Forthcoming in Studies in History and Philosophy of the Biological and Biomedical Sciences. PLEASE DO NOT CITE WITHOUT PERMISSION On our best behavior: optimality models in human behavioral ecology Catherine Driscoll Department of Philosophy North Carolina State University Campus Box 8103, Raleigh NC 27695-8103 Email: catherine_driscoll@ncsu.edu Abstract This paper discusses problems associated with the use of optimality models in human behavioral ecology. Optimality models are used both human and non-human animal behavioral ecology to test hypotheses about the conditions generating and maintaining behavioral strategies in populations via natural selection. The way optimality models are currently used in behavioral ecology faces significant problems, which are exacerbated by employing the so-called phenotypic gambit : i.e. the bet that the psychological and inheritance mechanisms responsible for behavioral strategies will be straightforward. I argue that each of several different possible ways we might interpret how optimality models are being used for humans face similar and additional problems. I suggest some ways in which human behavioral ecology might adjust how they employ optimality models; in particular, I urge the abandonment of the phenotypic gambit in the human case. Keywords: optimality models, human behavioral ecology, foraging theory
On our best behavior: optimality models in human behavioral ecology 1. Introduction Behavioral ecology is the study of animal behavior in ecological context, using Darwinian assumptions more precisely, behavioral ecology is in the business of determining the ecological conditions that are responsible for generating and maintaining animal behavioral strategies 1, via the action of natural selection. Possibly the most important tool used by the behavioral ecologists is the optimality model. Optimality models determine which of a range of possible behaviors would be fitness maximizing under a set of environmental conditions; where such a model fits, the behavioral ecologist can conclude that those conditions were in fact responsible for the origin of the behavioral strategy in question. Some philosophers have already commented on the use of such models in these disciplines (for example, Kitcher (1985, 1990) and Orzack and Sober (1994a, 1994b)). Human behavioral ecology, as its name suggests is, or is claimed by its participants to be, a component of the larger behavioral ecological project. Human behavioral ecology is the modern incarnation of what in the 1970 s was referred to as sociobiology (after the title of E.O. Wilson s famous book). Human behavioral ecology has responded fairly successfully to the critics of sociobiology, partly by drawing closer to general behavioral ecology the consequence is that its models are much more precise and its assumptions more explicit. However, some problems remain with the application of behavioral ecological models to human behavior, especially optimality models; these problems are in addition to those faced by standard non-human animal behavioral ecology. The aim of this paper is to show that several different possible ways in which we might interpret the human behavioral ecologists to be using optimality models all face serious problems; consequently human behavioral ecologists will need to rethink how these models are
used. The most natural interpretation of the human behavioral ecologists work is simply as behavioral ecology for humans: the human behavioral ecologists often claim to be doing the same work as, and certainly appear to be using the same models, inferences and assumptions that are employed in general behavioral ecology. However, non-human behavioral ecologists largely accept the so-called phenotypic gambit (i.e. assuming as a methodological shortcut that natural selection is the only evolutionary process acting on a behavioral strategy, and that behavioral strategies are controlled by simple genetic and psychological means). Unfortunately, inheritance and psychological mechanisms do make a difference to how a behavioral strategy can evolve, and in humans the constraints of psychology on the evolution of behavior are considerable: they make optimality models in humans prone to false positives, and make it harder to know how to adjust those models when they fail. The solution is to interpret human behavioral ecologists as doing something different with optimality models than are general behavioral ecologists. Unfortunately, the most plausible alternative interpretations leave human behavioral ecologists still facing significant (and similar) problems. My conclusion will be that human behavioral ecologists may need to rethink how they use optimality models in their work. My order of business will be as follows: in section two I will describe what optimality is and how optimality models test for it. Section three will explain how optimality models are used to understand the behavioral strategies of non-human animals and the problems behavioral ecologists face in these cases. In sections four and five I will argue that further problems arise when these models are employed in humans, and explain how social learning and practical reasoning make optimality models prone to false positives. Section six describes how human psychology makes it significantly harder to resolve failures of optimality models for human behavior. In section seven I will discuss two more reasonable alternative interpretations of the
human behavioral ecologists work with optimality models and show that these alternatives also face serious problems. Section eight describes how optimality models might be used in human behavioral ecology without facing these problems. 2. Optimality and the models that measure it In biology, a trait is optimal when it is maximally fit relative to other possible variants, i.e. an optimal trait is a trait that maximally contributes to an organism s potential reproductive success. In this section I d like to explain what maximally fit and the other possible variants really mean for optimality models. First let s address the notion of maximum fitness. One tool biologists have used to understand fitness is the adaptive landscape. Suppose we represent all the possible variation in a trait as points in a hyperspace where the various dimensions represent different attributes of the trait that can vary. Then, for any given environment we can assign a fitness distribution across that space. This distribution will have a variety of peaks and valleys the valleys will represent variants which are less fit, and the peaks variants which are more fit. So one thing that biologists might mean when they say a trait is maximally fit is that it is at the highest peak in such a space. However, in real organisms traits will rarely reach that highest peak because they are subject to various types of constraints constraints are responsible for some of the valleys in the fitness distribution, which natural selection cannot cross since it can only push traits uphill on a fitness landscape. In practice selection tends to push populations up the nearest peak that doesn t involve crossing a valley this may not be the highest peak in the landscape. Biologists refer to these nearest peaks as local optima, and usually when biologists say a trait is optimal they mean it is locally optimal maximally fit relative to the constraints the organisms in the population are facing.
