Download John Maynard Smith and the natural philosophy of adaptation

Biology and Philosophy (2005)
DOI 10.1007/s10539-005-9021-7
Ó Springer 2005
John Maynard Smith and the natural philosophy
of adaptation
ALIRIO ROSALES
Facultad de Humanidades y Educación, Escuela de Filosofı´a, Universidad Central de Venezuela,
Caracas, Venezuela; Author for correspondence (e-mail: [email protected])
Received 20 March 2005; accepted in revised form 18 August 2005
Key words: Adaptation, ESS, Evolutionary game theory, Evolutionary stability, Optimality,
Phenotypic evolution
Abstract. One of the most remarkable aspects of John Maynard Smith’s work was the fact that he
devoted time both to doing science and to reflecting philosophically upon its methods and concepts.
In this paper I offer a philosophical analysis of Maynard Smith’s approach to modelling phenotypic
evolution in relation to three main themes. The first concerns the type of scientific understanding
that ESS and optimality models give us. The second concerns the causal–historical aspect of
stability analyses of adaptation. The third concerns the concept of evolutionary stability itself.
Taken together, these three themes comprise what I call the natural philosophy of adaptation.
Introduction
‘This book is about a method of modelling evolution, rather than about any
specific problem to which the method can be applied’. Thus began the first
chapter of Evolution and the Theory of Games, and it expresses the side of John
Maynard Smith’s contribution to evolutionary biology that I want to explore in
this paper. In developing novel ways of modelling phenotypic evolution, Maynard Smith was simultaneously concerned with explaining adaptation and evolutionary change, and with the philosophy of biological modelling. He was thus
both a scientific theoriser and a natural philosopher of evolutionary biology. The
aim of this paper is to examine the philosophical underpinnings of Maynard
Smith’s work on evolutionary modelling, and to explore their ramifications.
The structure of the paper is as follows. I examine Maynard Smith’s views on
models and explanation in relation to three broad themes. The first is the role
of optimization and game theoretical models in explaining phenotypic adaptation. Following Maynard Smith, I argue that these models facilitate understanding by locating the actual in a space of possibilities. The second theme is
the causal–historical dimension to adaptationist theorising. I argue that stability analyses of adaptation are historical in something like Peter GodfreySmith’s ‘Modern History’ sense (Godfrey-Smith 1994). The third theme
concerns Maynard Smith’s concept of evolutionary stability. I argue that to
achieve a dynamical understanding of adaptation, evolutionary stability à la
Maynard Smith is not enough; evolutionary attainability is also critical.
Modelling adaptation
Theorising about adaptation did not proceed by way of building dynamical
models of change, as was the case in population genetics. Rather, it required
the development of novel interpretive concepts and the importation into
biology of mathematical methods from engineering and economics.1 One key
concept was that of an optimal adaptive strategy, later generalized in Maynard
Smith and Price’s (1973) concept of an evolutionarily stable strategy (ESS).
The mathematical methods included optimization theory and the theory of
games. For Maynard Smith, these methods provided a novel way of understanding biological structures and behaviours, and the diversity of life itself
(Maynard Smith 1978a, b).
The ESS concept was ‘useful in interpreting animal contests’, Maynard Smith
wrote, where ‘interpreting’ meant accounting for the selective forces responsible
for the evolution of the observed behaviours (1979, p. 475). More generally,
Maynard Smith argued that to theorise about adaptation, one needs to ‘start
out with a set of formal categories in terms of which one attempts to interpret
phenomena’ – such as an ESS (1987, p. 119). Such categories allow the theorist
‘to explain phenomena in terms of existing theory’ or to see that a given class of
phenomena ‘does not fit existing theory’ (ibid. p. 119). Put differently, these
categories are necessary to mediate the application of theory to the world.
As the opening quotation above illustrates, optimization and ESS models
provided a general framework for investigating the selective pressures shaping
phenotypic evolution, rather than hypotheses about particular evolutionary
histories.2 One important aspect of optimality modelling, emphasised repeatedly by Maynard Smith, is the need to impose constraints on the set of possible
phenotypes on which the selective process operates. He wrote:
It is clearly impossible to say what the ‘best’ phenotype is unless one
knows the range of possibilities. If there were no constraints on what is
possible, the best phenotype would live forever, would be impregnable to
predators, would lay eggs at an infinite rate, and so on. It is therefore
necessary to specify the set of possible phenotypes, or in some other way
describe the limits of what can evolve. (Maynard Smith 1978a)
1
These methods include calculus, optimal control theory, game theory and theories of comparative
statics in mathematical economics. It is interesting to note that the development of the ESS concept
brought about theoretical novelties within both evolutionary biology and game theory itself (cf.
Weibull 1985). Day (2005) provides an interesting review of the main modelling strategies in
theoretical evolutionary ecology, including a discussion of evolutionary game theory and its relation to the novel approach of adaptive dynamics.
2
Thus Maynard Smith wrote of the Hawk-Dove game that its goal is not to represent any
particular situation, ‘but to reveal the logical possibilities inherent in all contest situations’ (1982, p.
6). Similarly, Peter Abrams writes that optimality models are ‘aids for understanding adaptive
scenarios, rather than precise predictions about the outcome of evolutionary processes in specific
systems.’ (2001b, p. 274)
and
Whenever an optimisation or game theoretic analysis is performed an
essential feature of the analysis is a specification of the set of possible
phenotypes from among which the optimum is to be found. (Maynard
Smith 1982, p. 5)
The point made by Maynard Smith in these quotations has important
implications for the sort of scientific understanding produced by optimality
models. An optimal phenotype is not simply the best there is in a population, but
the best alternative given a specified range of possibilities. So optimality models,
either explicitly or implicitly, must specify a space of biological possibilities from
which the optimum is to be selected. To the extent that such models make
qualitatively correct predictions about the world, they explain actually observed
outcomes by locating them with reference to a set of alternative outcomes that
might have evolved, but did not. Thus there is a crucial modal dimension to the
scientific explanations that optimality and ESS models yield. This modal
dimension is something about which most philosophical accounts of explanation say little, but it plays a central role in the logic of optimality reasoning as
described by Maynard Smith. For him, ‘scientific theories say nothing about
what is right, but only about what is possible.’ (Maynard Smith 1989, p. 50).
Of course, models play a central role in some accounts of scientific explanation, e.g. Cartwright’s ‘simulacrum’ account of explanation and Hughes’ notion
of a ‘structural’ explanation (Cartwright 1983; Hughes 1993). However, neither
Cartwright nor Hughes addressed the modal dimension emphasised here, which
is crucial to the type of scientific explanation that ESS models yield. More
recently, Anya Plutynski (2004) has offered a pragmatist account of explanation
in classical population genetics in which the modal aspects of modelling are fully
taken into account. She argues for a context-dependent explanatory role of
models in terms of ‘proofs of possibility’. Plutynski’s modal view of populationgenetic models can be seen as complementary to the modal account of optimality
and ESS models offered here. Such models do not establish proofs of possibility
but comprise a framework for the theoretical understanding of adaptation.
Interpreted as optimal phenotypic configuration, adaptation does not make
sense unless it is embedded in a constrained space of phenotypic possibilities.
In his essay, Understanding Science, Maynard Smith argues for what might
be called a ‘structural’ conception of scientific understanding. He recalls
learning that a time delay in a control system increases the likelihood of
oscillations in the system (Maynard Smith 1989). So for any oscillating system,
be it the menstrual cycle or the number of hares and lynxes in Canada, it is
appropriate to look for a delayed feedback. The underlying assumption here is
that structure determines behaviour – it is the relations among the components
of the system that matter, not the sorts of objects involved. Understanding the
behaviour of the system involves understanding how the behaviour is
determined by the relations that hold between the parts. Typically the
behaviour is described mathematically, by means of a differential equation, but
understanding the equation is not enough:
If I claim to understand the behaviour of some system, I mean rather
more than that I understand the mathematical description of it. I mean
that I can in some way analyse it and play with it in my head, imagining
how it would behave in various circumstances. For want of an alternative, this ability can be described as having a ‘physical intuition’ about
a system. (Maynard Smith 1989, p. 227)
Physical intuition is complementary to the mathematical analysis of the
system. Now, analysing a system and playing with it in one’s head amounts to
constructing theoretical scenarios – models – in which one investigates how the
system behaves under different idealising assumptions.3 One explores how
robust the relational structure is under different conditions. So the features
essential for scientific understanding are structural features that are represented in models and equations.
What becomes of this structural conception of understanding in the context
of modelling adaptation? I suggest that structural understanding obtains once
the set of possible phenotypes, discussed above, is coupled with a fitness
function relating phenotypic performance to selective success in a given environment. For in effect, the functional relationship between fitness and trait
value constitutes a relational structure. So again, it is the relations among the
components of the evolving system, i.e. the variant phenotypes and the environment, not their intrinsic features that determine the system’s dynamics. We
arrive at the following view: theoretical understanding of adaptation is
achieved by locating actual phenotypes in a space of biological possibilities,
constructing a fitness function modulo a given environment, and exploring the
resulting evolutionary dynamics. Adaptation is understood through the fitnessphenotypic performance-environment relational structure.
