Prepared for the Northwest Philosophy Conference, Fall 2002

A new defense of adaptationismMark A. Bedau

Reed College, 3203 SE Woodstock Blvd., Portland OR 97202503-517-7337

http://www.reed.edu/~mabmab@reed.edu

Abstract

The scientific legitimacy of adaptationist explanationshas been challenged by Gould and Lewontin in “TheSpandrals of San Marco and the PanglossianParadigm.” The canonical response to this challenge isweak, because it concedes that the presuppositionthat an adaptationist explanation is needed cannot betested empirically. This paper provides a new kind ofdefense of adaptationism by providing such a test.The test is described in general terms and thenillustrated in the context of an artificial system—asimulation of self-replicating computer code mutatingand competing for space in computer memory.

It is a cliché to explain the giraffe’s long neck as an adaptation forbrowsing in tall tree. But the scientific legitimacy of such adaptive explanations iscontroversial, largely because of the classic paper “The Spandrals of San Marcoand the Panglossian Paradigm: A Critique of the Adaptationist Programme”published by Stephen Jay Gould and Richard Lewontin in 1979. The controversypersists, I believe, in part because the fundamental challenge raised by Gould andLewontin has not yet been met; in fact, it is rarely even acknowledged. Thispaper addresses this challenge and defends the scientific legitimacy of adaptiveexplanations in a new and deeper way.

First, some terminology.1 I will refer to claims to the effect that a trait is anadaptation as an adaptive hypothesis. A specific adaptive hypothesis is a claim tothe effect that a trait is an adaptation for some specified adaptive function, and ageneral adaptive hypothesis claims that a trait is an adaptation but identifies noadaptive function.2 An adaptive explanation of a trait explains its existence orpersistence as a result of adaptive evolution, i.e., by means of natural selectionfor that trait. And by adaptationism I mean the thesis that the activity ofpursuing adaptive explanations of the existence and nature of biological traits is anormal and legitimate part of empirical science.3

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The problem of adaptationism. Gould and Lewontin want a principledway to tell when adaptive explanations are needed, and they worry that this isimpossible. People deploy adaptive explanations without justifying them overnon-adaptive alternatives, such as appeals to architectural constraints or geneticdrift. If one adaptive explanation fails it is simply replaced by another, butsufficient ingenuity enables any trait can be given an adaptive explanation. Thegeneral adaptive hypothesis that a trait is an adaptation is treated as untestable.4The deeper worry is that the presupposition that a trait is an adaptation is reallyis untestable.5 There is a thicket of alternatives to adaptive explanations. How inprinciple can we tell when it is appropriate to pursue adaptationist branches?Gould and Lewontin summarize the predicament thus:

We would not object so strenuously to the adaptationistprogramme if its invocation, in any particular case, could lead inprinciple to its refutation for want of evidence. We might still viewit as restrictive and object to its status as an argument of firstchoice. But if it could be dismissed after failing some explicit test,then alternative would get their chance. (1979, pp. 258f).

The fundamental challenge, then, is to find some empirical test for generaladaptive hypotheses.6 If there cannot be done, then how can the practice ofgiving adaptive explanations be a normal and legitimate part of empiricalscience? In other words, the thesis of adaptationism would seem to be false.

The canonical response. So many of the responses to the challenge toadaptationism share the same basic form that this can be called the “canonical”response. In a nutshell, the canonical response is to concede that there is nogeneral empirical test for general adaptive hypotheses but construe this as partof normal empirical science.

Richard Dawkins nicely illustrates the cannonical response when heconsiders traits that might conceivably not be adaptations.7 He defendsadaptationism by pointing out that it is possible to test rival adaptive hypothesesby ordinary scientific methods, noting that “hypotheses about adaptation haveshown themselves in practice, over and over again, to be easily testable, byordinary, mundane methods of science” (Dawkins, 1983b, pp. 360f). Dawkins’scentral point is that specific adaptive hypotheses have observable consequences,so they entail empirical predictions and thus could be tested. Dawkins illustratesthis point with primate testes size. As it happens, primate testes size scalesroughly but not exactly with body size. If testes weight is plotted against bodyweight, there is considerable scatter around the average line.

A specific adaptive hypothesis is that in those species in whichfemales mate with more than one male, the males need biggertestes than in those species in which mating is monogamous orpolyganous: A male whose sperms may be directly competing withthe sperms of another male in the body of a female needs lots ofsperms to succeed in the competition, and hence big testes. Sureenough, if the points on the testis-weight/body-weightscattergram are examined, it turns out that those above theaverage line are nearly all from species in which females mate withmore than one male; those below the line are all frommonogamous or polygynous species. The prediction from the

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adaptive hypothesis could easily have been falsified. In fact it wasborne out… (1983b, p. 361)

This illustrates how specific adaptive hypotheses can be tested by ordinaryempirical methods.

Note, though, that Dawkins does not address the testability of generaladaptive hypotheses. Furthermore, the test for specific adaptive hypothesescannot be used to produce a test general adaptive hypotheses. The observableconsequences of a specific adaptive hypothesis depend on the specific functionhypothesized. Different functions may well entail different predictions. Forexample, the hypothesis that large primate testes are an adaptation fortemperature regulation would entail a quite different prediction about wherespecies fall in the testis-weight/body-weight scattergram. By contrast, thegeneral hypothesis that large testes are an adaptation for something or otherentails no prediction about where species fall in the scattergram. So, a generaladaptive hypothesis inherits no observational consequences from specifichypotheses. For this reason, Dawkins admits that general adaptive hypothesesare untestable. “It is true that the one hypothesis that we shall never test is thehypothesis of no adaptive function at all, but only because that is the onehypothesis in this whole area that really is untestable” (1983b, p. 361). In otherwords, Dawkins thinks the fundamental challenge to adaptationism cannot bemet.8

The evolutionary activity test. The cannonical response9 is weak. Itcapitulates in the face of Gould’s and Lewontin’s fundamental challenge byagreeing that there is no test for general adaptive hypotheses. But plenty of traitsare not adaptations, and adaptive explanations are often inappropriate. Is therereally no empirical way to tell whether adaptive explanations are in the offing? Ithink the answer is “Yes.” Thus, I part company with the canonical responseprecisely where it capitulates to Gould and Lewontin. As far as I know, this is thefirst response that takes the bull by the horns. I will explain how to test generaladaptive hypotheses and illustrate the method in one specific evolving system.

My test involves collecting and analyzing “evolutionary activity”information.10 From an abstract point of view an evolving system consists of apopulation of items with inherited features.11 The items participate in cycles ofbirth, life and death. Innovations are introduced into the population throughgenetic operations like mutation and crossover. Adaptive innovations tend to beinherited through the generations because of their beneficial effect on survival orreproduction. Maladaptive or neutral innovations tend to either disappear overgenerations or persist passively.