So what about what it means to say that the trait is optimal relative to the other possible variants? There are two possibilities. First, biologists might mean by this that T is the best relative to the set of reasonable bio-physically possible variants; i.e. those variants possible given the reasonable biological and physical constraints acting on organisms in a population. Second, biologists might mean that T is the most optimal of the variation actually available in past environments. The first of these two is the notion of optimality used most often in behavioral ecology because, even if natural selection can only optimize relative to the actual variation in a population, what that actual variation was in any particular case is usually unknown. So what natural selection will have optimized over (even under favorable circumstances) and what biologists build into their models will at times be different. So how do biologists employ this notion of optimality in optimality models? Optimality models are mathematical models that show that a trait T i of a range of possible variation T 1...T n would be maximally fit compared to T 1...T n if possessed by some organism type O facing conditions c 1...c n. The notion of an optimal model derives originally from engineering and has four main components: a strategy set, an optimization criterion, a fitness function and an analytical method (Maynard Smith, 1978, Parker and Maynard Smith, 1990). The strategy set is the set over which the optimization is supposed to be occurring in biological optimality models this will be the range of variants over which natural selection is supposed to be optimizing and as I discussed above, for most behavioral ecologists this is the set of biophysically possible variation. The optimization criterion is whatever characteristic of the items in the strategy set is being optimized in biological models, this is usually fitness or some reasonable correlate of fitness e.g. eggs fertilized per unit time, number of mates obtained, food collected per unit time, etc. The fitness function maps values of the optimization criterion
onto the members of the strategy set. The analytical method is the means used to determine which of the members of the strategy set, given the fitness function, is the optimum. A variety of different mathematical methods are used to do this. Optimality models are usually used to test the hypothesis that some trait is an adaptation to some particular set of environmental conditions. The idea is that optimality, where it is present, is a good indicator of the action of natural selection because no other evolutionary process is able to produce optimality as often as natural selection. Processes such as genetic drift, migration, mutation, meiotic drive and so forth may push traits to fixation in a population, but they are no more likely than chance to fix the fittest of the available traits. Natural selection, whilst hindered from producing optimality from time to time, is a force that causes fitter traits to spread, and therefore can produce locally optimal traits with a reasonable degree of regularity. This means that where we can show a trait was optimal, we can reason abductively that natural selection against the background conditions was very likely to have been the means by which that trait came to be present or prevalent in the population. 3. How optimality models are used in non-human animal behavioral ecology Behavioral ecology is a science that uses evolutionary assumptions to understand the way that animal behavioral strategies are the consequence of various ecological factors acting on them. Standard behavioral ecologists take organisms to possess (often complex, conditional) behavioral strategies. Behavioral ecologists use optimality models to try and understand the conditions that explain why animals behave as they do. A nice example of how optimality modeling can be used to understand a complex behavioral strategy comes from a study of the maternal behavioral strategy of the female parasitic jewel wasp by John Werren, (1980). The parasitic jewel wasp
lives and lays its eggs in blowfly pupae. When a female wasp is born, mates and emerges from a pupa she immediately flies off to find another pupa on which to lay her eggs. What she then does depends on different conditions. If she finds an unoccupied pupa, she will lay a mixture of sons and daughters with a heavy bias towards daughters (approximately 91.3% female to 8.7 % male). If she finds that the pupa is already occupied, then what she does depends on the size of her brood. Where she lays very few eggs, almost all will be male and will compete with the other males already in the pupa to fertilize some of the females; as brood size increases, she will tend to increase the proportion of females amongst the brood. Werren generated an optimality model for the female jewel wasp s maternal behavioral strategy, and determined what the optimal sex ratio for the female should be under the different conditions whether or not the pupae was occupied and the brood size the female would lay. When the pupae is only occupied by the wasp s own offspring, then all of the young wasps will be her genetic descendents, and thus the number of females is the limiting resource only a few sons are needed to fertilize all the females. When the pupae is already occupied, the female wasp needs to produce a sex ratio that will maximize the number of the females in the pupae that are fertilized by her own sons, whilst at the same time minimizing the amount of competition for mates amongst her sons. Hence when she lays few eggs, almost all of these can be male, since they will be able to fertilize many of the females of the previous wasp, competing with her sons, but not competing too much with each other. As she lays more eggs, adding more sons ceases to be useful, since competition between them begins to become a problem. Hence she will begin to add more females to her brood. Werren calculated the optimal percentages of males and females under the different conditions. His model fits the observed sex ratios quite well, but not perfectly. In Werren s study, the female wasp is engaging in a behavioral strategy under