The understanding achieved this way is phenotypic in that there is no
recourse to explicit genetic models of the traits in question. Maynard Smith has
pointed out that this way of thinking has an ‘obvious justification in analysing
the evolution of traits of whose genetics we know little or nothing’ (Maynard
Smith 1987, p. 128). This is a common justification of the approach, but it can
potentially obscure the role that optimality and ESS models play in providing
the sort of theoretical understanding of adaptation envisaged above. Optimality and ESS models provide understanding of how fitness varies with
phenotypic performance in a given environment (which includes other organisms), and how this affects the evolutionary dynamics. Such understanding
would be indispensable even if the genetical basis of the phenotypes were
3
The distinction between physical intuition and mathematical analysis mirrors the distinction
between the physical and formal–structural notions of model, emphasized by Suppes (1966). See
also Morgan and Morrison (1999), and French and Da Costa (2003).
known; it is not the sort of understanding that population genetical models can
supply. Alan Grafen expresses this point well:
The behavioural ecologist ... does not usually know the genetics underlying the character she studies. While she would be interested to know this
genetic system, it is not of primary importance. Her aim is to uncover the
selective forces that shape the character. The behavioural ecologist has to
hope in her ignorance that her method will work almost regardless of which
particular genetic system underlies the character..... (Grafen 1991, p. 5)
The crucial point, therefore, is that optimality and ESS models are not
simply ‘second best’ explanatory strategies that we adopt for lack of knowledge
of the underlying genetics; rather, they furnish a distinctive type of understanding, as explained above. It is also true, of course, that optimality and ESS
models are constructed under idealising assumptions concerning the underlying
genetics, i.e. we assume that the different phenotypes breed true, and that there
is sufficient mutation to allow each to invade (Grafen 1991). But the strategy of
idealization lies at the heart of all theoretical model building, as Maynard
Smith has repeatedly emphasized.4
A somewhat different way to put the point is this. Optimality and ESS
models explain adaptation by considering how fitness varies as a function of
phenotypic performance in a given environment (which includes other organisms); they are thus concerned with the ecological causes of fitness differences,
and their evolutionary consequences. Such ecological-dynamical understanding is of a different sort from that obtained from genetical theories of adaptation, which study the distribution of phenotypic effects produced by
favourable mutations, and more generally, from that obtained from standard
population genetics models, which are only indirectly concerned with phenotypic adaptation (Orr 1998, 2005a, b; Kirkpatrick 1996).5
4
Idealisation can be thought of as theorising under ‘as if’ assumptions; it is as pervasive in
theoretical evolutionary biology as it is in theoretical physics. There is a growing consensus concerning the cognitive role of idealisation in theory and model construction; see the contributions in
Shanks (ed.) (1998). The role of idealisation in the development of population genetics has been
analyzed by Margaret Morrison (2000, 2002).
5
H. Allen Orr has recently argued that genetical theories of adaptation fall into two classes:
phenotype-based and DNA sequence-based (Orr 1998, 2000, 2005a, b). (What distinguishes a
genetical theory of adaptation is that selection coefficients figuring in the models are not arbitrary:
they emerge as phenotypic effects from favorable mutational substitutions (Orr 2005a)). The
simplest phenotypic model is Fisher’s geometric model; Gillespie’s mutational landscape model is
Orr’s example of the DNA sequence approach. A basic difference between the two sorts of model is
that phenotype-based models explore a continuous space while DNA based models explore a
discrete space. Despite this, Orr’s discussion reveals some striking dynamical similarities between
the two types of model; in both cases, the phenotypic sizes of fixed favorable mutations fall off as
an approximate geometric sequence and the overall distribution of factors fixed during adaptive
walks is nearly exponential (at least among factors of larger effect). This has the philosophically
interesting consequence that there is a robust dynamical pattern governing adaptation at both the
phenotypic and the DNA sequence levels.
Explaining adaptation: history vs. equilibrium
A unifying aspect of the modelling strategies dealt with in the previous section
is an emphasis on equilibrium states. It is often argued that equilibrium approaches to adaptation are essentially ahistorical. Thus Douglas Futuyma
writes: ‘most of population genetics, all of optimization theory and much of
community ecology is framed in terms of equilibria rapidly approached; history plays no role in the theory and is wished away whenever possible in
interpreting data’. In a similar vein, Reeve and Sherman (1993) distinguish
questions of evolutionary history from questions of phenotype existence,
arguing that the latter are ahistorical in nature.
In The Evolution of Sex, Maynard Smith addressed this issue of equilibrium
vs. history, by contrasting questions about the origin and the maintenance of
adaptation:
So far I have discussed the maintenance of sexual reproduction and
genetic recombination. But what of their origins? Surely evolution theory should be concerned with the origins of adaptations, not merely with
their maintenance once they have arisen. There is much in this objection;
indeed, one of the things I have learned while writing this book is that
my own insight into the field may have been obscured by an obsession,
which I share with most population biologists, with equilibrium situations (1979, p. 6)
Maynard Smith went on to offer a pragmatic justification for focusing on
maintenance rather than origins, arguing that questions about the former are
more empirically tractable. He continued: ‘we have to concentrate on maintenance rather than origins because only thus have we any hope of testing our
ideas’ (ibid. p. 7).
Though Maynard Smith’s pragmatic point about testing may be valid, I
think the underlying contrast between ‘equilibrium’ and ‘history’ actually needs
to be questioned. It is true that optimality and ESS models cannot explain the
origins of complex adaptations, only their maintenance; but the type of
understanding they yield is nonetheless historical, in a sense. The key is to
distinguish between different historical timescales – what might be called ‘recent’ and ‘remote’ history. In explaining the maintenance of a trait, its preservation in recent evolutionary time is what matters. As Ron Amundson has
said: ‘equilibrium biology does have what we might call a shallow concept of
history, in that it is considered legitimate to infer that a trait of especially high
fitness relative to alternatives must at least have been maintained in the population by selection. But remote history is irrelevant’ (1996, p. 45). This idea
will be further unpacked below.
In his work on functional explanation, Peter Godfrey-Smith provides
a framework ideally suited for dealing with the temporal dimension of
adaptation at stake here.6 Indeed, in ‘A Modern History Theory of Functions’,
he refers explicitly to ESS theory:
A final illustration of the importance of the distinction between originating and maintaining selection is found in some of the literature
applying game theory to animal behaviour. An ESS, or evolutionary
stable strategy, is a strategy which, once prevalent in the population,
cannot be invaded by rival strategies. However, an ESS need not be a
strategy that can evolve from scratch in any situation. Often, a critical
mass of like-minded individuals is needed before a strategy becomes
stable. Thus to explain a behaviour by showing it to be an ESS is not
necessarily to explain how that behaviour originally became established.
Rather it is to point to the selective pressures responsible for the recent
maintenance of the strategy in the population (1994, p. 358).
What Godfrey-Smith says about ESS models applies to optimality models as
well. Such models aim to determine the possible selective forces responsible for
the recent maintenance of traits. These forces typically act over many generations, but many generations can be a point in time within a phylogenetic
frame of reference: that is, such a period would hardly count as historical in the
latter frame.
The core of the ‘Modern History’ approach is that biological functions are
dispositions or effects of traits which explain their recent maintenance under
natural selection. To make a claim about functions is to make a claim about
the recent past, not the ancient one. If traits are conceptualised as adaptive
strategies, as strategies maximising some component of fitness, then their
maximising effect on fitness, relative to a certain environment, explains their
recent maintenance under selection. Therefore, the phenotypic understanding
of adaptation outlined in the previous section can be considered historical,
despite its focus on maintenance rather than origins. My point is that the
temporal dimension of such evolutionary causation is to be found in modern
history, in Godfrey-Smith’s sense.
Godfrey-Smith (1993) reserves the term ‘functional explanation’ for explanation of the recent maintenance of a trait, and ‘evolutionary explanation’ for
the original establishment of the trait. I suggest that what Godfrey-Smith calls
functional explanation is really a type of evolutionary explanation with a
shortened temporal dimension. For the inter-generational time over which
traits are selectively maintained can also be considered evolutionary history.
This conceptualisation allows us to retain the idea that adaptive explanations
are always about the causal history of a trait (as Brandon 1990 insists). For
recent maintenance is causal history, albeit over a shorter timescale than original establishment.
6
See also Godfrey-Smith (1993). For an application of the ‘modern history’ framework to a
different but related philosophical problem, see Kaplan and Pigliucci (2001).
If this is right, it implies that adaptive explanations based on equilibrium
states, of the sort furnished by ESS and optimality theory, do qualify as causal.
However, Elliott Sober (1983) has contrasted what he calls ‘equilibrium
explanation’ with ‘causal explanation’, thus apparently implying that the former is non-casual. Sober’s prime example of an equilibrium explanation is
Fisher’s explanation of why many species have a sex ratio close to 1:1. (Fisher’s
argument is a precursor of the ESS concept, as Maynard Smith (1982) notes.)
Fisher (1930) observed that if a given population consisted mainly of females, it
would pay to produce more sons than daughters, as more grandchildren would
then be produced. If males were predominant, it would pay to produce more
daughters than sons. But if there were equal numbers of males and females in
the population, there would be no selective advantage to producing a preponderance of either daughters or sons. Therefore, the 1:1 ratio is evolutionarily stable.