The crux of the evolutionary activity method is to observe the extent towhich items resist selection pressures, for resistance to selection is evidence ofadaptation. Since an item is subjected to selection pressure only when it is activeor expressed, I call this evolutionary “activity” information.12 Simplebookkeeping collects an historical record of items’ activity—the extent to whichitems have been subjected to selection pressure, i.e., the extent to which theiradaptive value has been tested. The bookkeeping increments an item’s currentactivity as long as it persists, yielding its cumulative activity. If the item (e.g.,gene) is inherited during reproduction, its cumulative activity continues to beincremented by the child’s current activity. In this way our bookkeeping records

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an item’s cumulative activity over its entire history in the lineage.13 Cumulativeactivity sums the extent to which an item has been tested by selection over itsevolutionary history.

Every time an item is exposed to natural selection, selection can providefeedback about the item’s adaptive value. Obviously, an item will not continue tobe tested by natural selection unless it has passed previous tests. So, the amountthat an item has been tested by selection reflects how successfully it has passedthe tests. If a sufficiently well-tested item persists and spreads through thepopulation, we have positive evidence that it is persisting because of its adaptivevalue.

But natural selection is not instantaneous. Repeated trials might be neededto drive out maladaptive items. So exposure to some selection is no proof ofbeing an adaptation. Thus nonadaptive items will generate some “noise” inevolutionary activity data, To gauge resistance to selection with evolutionaryactivity we must filter out this nonadaptive noise. We can do so by determininghow activity will accrue to items persisting due just to nonadaptive factors likerandom drift or architectural necessity. A general way to measure the expectedevolutionary activity of nonadaptive items is to construct a neutral model of thetarget system: a system that is similar to the target in all relevant respects exceptthat none of the items in it has any adaptive significance. (I give a concreteexample below.) The accumulated activity in neutral models provides a no-adaptation null hypothesis for the target system that can be used to screen offnonadaptive noise. If we observe significantly more evolutionary activity in thetarget system than in its neutral shadow, we know that this “excess” activitycannot be attributed to nonadaptive factors. It must be the result of naturalselection, so the items must be adaptations.14

Adaptation of Evita genotypes. I will illustrate the evolutionary activitytest for adaptations in Evita, a simple artificial evolving system that “lives” in acomputer.15 Somewhat analogous to a population of self-replicating strings ofbiochemical RNA, Evita consists of a population of self-replicating strings ofcustomized assembly language code and residing in a two-dimensional grid ofvirtual computer memory. The system is initialized with a single self-replicatingprogram. When Evita runs, this ancestral program copies each of its instructionsinto a neighboring spot on the grid, thereby producing a new copy of theprogram— its “offspring.” Then this offspring and its parent both startexecuting, and each makes another copy of itself, creating still more offspring.This process repeats indefinitely. When space in computer memory runs low andoffspring cannot find unoccupied neighboring grid locations, the older neighborsare randomly selected and “killed” and the offspring move to the vacated space.Innovations enter the system through point mutations. When a mutation strikesan instruction in a program, the instruction is replaced by another instructionchosen at random.16 With a moderate mutation rate new kinds of programs arecontinually spawned.17 Many are maladaptive but some reproduce more quicklythan their neighbors, and these tend to spread through the population, causingthe population of strings to evolve over time.

Evita is explicitly designed so that the programs interact only bycompeting for space.18 On average, programs that reproduce faster will supplanttheir reproducing neighbors. Most significant adaptive events in Evita are

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changes in reproduction rate, so for present purposes a genotype's fitness can beequated with its reproduction rate.19 Evita has a clear distinction betweengenotype and phenotype. A given genotype is simply a string of computer code.If two programs differ in even one instruction they have different genotypes.But two genotypes might produce exactly the same behavior—the samephenotype. If a program includes instructions that never execute, theseinstructions can mutate freely without affecting the operation of the program.Thus multiple genotypes—without phenotype distinction and so with exactly thesame fitness—may then evolve through random genetic drift.

To gather evolutionary activity data in Evita two issues must be settled.First, one must decide which kind of item to observe for adaptations. We willobserve whole genotypes. Second, one must operationalize the idea of agenotype’s being tested by natural selection. A plausible measure of this isconcentration in the population. The greater the genotype’s concentration, themore feedback that selection provides about how well adapted it is. Agenotype’s cumulative evolutionary activity, then, is just the sum of itsconcentration over time.

In order to discern how much of Evita’s genotype activity can beattributed to the genotypes’ adaptive significance, we create a “neutral shadow”of it (recall the discussion above). The neutral shadow is a population of nominal“programs” with nominal “genotypes” existing at grid locations, reproducingand dieing. These are not genuine programs with genuine genotypes; theycontain no actual instructions. Their only properties are their location on the grid,their time of birth, the sequence of reproduction events (if any) they go through,and their time of death.

Each target Evita run has a corresponding neutral shadow.20 Certainevents in the target cause corresponding events in the shadow, but events in ashadow never affect the target (hence, the ‘shadow’ terminology). The frequencyof mutation events in the shadow is copied from the Evita target. Whenever amutation strikes a shadow “program” it is assigned a new “genotype.” Thetiming and number of birth and death events in the neutral shadow is alsopatterned exactly after the target. Shadow children inherit their parent’s“genotype” unless there is a mutation, in which case the shadow child is assigneda new “genotype.” The key difference is that, while natural selection typicallyaffect which target program reproduces, random selection determines whichshadow “program” reproduces. So shadow genotypes have no adaptivesignificance whatsoever; their features like longevity and concentration—andhence their evolutionary activity—cannot be attributed to their adaptivesignificance. At the same time, by precisely shadowing the births, deaths, andmutations in the target, the neutral shadow shows us the expected evolutionaryactivity of a genotype in a system exactly like Evita except for being devoid ofnatural selection. The neutral shadow defines a null hypothesis for the expectedevolutionary activity of genotypes affected by only non-adaptive factors such aschance (e.g., random genetic drift) or necessity (e.g., the system's underlyingarchitecture).

Figure 1 about here

Evita’s evolutionary graphs depict the history of the genotypes’ activity ina given Evita run.21 Whenever one genotype drives another to extinction by

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competitive exclusion, a new wave arises as an earlier one dies out. Multiplewaves coexist in the graph when multiple genotypes coexist in the population,and genotypic interactions that affect genotype concentrations are visible aschanges in the slopes of waves. The key point to appreciate about the graphsthat the big waves in these diagrams correspond to significant adaptationsamong the genotypes. We can see this clearly in Figure 1 by comparing a typicalEvita evolutionary activity graph (above) with an activity graph of its neutralshadow (below). These graphs are strikingly different.22 Leaving aside theancestral wave, the highest waves in the Evita are orders of magnitude higherthan those in the neutral analogue.23 This is clear evidence of how the size of agenotype's evolutionary activity waves in Evita reflects the genotype's adaptivesignificance. In the Evita target, at each time one or a few genotypes enjoys aspecial adaptive advantage over their peers, and this is reflected by theircorrespondingly huge waves. The change in dominant waves reflects a newadaptation out competing the prior dominant adaptations. In the neutralanalogue, by contrast, a genotype's concentration reflects only dumb luck, so nogenotype activity waves rise significantly above their peers.24

Figure 2 about here

Figure 2 shows more detail of the evolutionary activity during thebeginning of the Evita run in Figure 1, with the average population fitnessgraphed below. The activity graph is dominated by five main waves, the firstcorresponds to the ancestral genotype and the subsequent waves correspond tosubsequent adaptations.25 Miscellaneous low-activity genotypes that never claima substantial following in the population are barely visible along the bottom ofthe activity plot. Comparing the origin of the waves with the rises in averagepopulation fitness shows that the significant new waves usually correspond tothe origin of a higher fitness genotype. Detailed analysis of the specific programthat makes up the genotypes with high activity, we see that the major adaptiveevents consist of shortening a genotype's length or copy loop.26

Figure 3 about here

The moral is that significant evolutionary activity waves are adaptations.They correspond to genotypes that are persisting and spreading through thepopulation because of their relative adaptive value. Natural selection ispromoting them because of their relative reproduction rate; they flourishbecause of selection for this, so they are adaptations. The evidence for the moralhas three parts. First, new significant waves coincide with significant jumps inaverage population fitness. This shows that the new genotype spreadingthrough the population and making the new wave is an adaptive advantageover its predecessors. Second, microanalysis of the genotypes in the new wavesreveals the genetic novelties that create their adaptive advantage. Third, in aneutral model in which chance and architectural necessity are allowed full reignand natural selection is debarred by fiat, no genotypes make significant waves.So, the major evolutionary activity waves in Evita could be produced only bycontinual natural selection of those genotypes, and natural selection of thegenotypes must be due to selection for their adaptive value.

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Neutral variant genotypes are an exception to this moral, but they provethe rule. Notice that the second fitness jump in Figure 2 corresponds to densecloud of activity waves. Figure 3 is a blowup of these waves. The genotypes inthis cloud differ from each other only by mutations at an unexpressed locus, sothey all use exactly the same algorithm. They are neutral variants of oneanother—different genotypes with exactly the same phenotype.27 So the neutralvariants are one and the same phenotypic adaptation. Each genotypic instancesof the phenotype is an adaptation because it is persisting due to its adaptivevalue.28

Conclusion. The sign that an evolutionary process is creating adaptationsis that its activity data is significantly higher than what you would expect ifselection were random. If activity waves rise above the noise generated in a no-adaptation neutral model, then you know the corresponding items areadaptations even if you are ignorant about the adaptive functions. The activitydata shows that some adaptive explanation is needed even if it silent about themerits of any specific explanation.29 In other words, the evolutionary activitymethod tests general adaptive hypotheses.30

Thus, the evolutionary activity test directly responds to the fundamentalchallenge to adaptationism.31 The test does not assume that traits are adaptationsbut tests whether they are. Adaptive “just-so” stories have no place here; suchstories propose specific adaptive hypotheses and these are not at issue. The issueis general adaptive hypotheses, and these are accepted skeptically and only ifdemanded by the empirical evidence.32 The activity method avoids the problemsof the canonical response, and it makes the question of adaptation objective andempirical. Sometimes adaptive presuppositions are false and the activity datashow this. When activity data justify taking the adaptive stance, the stance isadopted on the basis of empirical evidence against nonadaptive alternatives. Sowe can pursue the adaptationist program constructively and self-critically.

Gould and Lewontin said that they “would not object to strenuously tothe adaptationist programme if its invocation, in any particular case, could lead inprinciple to its refutation for want of evidence” (1979, pp. 258f). The evolutionaryactivity method provides just the sort of tool that Gould and Lewontin sought.So if we can take them at their word, they should now withdraw theirobjection.33

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References

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Figure 1

Evolutionary activity waves in Evita (top) and its neutral shadow (bottom). Notethat the activity scale on the neutral shadow is inflated by a factor of three, inorder to highlight the neutral shadow waves (barely visible along the bottom).

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Figure 2

Evolutionary activity graph (above) and average population fitness (below) froma typical Evita run. The adaptive advantage of the genotypes causing the salientwaves is indicated. The start of a significant wave generally corresponds to anincrease in fitness. Note the cloud of neutral variants that cause one of the fitnessjumps and act in the population like a single phenotype. These neutral variantsare more fit than genotype 33aaf because they require one fewer instruction perexecution of the copy loop. Note also that the significant wave due to genotype32abl does not cause a significant fitness increase; it is nearly phenotypicallyequivalent to 32aaV because it executes only one fewer instruction perreproduction event than 32aaV.

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Figure 3

Blow up of the evolutionary activity graph in Figure 2, showing the neutralvariants that cause the fitness increase just after time step 1000.

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Notes

1 The controversy over adaptationism is caused partly by terminology, so let me clarifymine. The notion of how well an organism fits or is adapted to its environment is the organism’sadaptedness in that environment. A trait is adaptive (in an environment) just in case it benefitsthe organism (in that environment), i.e., it increases the organism’s propensity to survive orreproduce. Optimal traits are those that (in the context and given a set of constraints) could notbe more adaptive. Now, an adaptation is a trait produced by the process of natural selection for that trait. (More about adaptations: See Sober 1984 for the distinction between selection for atrait and selection of a trait.) For example, the whale’s fins are presumably an adaptation forswimming. We can apply the property of being an adaptation to other levels of analysis byaveraging over traits and individuals. In particular, below I will talk of genotypes (a completeset of traits) as adaptations and as adaptive. A genotype is adaptive if on average individualswith that genotype are adaptive. A genotype is an adaptation if it is persists through theaction of natural selection, that is, if on average the individuals with that genotype have beenselected for their possession of that genotype, i.e., if the traits in that genotype areadaptations.

Some (e.g., Gould and Lewontin 1979, Williams 1966) have suggested that a trait couldbe selected for without being an adaptation, but I side with Sober’s (1984) refutation of this.The process of producing adaptations is also sometimes called “adaptation,” as when we speakof the adaptation of a population to its environment by means of natural selection. I will avoidthis ambiguity by calling this process “adaptive evolution.” A trait is adaptive only relativeto an environment, and that environment typically includes other evolving organisms. So theenvironment for adaptation might itself be changed by the process of adaptive evolution.

