A Common, Conceptual Framework for Behavioral Ecology and Evolutionary Psychology

Evolutionary Psychology
www.epjournal.net – 2007. 5(2): 275-288
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Original Article
A Common, Conceptual Framework for Behavioral Ecology and Evolutionary
Psychology
Donald W. White, Department of Psychology, Simon Fraser University, Burnaby, BC, Canada.
Lawrence M. Dill, Behavioural Ecology Research Group, Department of Biological Sciences, Simon Fraser
University, Burnaby, BC, Canada. E-Mail: ldill@sfu.ca. (Corresponding author)
Charles B. Crawford, Department of Psychology, Simon Fraser University, Burnaby, BC, Canada.
Abstract: Since evolutionary psychology and behavioral ecology have much in common
despite their using different objects for their study, one might expect these disciplines to
share a common conceptual framework with associated definitions. Unfortunately, such
agreement does not entirely exist. To address the problem, we propose a common,
conceptual framework, the Adaptive Behavioral System (ABS), which organizes behavior
within an evolutionary framework around an organism’s life history tasks. An ABS
includes strategies that use decision rules and employs tactics administered by a
hypothesized construct, the Evolved Processing Unit (EPU). The ABS also includes
observed or predicted behavior which can be tested experimentally – the ultimate test of
construct validity. Use of the proposed framework should help the two disciplines focus
on their common, core business of behavior and, ultimately, be to the benefit of both.
Keywords: Strategy, Tactics, Decision Rule, Adaptive Behavioral System, Fitness
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Introduction

Evolutionary psychology and behavioral ecology share many things – despite their
choosing humans or non-humans respectively as the objects for their study (Hutchinson and
Gigerenzer, 2005). Both accept the principles that organisms obtain information from their
environment and, on the basis of that information and within the limits imposed by their
genotypes and other constraints, produce a response (Giske et al., 2003). Both regard
adaptations as traits honed by natural selection (Alcock, 2005; Buss, 2004). Both focus on
the informational inputs, decision rules, and outputs which organize and influence an
organism’s behavioral interactions with its environment (Gross, 1966; Cosmides and

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Adaptive Behavioral System
Tooby, 1987, 1995), and both hold that, ultimately, the study of an organism’s behavior is
the business of their discipline (Hutchinson and Gigerenzer, 2005).

Given such commonalties, one might expect the two disciplines to share a
conceptual framework and definitions for its constructs. Testing theories developed in one
discipline on the organisms of the other can only be to the benefit of both (Hutchinson and
Gigerenzer, 2005). At the present time, however, much of the benefit that might be
obtained from exchanging information between evolutionary psychology and behavioral
ecology seems unnecessarily out of reach. Rather than sharing a common framework and
definitions, the terminology and constructs used to refer to and describe behavioral systems
and their constituent parts not only vary between the disciplines but also to a certain extent
within the disciplines themselves.

The same information processing units are sometimes referred to as Darwinian
algorithms, sometimes as psychological mechanisms, or mental organs, or sometimes
simply as strategies (Buss, 2004; Cosmides and Tooby, 1987; Gaulin and McBurney,
2004). Evolutionary psychology frequently uses the label “psychological mechanism” to
refer indiscriminately to tactics, decision rules, and to the organizing mechanism which
utilizes these constructs. Psychological mechanisms now include everything from
information processing systems to the affective states of hate and fear (Buss, 1995). In
behavioral ecology, decision rules may be referred to as “rules of thumb”; in evolutionary
psychology, as heuristics (Hutchinson and Gigerenzer, 2005). In both evolutionary
psychology and behavioral ecology, decision rules and outputs are described variously and
often interchangeably as strategies, tactics, or adaptations (Borgerhoff Mulder, 2007; Buss,
2004; Gross, 1996).

At an even deeper level, the tenets of behavioral ecology and evolutionary
psychology are often seen at odds. Despite the acceptance by both disciplines that natural
selection is the force that shapes adaptations and that adaptations are selected and evolve if
they promote inclusive fitness (Alcock, 2005; Buss, 2004), the status assigned to fitness
within the investigations of the two disciplines is often quite different. Behavioral
ecologists assume that increased fitness benefits, direct or indirect, underlie the spread of
any adaptation (Alcock, 2005). Kaplan and Gangestad (2005) concur that evolutionary
psychology’s examination of adaptations connects it intimately to life history theory and,
thus, to an examination of an organism’s fitness-maximizing strategies. However, Buss
(1995) and Tooby and Cosmides (2005) regard humans as “adaptation executors” not
“fitness strivers” or “fitness pursuers.” Buss (1995) also argues that if humans were fitness
strivers, we would attempt to maximize reproductive output to the exclusion of all else – a
perspective not shared by behavioral ecologists. Tooby and Cosmides (2005) state that one
reason evolutionary psychology is distinct from anything resembling human sociobiology
“lies in its rejection of fitness maximization as an explanation for behavior.”

Despite this difference in theoretical position much research in the two disciplines is
structured similarly. Behavioral ecologists almost always ignore the number of offspring
produced and study, instead, how a particular adaptation contributes to some fitness proxy,
for example, net energy intake rate (e.g., Giraldeau and Caraco, 2000; Figure 6.1).
Evolutionary psychologists may study mate choice (Buss and Schmitt, 1993) or resource
distribution (Smith, Kish and Crawford, 1987). Reeve and Sherman (2007) argue that any
test of a hypothesis concerning the adaptive significance of human behavior must include a
fitness value or payoff, but this need not be number of offspring. It could as easily be
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Adaptive Behavioral System
men’s copulatory frequency or nutritional outcomes for mothers (Borgerhoff Mulder,
2007), and one need not resort to counting babies (Crawford, 1993).

Borgerhoff Mulder (2007) argues that only the more extreme positions (always or
never counting offspring) are problematic and are largely responsible for creating the
division between the disciplines. What the researcher measures depends entirely on
whether or not they define an adaptation in terms of its current function or in terms of its
evolutionary development. Reconciling the differences and increasing communication
between the disciplines, therefore, may be mainly a matter of moderating these extreme
positions.

Communication between the disciplines and calls for its improvement are nothing
new (Daly and Wilson, 1999; Hutchinson and Gigerenzer, 2005). But to easily evaluate
and apply the theories and findings of one discipline to the experimental subjects of the
other requires a common conceptual framework with agreed-upon definitions. The purpose
of the present paper is to propose exactly that. First, we describe a conceptual framework
that gives an evolutionary (i.e., adaptive) context for organizing and examining
informational inputs, decision rules, and outputs. Next, we define the framework’s
component parts. We then give examples (based on instances found in the literature) of
how both simple and complex behaviors of both humans and non-human species can be
analyzed within our framework. Lastly, we provide a discussion and some conclusions
about the framework’s utility for behavioral ecology and psychology.
The Adaptive Behavioral System (ABS)
Description

An adaptive behavioral system (ABS) is a conceptual framework which analyzes
and organizes behavior within an evolutionary context with reference to an organism’s life
history tasks. It regards mechanisms with behavioral outputs as adaptations, at least in
terms of the environment in which the mechanisms evolved. Thus, like Hutchinson and
Gigerenzer’s (2005) argument of “ecological rationality”, the ABS avoids the necessity of
demonstrating that today’s environment is the same as the “environment of evolutionary
adaptedness” (Tooby and Cosmides, 1992). The primary assumption of an ABS is that the
mechanism has been shaped by natural selection and has evolved because of its
contribution to an organism’s inclusive fitness.

An ABS looks first at behavior in terms of the general and specific life history
(adaptive) problems it might address. It then considers three stages or processes of
decision-making and response production: (1) Inputs - information (in one form or another)
from the internal and external environments of the organism; (2) Information processing
and selection of outputs using particular decision rules; and, (3) Outputs – the behavioral,
somatic, affective, and cognitive tactics which are finally applied and may be typically
observed.

The “Mechanism” Problem: Process or Structure?

Considerable imprecision currently exists, at least in the literature of evolutionary
psychology, in distinguishing between the structure which accomplishes the information
processing-decision making and the processes which it runs. As already noted in the
Introduction, evolutionary psychology frequently uses the label “psychological
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mechanism” to refer to tactics, decision rules, and to the physical structure which
administers them. Part of the confusion may be due to the fact that we usually do not
know the exact neurological components which accomplish information processing-
decision making tasks. Part may come from the inexactitude of the terminology. When
authors refer to a “mechanism”, it is not always clear whether they are referring to a
structure or a process. As a means of avoiding this confusion, the ABS includes a
hypothetical structure for the second stage, the Evolved Processing Unit (EPU).

The Evolved Processing Unit (EPU)

Although much of the current terminology used in behavioral analysis comes from
game theory (e.g., strategies, tactics, etc.), a more useful analogy for the information
processing, decision-making structure might exist in the field of computing. A computing
system can be regarded as being organized not unlike an ABS: its complex structure is
typically broken into input (and input devices), processing or computing, and output (with
output devices). The physical structure which carries out the computing or information
processing is the Central Processing Unit or CPU. If the term “evolved” is substituted for
“central”, CPU become EPU, the acronym for “evolved processing unit.” The Evolved
Processing Unit (EPU) is the information processing-decision making structure of the ABS.

The function of an EPU is to run the inherited, genetic programs (the decision rules
by which an organism’s response to environmental input is determined), to select the
particular tactics employed by the strategy (Gross, 1996), and to trigger a particular
response. Thus, an EPU receives environmental input, processes that input in terms of
possible costs and benefits (the particular “if-then” program which the EPU runs), and
selects an appropriate output. This does not preclude the possibility that learned
“programs” may also mediate behavior and should sometimes be included in an ABS. As
an evolved processing unit, however, the EPU is constrained by natural selection acting on
genetically transmissible information. The ability or tendency to learn “programs” might
more appropriately be regarded as a tactic which the EPU invokes (See Definition of
Terms).

The Boundaries of an ABS

While all adaptations function ultimately within the context of inclusive fitness the
scope of a particular ABS, i.e., its boundaries, is largely determined by the specific problem
it addresses. Defining the scope of that problem is largely at the discretion of the
researcher. This discretion is similar to that required of a modeler in setting the strategy set
and rules of thumb for a proposed behavioral model (Hutchinson and Gigerenzer, 2005).
An ABS is not “a basic building block” or indivisible unit of behavior. Smaller ABSs may
be nested in one or more larger, more encompassing ABSs, and may be shared by others
which are non-related. For example, an ABS for ingesting available food when hungry can
be organized within an ABS for foraging; an ABS for foraging may also include an ABS
for searching for the food to be ingested and an ABS for fighting. The ABS for fighting
could also be included in other ABSs which have nothing to do with food (e.g., mate
competition). Thus, an ABS may be constructed by someone wishing to analyze a
particular response to a particular stimulus or by someone wishing to take a broader view.
The boundaries of the ABS are largely determined by the boundaries of the task to which it
is applied.
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A desired goal of any conceptual system should be not only to explain observed
behavior but also to produce predictions which can be subsequently tested by experiment
(Hutchinson and Gigerenzer, 2005). Such predictions are the foundation of any scientific
investigation and their confirmation will also provide the support required to substantiate
the validity of any hypothesized ABS. Thus, an ABS should not only describe and explain
the behavior observed in a particular species, but it should describe and explain it in a way
that generates testable predictions. This is accomplished in the final component of an ABS
description, the Observed/Predicted Behavioral Output.

Definition of Terms

The inputs, decision rules and outputs included in an ABS are broken into and analyzed
according to the following categories: General Adaptive Problem; Specific Adaptive
Problem; Strategy; EPU; Decision Rule(s); Tactic(s); and, Observed/Predicted Behavioral
Output.
Adaptive Behavioral System (ABS): a conceptual framework which organizes the
informational inputs, decision rules, and behavioral outputs by which an organism attempts
to interact adaptively with its environment.
General Adaptive Problem: Ultimately, a problem of survival or reproduction, (the
determinants of fitness), but may refer to a specific set or sub-set of problems in a
hierarchy, e.g., mate selection.
Specific Adaptive Problem: The specific problem which the strategy, EPU, decision
rule(s), and tactics address.
Strategy: A summary description of one or more decision rules used to achieve a goal (i.e.,
solve a specific problem of survival/reproduction) through the use of its selected tactics and
which is in evolutionary competition with other strategies.
EPU: a physiological and/or morphological structure by which a strategy is instantiated.
Decision Rule: An operational definition of the strategy encoded in the EPU.
Tactics: cognitive, affective, somatic, and, ultimately, behavioral outputs of a decision rule
by which accomplishment of the strategy is attempted.
Observed/Predicted Behavioral Output: Behavior typically observed or predicted as a
result of the decision rule.

Selected Examples of Adaptive Behavioral Systems

The following examples were selected to represent both simple and complex behavior
of both humans and non-human species, and many use observed behavior cited in the
literature of behavioral ecology and/or psychology. The intention is to demonstrate the
range of behaviors which can be captured by the ABS framework. Because one of the
major benefits derived from including an EPU within an ABS is the clear separation
created between the physiological or morphological mechanism and the program which it
runs, one or more EPUs consisting of sets of neurons are hypothesized for each of the
examples provided. These EPUs are included purely for illustrative purposes, and we do
not wish to imply that these exact neural mechanisms actually exist and perform the
described duties (although they may). These hypothetical structures merely describe how
neural networks might be involved in solving the particular ecological problem and
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acknowledge that larger problems often consist of several smaller problems. Other
neurophysiological structures could also be proposed.

Choosing Where to Live
Species: Planarian.
General Adaptive Problem: Survival.

Specific Adaptive Problem: Choosing a safe habitat.
Strategy: Select the habitat with the lowest risk of being detected by a visual predator.
EPU: Two paired, fronto-lateral photoreceptors which fire at a rate based on the intensity of
stimulation (faster for brighter light), and a set of a few neurons which compares the firing
rates of the photoreceptors and stimulates muscles contra-lateral to the side with the lowest
firing rate or intensity.
Decision Rule(s): Always choose the darkest available location.
Tactic(s): Muscle contractions leading to l/r turn.
Observed/Predicted Behavioral Output: Planarians move towards and gather in dark areas.

Obtaining Energy to Live (1)
Species: All.
General Adaptive Problem: Survival.
Specific Adaptive Problem: Obtaining/replacing energy used in metabolic activities.
Strategy: Intake energy whenever needed.
EPU: Sets of neurons which fire when cellular activity decreases, or when energy reserves
(e.g. fat) are low, or are being depleted, and which initiate foraging behavior for the
appropriate food.
Decision Rule(s): Hunt for food when reserves are low.
Tactic(s): Search, capture, eat.
Observed/Predicted Behavioral Output: Foraging, feeding.

Obtaining Energy to Live (2)
Species: Chickadee.
General Adaptive Problem: Maximizing net energy intake rate (NEIR).
Specific Adaptive Problem: Choosing between habitat patches with variable amounts of
food.
Strategy: Remain in a patch if NEIR is above the habitat average.
EPU(s): One set of neurons fires at a “many captures” rolling average rate (habitat
average). A second set of neurons suppresses firing of the first set by an amount
corresponding to a memory trace of migration travel time/cost between patches. A third set
of neurons clocks time since last prey capture and if the time exceeds the inverse of the
adjusted “many captures” rolling average rate, the chickadee leaves the current patch to
forage elsewhere.
Decision Rule(s): Change patches when time between captures exceeds giving up time
(GUT).
Tactic(s): Remain/depart, foraging, feeding.
Observed/Predicted Behavioral output: Chickadee leaves/stays in patch when food
availability is low/high (Smith and Sweatman, 1974).

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Avoiding Toxic Food
Species: Human.
General Adaptive Problem: Avoiding toxic substances.
Specific Adaptive Problem: Preventing the assimilation of allylisothiocynate when ingested
as it prevents normal uptake of iodine into thyroid tissue.
Strategy: Expel any food ingested containing allylisothiocynate (especially when most
susceptible).
EPU: A group of neurons sensitive to the presence of allylisothiocynate which trigger
gagging, spitting, and/or vomiting when they fire, but whose firing threshold increases with
development.
Decision Rule(s): gag, spit, vomit if allylisothiocynate detected.
Tactic(s): Gagging, spitting, vomiting.
Observed/Predicted Behavioral output: Gagging, spitting, vomiting (especially during
childhood) whenever broccoli and/or Brussels sprouts are ingested (Nesse and Williams,
1994).

Finding a Mate (1)
Species: Moth - Male.
General Adaptive Problem: Reproduction.
Specific Adaptive Problem: Locating a female partner.
Strategy: Locate a female partner by backtracking a pheromone plume.
EPU: One group of neurons which detects wind direction, one group which fires when
female pheromone detected and triggers up-wind flight; a second group of neurons which
detects cessation of firing in first set and triggers back and forth cross-wind flight.
Decision Rule(s): Head upwind when pheromone detected; cast back and forth if plume
suddenly disappears.
Tactic(s): Fly, turn l/r.
Observed/Predicted Behavioral Output: Males approach a female dispensing pheromone
(Kennedy, 1983).

Finding a Mate (2)
Species: Scorpionfly - Male.
General Adaptive Problem: Reproduction.
Specific Adaptive Problem: Securing mating opportunities.
Strategy: Choose the mating tactic with the greatest expected fitness given current
constraints.
EPU: Assuming a sexual interest has been triggered, a set of neurons fires at a rate
commensurate with the scorpionfly’s condition – at a high rate if the scorpionfly is capable
of capturing food, at a low rate if it is in relatively good condition but not good enough to
capture a food item. If the neuron set fires at a high rate, a food item is caught and used to
attract females; if the neuron set fires at a low rate, a salivary mass is secreted and used to
attract females; if the neuron set fails to fire, the male searches for and attempts forced
copulation with a female.
Decision Rule(s): If food availability and physical condition permit, capture prey and offer
it to female (preferred tactic); if no prey can be captured but physical capability exists, offer
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salivary mass to female (second choice); if no prey can be captured but not physically
capable of secreting salivary mass, attempt forced copulation (third choice).
Tactic(s): Prey capture, prey offering, saliva secreting/offering, forced copulation.
Observed/Predicted Behavioral Output: Depending on the physical capability of a male
scorpionfly and the availability of food items, capable males choose first to fertilize a
female by offering a potential mate captured prey, second a gift of salivary mass. If all else
fails, a male attempts forced copulation (Thornhill, 1981).

Increasing Number of Offspring
Species: Meadow vole - Male.
General Adaptive Problem: Maximizing number of direct descendents.
Specific Adaptive Problem: Overcoming limited reproductive potential of a single female.
Strategy: Maximize the number of females inseminated.
EPU: Neurons which fire and increase hormonal levels when a female is encountered;
another set of neurons which “records” a memory (sight/smell) of a female after
intromission-ejaculation and fires, inhibiting the firing of the first set when a “familiar”
female is later encountered; a third group of neurons which fires when it detects increased
hormonal levels and triggers sexual approach to the female.
Decision Rule(s): Only approach novel females.
Tactic(s): Sexual interest/disinterest, approach, avoidance.
Observed/Predicted Behavioral output: male preference for novel females, mate switching,
polygyny (Dewsbury, 1981).

Improving Viability of Offspring (1)
Species: Bushcrickets.
General Adaptive Problem: Maximizing quality of offspring.
Specific Adaptive Problem: Choosing the highest quality male.
Strategy: Always select the loudest calling male.
EPU: “A simple negative feedback loop in the ear of bushcrickets which adjusts the
sensitivity of the ear according to the loudness of the signal. The consequence is that the
female’s brain is totally unaware of all but the loudest male cricket in the vicinity.” (Römer
and Krusch, 2000). A set of a few neurons which compares the firing rates of the two ears
(audio receptors) and stimulates muscles on the side with the lowest firing rate or intensity.
Decision Rule(s): Approach the loudest calling male.
Tactic(s): turn l/r, approach.
Observed/Predicted Behavioral Output: “The consequence behaviorally is a rule of thumb
for mate choice of simply heading toward the male that appears loudest” (Römer and
Krusch, 2000).

Improving Viability of Offspring (2)
Species: Human – Female.
General Adaptive Problem: Survival of offspring.
Specific Adaptive Problem: Ensuring adequate paternal support for mother and offspring.
Strategy: Accompany ovulation with sexual interest only when in a committed
relationship.
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EPU: One group of neurons which fires at an increasing rate when it detects an internal
state of “romantic commitment” to a male partner (output from another ABS); another
neuronal group which fires when it detects the hormone precursor of ovulation; and a third
which fires (triggering sexual interest and associated behaviors) when it detects the firing
of both of the other neuron sets.
Decision Rule(s): Generate state of sexual interest when ovulating, but only if committed to
a partner.
Tactic(s): Sexual interest (becomes input to appropriate sexual behavior ABS).
Observed/Predicted Behavioral Output: Only women committed to a relationship
demonstrate a change in sexual desire over the menstrual cycle, with the peak occurring
around the time of ovulation (Pillsworth, Haselton and Buss, 2004).

Avoiding Predators and Disease
Species: Human.
General Ecological Problem: Survival.
Specific Ecological Problem: Maintaining good health by avoiding disease.
Strategy: Avoid likely contagious carriers/sources of disease.
EPU: A set of neurons which records memory (physical appearance, smell, etc.) of average
ingroup members, a second set of neurons which compares present conspecifics with
memories of ingroup members and fires when non-normative comparisons are detected,
and a third set of neurons which trigger an internal state of disgust and other behavioral and
cognitive responses that compel avoidance.
Decision Rule(s): Avoid any conspecific with physical/behavioral features that are judged
to be normatively unusual.
Tactic(s): Encoding, retrieval, and comparison of memories; avoidance, disgust.
Observed/Predicted Behavioral Output: avoidance of and prejudice towards those who are
asymmetrical, disabled, or members of unfamiliar ethnic outgroups (Schaller, Park and
Faulkner, 2003).