Sober argues that a causal explanation would show how the population got
to its present state from some specified starting point, i.e. it would trace a
dynamical path from the initial conditions to the present state. But Fisher’s
account does not do this:
In fact, Fisher’s account shows why the actual initial conditions and the
actual selective forces don’t matter; whatever the actual initial sex ratio
had been, the selection pressures that would have resulted would have
moved the population to its equilibrium state. Where causal explanation
shows how the event to be explained was in fact produced, equilibrium
explanation shows how the event would have occurred regardless of
which of a variety of causal scenarios actually transpired. (Sober 1983, p.
202)
So according to Sober, a causal explanation focuses exclusively on the actual
trajectory of the population; while an equilibrium explanation shows that the
actual trajectory is in a sense irrelevant, for the actual starting point makes no
difference to the evolutionary outcome.
It is true that Fisher’s argument does not say anything regarding actual
initial conditions or the population’s actual trajectory. But Fisher’s equilibrium
argument is still causal, albeit in a ‘modal’ way. The argument shows how the
population would evolve to an equilibrium state from any set of initial
conditions. There may be equilibrium explanations that lack such a modaldynamic structure and are formulated only in terms of equilibrium and not
dynamical convergence, but Fisher’s argument is clearly dynamic in the proposed modal sense. The argument specifies the selective forces that would cause
any population, starting from any initial conditions, to evolve to a 1:1 sex ratio.
So Fisher’s sex ratio argument is causal, but it concerns possible causal histories rather than just the actual one. In like manner, equilibrium predictions of
optimality and ESS models can be made compatible with a manifold of different actual trajectories, but the explanations that such models yield are
nonetheless causal. Sober’s distinction between ‘causal’ and ‘equilibrium’
explanations is in reality a distinction between two different species of causal
explanation. As we will see below, the fact that some equilibrium explanations
are also causal is connected to a point that emerged in discussions that came
after Maynard Smith’s original ESS formulation, namely that evolutionary
stability involves both equilibrium and dynamical attainability.
To summarize, the type of explanation of adaptation yielded by optimality
and ESS models concerns the maintenance, rather than the origins, of adaptive
traits, in the first instance. However, such explanations are both historical and
causal, despite what has sometimes been thought. Godfrey-Smith’s ‘modern
history’ account of functions provides the appropriate timescale for the
explanation of adaptation by phenotypic models, and opens a way to broaden
what we understand as the causal history of a system. How this relates to the
notion of evolutionary stability is the subject of the next section.
Evolutionary stability and history
In this section I explore further the historical side of ESS arguments by analysing refinements of the notion of evolutionary stability. As with all theoretical
models, optimality and ESS models do not relate to empirical reality in any
straightforward way, for they require idealising assumptions. Only under those
assumptions do they provide the sort of theoretical understanding envisaged
above. Such understanding, however, neither implies nor requires that populations in fact be at the optimal configurations predicted. As is well known,
biological complexities such as frequency-dependence and environmental
variability conspire against fitness maximisation and stability. As Hofbauer
and Sigmund note, ‘the idea that a population will somehow evolve until it
happily reaches the safe heaven of evolutionary stability is not always valid’
(1998, p. xxiv).
Theoretical developments within evolutionary ecology have shown how
evolutionary stability involves dynamical relations between predicted equilibria
and their evolutionary attainability (Eshel and Motroo 1981; Eshel 1983;
Taylor 1989; Christiansen 1991). These developments mean that Maynard
Smith’s original ESS concept needs modification. For a state that is an ESS
may not be reachable by a population from its actual starting point. As Troy
Day says, ‘evolutionarily stable strategies can be evolutionarily unattainable’
(2005, p. 292). To address this problem, some authors have argued that the
notion of evolutionary stability should be defined more carefully, to include
evolutionary attainability (Christiansen 1991).
The importance of evolutionary attainability can be explained as follows.
Maynard Smith’s original ESS concept describes a situation where the fitness
of an individual is affected by what others are doing in a population. The basic
ESS argument establishes what happens once a population has become evolutionarily stable: the (average) population composition would not change if
threatened by potential invaders. The strategy adopted at the monomorphic
stable state is superior to any strategy used by an invading mutant or immigrating group. The result is that the population ‘will remain effectively
monomorphic, with all individuals using the ESS’ (Hines 1987, p. 200).
However, the biological meaning of an ESS is not complete unless one can
determine that a population will remain at an evolutionary stable state in the
face of perturbations; the original definition of an ESS does not guarantee this.
An early concern with a dynamical understanding of evolutionary equilibria
was expressed in Taylor and Jonker (1978):
An equilibrium state, if undisturbed, should persist, since all existing
strategies are equally fit. However, in real life, an equilibrium state will
surely be disturbed, so that we are only interested in it if the system, once
disturbed, returns to the equilibrium state. If this is the case, the equilibrium is said to be stable (Taylor and Jonker 1978, p. 146, emphasis in
original.)
Recast in these terms, a genuine evolutionary equilibrium requires that two
local stability conditions be satisfied (Taylor 1989; Christiansen 1991). One
coincides with the ESS condition, the other requires that a population situated
near an evolutionary equilibrium (ESS) converges to such an equilibrium
(Christiansen 1991). An ESS precludes possible invading mutant strategies
from modifying the current stable population composition. Convergence stability ensures that the population has a tendency to move towards the stable
state: variants that bring the population closer to the ESS will be selectively
favoured. The type of stability that is crucial for evolutionary history includes
both the ESS condition and the convergence (i.e. attainability) condition. I
suggest that the shift from ESS alone to ESS-plus-attainability yields a causal–
historical conception of evolutionary stability, where ‘causal–historical’ is
understood in the modal and ‘modern history’ senses elaborated above. The
erroneous idea that optimality and game-theoretic explanations are ahistorical
and non-causal probably arises from defining evolutionary stability in terms of
the ESS condition alone. Once dynamic convergence to ESS is included in the
definition of stability, the causal–historical dimension of these explanations
becomes immediately apparent.
This point can be appreciated from a different angle, by a brief excursion
into history. Early pioneers of optimality analysis, such as Richard Levins,
took an explicitly dynamical approach; this was clear from Levins ‘optimization principle’, according to which ‘populations will differ in nature in the same
direction of their optima’ (Levins 1970, p. 399). The same is true of more recent
life-history theorists who have used perturbation techniques to investigate how
optima move when conditions change (Hernández and León 1995). Though
ESS modelling began as an extension of optimality analysis to frequencydependent contexts, the dynamic dimension was initially obscured. The first
precise definition of an ESS was given by Maynard Smith and Price (1973), in
their study of the evolution of ritualised animal fights. The ESS concept as
defined by Maynard Smith and Price turned out to satisfy the Nash
equilibrium condition of game theory; as a result, the ESS concept was seen as
limited to equilibrium situations in which dynamics and history played no role.
So the use of the classical ESS concept made history irrelevant to phenotypic
analyses of adaptation, and evolutionary stability was regarded as an ahistorical notion. But, as we have seen, the ESS condition turned out not to be the
whole story for understanding evolutionary stability. Indeed it could not have,
for evolutionary adaptation is a dynamic, historical process. The recognition
that stability involves ESS-plus-attainability brings history back, in the form of
maintenance in ‘modern history’, into the theoretical understanding of adaptation. Evolutionary attainability introduces possible dynamical trajectories
for a given equilibrium.
Three related topics deserve philosophical attention. Recent work has
explored whether different approaches to the study of adaptive evolution,
namely optimization, game-theoretic and quantitative-genetic, lead to similar
predictions about evolutionary dynamics and equilibria (Gomulkiewicz 1998).
This has led to a clarification of the inter-relationships between the modelling
approaches and has made their assumptions more transparent (Abrams 2001a;
Day and Taylor 2003). Philosophy can do an important job here in exploring
the structure of the conceptual framework we have for understanding phenotypic adaptation. The second topic concerns the role of frequency-dependence
in producing stable fitness minima that could be involved in situations of
competition and disruptive selection (Abrams et al. 1993; Abrams 2001b).
Indeed, Day emphasises how the distinction between an ESS and convergence
stability ‘illustrates the potential for a trait to evolve to a point at which natural
selection becomes disruptive’ (Day 2005, p. 295). The third topic concerns the
theory of long-term selection, which ‘was originally suggested as a means of
closing the gap between the genotypic and the phenotypic approaches to
natural selection’ (Eshel and Feldman 2001, p. 161); see also Hammerstein
(1996). According to the proponents of this approach, ESS stability holds for
long-term selection in phenotypic space but not for the short-term selection
analysed by population genetics models, that operates in genotypic space
(Eshel 1996). It remains to be assessed whether long-term selection would be
compatible with the modern history framework presented here. But an analysis
of these topics will have to wait for another occasion.
