Download On our best behavior: optimality models in human behavioral ecology

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PLEASE DO NOT CITE WITHOUT PERMISSION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: [email protected]
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 Ti of a range of possible variation T1...Tn
would be maximally fit compared to T1...Tn if possessed by some organism type O facing
conditions c1...cn. 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 c1...cn 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 Ts 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):
R= ∑
∑
n
i =1
n
Ts λi pi ei − sTs
i =1
Ts λi pi hi + Ts
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 c1...cn
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
behavioral strategies are produced by the same mechanisms, and they must do this if they are to
test whether the mechanisms in question are producing optimal behavioral strategies reliably.
Second, there is more to being an optimal mechanism than just generating optimal behavioral
strategies — for example, a mechanism needs to be computationally and energy efficient.
8. How optimality models can be successfully employed in human behavioral ecology
So far I have explained some problems for the way optimality models are currently used in
human behavioral ecology. However, I don’t think that all is lost as far as optimality models are
concerned. The central problem that faces human behavioral ecology is that the phenotypic
gambit they employ is a considerably worse bet for studying human behavioral strategies than it
is for studying non-human animal behavioral strategies. However, the problems for humans are
exaggerated forms of the problems that exist for using optimality models in general behavioral
ecology; the phenotypic gambit in both cases leads to ignoring psychological and inheritance
mechanisms, and in both cases this can cause problems. But just as we saw in section 3, these
problems with optimality models can be solved or ameliorated (as they were for Werren’s wasps)
by studies that pay attention to the underlying psychological and inheritance mechanisms (among
other things) of those behavioral strategies. Werren was lucky: his bet that the female wasp’s
strategy would be optimal regardless of the nature of the underlying mechanisms turned out not
to be wildly wrong (although it was partly wrong, and the reason was (in part) the nature of those
mechanisms). However, such a bet would be much more unreliable in the human case; it is
much less likely that, in particular, the way human psychological mechanisms produce
behavioral strategies will be straightforward enough for bets like Werren’s to turn out well.
Actually using psychological information is very important to help the human behavioral
ecologists avoid applying optimality models to those cases for which they cannot be used; the
phenotypic gambit probably needs to be avoided in the human case.
This leaves the human behavioral ecologists with two options. The first option is to
continue as they are, but integrate information about the inheritance mechanisms and
psychological underpinnings of human behavioral strategies to help them decide where their
models might be applicable. In these cases they might be able to find human behavioral
strategies that could be subject to straightforward selection, or that weren’t the consequence of
learning mechanisms or heuristics in the way that excluded those uses described in other
sections. This however, may leave them with relatively little material to study; I expect not
many human behavioral strategies will turn out to be like this. It also would require them to
acquire and employ this information in a way for which many of them do not have the training.
The second option, which I think makes more sense, is for the human behavioral
ecologists to integrate their work into a broader evolutionary social science. The science I have
in mind would permit the evolutionary study of psychological mechanisms as well as behavioral
strategies, and hence would permit the scientists concerned to evaluate the functional properties
of learning and general purpose mechanisms as well as mechanisms specifically responsible for
particular behavioral strategies. It should also involve the study and evaluation of the
computational features and tradeoffs of the mechanisms responsible for human behavior. In this
context, initial studies of the adaptiveness of particular behavioral strategies would become
material that could be used to predicting the existence of or help to evaluate the functional
contribution of the features of psychological mechanisms; however, because these studies could
in this context be provisional or else combined with information about psychological constraints
and inheritance, there would be less likelihood of falling into the traps discussed in section 3 or
5-7. Such a science is not currently practicable: the most obvious candidates for cooperators
with the human behavioral ecologists are cognitive psychologists and anthropologists interested
in evolutionary questions, and most of these are those currently working in evolutionary
psychology and dual inheritance theory. The evolutionary psychologists’ success in identifying
mechanisms which are plausible candidates to be adaptations has been fairly limited (and the
best cases are in areas in which the human behavioral ecologists are not doing much research) —
dual inheritance theorists have had more success but are still in the early stages of their project.
More particularly, methodological and theoretical differences (such as their views on whether
mechanism adaptations would produce optimal behavioral strategies in current environments)
have limited cooperation between these fields. Hopefully in the long run such differences can be
overcome.
Acknowledgements
Many thanks to Stephen Stich, John Carroll and several anonymous referees for their very
helpful comments on earlier drafts of this paper.
References
Alexander, R. D. (1990). Epigenetic rules and darwinian algorithms: The adaptive study of
learning and development. Ethology and Sociobiology, 11, 241-303.
Blurton Jones, N. G. (1990). Three sensible paradigms for research on evolution and human
behavior? Ethology and Sociobiology, 11, 353-359.
Boyd, R., & Richerson, P. J. (1985). Culture and the evolutionary process. Chicago/London:
University of Chicago Press.
Gigerenzer, G. (2000). Adaptive thinking. Oxford: Oxford University Press.
Gigerenzer, G., & Todd, P. M. (1999). Simple heuristics that make us smart. Oxford: Oxford
University Press.
Gould, S. J., & Lewontin, R. (1979). The spandrels of san marco and the panglossian paradigm:
A critique of the adaptationist programme.
Grafen, A. (1984). Natural selection, kin selection and group selection. In J. R. Krebs, & N. B.
Davies (Eds.), Behavioural ecology: An evolutionary approach (pp. 62-84). Oxford: Blackwell
scientific publications.
Hawkes, K. (1991). Showing off: Tests of an hypothesis about men's foraging goals. Ethology
and Sociobiology, 12, 29-54.
Hawkes, K. (1993). Why hunter gatherers work: An ancient version of the problem of public
goods. Current Anthropology, 34, 341-351.
Henrich, J., & Gil-White, F. J. (2001). The evolution of prestige: Freely conferred deference as a
mechanism for enhancing the benefits of cultural transmission. Evolution and Human Behavior,
22, 165-196.
Irons, W., & Cronk, L. (2000). Two decades of a new paradigm. In L. Cronk, N. Chagnon, & W.
Irons (Eds.), Adaptation and human behavior (pp. 3-26). New York: Aldine de Gruyter.
Kaplan, H., & Hill, K. (1992). The evolutionary ecology of food acquisition. In E. A. Smith, &
B. Winterhalder (Eds.), Evolutionary ecology and human behavior). New York: Aldine de
Gruyter.
Kitcher, P. (1985). Vaulting ambition. Cambridge Massachusetts: MIT press.
Kitcher, P. (1990). Developmental decomposition and the future of human behavioral ecology.
Philosophy of Science, 57, 96-117.
Krebs, J. R., & Davies, N. B. (Eds.) (1997). Behavioral ecology: An evolutionary approach (4th
ed.). Oxford: Blackwell Science Ltd.
Maynard Smith, J. (1978). Optimization theory in evolution. Annual Review of Ecological
Systems, 9, 31-56.
Orzack, S. H., & Sober, E. (1994a). How (not) to test an optimality model. Trends in Ecology &
Evolution, 9, 265-267.
Orzack, S. H., & Sober, E. (1994b). Optimality models and the test of adaptationism. American
Naturalist, 143, 361-380.
Oster, G. F., & Wilson, E. O. (1978). Caste and ecology in the social insects. Princeton, NJ:
Princeton University Press.
Parker, G. A., & Maynard Smith, J. (1990). Optimality theory in evolutionary biology. Nature,
348, 27-33.
Seger, J., & Stubblefield, J. W. (1996). Optimization and adaptation. In M. R. Rose, & G. V.
Lauder (Eds.), Adaptation (pp. 93-123). San Diego: Academic Press.
Shuker, D. M., & West, S. A. (2004). Information constraints and the precision of adaptation:
Sex ratio manipulation in wasps. Proceedings of the National Academy of Sciences, 101, 1036310367.
Smith, E. A. (2000). Three styles in the evolutionary analysis of human behavior. In, Adaptation
and human behavior: An anthropological perspective (pp. 27-46). New York: Aldine de Gruyter.
Smith, E. A., Borgerhoff Mulder, M., & Hill, K. (2001). Controversies in the evolutionary social
sciences: A guide for the perplexed. Trends in Ecology & Evolution, 16, 128-135.
Smith, E. A., & Winterhalder, B. (1992). Natural selection and decision making: Some
fundamental principles. In E. A. Smith, & B. Winterhalder (Eds.), Evolutionary ecology and
human behavior (pp. 25-60). New York: Aldine de Gruyter.
Sterelny, K. (2003). Thought in a hostile world: The evolution of human cognition. Oxford:
Blackwell publishing.
Werren, J. H. (1980). Sex ratio adaptations to local mate competition in a parasitic wasp.
Science, 208, 1157-1159.
Notes
1
A behavioral strategy is a complex behavioral disposition; I contrast this with overt behavior
(i.e. actual token movements in which an animal might engage).
2
For a more complete discussion of all of these possible causes of failure, see Oster and Wilson,
(1978), Seger and Stubblefield (1996).
3
Kaplan and Hill think that roughly the same model applies to human foraging in other types of
environment and that the foraging strategy is a general human behavioral adaptation. They
aren’t, of course, suggesting that the Ache have a unique foraging adaptation!
4
Sterelny (2003, pp.99-101; 236-239) has also argued that the human behavioral ecologists’ use
of optimality models is problematic, in two ways. First, optimality models for humans need to
include as conditions the effects of niche construction, which involves complex interactions with
behavior and leaves few traces in the archaeological record; and second, that human behavioral
ecologists do not use optimality models in a way that permits them to discover the constraints on
human behavior. While Sterelny’s first criticism may be true to some degree, I am less
convinced about the second — see the example with Kaplan and Hill verses Hawkes in section 6.
5
This observation has also been made by Philip Kitcher (1990).
6
There is one kind of cultural evolution that is an exception to this argument: suppose
individuals vary in their cultural traits, and pass on these traits to their children. If those traits
vary in regard to their fitness, such that those who have fitter traits have more children than those
who do not, then cultural traits can spread through a population via natural selection — though
not a kind of selection that involves changes in gene frequencies.
7
A referee has suggested that perhaps an account of representation such as Sterelny’s (2003)
might get around part of this problem and part of the next. Sterelny argues that the origin of
human representation derived partly from their needing to track robust (i.e. unlikely to fail) cues
in their environment in order to behave adaptively. Multi-cue tracking is more robust than single
cue or proxy tracking (i.e. tracking a single condition merely associated with the fitness-affecting
cues). Presumably the argument derived from Sterelny’s view is that this suggests that most
human representations of the environment include the conditions that make behavior adaptive,
and not mere proxies for those conditions; this in turn means we can expect most of X to be
represented in M after all. However, the problem with such arguments is that multi-cue tracking
places greater computational demands on psychological mechanisms than proxies; these
demands also increase errors or slow the mechanism down. How well proxies do adaptively
verses multi-cue tracking will depend on how well accuracy verses difficulty trade off for
particular organisms and environments. Sterelny thinks that these computational demands will
be limited in human environments, because humans cooperate in sharing information — human
social environments are informationally “translucent”. The question is whether that means the
whole environment is translucent, and whether translucency (accuracy of information) means
tractability in deploying information. Sterelny seems to bet it will be; I’m betting it won’t.