Natural selection must be distinguished from so-called “sexual” selection—the processby which a trait is selected for its attractiveness to mates. I side with those who classifysexual selection as a kind of natural selection. So, for me, a product of sexual selection, such asthe peacock’s extravagant tail, is an adaptation, even though it is may be detrimental tosurvival, because it increases reproductive success. For more on the controversy about sexualselection, see Cronin (1992) and Spencer and Masters (1992).

There is a controversy about how far back into the past selective explanations mustreach for a trait to be an adaptation. Gould and Vrba (1982) called attention to this issue withthe neologism ‘exaptation’, which refers to an item that now performs one function but whichoriginated to perform either a different function or no function. (The traditional biological termfor these items is ‘preadaptation’.) Some think that an adaptation for some function must haveoriginally arisen by natural selection to perform that function. For these people exaptations arenot adaptations (Gould and Lewontin 1979, Gould and Vrba 1982, Sober 1984). Others think thatit is sufficient for a trait to be an adaptation if its recent persistence is due to natural selection.For these people an exaptation can be an adaptation (Brandon 1990, Kitcher 1993, Godfrey-Smith 1994b, Sterelny and Griffiths 1999).

Behind this semantic disagreement about whether exaptations are “adaptations,”everyone agrees that exaptations exist and that natural selection can maintain an item becauseof the function it now performs, even if the item did not originate for that reason. As a matter ofsemantics, I view exaptations as a special subset of adaptations, because I view anythingmaintained because of selection for a function as an adaptation. Regardless of their semanticclassification, exaptations highlight that items can acquire new functions and be maintainedby natural selection because of those new functions. Any attempt to explain the process ofadaptive evolution must allow and explain exaptations.

2 A general adaptive hypothesis expresses the presupposition that the trait has someadaptive explanation. An example is the claim that large primate testes are an adaptation.An example of a specific adaptive hypothesis is the claim that large primate testes are anadaptation for producing more sperm.

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3 My use of the term “adaptationism” captures what I believe is the central issue, but I

should emphasize that the term is sometimes used in other ways. Most of the discussion ofadaptationism in the literature concerns the use of optimality models (and related models) forexplaining adaptations (e.g., Dupré 1987, Orzak and Sober 1994, Orzack and Sober 2001), butadaptationism (as I understand it) transcends optimality and concerns any kind of adaptiveevolutionary explanation. What I mean by “adaptationism” also differs from Elliot Sober’sthesis by the same term (Sober 1987, 1993, Orzack and Sober 1994). Sober’s thesis is thatadaptive explanations are sufficient to explain most (nonmolecular) traits in mostpopulations—a position which I call panadaptationism (n.b., not Sober’s term).Panadaptationism entails (1) that natural selection explains most (nonmolecular) traits in most populations, and (2) that natural selection plays the only important role in the explanation ofthose traits. Adaptationism (as I mean it) entails neither (1) nor (2). The practice of givingadaptive explanations can be a normal and legitimate part of empirical science without mosttraits of most organisms being explained primarily by natural selection.

Following Peter Godfrey-Smith (2001) but deviating slightly from his terminology, weshould distinguish two more theses. One is the claim—which I call limitedadaptationism —that natural selection is the only satisfactory explanation of some limited andspecial set of traits. The special traits typically are striking complex adaptive structures likethe eye or the brain. By contrast, adaptationism (as I mean it) takes no stand on which specifictraits are adaptations; it just requires that we can distinguish adaptations from non-adaptations and support adaptive explanations with normal empirical science. A furtherclaim—which I call methodological adaptationism —holds that the best way to understandevolutionary phenomena is to assume traits are adaptations and to investigate what they arewell designed for in order to, among other things, notice deviations from such designs andthereby to infer evolutionary constraints. This view can be found in Maynard Smith’sdiscussions involving optimization models: “in testing a model we are not testing the generalproposition that nature optimizes, but the specific hypotheses about constraints, optimizationcriteria, and heredity” (Maynard Smith 1978, quoted in Dawkins 1982, p. 49). In contrast,adaptationism (as I mean it) claims not that it is heuristically valuable to view naturethrough adaptive lenses, but that the empirical lenses can reveal whether and how naturalselection actually shapes evolutionary processes.

I distinguish adaptationism from pan-, limited and methodological adaptationismbecause I think that the fundamental challenge for adaptationism concerns adaptationismitself; panadaptationism, limited adaptationism, and methodological adaptationism aredistractions.

4 As Lewontin puts it, “the adaptationist program makes of adaptation a metaphysicalpostulate that … cannot be refuted” because the presupposition that a trait is an adaptation isnever questioned (Lewontin 1977/1985, p. 76).

5 It might be uncontroversial that some specific traits are adaptations, but those are theexception.

6 One might suspect that the problem for adaptationism is just blanket verificationismof the sort espoused logical positivists early last century. This would make the problemuninteresting because verificationism has been completely discredited. Virtually no one todaywould hold the practice of normal empirical science hostage to a priori verificationism.However, the problem for adaptationism cannot be dismissed this easily. It is grounded inconcerns that specifically apply to adaptive explanations. Those who reject blanketverification still have to explain how the presupposition of adaptive explanations can beempirically verified. The fundamental challenge is to provide some empirical test for whethertraits are adaptations.

7 To see Dawkins’s canonical response one must avoid the distraction of methodologicaland limited adaptationism, for Dawkins supports these too. Dawkins supports methodologicaladaptationism when he notes that assuming that a trait is an adaptation—indeed, an optimal

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adaptation given the assumption that a certain set of constraints exist—can be a usefulheuristic procedure, a productive working hypothesis for generating testable predictions(Dawkins 1982). But since methodological adaptationism consciously and deliberately takes nostand on whether traits actually are adaptations, it fails to take the Gould and Lewontin bullby the horns. Dawkins also defends limited adaptationism when he emphasizes that complexorgans and behavior are quite likely to be adaptations. E.g., “[t]he working hypothesis thatthey must have a Darwinian survival value [i.e., that they are adaptations] isoverwhelmingly strong” (Dawkins 1982, p. 43), and his statement that he “shall only beconcerned with those aspects of morphology, physiology, and behavior of organisms that areundisputedly adaptive solutions to problems” (1983a, p. 17). But limiting attention only touncontroversial adaptations also just side-steps the fundamental challenge.