different conditions of occupied or not occupied pupae, and under different brood sizes, the wasp does different things. Werren appeals to a variety of ecological conditions (male and female reproductive contributions, male-male competition, constraints on jewel wasp mothers etc.) as proposed selection pressures acting on the female wasp s strategy, and predicts how these will affect the resulting sex ratio; these are the conditions c 1...c n of the optimality model described above. So what exactly is Werren doing when he identifies the conditions that make the jewel wasp s strategy optimal (or near-optimal)? Let s look at what the behavioral ecologists from an important collection in behavioral ecology have to say about optimality modeling: The argument for using optimality models in behavioural ecology is that natural selection is an optimizing agent, favouring design features of organisms which best promote an individual s propagation of copies of its genes into future generations. Behaviour patterns clearly contribute to this ultimate goal, so we expect individuals to be designed as efficient at foraging, avoiding predators, mate choice, parenting, and so on from our knowledge of prey available and morphological constraints, we could predict how our starling should select prey so as to maximize its rate of food delivery to its brood. If the model fails to predict the observed behaviour, we can then use the discrepancies to help identify which of our assumptions are incorrect. (Krebs and Davies, 1997, pp.6-7) Krebs and Davies seem to be saying that the ecological conditions identified by the model are the conditions that have acted by natural selection to make the behavioral strategy in question highly fit. In Werren s case, the claim is that the fit of the model to the data suggests that Werren s proposed selective conditions are the right ones, and the wasp s behavioral strategy really is an adaptation to those conditions; this claim employs the inference described at the end of the last section. It is deciding what to do when optimality models fail that causes problems for behavioral ecology. Properly constructing optimality models is hard; they include many different assumptions, and any or all of these might be responsible for the lack of fit of the observed behavioral strategy. First, consider the strategy set. If this is not constructed properly, the
predicted and observed optimal trait may differ since the model will be optimizing over a different collection of traits than selection is presumed to have done. However, properly constructing the strategy set requires the scientist to determine the phenotypic possibilities; and there may be significant and difficult to discover developmental constraints or genetic interactions that prevent certain types of variants from arising. In the case of behavioral strategies, the phenotypic possibilities will also be limited by how and how far the relevant psychological mechanisms can also evolve. Second, the model might fail because the conditions and constraints might have been identified incorrectly they may be the wrong conditions or constraints (because the scientist incorrectly determined which were important or missed some that were not obvious); or those constraints may interact in unexpected ways. Third, the model may have an unsuitable optimization criterion i.e. unsuitable measures of fitness may have been chosen. While some measures of fitness are relatively obvious (such as eggs laid, or (perhaps) food collected per unit time) it is less obvious how to determine the suitable measures of fitness, say, for bones or for vision. Fourth, the analytical method may be unsuitable or incomplete: many optimality models may fail, for example, because they do not take into account tradeoffs between the fitnesses of individual traits and other traits the organisms possesses; this is exacerbated if these tradeoffs require the integration of different sorts of fitness measures. Finally, the trait in question may not be an adaptation at all: natural selection might not be acting in this case or might only be acting weakly. The trait might only be a developmental or genetic side effect or spandrel ; it may not be inherited in such a way that natural selection can act on it; or the most optimal variant may never actually have arisen in the ancestral population 2. Now, technically, these problems only arise when the observations of the behavioral
strategy in question do not fit the predictions of the optimality model and the model needs to be adjusted; given a perfect fit, one could reasonably claim that one has gotten all the above elements of the model correct. The trouble is that in practice almost no optimality model, even a good one, fits the observed behavioral strategy perfectly Werren s case, for example, doesn t account for all the observed variation in the wasps behavior (and many such models are much worse). The difficulty lies in determining what any remaining gap between the predicted strategy and the observed one is telling us: which of the numerous problems described above is the problem with our model? Generally, the behavioral ecologists tend not to assume that there is anything fundamentally wrong with the model s assumptions about selection, or about the genetic and psychological constraints on the strategy set; instead, they restrict themselves to looking for the source of the problem in the conditions, constraints and tradeoffs represented in the model. This methodological strategy is what Alan Grafen (1984) calls the phenotypic gambit: behavioral ecologists model the behavioral strategies they observe as if there were a haploid locus at which each distinct [behavioral] strategy was represented by a distinct allele, as if the payoff rule [the fitness function] gave the number of offspring for each allele, and as if enough mutation occurred to allow each strategy a chance to invade (Grafen, 1984, pp. 63-64). The reason for the gambit is, presumably, that discovering the conditions and constraints (and to a lesser extent, tradeoffs) is the purpose of optimality models in behavioral ecology. By using it, behavioral ecologists bet that a stepwise process of refining the conditions, constraints and tradeoffs described on the model will eventually capture all or most of the important causes of the evolution of the behavioral strategy in question. Of course, this is a controversial assumption. One concern is that it means that the behavioral ecologist is not sufficiently open to the possibility that occasionally, some of their