Concluding remarks
Maynard Smith’s pioneering work in evolutionary ecology produced novel
modelling strategies aimed at the explanation of phenotypic adaptation. These
modelling strategies involve a modal dimension and yield a ‘structural’
understanding of adaptation; as we have seen, both the modal and the structural aspects were part of Maynard Smith’s general philosophical stance
towards modelling in science. In this paper, I have tried to formulate some of
the philosophical questions arising from optimality and ESS modelling, and
have offered some preliminary answers. Besides the problem of elucidating the
type of scientific understanding that optimality and ESS models provide, there
was the central question of whether the notion of evolutionary stability is
historical and if so, what sense of history it involves. This in turn led to an
analysis of how the concept of evolutionary stability has been modified since
the original ESS formulation. If my analysis is correct, then the refinement of
the notion of evolutionary stability to include both an equilibrium and an
attainability condition shows that stability analyses of adaptation are causal–
dynamical in nature, and do involve a historical dimension, in the ‘modern
history’ sense specified above. The questions posed here are meant to provoke
further analyses of the natural philosophy of adaptation.
Acknowledgements
I wish to thank Jesús Alberto León, Elliott Sober and Samir Okasha for their
comments on an early draft of this paper, and an anonymous referee for
valuable suggestions. The ideas developed here have benefited from discussions
with David Hull, Peter Abrams, Steven Orzack, Paul Griffiths, Greg Cooper,
Robert Brandon, Jim Lennox, Diego Rodriguez, Jesús Alberto León, H. Allen
Orr, Steven French and Otávio Bueno.
References
Abrams P.A., Matsuda H. and Harada Y. 1993. Unstable fitness maxima and stable fitness minima
in the evolution of continuous traits. Evol. Ecol. 7: 465–487.
Abrams P. 2001a. Modelling the adaptive dynamics of traits involved in inter- and intraspecific
interactions: an assessment of three methods. Ecol. Letts 4: 166–175.
Abrams P. 2001b. Adaptation, optimality models and tests of adaptive scenarios. In: Orzack S.H.
and Sober E. (eds), Adaptationism and Optimality, Cambridge University Press, pp. 273–302.
Amundson R. 1996, Historical development of the concept of adaptation. In: Rose M. R. and
Lauder G. (eds), Adaptation, Academic Press, pp. 11–53.
Brandon R. 1990. Adaptation and Environment. Princeton University Press, Princeton, NJ.
Cartwright N. 1983. How the Laws of Physics Lie. Oxford University Press, New York.
Christiansen F.B. 1991. On conditions for evolutionary stability for a continuously varying character. Am. Nat. 138: 37–50.
Darwin C. 1859. On the Origin of Species by Means of Natural Selection, Edited by J.W. Burrow,
Penguin Classics, 1968.
Day T. and Taylor P. 2003. Evolutionary dynamics and stability in discrete and continuous games.
Evol. Ecol. Res. 5: 605–613.
Day, T. 2005. Modelling the ecological context of evolutionary change: déjà vu or something new?
In: Cuddington K. and Beisner E. (eds), Ecological Paradigms Lost: Routes to Theory Change,
Academic Press.
Eshel I. 1983. Evolutionary and continuous stability. J. Theor. Biol. 103: 99–111.
Eshel I. 1996. On the changing concept of evolutionary population stability as a reflection of a
changing point of view in the quantitative theory of evolution. J. Math. Biol. 34: 485–510.
Eshel I. and Motro U. 1981. Kin selection and strong evolutionary stability of mutual help. Theor.
Popul. Biol. 19: 420–433.
Eshel I. and Feldman M. 2001. Optimality and evolutionary stability. In: Orzack S.H. and Sober
E. (eds), Adaptationism and Optimality, Cambridge University Press, New York, pp. 161–190.
Fisher R.A. 1930. The Genetical Theory of Natural Selection. Clarendon Press, Oxford.
French S. and Da Costa N.C.A. 2003. Science and Partial Truth: A Unitary Account to Models
and Scientific Reasoning. Oxford University Press, New York.
Godfrey Smith P. (1993). Functions: consensus without unity. Pacif. Philos. Quart. 74: 196–208.
Reprinted in Hull D.L. and Ruse M. (eds), 1998, The Philosophy of Biology, Oxford University
Press, New York, pp. 280–292.
Godfrey-Smith P. 1994. A modern history theory of functions. Nous 28: 344–362.
Hammerstein P. 1996. Darwinian adaptation, population genetics, and the streetcar theory of
evolution. J. Math. Biol. 34: 511–532.
Grafen A. 1991. Modelling in behavioural ecology. In: Krebs J.R. and Davies N.B. (eds),
Behavioural Ecology, 3Blackwell, Oxford, pp. 5–31.
Gomulkiewicz R. 1998. Game theory, optimization, and quantitative genetics. In: Dugatkin L.A.
and Reeve H.K. (eds), Game Theory and Animal Behavior, Oxford University Press, NewYork,
pp. 283–303.