8 Of course, evidence for a specific function is a fortiori evidence for some function, socorroborating a specific adaptive hypothesis also corroborates the corresponding generalhypothesis. But we cannot test all possible specific adaptive hypothesis for a trait (that isDawkins’s point in the quote above). So testing specific hypotheses provides no test for generalhypothesis.

9 To appreciate how canonical this response is, consider some other well-knownresponses. Daniel Dennett offers a principled reason for embracing the untestability ofadaptive presuppositions. He gives many examples of adaptive stories that are “too obviouslytrue to be worth further testing” (1995, p. 246). This is limited adaptationism, and as such itignores the problem of adaptationism. Dennett also embraces a sweeping position near topanadaptationism, saying that adaptationism “plays a crucial role in the analysis of everybiological event at every scale from the creation of the first self-replicating macromolecule onup” (1995, p. 238). But this also is no help with the problem of adaptationism. Dennettemphasizes that reverse engineering explains adaptations. Reverse engineering is typicallyapplied to artifacts. It is the search for the reasons for an artifact’s features, the reasons why itwas engineered to have those features. Biological adaptations are not artifacts literallydesigned by some creator, of course, but Dennett points out that they are nevertheless crafted bynatural selection, and this is enough for reverse engineering to work.

Darwin’s revolution … permits [reverse engineering] to be reformulated. Insteadof trying to figure out what God intended, we try to figure out what reason, ifany, ‘Mother Nature’—the process of evolution by natural selectionitself—‘discerned’ or ‘discriminated’ for doing things one way rather thananother. … [E]ven at the molecular level, you just can’t do biology without doingreverse engineering. (1995, p. 213).

Note that reverse engineering aims to discover specific adaptive functions. It presumes that thetrait in question is designed by natural selection, i.e., that it is an adaptation. This presumptionis not something that reverse engineering can test. When engaged in reverse engineering, if onespecific adaptive hypothesis fails, your job is to keep searching for the best specific adaptivehypothesis.

Reverse engineering and methodological adaptationism have similar benefits. Giventhe assumption that something is an adaptation or, indeed, optimally designed, we can testdifferent hypotheses about selective forces, heritable factors, etc., by seeing if optimalitypredictions given those constraints match what we observe. “The bootstrapping evidence thatwe have in fact located all the important constraints relative to which an optimal designshould be calculated is that we make that optimizing calculation, and it turns out to bepredictive in the real world” (1983, p. 353). So Dennett views adaptationism as a usefulexplanatory strategy. He likens it to “intentional stance” instrumentalism (Dennett 1971) withregard to the mind.

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Adaptationism and mentalism (intentional systems theory) are not theories inone traditional sense. They are stances or strategies that serve to organize data,explain interrelations, and generate questions to ask Nature. Were theytheories in the ‘classical’ mold, the objection that they are question begging orirrefutable would be fatal, but to make this objection is to misread their point.(1983, p. 353)

Dennett admits that adaptive presuppositions are virtually untestable, but he sees this not as aflaw but as a harmless property of all research strategies.

Dennett, like Dawkins, illustrates the canonical response to Gould and Lewontin. Heconcedes that there is no general empirical test for adaptations, but explains this away asnormal science. In fact, Dennett views any general test for adaptationism as misguided pre-Darwinian essentialism. “We commit a fundamental error if we think that if we want toindulge in adaptive thinking we need a license and the only license could be the possession of astrict definition of or criterion for a genuine adaptation” (1995, p. 247). The central job of thispaper is to produce an empirical criterion of just this sort.

Ernst Mayr and Alexander Rosenberg provide two further illustrations of the canonicalresponse to Gould’s and Lewontin’s fundamental complaint against the adaptationist program.Mayr thinks that evolutionary change is the result of two causal mechanisms: chance andselection. Mayr thinks that, while particular adaptive hypotheses can be falsified, one cannever directly prove that chance explains some biological phenomenon or process. Instead, onecan show the hand of chance only indirectly and “tentatively” (Mayr 1983, p. 151), by ruling outall adaptive explanations that have been considered so far. As Kitcher puts it, Mayr thinksthat selection “is the only game in town” (1985, p. 233). But if it is impossible to test whetherchance explains some trait, then it is impossible to settle whether the trait is an adaptation.Thus, the fundamental presupposition of adaptive explanations cannot be tested, so Mayr ineffect accepts Gould and Lewontin’s fundamental criticism. But Mayr considers this to be normalscientific practice: “the strategy to try another hypothesis when the first fails is a traditionalmethodology in all branches of science” (1983, p. 151).

Rosenberg (1985) emphasizes that the theory of natural selection is an extremal theory ,like Newtonian mechanics or quantum mechanics, in that it treats the objects in its domain asbehaving in such a way as to maximize or minimize the values of certain variables.

In the theory of natural selection, the [extremal] strategy is exemplified in theassumption that the environment acts so as to maximize the rate of proportionalincrease of the fittest hereditarily-similar subset of a species. This strategy is crucialto the success of these theories because of the way it directs and shapes the researchand applications that are motivated by the theories. Thus, we hold that a systemalways acts to maximize the value of some mechanical variable. If our measurements ofthe value of that variable in a experimental or observational setting diverge from thepredictions of the theory and the initial conditions, we never infer that the system isfailing to maximize the value of the variable in question. Rather, we assume that ourspecification of the constraints under which it is actually operating is incomplete. (p.238)

Rosenberg thinks that the extremal character of the theory of natural selection explains andredeems those features of adaptationism that Gould and Lewontin find objectionable.

… Gould and Lewontin’s criticism of the adaptationalist program reflects real andunavoidable features of the theory that animates it: The refutation of oneadaptationalist “story” does result in the provision of another; the failure to find anyis considered a scientist’s failure, not a theoretical falsification…. There is nothing

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methodologically wrong with constructing a new adaptationalist explanation of agiven “form, function, or behavior” when an older one has been rejected; indeed, thetheory of natural selection requires it by virtue of its extremal character. (p. 240)

Rosenberg’s response exactly fits the canon: He concedes that adaptive presuppositions are nottested but explains this away as normal scientific practice.

Note that Mayr’s and Rosenberg’s positions have the characteristic drawback of thecanonical response: They do not solve the problem of adaptationism, for neither explains howwe can determine when natural selection should be invoked. For example, the extremalcharacter of the theory of natural selection does nothing to indicate when it is appropriate todeploy that theory.

10 Norman Packard and I developed and applied this method to a number of systemsover a number of years with the help of students and colleagues. See Bedau and Packard 1992,Bedau 1995, Bedau 1996a, Bedau and Brown 1997, Bedau, Snyder, Brown, and Packard 1997,Bedau, Snyder, and Packard 1998, Bedau, Joshi, and Lilly 1999, Rechtsteiner and Bedau1999a,b.

11 The system might contain many different kinds of evolving entities, at differentlevels of analysis. I use the term ‘item’ for maximum generality. The items come from a widerange of possibilities, ranging from individual alleles to whole genotypes and beyond.

12 Perhaps “selection test” information would be better terminology.13 The cumulative activity of an item at a given time is not determined solely by its

intrinsic state at that moment. It is also affected by all its activity over its previous history inthe evolving system. Many (e.g., G. C. Williams 1966, and R. M Burien 1992) have emphasizedthat adaptation is a historical concept; something is an adaptation not in virtue of whatfunction it performs but in virtue of its causal etiology. This historical dimension is part ofwhat has made adaptations especially difficult to identify. So it is worth emphasizing thatevolutionary activity information is historical information.

14 Although the evolutionary activity method is novel, the essential logic behind itshould be familiar. William Wimsatt has said that using neutral (random selection) models inbiology as null hypotheses is “common and important” (1987, p. 28). David Raup makeseffective use of neutral models in paleontology. For example, Raup wondered whether theextinction of the trilobites could be explained simply as bad luck, analogous to a gambler’s ruinin a fair game, so he constructed a neutral model in which trilobite cladogenesis is “a purelyrandom process with average rates of speciation and extinction no different from the averagesfor other organisms” (1987, p. 123). He found that probability of the trilobites going extinct dueto bad luck was vanishingly small. This illustrates the essential features of my test foradaptations.

Kimura’s neutral theory of molecular biology (Kimura 1983) is also often used as a nullhypothesis, against which the action of natural selection is gauged. Kimura’s neutral theorypredicts that molecular changes with little or no adaptive significance will occur much morerapidly, and lots of data support it. This reasoning, but run in the other direction, is therationale behind my test: We can find significant adaptations by detecting those changes thatpersist longer than chance could explain.

At the molecular level my test for adaptations has implications for the relative ratesat which introns and exons change. Chromosomes consist of two kinds of genetic segments: thosethat are unexpressed, called introns, and those that are expressed, called exons. Introns changemore quickly than exons, presumably because the introns have little or no function and so areunconstrained by their function. The so-called “molecular clock”—the background rate ofchange of genetic molecules—ticks at the rate of change for introns. Exons can be discriminatedin part by the fact that they change more slowly than the molecular clock. My neutral modelsare analogous to the molecular clock. They provide what you might call a “gene” or “genotype”clock. In each case, comparison with these neutral “clocks” are used to discern when natural

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selection is retaining some evolutionary novelty longer than would be expected just from theoperation of the clock.

John Beatty investigated the use of random drift models in the adaptationism debate.This is essentially what I am proposing. Beatty concluded that the case for viewing drift as anull hypothesis for natural selection “is, at best, very messy” (1987, p. 54). But he admits thatpredictions based on drift hypotheses can be standard null hypotheses. For example, thehypothesis that the evolution of some item in some system is primarily a matter of randomdrift would predict a certain probability distribution of activity data—exactly thedistribution produced by a neutral model. The null hypothesis is that the activity observed inthe target system will be taken from the same distribution. So, Beatty’s skepticism about drifthypotheses does not undermine the activity test’s use of pure drift models.

15 Created by C.Titus Brown, Evita is inspired by Tierra (Ray 1992) and its derivativeAvida (Adami and Brown 1994), but it is much simpler because it disallows the kind ofinteractions that lead to parasitism and the other interesting evolutionary phenomenaobserved in Tierra. Its simplicity makes it an especially simple and clear illustration of howgraphing evolutionary activity reveals a system’s evolutionary dynamics.

16 The probability that a given program suffers a mutation somewhere is proportionalto its length. While the probability that a given program is mutated is independent of the sizeof the population of programs, the probability that a mutation occurs somewhere in thepopulation is obviously proportional to the population size. Typically, mutation rates arespecified in terms of 10-5 mutations per time step: that is, a mutation rate of m would mean thata given codon would mutate on average once every 105/m time steps. This means, for example,that in a run with 1600 creatures with an average length of 30 instructions, a mutation rate of 1would cause one mutation somewhere in the population approximately every other time step.

17 If the mutation rate is too low, there is no significant genetic change in thepopulation. If the mutation rate is too high, the population dies out almost immediatelybecause no successfully reproducing creature can survive the bombardment of mutations longenough to reproduce.

18 In each update of the system the program at each occupied grid spot executes a fixednumber of instructions. This processing time is allocated in a way that is unbiased by spatialposition; hence, no organism gains an advantage from its location on the grid. In fact, the onlyadvantage grid position can provide is the relative fitness of the surrounding population: yourfitness depends on your reproduction rate relative to your neighbors. So competition in thissystem is spatially localized.

19 Shorter programs are more resistant to mutations (recall above), so a program's lengthinfluences its copy fidelity and, thus, its representation in future generations. Thus,reproduction rate is not a perfect measure of evolutionary success. Nevertheless, at themutation rates used here, evolutionary success overwhelmingly reflects reproduction rate, so ourdefinition of fitness is more than adequate for present purposes. When fitness values aredisplayed below, we define fitness as 30 times reproduction rate to prevent them from beinginconveniently small. So, for example, a genotype that reproduces in 100 instructions has areproduction rate of 0.01 and a fitness of 0.3.

20 Actually, it has an indefinite number of them, due to random sampling differences—aqualification I will usually ignore.

21 In the Evita activity graphs shown here, the Evita system parameters were allidentical except for mutation rate and elapsed time. Each genotype in a given run is given aunique name of the form Nxxx, where N is a number indicating the genotype's length and xxx isa three-character string (in effect, a base 52 number) indicating the genotype's order oforigination among genotypes of that length. For example, 32aac is the third length 32 genotypeto arise in the course of a given run.

The grid size was 40 x 40, so when the grid filled up the population consisted of about1600 self-reproducing programs. I have pruned out irrelevant data about transitory genotypes by

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graphing only those genotypes that had at least five instances in the population at some time.This removes some of the “little hairs” created by nonadaptive noise.

22 Note that the activity scale (y-axis) in these two plots is roughly comparable, exceptthat activity on the bottom is expanded by a factor of three to make the neutral model activityeasier to see.

23 The other salient difference between the normal and neutral runs—that the ancestralgenotype persists about four times longer in the neutral run—is due to the fact that, since theneutral ancestral genotype need never compete with better adapted genotypes, its initialnumerical advantage as ancestor carries more weight.

24 This difference between the normal and neutral activity data can be used to quantifythe adaptive evolutionary activity in evolving systems. See Packard and Bedau references inthe note above.