basic assumptions may be wrong for example, Gould and Lewontin (1979) argue that behavioral ecologists ought to seriously consider the possibility that some of the behavioral strategies they study are not adaptations; and that behavioral ecologists sometimes go so far that their general belief that these strategies are adaptations appears unfalsifiable. Furthermore, there is some empirical evidence to think that the gaps in these models are sometimes due to deviations from the expectations about inheritance, mechanism and evolution that the phenotypic gambit involves. For example, it may be that part of what caused the deviation from optimality on Werren s model was the effects of the types of cues the jewel wasp was using to determine when to lay a male biased ratio (Shuker and West, 2004). Shuker and West s study only imperfectly supports Werren s original model because it shows that the mechanisms responsible for the wasp s behavioral strategy are not exactly as Werren s model assumes; instead, the real mechanisms only approximate the ideal strategy. This suggests that, in practice, the background assumptions of even good models need ultimately to be tested the psychological (or other proximate) mechanisms and inheritance mechanisms (the genes and developmental and epigenetic processes responsible for biologically transmitting behavioral strategies) need to be deliberately examined. The phenotypic gambit is, therefore, problematic. 4. How optimality models are used in human studies Much like general behavioral ecologists, human behavioral ecologists believe that human social behavior reflects strategies that would enhance inclusive fitness in environments similar to those of past human evolution (Irons and Cronk, 2000, p. 5). At least superficially, human behavioral ecologists understand themselves to be doing similar work to the general behavioral ecologists: We use the phrase human behavioral ecology chiefly because it is the label used most often by those doing similar work in non-humans, thereby accentuating the
fact that his approach has its roots in animal behavior studies or ethology. (Irons and Cronk, 2000, p. 3). If this is so then human behavioral ecologists should also be interested in determining to which conditions human behavioral strategies are adaptations. This does seem to be what they take themselves to be doing: The key assumptions of HBE include its ecological selectionist logic [which] consists of analyzing any behavioral phenomenon by asking What are the ecological forces that select for behavior X? The ecological part of this logic means that HBEs usually look to environmental features (e.g. resource density, competitor frequency) and examine the covariation in these features and the behavior of interest (e.g. territorial defense). The selectionist aspect means that predictions about this covariation are derived from expectations about what patterns we expect natural selection to favor. (Smith, 2000, p.29). So one natural interpretation of how optimality models are used by human behavioral ecologists is as a way to identify the ecological conditions selecting for human behavioral strategies, just as behavioral ecologists do with non-human animals. They also, like the general behavioral ecologists, employ the phenotypic gambit (Smith, 2000, Smith and Winterhalder, 1992), so they are agnostic about the psychological and inheritance mechanisms which underlie human behavioral strategies. Let s look at an example of work in human behavioral ecology: in Kaplan and Hill (1992) the authors argue that a standard model for foraging a model of optimal prey choice taken from general behavioral ecology roughly fits the behavior of various foraging people, including the Ache of Paraguay. What this model represents is the prey choice strategy which is maximally fit under certain conditions, (e.g. a random distribution of prey, and a random distribution of energy values of prey, among other things). The model uses calories gained per hour (e) as a measure of the fitness of a prey choice strategy. The Ache encounter a variety of plants and animals when they go out foraging; these animals need to be caught and the plants need to be processed, which uses time and burns calories. Sometimes the Ache could actually come out ahead in terms of calories per unit time spent foraging even
including the extra time spent searching if they ignore certain types of prey and keep on looking for something which will yield them more calories after all the processing is done. The Ache need to decide which animals or plants are worth pursuing what their prey set should be. An individual maximizes calories gained per hour of foraging if they construct their prey set as follows. First, they need to determine the profitabilities of all the potential prey items in their environment. The profitability of a prey item is its total calorie value (e) divided by the time it takes to extract that return (h) (i.e. catch, kill and butcher an animal; process a plant). They should then add the most profitable prey type to their prey set, and each additional prey type in order of profitability until the profitability of the additional item is less than the average foraging return rate (R) would be based on the items already in the prey set. Items with a profitability lower than R should be rejected. R is determined as follows, where T s is the search time, λ is the rate at which the prey item is encountered per hour, s is the calories per hour used searching and p is the probability of taking the prey (p is always either 1 or 0): n R= i = 1 n i= 1 T λ p e st s s i i i T λ p h + T i i i s s R is the average rate of calories per hour gained by foraging for the items in your prey set. If the profitability of a new prey is higher than R, then adding it to the prey set will raise the value of R. If the above interpretation of the human behavioral ecologists work is correct then in this case Kaplan and Hill are trying to understand the selective pressures operating on the Ache s (or rather, the general human) 3 foraging strategy, much as Werren was trying to understand the pressures acting on the jewel wasp or Krebs and Davies with their starlings. On this interpretation Kaplan and Hill infer that the conditions on the model are causally responsible for
the behavioral strategy because they are selection pressures that acted to fix it in the human population; these conditions are those under which the components of the prey choice strategy are manifested taking different animals under different conditions and hence become subject to selection. In other words Kaplan and Hill infer straightforwardly from the Aches behavioral strategy fitting their optimality model to its being an adaptation to the conditions on that model. In the next section and the one that follows, I am going to argue that using optimality models this way in human behavioral ecology that is, whilst using the phenotypic gambit is seriously problematic, even more so than for it is in general behavioral ecology 4. Most of the problems for general behavioral ecology arise where there is a failure of fit in a model. However, in the human case it is also possible to have clear false positives (i.e. where the model fits the observed behavioral strategy B but where B was not selected for against conditions c 1...c n described by the model). These false positives are due to the nature of human psychology, and hence in the human case, the phenotypic gambit deliberately setting aside possible complications in psychology and inheritance mechanisms also leads human behavioral ecologists to misinterpret the apparent successes of their models. Moreover, in the human case, there are extra complications for correcting model failures because the relative complexity of human psychology and the even more complicated relationship between psychology and behavior mean there are psychological tradeoffs and constraints beyond those found in nonhuman animals. In the next section (section 5) I will explore why the problems with false positives arise; in the section after that (section 6) I will address the problems that arise when the model fails.
5. Optimality models in human behavioral ecology face false positives The only situation in which an optimality model is at all likely to yield a false positive is where some optimizing process other than natural selection is responsible for a behavioral strategy B s occurrence and optimality in the population relative to the conditions described on the model (let s assume the fit of this strategy to the model is perfect, for now), and consequently where it is not true that B occurs because natural selection fixed B relative to those conditions. In human beings there are two processes (at least) that can lead to the generation of optimal behavioral strategies without the environmental conditions that make them optimal being causally responsible for their existence via natural selection. One of these is practical reasoning or learning and the other is cultural evolution. 5.1. Practical reasoning or learning The first way in which human beings can end up engaging in locally optimal behavioral strategies is via a variety of psychological mechanisms that we might term individual learning or practical reasoning mechanisms. Human beings have the ability to form goals or desires for things and make plans that direct their behavior and are able to make those goals come to pass. One obvious way in which people might come to behave in ways that are optimal in their environments is if they a) possess goals which correlate well with fitness across many different sorts of environments and b) possess psychological mechanisms which are very good at determining the consequences of actions and permit human beings to achieve their goals reliably. Very few of such mechanisms are necessarily specific to any particular type of behavioral strategy: such mechanisms probably interact in many different ways to produce different sorts of behavioral strategies in different contexts. In the case of the Ache, then, we could explain the
optimality of their behavioral strategy by appealing to the fact that first, the Ache have goals which correlate with fitness i.e. to maximize their production of food, and second, they have figured out, probably using a combination of causal and inductive reasoning, that they get more food per foraging trip if they ignore low profitability items in favor of continuing to search for more profitable ones. Notice, then, that if the prey choice strategy was produced by one or more practical reasoning mechanisms in response to the conditions in the Ache s environment, the hypothesis that that strategy was fixed by natural selection as a response to the conditions described on Kaplan and Hill s model is false. I think that natural selection can be said to act on behavioral strategies (as opposed to psychological mechanisms), but it does so via the underlying mechanism(s); the mechanism(s) increase in the population because (in part) of the fitness benefits of the behavioral strategy or strategies they produce. However, in the case of a behavioral strategy B generated by a reasoning mechanism, B s presence or prevalence in the population may have nothing to do with B s fitness contribution in the past; instead B may occur because it is caused by a reasoning mechanism and the reasoning mechanism was selected for producing a variety of other behavioral strategies in the past. In our case, the Ache s reasoning mechanism was fixed in the Ache s (and our) ancestors because it was able to produce many different and novel adaptive behavioral strategies in response to a variety of different and novel sorts of conditions. It is quite possible that the actual conditions which led to the fixation of this reasoning mechanism did not include prey choice of the kind in which the Ache are currently engaging. In that case, the prey choice strategy as such does not have any evolutionary history of its own (including any involving natural selection), and the hypothesis that it has such a history is false.
5.2. Cultural evolution Another way in which behavioral strategies can end up being optimal is via a process called cultural evolution, (sometimes called gene culture co-evolution ) 5. Many behavioral strategies sometimes even complex behavioral strategies are transmitted from generation to generation via social learning i.e. where one person learns a behavioral strategy from another. Cultural evolution occurs when frequencies of a behavioral strategy in a population change due to differences in the rate at which that behavioral strategy is socially learned. Some cultural evolutionary processes can result in the spread of optimal behavioral strategies through a population. One form of cultural evolution that can lead to the optimization of behavioral strategies occurs where individuals have one or more ways of distinguishing between strategies on the basis of their fitness. This does not mean that individuals need to track fitness per se, but it does require that they have mental representations of things that tend to correlate well with fitness in their environments. For example, one fitness correlate might be food produced per unit of invested effort ; this might be represented as a goal or desire in people s minds. Suppose individuals can reliably choose, from among the possible behavioral variants, that variant which best achieves or maximizes that correlate of fitness. For example, they might be fairly reliable at determining which among a variety of foraging strategies creates the most food. Then the best technique will spread through the population. Over time, additional variants arise in the population as individuals come up with ways to improve their behavioral strategies, and these improvements in turn get selected. This process, called horizontal transmission with direct bias (Boyd and Richerson, 1985) can lead to the local optimization of some behavioral strategy in its environment.
The Ache might have come by their foraging strategies in just this way by one or more individuals hitting on a good technique that the others observed, adopted and gradually improved on. Another way in which the same sort of process can happen is where some individual A, instead of using her own judgment when trying to decide which behavioral strategy to acquire, learns from some prestigious member B of A s group. If B is deferred to by other members of A s group A can use that deference as an indicator that B s behavioral strategies are thought to be good or the best. By acquiring B s behavioral strategy, A can acquire a fitter behavioral strategy than she would by chance. In any population where individuals use this copy the prestigious learning heuristic and where there are occasional improvements on a behavioral strategy, that strategy can also eventually be optimized (Henrich and Gil-White, 2001). This is another way the Ache might have come by their optimized foraging strategies individual Ache learn their foraging strategies from prestigious individuals, and this, with occasional improvements in technique, led to optimization. If the Ache acquire their prey choice strategy in some of these ways, then the hypothesis that that strategy was fixed by natural selection in response to the conditions on Kaplan and Hill s model is false. Just as with practical reasoning, behavioral strategies culturally transmitted by these means are not present in the population because they increased the fitness of those which had them; and the psychological mechanisms which acquire them were not necessarily selected for because they acquired the particular behavioral strategies we see now 6. 6. It is difficult to manage failures of optimality models for humans The second problem that arises for using optimality models for humans in the same way as they are used in general behavioral ecology is that model failures will be more difficult to account for
than they would be in non-human animals. As we saw in section 3, optimality models can fail for a variety of reasons they fail, for example, where there are genetic, developmental or psychological constraints acting on the behavioral strategy being studied; or where some of the conditions described in the model are not correct; or where the optimization criterion is unsuitable, and so on. Now, sometimes optimality model failures can be enlightening for the human behavioral ecologists. Take for example our case of the Ache. Ache generally do take animals that fall above the average foraging return rate; however, male Ache tend to ignore small game with fairly high profitabilities. This was a puzzling failure of the prey choice model until some human behavioral ecologists collected some evidence that prey choice for male foragers is a trade off between showing off and thus gaining social status and sexual opportunities and getting food to eat (Hawkes, 1991, 1993). However, not all failures of optimality models are as easy to resolve (and indeed, Hawkes suggestion may not account for all the lack of fit in Kaplan and Hill s model). One important type of failure is where the behavioral strategy turns out not to be optimal because the underlying psychological mechanisms are subject to certain sorts of psychological or computational constraints. This is a particular problem for human behavioral ecology because the complexity of human psychology suggests these sorts of constraints may be more common in human than in non-human animal psychology. For example, human behavioral strategies are, and need to be, highly plastic and sensitive to their environment. This is achieved by using mechanisms that process a lot of environmental information in order to adjust behavioral responses appropriately. However, the information these mechanisms have to work on and the time they have to do it are highly limited, and the possible outputs are thus highly constrained. Consequently, as some evolutionary psychologists (Gigerenzer, 2000, Gigerenzer and Todd, 1999) have argued these mechanisms often take the form of heuristics that
approximate the best or most rational decision under those conditions. Human reasoning may be locally optimal but its adaptive landscape is highly constrained a model that employed these additional constraints would show it to be optimal. However, human behavioral ecologists rarely take these kinds of computational constraints into account when building their models. More often than not, the constraints invoked are those imposed by the individuals environment rather than the computational limits of their psychology. Kaplan and Hill (1992), for example, discuss the way that limited information about the prey available in any environment might affect an individual s foraging success; this may account for some of the lack of fit on prey choice and other models. However, this is a discussion of the limits of the information available from the environment, rather than limits on the use of information imposed by psychology. There is an obvious reason why: investigation of computational constraints (and the consequent abandonment of the phenotypic gambit) is easier in many non-human animals than it is in humans; determining the mechanisms used by jewel wasps is probably easier than determining the mechanisms used by humans. 7. Alternative interpretations of the use of optimality models in human behavioral ecology Human behavioral ecologists are aware that mechanisms for individual and social learning and reasoning are likely to be part of the range of mechanisms that are involved in generating adaptive behavioral strategies; indeed, they often argue that it is likely that at least some behavioral strategies are generated by such mechanisms (see, for example, Alexander (1990), Smith et al. (2001)). As we have seen above, however, it is not obvious how human behavioral ecologists can square this approach with a straightforward selectionist logic if behavioral strategies are the consequence of certain kinds of psychological mechanisms, then they are not
adaptations to the selection pressures against which they appear optimal. So is there any way to interpret how human behavioral ecologists are using optimality models other than via a straightforward selectionist logic? There are at least some reasonable possibilities. One plausible interpretation is that the human behavioral ecologists believe that optimality models allow them to determine what the conditions are that provoke the components of human behavioral strategies i.e. what are the current causes of variation in overt behavior generated by cognitive mechanisms: HBE usually frames the study of adaptive design in terms of decision rules or conditional strategies having the general form In context X, do α, in context Y, switch to β. Thus HBEs tend to focus on explaining behavioral variation as adaptive responses to environmental variation; they assume that this adaptive variation is governed by evolved mechanisms that instantiate the relevant conditional strategy or decision rule. (Smith, 2000, p. 30, his italics). Also consider the following: If we assume that people behave adaptively (maximize fitness) and we attend to the material costs and benefits of behavior, and the ways they affect RS, we can use our assumption to enable us to understand variation and differences in behavior, how behavior may be expected to change as circumstances change, and thus to explain differences in behavior between populations. (Blurton Jones, 1990, p. 355) On this interpretation, optimality models allow human behavioral ecologists to identify the current X and Y that generate overt behaviors α and β, thus allowing them to understand the component behavioral dispositions that make up the behavioral strategy, and therefore predict or explain when and where we might expect α and β to occur. The idea is that natural selection acts on the behavioral strategy or underlying mechanism such that that mechanism will generally produce adaptive responses to environmental conditions. Therefore, if we assume that natural selection generates mechanisms that produce optimal behavior, we can predict that the set of conditions X (which render α optimal) will generate α; or else explain α s presence in an environment in terms of the presence of X in that environment.
Now, using optimality models in this way is at least as problematic as using the standard selectionist inference from an optimality model, because it requires us to assume that the underlying strategy/mechanism is definitely sufficiently well designed by natural selection to reliably generate optimal behavior (as well as that one has the model s other assumptions right). Of course, not only does natural selection not generate highly adaptive behavioral strategies all the time, but even where the behavioral strategies are highly adaptive that might not mean the components of the strategies will be adaptive, because the constraints on the underlying psychological mechanisms might be such that no individual element of the resulting behavioral strategy will appear optimal under the circumstances in which it is produced. Furthermore, where behavioral strategies are acquired by social learning, it is even more dangerous to assume that the strategies in question will be highly adaptive. Cultural evolution is much more unreliable at generating highly adaptive behavioral strategies than natural selection: cultural evolution occurs at a remove from the feedback processes that can secure the high fitness of biologically transmitted behavioral strategies. Even given this adaptationist assumption is reasonable, there are still problems with this use of optimality modeling. The first problem is that X may not really explain why α and β occur. Suppose that in X the behavioral strategy B generates α and in Y B generates β. The human behavioral ecologists think (if this interpretation is correct) that X and Y explain α and β presumably, because X and Y are causally responsible for α and β. If this is true, it will have to be because (as the human behavioral ecologists seem to recognize above) there is a highly adaptive psychological mechanism M underlying B that generates α and β by being caused to do so by X and Y. Psychological mechanisms generate behavior by operating on various types of information in the form of mental representations, so a very natural way to understand the being
caused to do so is that M receives information about and represents X and Y and this leads it to generate α and β. So we might understand the inference from the optimality of α and β in X and Y to an explanation for α and β in terms of X and Y as follows: M always generates optimal behavior for the conditions, and so when M generates α and β this is because these are optimal behaviors for X and Y, and because M is representing information about X and Y. So when we know the conditions under which α and β are highly adaptive, we know which conditions M is representing, and hence which conditions are currently causally responsible for α and β via M. Hence we have an explanation for α and β in terms of those conditions. However, this inference is mistaken. Many psychological mechanisms don t (and don t need to) employ representations of X or Y, or even all of any component conditions of X or Y, in order to make an optimal or near-optimal choice of behavior (i.e. α and β) when X or Y obtain. 7 As I discussed in the previous section, many of the psychological mechanisms employed in reasoning and learning may be heuristics heuristics are useful precisely because they don t require an individual to have all the relevant information in order to generate a near-optimal behavioral response (given all of the cognitive and ecological constraints on that choice). Sometimes, the individual is not even using representations of the conditions which make their behavior optimal, but instead some condition connected or co-varying with X in their environment: for example, when an individual I uses prestige biased social learning to acquire α, the conditions X that make α optimal are not what I represents in order to acquire α. Instead I represents the prestige of another individual B, which happens to co-vary with the optimality of certain types of behavior (including α) in I s social group. Even where humans are using reasoning processes that are not heuristic in character, not all of X need be appealed to in order to produce appropriate behavior. Individuals have goals or desires which represent fitness
correlates, and determine which behavioral strategy to acquire simply by reasoning inductively observing which behavioral strategy has the consequence of producing the best results relative to that fitness correlate. To do this they do not need to determine why that behavior leads to those consequences, or how the environment makes that behavioral response appropriate. For example, the Ache probably do not go through any complex mathematical reasoning representing profitabilities and average foraging return rates when determining their prey choice. Instead, they might simply have noticed that they do better overall when they only take the high profitability prey up to a certain threshold and ignore the other ones. Nevertheless, inductive reasoning of this sort is a reliable way of acquiring fit behavioral strategies. Consequently, X need not really explain why α and β occur. The second problem is one of prediction. Not appreciating the fact that many psychological mechanisms underlying behavioral strategies may be heuristic or not using all available information makes it more difficult for the human behavioral ecologists to predict reliably human behavior in novel contexts. If the human behavioral ecologists don t know reasonably accurately what the conditions are that are represented in the process that generates α, they won t be able to tell which contexts will or will not generate α. For example, human reproductive behavior no longer fits idealized reproductive strategy models in countries that have undergone the demographic transition this is possibly because the psychological mechanisms that lead to these decisions are heuristics. If the above is true, then optimality models are not reliable ways to help determine the components of human behavioral dispositions in such a way that human behavioral ecologists can use them to help predict and explain variation in overt behavior across environments. However, there might be other ways to interpret how the human behavioral ecologists are using
optimality models for example, one of the above quotations, while describing the human behavioral ecologists commitment to understanding human behavioral dispositions, recognizes that these dispositions will be generated by an underlying psychological mechanism or mechanisms. So while the human behavioral ecologists are agnostic about what exactly these mechanisms might be like, they might still hope to learn whether these mechanisms are likely to be adaptations via optimality modeling. Perhaps the idea is that discovering that some behavioral strategy B is optimal is evidence that the underlying mechanism(s) M is an adaptation. However, using optimality models this way won t work either. Finding that B is optimal does not allow us to make the same claim for the particular mechanism M that generates B. The reason is that to make the inference that M had a selective history on the same terms as we ve done it for B, we would have to show that M is optimal. Now it s quite possible, of course, for a mechanism to be (locally) optimal without managing to produce optimal behavioral strategies (perhaps because of complex computational and informational constraints on the mechanism, or competing demands from the various types of behavioral strategies for which M might be responsible). However, if we are to infer reliably from M s producing optimal behavioral strategies to M s being an adaptation then M will have to be producing a wide variety of optimal behavioral strategies, not just one; M, like many psychological mechanisms, may be involved in the production of lots of different behavioral strategies. Of course, the human behavioral ecologists might only be meaning to make an assembly test of the claim that the underlying mechanism is an adaptation by testing all of the behavioral strategies generated by that mechanism, we will be able to determine whether it is an adaptation. This, however, raises further problems for the human behavioral ecologists, especially given their commitment to the phenotypic gambit: first, it will be tricky for the human behavioral ecologists to determine which