Hernández M.J. and León J.A. 1995. Evolutionary perturbations of optimal life histories. Evol.
Ecol. 9: 478–494.
Hines W.G.S. 1987. Evolutionary stable strategies: a review of basic theory. Theor. Popul. Biol. 31:
195–272.
Hofbauer J. and Sigmund K. 1998. Evolutionary Games and Population Dynamics, Cambridge
University Press.
Hughes R.I.G. 1993. Theoretical explanation. In: French P.A. and Uehling Jr. T. E. (eds), Midwest
Studies in Philosophy: Philosophy of Science, Vol. 18, University of Notre Dame Press, pp. 132–
153.
Kaplan J.M. and Pigliucci M. 2001. Genes ‘for’ phenotypes: a modern history view. Biol. Philos.
16: 189–213.
Kirkpatrick, M. 1996. Genes and adaptation: a pocket guide to the theory. In: Rose M.R. and
Lauder G.V. (eds), Adaptation, Academic Press, pp. 125–146.
Levins R. 1970. Fitness and optimization. In: Kojima (ed.), Mathematical Topics in Population
Genetics, Springer-Verlag, Berlin, pp. 389–400.
Maynard Smith J. 1978a. Optimization Theory in Evolution, Annu. Rev. Ecol. Syst. 9: 31–56.
Reprinted in Sober E. 1984. (ed.), Conceptual Issues in Evolutionary Biology, MIT Press,
Cambridge, MA, pp. 298–315.
Maynard Smith J. 1978b. The Evolution of Sex. Cambridge University Press, Cambridge.
Maynard Smith J. 1979. Game theory and the evolution of behaviour. Proc. Roy. Soc. Lon. B 205:
475–488.
Maynard Smith J. 1982. Evolution and the Theory of Games. Cambridge University Press,
Cambridge.
Maynard Smith J. 1987. How to model evolution. In: J. Dupré (ed.), The Latest on the Best: Essays
On Evolution and Optimality, MIT Press, pp. 119–131.
Maynard Smith J. 1989. Did Darwin get it Right? Essays on Games, Sex and Evolution. Chapman
and Hall, New York.
Maynard Smith J. and Price G.R. 1973. The logic of animal conflicts. Nature 246: 15–18.
Morgan M. and Morrison M. (eds), 1999. Models as Mediators. Cambridge University Press, New
York.
Morrison M. 2000. Unifying Scientific Theories: Physical Concepts and Mathematical Structures.
Cambridge University Press, New York.
Morrison M. 2002. Modelling populations: Pearson and Fisher on Mendelism and biometry. Brit.
J. Philos. Sci. 53: 39–60.
Orr H.A. 1998. The population genetics of adaptation: the distribution of factors fixed during
adaptive evolution. Evolution 52: 935–949.
Orr H.A. 2000. Adaptation and the cost of complexity. Evolution 54: 13–20.
Orr H.A. 2005a. Theories of adaptation: what they do and don’t say. Genetica 123: 3–13.
Orr H.A. 2005b. The genetic theory of adaptation: a brief history. Nat. Rev. Genet. 6: 119–127.
Parker G.A. and Hammerstein P. 1985. Game theory and animal behaviour. In: Greenwood P. J.,
Harvey, Paul H. and Slatkin M. (eds), Evolution: Essays in Honour of John Maynard Smith,
Cambridge University Press, pp. 73–94.
Plutynski, A. 2004. Explanation in classical population genetics. Philos. Sci. Proceedings Part II:
1201–1215.
Reeve H.K. and Sherman P.W. 1993. Adaptation and the goals of evolutionary research. Quart.
Rev. Biol. 68: 1–32.
Shanks N. (ed.), 1998. Idealization IX: Idealization in Contemporary Physics, Poznan Studies in
the Philosophy of the Sciences and the Humanities, vol. 63. Rodopi.
Sober E. 1983. Equilibrium explanation. Philos. Stud. 43: 201–210.
Suppes P. 1966. A comparison of the meaning and uses of models in mathematics and the empirical
sciences. In: H. Freudenthal (ed.), The Concept and the Role of Models in Mathematics and
Natural and Social Sciences, D. Reidel, pp. 163–177.
Taylor P.D. 1989. Evolutionary stability in one-parameter models under weak selection. Theor.
Popul. Biol. 36: 125–143.
Taylor P.D. and Jonker L.B. 1978. Evolutionary stable strategies and game dynamics. Math.
Biosci. 40: 145–156.
Weibull J. 1985. Evolutionary Game Theory. MIT Press, Cambridge, MA.