25 Notice that the fourth salient wave (due to genotype 32abl) does not correspond to asignificant fitness jump. This genotype is well adapted, but it is not significantly betteradapted than its main predecessor: genotype 32aaV. The waves from 32aaV and 32abl coexistfor so long because the two genotpyes are nearly neutral variants. In fact, the fitness of thesecond wave (32abl) exceeds that of the first wave (32aaV) by about only 0.5%. Theinteractions among the three salient waves between updates 4000 and 5000 have a similarexplanation. They are a significant improvement (5% fitness advantage) over the genotypesthat they drive extinct, but they differ from one another by much less (less than 2%).

26 The second major activity wave corresponds to genotype 33aaf. Microanalysis showsthat its shorter length gives it a significant fitness advantage. The next rise in fitnesscorresponds to a cloud of low-lying activity waves; more on this in a moment. The big fitness risein the middle of the graph corresponds to the introduction of genotype 32aaV. Its fitnessadvantage is due to its shorter overall length as well as its shorter copy loop. The last verylarge wave corresponds to genotype 30aab, another genotype with a fitness advantage due toshorter length.

27 The unexpressed locus was created by a mutation that produced a non-templateinstruction inside the template surrounding the copy loop, saving one executed instruction perloop traversal and thus significantly increasing fecundity. Once this mutation has occurred,almost any other mutation at the same locus creates another non-template instruction with anidentical fitness benefit, so a cloud of neutral variants quickly grows. Since these neutralvariants have exactly the same fitness advantage over their predecessors, and since they allare just one mutation away from each other, they engage in adaptive interactions withcompeting genotypes effectively as a single higher-level phenotype.

28 If a cloud contains enough neutral variants, each individual wave might be smallenough that it would not be significantly different from the waves produced in a neutral model.The neutral variants in Figure 3 stand out from neutral activity waves, but not by a lot. (Theneutral model produces waves like the “little hairs” along the bottom of the Figure.) So a newadaptation could get lost in a big enough cloud of neutral variants, so my test for adaptationsmight miss some adaptations. These false negatives can happen because we are applying thetest to suboptimal items. In Evita, adaptations are not really genotypic but phenotypic, so itwould be better to collect activity data of phenotypes. If we did so, the activity of the neutralvariants would all be lumped into a single salient wave on a par with the other large waves.We observed genotype activity simply because it is so easy to get genotype data. This practicalexpedient is still quite revealing about the adaptive dynamics, and it clearly indicates whenadaptive explanations are in the offing.

29 For example, inspection of Figure 2 shows that the big wave in the middle producedby genotype 32aaV is an adaptation but it does not show what makes it better than its peers.We can usually discover a genotype’s adaptive advantage by independent microanalysis, as wedid for 32aaV.

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Genetic hitchhikers and genetic drift introduce some complications in this analysis. I

discuss these details in a forthcoming monograph.30 It goes without saying that this test has no panadaptive implications or

presuppositions. There are many maladaptive genotypes in Evita; their activity creates the“little hairs” along the bottom of the wave diagrams. Furthermore, if Evita’s mutation rate istoo high or too low, then natural selection fails to work. In either case, there are plenty ofevolutionary activity waves but they are not significantly different from neutral activitywaves, so none are adaptations. The adaptive chips (if any) fall where they may, and theactivity method finds them.

This evolutionary activity test applies to evolutionary systems across the board, and itapplies across levels of analysis. We have seen it applied to whole genotypes and ourdiscussion of neutral variants in Evita generalized the test to phenotypes. It is easy to apply itto alleles and other possible targets of natural selection, such as clusters of genes orrelationships among organisms. To apply the evolutionary activity method we must choosewhat items to observe and how to increment their activity.

These choices affect how the activity data looks, so we must remember our choiceswhen interpreting evolutionary activity data. Consider one locus with three alleles—A, B, andC—and assume there is an adaptive evolutionary cycle among these alleles: Allele A isreplaced by allele B, which is replaced by allele C, which is replaced by allele A, which isreplaced by allele B, etc. Each of these alleles is an improvement on its predecessor, but none isglobally optimal because fitness is context sensitive. If we collected activity data for eachinstance of each kind of allele and incremented it according to its concentration in thepopulation, the activity diagram would show a continual stream of new waves. This mightseem to violate the lesson that new waves indicate new adaptations. The subsequent instancesof the alleles seem not to be “new” adaptations but just new instances of “old” adaptations. Thesame problems can arise at other levels of analysis, such as genotypes. An analogous problemarises when there is a succession of “new” alleles that are neutral variants.

To clarify this issue, one must distinguish kinds of alleles from instances of those kinds,and then one must choose which to examine. If we are interested in the evolution of newinstances of alleles, then we should collect activity data for each instance of each kind ofallele (the first instance of A, the second instance of A, …, the first instance of B, the secondinstance of B, etc.). The resulting data would appear as in the top of Figure pgs.waves. In thiscase, when evolution produces a new instance of an allele, the activity diagram will show thestart a new wave because it is a new example of the adaptive evolution of an allele instance. Sothe new waves do correspond to new adaptations. On the other hand, if we are interested in theevolution of new kinds of allele, then activity counters should be attached to each kind ofallele (A, B, C). In this case, when evolution produces a new instance of an allele, the activitydiagram will show an increase in slope of an existing wave, as in the bottom of Figurepfs.waves. With each turn of the A-B-C cycle, the three waves for A, B, and C would each inturn step upwards and then level out. The only new waves in the diagram would correspond tothe origin of the first instance of each kind of allele. As before, the new waves correspond tonew adaptations at the level of analysis we have chosen to observe.

31 I illustrated the method in an artificial evolving system because it is so easy to getevolutionary activity data from computer models. Although it is harder to get analogous datafrom natural systems, it is possible and the method applies in the same way regardless ofwhere the data originated. All that is needed is a time series of current evolutionary activityincrements for the item being observed. The only real hurdle to measuring evolutionary activityin natural systems is the practical problem of getting the data. This is the familiar sort ofpractical difficulty that any empirical science must face.

I have measured evolutionary activity statistics in the fossil record, for example, andcompared it with evolutionary activity in various artificial systems (Bedau et al. 1997, 1998).In an analogous way, a time series of concentrations of RNA species in RNA evolution in flow

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reactors could be processed to reveal the activity waves of significant adapting RNA species.The same holds for the evolution of almost any other natural evolving system, such as bacterialecologies. Collecting the relevant data would be hard, but it is within the realm of possibilitytoday. Automated methods for sequencing genomes and measuring gene expression levels willsoon provide an unprecedently detailed picture of gene-level evolutionary activity in naturalsystems. It is probably easier to gather evolutionary activity information about evolution innon-biological natural systems. A wealth of information about cultural and technologicalevolution is reflected in patent records, financial transaction data, newspaper stories, and thelike. Such data are increasingly available in electronic form, making it relatively easy toapply an evolutionary activity analysis. Evolution in some natural systems is too slow orinaccessible to produce the data needed for an evolutionary activity analysis.

32 My solution to problem for adaptationism allows general adaptive hypotheses to beadjudicated by the normal procedures of empirical science. Some other responses to Gould andLewontin similarly construe adaptationism as a contingent thesis to be settled by normalempirical biology (e.g., Brandon 1990, West-Eberhard 1992, Sober 1993, Orzack and Sober 1994,and Sterelny and Griffiths 1999). But the evolutionary activity test is significantly differentbecause it alone abandons the canonical response. We can see this point nicely if we considerSober’s version of the canonical response. (Although Sober focuses on panadaptionism and avariety of weaker theses, his approach can be generalized to a defense of adaptationism.)

The essence of Sober’s response is this. To determine whether natural selection explainssome trait in some system, you propose a specific hypothesis about the trait’s adaptive functionand then see whether this hypothesis makes empirical predictions that are hold true for thesystem in question. Developing the hypothesis involves making a simplified model of thefactors at work in the system, including such things as the genetic structure of the system andthe strength of genetic drift. Consider a simple example concerning the size differences betweenthe sexes (Sober 1993). In many species males are larger than females on average. Onehypothesis to explain this is that large relative male size is an adaptation due to sexualselection—larger males tend to win mates. This simple model predicts that relative male sizewill be proportional to the sex ratio of breeding groups (number of females per male): Males andfemales should have roughly equal size in monogamous groups, and in polygamous groups malesize should increase with the sex ratio. When Clutton-Brock and Harvey (1977) tested thismodel in a number of primate groups, they found that the sex ratio of breeding groups correlatedwith the degree to which males were larger than females. Although the model does notexplain why and how the data are scattered above and below the best-fitting regression line,the general trend predicted by the model is borne out by the data.

The crucial difference between Sober’s method and the evolutionary activity method isthat Sober tests a specific adaptive hypothesis. He constructs a model of the selective forcesinfluencing the specific adaptation, and then checks the predictions of that model. BecauseSober is testing a specific model, if the empirical data fail to support that model, he is free toconsider and test different models. Failure of some specific adaptive hypotheses does not showthat a trait in not an adaptation. So Sober’s method can confirm that a trait is an adaptation byconfirming a specific adaptive hypothesis, but it cannot disconfirm all possible adaptivehypotheses, so it cannot disconfirm any general adaptive hypotheses. By contrast, theevolutionary activity method directly tests general adaptive explanation hypotheses. Failureto pass the activity test cuts off any further search for an adaptive explanation. So theevolutionary activity method answers the question whether the trait is an adaptation, and itdoes this without making any assumptions about specific adaptive functions.

Sober implies that a general adaptive claim that there exists an adaptive explanationis not falsifiable for it is an existence claim and “existence claims are not falsifiable in Popper’ssense” (1993, pp. 129). Sober’s argument is that you can confirm that a trait has an adaptiveexplanation by providing and corroborating one such explanation, but to falsify the claiminvolves examining and rejecting all possible adaptive explanations, and this in general is

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impossible. However, the evolutionary activity method does test exactly what Sober says isimpossible to test: whether some item deserves an adaptive explanation. The claim that atrait has an adaptive explanation makes a presupposition—that the trait is anadaptation—and it is this presupposition that that the evolutionary activity method tests.So, the evolutionary activity method can falsify the existence claim about adaptiveexplanations by falsifying the presupposition of all such explanations.

Sober (1993, Orzack and Sober 1994) points out that if you confirm enough specificadaptive hypotheses about individual traits, then you can build up evidence forpanadaptationism (and thus for adaptationism). But the process of gathering the requisiteevidence would be extremely time consuming, requiring the entire careers of many biologists.

It is important here to ward off a potential misunderstanding about models. Sober’smethod essentially involves modeling a target system and testing whether the modelaccurately predicts the target’s behavior. Since the model depends on various assumptionsabout specific adaptive function, the strength of selection, etc., those assumptions can be revisedif the model’s predictions do not describe the target system. In other words, the method istesting the adequacy of a particular representation of the target system. The evolutionaryactivity method does not test a representation of the system; instead, it gathers activityinformation from the target system and analyzes it. I used the artificial evolutionary modelsEvita, Echo, and the Bugs to develop and explain the activity method. But those models are notrepresentations (“models”) of some natural system. Rather, they are artificially constructedtargets that are investigated in their own right. Our concern is whether those systemsthemselves exhibited adaptive evolution, not whether they adequately represent adaptiveevolution in some other system.

Of course, the activity method does use neutral models to filter the activityinformation from target systems. Making a neutral model of a target system requires makingvarious assumptions about the target system, for the neutral model must be just like the targetexcept that it has random selection where the target might have natural selection. But once anappropriate neutral model has been constructed, the modeling process is over. The activitymethod uses the same neutral model to analyze many evolutionary histories in a given targetsystem. This neutral model cannot be revised in response to a failed test of the adaptivepresupposition.

So, the evolutionary activity test’s response to the problem of adaptationism isfundamentally different from Sober’s response. The two responses are complementary; they justaddress different questions. In a sense the evolutionary activity response is prior and morefundamental than Sober’s response, because it addresses the presupposition of the question thatSober addresses. The same general point applies to other proposals for how to empirically testadaptationism (e.g., Brandon 1990, West-Eberhard 1992, Sterelny and Griffiths 1999), such aschecking for correlation between aspects of traits and aspects of the environment, checking theeffects on environmental efficiency of altering traits, and comparing the environmental successof naturally occurring variants with respect to traits. These methods can all shed valuablelight on whether a trait is an adaptation, but they do so by exploring specific adaptivehypotheses. The evolutionary activity method can be applied whether or not one knows aboutspecific adaptive functions. These other methods complement the evolutionary activitymethod, which is prior and more fundamental.

33 For helpful comments or discussion, thanks to Peter Godfrey-Smith, Tom Ryckman,Chris Stephens, Phil Anderson, as well as audiences at the University of Oklahoma, theUniversity of Washington, Washington University, the Center for Humanities at Oregon StateUniversity, the Center for Cognitive Studies at Tufts University, the Santa Fe Institute, thefirst Genetic and Evolutionary Computation Conference, the fourth European Conference onArtificial Life, and Artificial Life VI. Thanks to the University of Oklahoma, its ZoologyDepartment, and Professor Tom Ray, for hospitality and support while some of this paper waswritten.