a code and its stabilization.
“To make a distinction between adaptation and novelty implies that novelties represent a class of phenotypic change that differs from adaptive variation and hence constitutes a separate evolutionary problem in the same way that speciation is an evolutionary problem that is distinct from adaptation (…). Two possibilities can justify a distinction between adaptation and novelty: (1) the mechanisms involved in the generation of novelty may be different from those underlying variation and adaptation, and (2) the consequences of novelties could influence the dynamics of phenotypic evolution in a way that differs from variational change” (Müller and Newman (2005), p. 488).
The issue of “emergence of adaptability” is very close to the “emergence of evolvability”, as opposed to evolution; Wagner and Altenberg (1996) forged this term in order to focus of the question of variability—rather than variation, which fuels the ordinary population genetics of adaptation—through considerations akin to evolutionary computation.
In the last case, some simulations simulate only a pattern of evolution, with no hypothesis concerning the process of evolution—this is the case of the simulations used to corroborate the punctuated equilibria pattern in macroevolution, or Mc Shea’s diffusion model of increasing complexity (2005).
For this reason, Wright (1932) forged the Shifting Balance Theory which states that, in order for a population to reach the highest fitness peak, it has to be driven down by genetic drift, in the next valley in order to be able to climb up a higher peak. Small size of populations and eventual spreading of innovations after the new climbing are the correlates of this theory. See Coyne et al. (1997) for a current perspective on the SB theory. Gavrilets (2003) challenged the intuitive truth of the Shifting Balance Theory, arguing that the role we attribute to drift in allowing a descent of a valley is an effect of the 3-dimensional character of the usual picture, whereas real fitness landscapes have a lot more than 3 dimensions, because there are a lot more than 2 alleles to model. And in those realistic cases, the topology of multidimensional fitness landscapes allows “tunnels” of stable fitness that imply communications between several peaks with no need of decreasing fitness.
On this difference between fixed and open fitness function, see Taylor (2001). Taylor is very concerned with the limits to creating open-ended evolution in algorithmic artificial systems, and to this respect this difference proves essential, together with the difference—not emphasized here—between embedded coding and manifest coding (like in CAs). Sayama et al. (2003), also surveying the possibility of open-ended evolution and increase of complexity, highlight the impact of this difference.
This might in the end prove to be the main difference between simulations and the biosphere. See Bedau et al. (1998) and Huneman (forthcoming) on this issue.
Measures of complexity are numerous, as we know, and Adami (2002) gave a complete survey of the question applied to biology, arguing for the impossibility of a general trend towards complexity because the relevant complexity measure has to be an information measure in the niche of the organism.
For another way of weakening the traditional requisites of emergence see Batterman (2002), Chap. 8.
Nevertheless, other kinds of reason may compel us to talk about emergent processes in the first place—or so I argue in Huneman (2009).
Speaking of “calculi of emergence”, Crutchfield (2002) claims, like Wolfram (1984), that each natural process can be thought as a machine computing the outcome, so that various classes of machines reveal classes of natural processes: the true definition of emergence would therefore be given by Wolfram’s class III and IV cellular automata.
In the same sense, Kauffmann (1993) argued that systems like hydrophilic and hydrophobic lipids that assemble themselves into a kind of cell are instances of ontological emergence since the “cell” acts as a whole in a novel way (compared to the previous actions of the molecules).
For example Pattee (1989) says that “the most important type of emergence” is “measurement itself”.
Following, Wimsatt (1997, 2007), this is strictly entailed by the contrast between aggregative and emergent properties: if aggregativity fails when it comes to emergent properties, then the possibility of measure ensured by linearity and homogeneity of aggregative properties is no more guaranteed, and another kind of measure comes into play.
On the basis of this requisite, Rueger (2001) grounded his theory of emergence as robust supervenience—deprived of the clause of individual stability of the phenomenon.
This requisite is also formulated by Gilbert (2002) in the context of his finessing of Schelling’s model of segregation and clustering (the clusters have to be robust with respect to replacements of agents).
Note that Crutchfield and Hanson (1993), when they define a vocabulary to talk about what happens in some simulations (emergent computations in this case, e.g. GA for getting the best CA applied to a given problem), seem to undertake the same search for a causal perspective on criteria of emergence in simulations. I try to give reasons why this is indeed a causal story.
See also Symons (this volume).
The usual Top–down and Bottom–up terms are not used here because my interest is in the causal process embedded in the phenomenon, not the kind of explanation. Of course, convergence of genetic algorithms hangs on mathematical structures, recently illuminated by some results reached in the framework of the Freydlin Wentzell theorem (Cerf 1998). Hence, it may seem illegitimate to speak here of causation—one could claim that everything here takes place in mathematics, with no causation. But the same argument would contest that there is causality in physics since mathematical physics exists, which is absurd.
However, there exists some CA such that the Local Forward approach yields a Global Backward approach: think of a rule such that “if next neighbour is white then black, if black then white”, and an initial state with all the odd cells white and all the even cells black. In this case for any n you can directly get the global state of the CA.
Once again, emergence so characterized does not entail unpredictability. Batterman (2002) also weakens the classical requirements of emergence.
A similar claim is made by Corning (2002) who defends a concept of ontological emergence as synergy: “in evolutionary processes, causation is iterative: effects are also causes. And this is equally true of synergetic effects produced by emergent systems. In other words, emergence (as I have defined it) has been the underlying cause of the evolution of emergent phenomena in biological evolution”.
The connection between (what he calls “strong”) emergence and selection is also made, although differently, by Bar Yam through the idea of global constraints acting globally and indiscriminately on the parts. (“Selection is fundamental to both type 2 strong emergence and evolution”, Bar Yam 2004, p. 23).
See Shalizi et al. (2006), on CA with rule 22.
Langton’s loop is a CA that creates a loop; it has been improved into SDSR loop and Evoloop, which self-replicates while dissipating its “ancestors”. What is interesting here is that the self-replication is not a feature of the building blocks (the cell in the automaton) but it somehow emerges during the process.
In terms of cellular automata one can easily verify what is at stake. There are two kinds of perturbations: changing the state of a cell within a pattern, changing the states of cells around the pattern. Some emerging patterns (the ones we call robustly emergent) are such that those two kinds of perturbation don’t change anything. After some steps the CA behaves exactly as it used to do, still displaying the same global regularity that qualified it as robustly emergent. For a study of the conditions of stability of those patterns see Humphreys (this volume).
Robustness of the emergent level is guaranteed, here, in a way close to the “generative entrenchment” emphasized by Wimsatt and Schank (1988), in the sense that since an emergent novelty is used by the system to build other features, its modification is likely to be very expensive, and then is not likely to happen; novelty gets entrenched.
On refining the definition of computational emergence in order to avoid inclusion of random patterns, see Huneman (2009).
Or chaotic as in CA 22 (Shalizi et al. 2006).
Yet examples of robustness are not only a fact of evolutionary theory and new selective regimes. They are pervasive in biochemistry and molecular biology. Laughlin et al. (2000) write about protein folding: “Even more pervasive is the observation of the non-uniqueness of the sequence that folds into a protein with a particular structure, say that of myoglobin. This happens reliably for sequences that appear almost randomly related to each other, so it would appear that small perturbations of the underlying system still preserve myoglobiness, which could then be regarded as an emergent collective property”. (33) Notice that Laughlin, Pines et al.’s concept of “protection” comes close to our sense of “robustness”.
See also examples in Keller (1999).
“The results of the tournaments show that the various proposed adaptive advantages of sex tested, improve the performance of sex, vis-à-vis non-sexual alternatives. However, none of these features on their own give a definite advantage over asexuality. Only when gamete selection mechanism is modelled, such as spermatozoa selection, together with a selection mechanism of males, sex becomes indisputably superior to no-sex. (…) The simulation results clearly show that not all the features proposed to favour the emergence and/or maintenance of sex have the same weight in terms of their effect on the evolutionary dynamics. Although all the features that had been reported to favour the maintenance of sex (…) they did it in widely different degrees. The single most successful feature tested, favouring sex, was gamete selection. An evolutionary advantage gamete selection may provide is the opportunity for selection to act on haploid gametes, where single strings of alleles may be expressed without a shielding effect of the twin alleles. This provides an opportunity for efficiently weeding out incompatible combination of alleles, deleterious mutations, and other non-adaptive genetic features, prior to the production of costly organisms that are then subjected to natural selection”. (Jaffe 2004, p. 48) Those considerations lead the author to say in conclusion that “our study strongly suggests that an important adaptive advantage of sexual reproduction is the opening of new levels of selection” (p. 50).
Maynard-Smith (1978).
Alleles whose only effect is to distort meiosis by some mechanism so that they after the meiosis more represented than the alleles on the same or on the other chromosome; this is most of the time deleterious for the organism.
“During the emergence of a new unit, population structure, local diffusion in space (Ferriere and Michod) and self structuring in space (Boerlijst and Hogeweg) may facilitate the trend toward a higher level of organization, culminating in an adaptation that legitimizes the new unit once and for all. Examples of such adaptations include the cell membrane in the case of the transitions from genes to groups of cooperating genes, or… the germ-line or self policing functions, in the case of the transitions from cells to groups of cooperating cells, that is, multicellular organisms” (Michod 1999, p. 42).
Michod emphasizes that “conflict mediation” is a process that is pervasive and makes possible adaptability at the new level. “During the origin of each new kind of individual, conflict mediation is a necessary step, otherwise new adaptations at the new level cannot evolve, for there is no clearly recognizable (by selection) unit, no individuality. The evolution of conflict mediation is necessary for adaptation at the new level” (Michod 1999).
See Huneman (forthcoming) for an investigation of the connection between compositional evolution in computer sciences and issues in evolutionary biology.
Note that, contrary to the measurement criterion, the causal criterion seems less neutral regarding what is primo sensu emerging. In effect, if we show that properties (or events, or facts) are the relata of causal relations, then this category will be the emergent in proper sense (for example Humphreys (2004) holds properties for being those causal relata; thereby what is emergent for him must be, above all, properties).
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I am grateful to Sara Franceschelli, Paul Humphreys and John Symons for their insightful criticisms and comments on this article.
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Huneman, P. Emergence and Adaptation. Minds & Machines 18, 493–520 (2008). https://doi.org/10.1007/s11023-008-9121-7
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DOI: https://doi.org/10.1007/s11023-008-9121-7