Discussion


As soon as one begins to analyze an organism’s observed behavior within an ABS,
several issues become apparent. First, as obvious as it seems, both evolutionary psychology
and behavioral ecology should distinguish between processes in which behavior is involved
and processes where it is not (Cosmides and Tooby, 1987). Both disciplines focus on
behavior for their studies (Hutchinson and Gigerenzer, 2005), and, ultimately, an ABS
should do the same. The problem of an organism’s obtaining energy to sustain cellular
activity might be framed in terms of chemical transport across a cell membrane, feeding
behavior, or patch choice, but both behavioral ecology and evolutionary psychology are
concerned primarily with the latter two. Second, as noted previously, behavioral analysis
can occur on many levels, and no level of analysis is more “correct” than any other. Both
foraging and patch choice also include feeding. Patch choice includes foraging as well.
The scale of the ABS described is determined by the level of analysis desired by the
researcher. Furthermore, the researcher can conceptualize several ABSs as being
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structured hierarchically – the output of one ABS becoming the input of another –
reminiscent of Tinbergen’s model of behavior (Tinbergen, 1951).

Each major task or problem can be broken into tasks and sub-tasks. The output of
any set of decision rules may accomplish a task directly (through a tactic), or may lead to
another set of decision rules (another strategy). Some problems are solved by solving two
or more concurrent problems (Hutchinson and Gigerenzer, 2005) or by solving two or more
consecutive sub-problems, i.e., solution of the first sub-problem provides the informational
input needed to solve the second sub-problem, and so on. Some problems may require
solving a number of concurrent sub-problems which may not be linked directly, but the
solutions of which are all needed to solve the “meta” problem. In the example of sexual
interest in the human female, detection of both a committed relationship and ovulation
were required to produce the increased interest. A variation of concurrent sub-problems is
described by Hutchinson and Gigerenzer (2005) where a non-sequential combination of
cues (information from any subset of several cues within a larger set) is required to reach a
threshold triggering the behavior.

Another issue mentioned earlier but deserving further elaboration concerns the
Evolved Processing Unit (EPU). Ironically, one of the greatest benefits to evolutionary
psychology and behavioral ecology in defining the EPU might be that once defined, the
EPU can be set aside and attention shifted elsewhere. Behavioral ecologists and
evolutionary psychologists are concerned primarily with how organisms respond to their
environments (their decision rules and behavior), rather than with the morphological or
physiological mechanism which administers those processes (Cosmides and Tooby, 1987).
Concern with the mechanism itself is more properly the interest of disciplines such as
neuroethology (Camhi, 1984), and great strides are being made in elucidating the neural
basis of decision making (Schultz, 2004). This does not mean, however, that it is not
beneficial to include the EPU when considering an ABS. Defining its hypothetical
structure does much to eliminate the confusion between structure and process which
currently exists. Furthermore, as can be seen in the examples provided above, describing
even a hypothetical structure may be valuable in helping to analyze the process(es)
undertaken by the organism in its problem solving or cost-benefit evaluation and that
analysis can produce other testable hypotheses.

Another benefit obtained from defining an EPU within the framework of the ABS,
is the clarification it gives to the “behavior-as-adaptation” confusion often encountered in
the literature. Despite how both behavioral ecology and evolutionary psychology
sometimes refer to it (Alcock, 2005; Buss, 2004), behavior is not an adaptation; it is the
tactical output of an adaptation. By defining behavior as a tactic employed by an EPU, the
ABS eliminates this problem and this source of possible confusion.

Each ABS and, thus, each set of decision rules it includes, is bounded by the
perceived environmental information relevant to the particular adaptive problem being
considered. Similarly, all tactics available to the organism are limited by the constraints of
the possible phenotype set available to the animal and the morphological and physiological
constraints on what the animal can/cannot do including its “evolutionary baggage” (Gould,
1989).

The organizational structure of the ABS also provides for current theories of state
dependence (Clarke and Mangel, 2000). If secretion of a hormone is a tactic, and the
presence of the hormone brings about a particular internal state, then that state must also be
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