Coincidentally, I ended up reading Evolutionary Psychology: Controversies, Questions, Prospects, and Limitations today, and noticed that it makes a number of points that could be interpreted in a similar light: in that humans do not really have a “domain-general rationality”, and that instead we have specialized learning and reasoning mechanisms, each of which are carrying out a specific evolutionary purpose and which are specialized for extracting information that’s valuable in light of the evolutionary pressures that (used to) prevail. In other words, each of them carries out inferences that are designed to further some specific evolutionary value that helped contribute to our inclusive fitness.
The paper doesn’t spell out the obvious implication, since that isn’t its topic, but it seems pretty clear to me: since our various learning and reasoning systems are based on furthering specific values, our philosophy has also been generated as a combination of such various value-laden systems, and we can’t expect an AI reasoner to develop a philosophy that we’d approve of unless its reasoning mechanisms also embody the same values.
That said, it does suggest a possible avenue of attack on the metaphilosophy issue… figure out exactly what various learning mechanisms we have and which evolutionary purposes they had, and then use that data to construct learning mechanisms that carry out similar inferences as humans do.
Quotes:
Hypotheses about motivational priorities are required to
explain empirically discovered phenomena, yet they are not
contained within domain-general rationality theories. A
mechanism of domain-general rationality, in the case of
jealousy, cannot explain why it should be “rational” for
men to care about cues to paternity certainty or for women
to care about emotional cues to resource diversion. Even
assuming that men “rationally” figured out that other men
having sex with their mates would lead to paternity uncertainty, why should men care about
cuckoldry
to begin
with? In order to explain sex differences in motivational
concerns, the “rationality” mechanism must be coupled
with auxiliary hypotheses that specify the origins of the sex
differences in motivational priorities. [...]
The problem of combinatorial explosion.
Domain-general theories of rationality imply a deliberate cal-
culation of ends and a sample space of means to achieve those
ends. Performing the computations needed to sift through that
sample space requires more time than is available for solving
many adaptive problems, which must be solved in real time.
Consider a man coming home from work early and discovering his wife in bed with another man. This circumstance
typically leads to immediate jealousy, rage, violence, and
sometimes murder (Buss, 2000; Daly & Wilson, 1988). Are
men pausing to rationally deliberate over whether this act
jeopardizes their paternity in future offspring and ultimate
reproductive fitness, and then becoming enraged as a consequence of this rational deliberation? The predictability and
rapidity of men’s jealousy in response to cues of threats to
paternity points to a specialized psychological circuit rather
than a response caused by deliberative domain-general rational thought. Dedicated psychological adaptations, because
they are activated in response to cues to their corresponding
adaptive problems, operate more efficiently and effectively for
many adaptive problems. A domain-general mechanism
“must evaluate all alternatives it can define. Permutations
being what they are, alternatives increase exponentially as the
problem complexity increases” (Cosmides & Tooby, 1994, p.
94). Consequently, combinatorial explosion paralyzes a truly
domain-general mechanism (Frankenhuis & Ploeger, 2007). [...]
In sum, domain-general mechanisms such as “rationality” fail to provide plausible alternative explanations for
psychological phenomena discovered by evolutionary psychologists. They are invoked post hoc, fail to generate
novel empirical predictions, fail to specify underlying motivational priorities, suffer from paralyzing combinatorial
explosion, and imply the detection of statistical regularities
that cannot be, or are unlikely to be, learned or deduced
ontogenetically. It is important to note that there is no
single criterion for rationality that is independent of adaptive domain. [...]
The term
learning
is sometimes used as an explana-
tion for an observed effect and is the simple claim that
something in the organism changes as a consequence of
environmental input. Invoking “learning” in this sense,
without further specification, provides no additional explanatory value for the observed phenomenon but only
regresses its cause back a level. Learning requires evolved
psychological adaptations, housed in the brain, that
enable
learning to occur: “After all, 3-pound cauliflowers do not
learn, but 3-pound brains do” (Tooby & Cosmides, 2005, p.
31). The key explanatory challenge is to identify the nature
of the underlying learning adaptations that enable humans
to change their behavior in functional ways as a consequence of particular forms of environmental input.
Although the field of psychology lacks a complete
understanding of the nature of these learning adaptations,
enough evidence exists to draw a few reasonable conclu-
sions. Consider three concrete examples: (a) People learn
to avoid having sex with their close genetic relatives (learned incest avoidance); (b) people learn to avoid eating foods that may contain toxins (learned food aversions); (c)
people learn from their local peer group which actions lead
to increases in status and prestige (learned prestige criteria). There are compelling theoretical arguments and empirical evidence that each of these forms of learning is best
explained by evolved learning adaptations that have at least
some specialized design features, rather than by a single
all-purpose general learning adaptation (Johnston, 1996).
Stated differently, evolved learning adaptations must have
at least some content-specialized attributes, even if they
share some components. [...]
These three forms of learning—incest avoidance, food
aversion, and prestige criteria—require at least some content-specific specializations to function properly. Each op-
erates on the basis of inputs from different sets of cues:
coresidence during development, nausea paired with food
ingestion, and group attention structure. Each has different
functional output: avoidance of relatives as sexual partners,
disgust at the sight and smell of specific foods, and emulation of those high in prestige. It is important to note that
each form of learning solves a different adaptive problem.
There are four critical conclusions to draw from this
admittedly brief and incomplete analysis. First, labeling
something as “learned” does not, by itself, provide a satisfactory scientific explanation any more than labeling
something as “evolved” does; it is simply the claim that
environmental input is one component of the causal process
by which change occurs in the organism in some way.
Second, “learned” and “evolved” are not competing explanations; rather, learning requires evolved psychological
mechanisms, without which learning could not occur.
Third, evolved learning mechanisms are likely to be more
numerous than traditional conceptions have held in psychology, which typically have been limited to a few highly
general learning mechanisms such as classical and operant
conditioning. Operant and classical conditioning are important, of course, but they contain many specialized adaptive
design features rather than being domain general (Ohman
& Mineka, 2003). And fourth, evolved learning mechanisms are at least somewhat specific in nature, containing
particular design features that correspond to evolved solutions to qualitatively distinct adaptive problems.
Coincidentally, I ended up reading Evolutionary Psychology: Controversies, Questions, Prospects, and Limitations today, and noticed that it makes a number of points that could be interpreted in a similar light: in that humans do not really have a “domain-general rationality”, and that instead we have specialized learning and reasoning mechanisms, each of which are carrying out a specific evolutionary purpose and which are specialized for extracting information that’s valuable in light of the evolutionary pressures that (used to) prevail. In other words, each of them carries out inferences that are designed to further some specific evolutionary value that helped contribute to our inclusive fitness.
The paper doesn’t spell out the obvious implication, since that isn’t its topic, but it seems pretty clear to me: since our various learning and reasoning systems are based on furthering specific values, our philosophy has also been generated as a combination of such various value-laden systems, and we can’t expect an AI reasoner to develop a philosophy that we’d approve of unless its reasoning mechanisms also embody the same values.
That said, it does suggest a possible avenue of attack on the metaphilosophy issue… figure out exactly what various learning mechanisms we have and which evolutionary purposes they had, and then use that data to construct learning mechanisms that carry out similar inferences as humans do.
Quotes: