Evolutionary Explanations for ‘Irrationality’Leeann Breeze
“In formal logic, a contradiction is the signal of defeat, but in the evolution of real knowledge it marks the first step in progress toward a victory.”
- -Alfred North Whitehead
EVOLUTIONARY PSYCHOLOGY
& reasoning todayAll humans today carry cognitive traits that served to help our
ancestors survive and reproduce in prehistoric environments.
Today’s world is much DIFFERENT than the worldin which our ancestors lived and evolved…So the traits we observe today may have been valuable in the past, but some no longerserve any evolutionary advantage, given the nature of modern environments
This includes the psychological strategies we have evolved to use…
EVOLUTIONARY PSYCHOLOGY& reasoning today
…THUS NON-NORMATIVE REASONING EXISTS TODAY
Because we evolved cognitive modules that served for efficiency, reproductive/social
success, and environmental safety/typicality in prehistoric contexts
So, where Alfred Whitehead is concerned, the contradiction between Normative and Descriptive theory is a failure for formal logic (which is
hard to argue against) BUT the fact that we evolved to demonstrate this contradiction because of ecologically sound reasoning marks the
success of “the evolution of real knowledge”
HOW IS IRRATIONALITY ADAPTIVE?
LET’S BEGIN DEMONSTRATING THE ECOLOGICAL BENEFIT OF IRRATIONAL
BEHAVIOR AND COGNITIVE PROCESSES BY OUTLINING THE FORCES THAT
GUIDED THE DEVELOPMENT OF HUMAN COGNITIVE TRAITS, ACCORDING TO
EVOLUTIONARY THEORY
PRINCIPLES OF EVOLUTION by Natural Selection, from Darwin1. Traits show variation
2. Some variation is heritable
3. Individuals differ in fitness (the number of offspring they are able to produce)
4. A correlation exists between phenotype and fitness
EVOLUTIONARY INTERPRETATIONS
A trait’s adaptiveness is determined by its frequency in the population of interest
An adaptive phenotype will have an advantage for personal fitness, those who exhibit it will more frequently survive to reproductive age,
and the trait will be inherited by offspring, increasing the trait’s frequency in the
population
EVOLUTIONARY TIMEHumans are biological creatures programmed
by evolution to act, think, feel, and learn in ways that have fostered survival over many
past generations.
Traits we see today exist because they survived challenges of past environments in
which our ancestors lived
EVOLUTIONARY INTERPRETATIONS
& REASONSince cognition is not a physical trait, selection acts upon manifested behaviors that result from cognitive ability or task construal. Evolutionary psychology works on the assumption that cognitive traits we observe today developed as responses to problems our ancestors faced over thousands of years of evolution in prehistoric, savanna-style, hunter-gatherer, societies that relied on social interaction to thrive
EVOLUTIONARY PSYCHOLOGY“Evolved psychological mechanisms are
functional; they function to solve recurrent adaptive problems that confronted our ancestors.”
–David Buss interview in Barker, 2006, pp. 69-70
RATIONALITY & EEA
According to hypotheses that reference the Environment of Evolutionary Adaptiveness(EEA), our mental modules have structures that are better adapted to past environments than the present.
WHERE RATIONALITY IS CONCERNED:
Where do these environmental discrepancies apply?
EVOLUTIONARY INTERPRETATIONS
& RATIONALITYBecause our cognitive modules evolved to serve SURVIVAL AND REPRODUCTION in the highly social, hunter-gatherer, savannah-style EEA, our reasoning abilities today do not appear to fit modern normative theories of logic…resulting in apparent reasoning “errors” defined as mismatching between descriptive and normative models of logic
EVOLUTION and CHANGES
The rapidly changing technological environment in which we live makes these previous adaptations
seem even more out-of-date in their modern context
Because even today, we appear to be designed to more readily respond to tasks with the influence of:
1. Typicality of events & Natural Sampling2. Social Contexts
3. Time/Effort-Saving Heuristicseven when these strategies produce obviously
incorrect responses to modern problems
LET’S REVIEW:
WHAT IS RATIONAL? BARON: anything that helps us achieve
our goals DAWES: rationality is avoidance of self-
contradiction Ascribing to formal (normative) rules of logic
Empirical demonstrations of irrationality
WASON 4-CARD PROBLEM
BAYESIAN INFERENCE
PROBABILITY ESTIMATES
PART I: WASON 4 CARD PROBLEMLeda Cosmides and John ToobyThe Scenario:
GROUP 1: 4 cards are on a tableThere is ONE RULE:
To have a B, there must be 21 or higher on the other side
WASON 4 CARD PROBLEM
GROUP 1: What is the maximum number of cards you must check to be SURE this rule is
satisfied?
WASON 4 CARD PROBLEM
GROUP 2: 4 cards are on a tableThere is ONE RULE:
To have a beer a person must be 21 or older
WASON 4 CARD PROBLEM
GROUP 1: What is the minimum number of cards you must check to be SURE this rule is
satisfied?
WASON 4 CARD PROBLEM
FINDINGS: although the 2 problems have the same logical structure, less than 25 percent of college students can solve the problem for group 1, but roughly 75 percent of college students answer the problem of group 2 correctly
After re-designing the problem to eliminate issues of familiarity, Cosmides and Tooby conclude
that we seem to be predisposed to more easily solve the problems that involve
CHEAT DETECTION
WASON 4 CARD PROBLEM& irrationality
Therefore, people seem to violate Dawe’s definition of rationality by failing to be consistent when the 2 problems have the same logical structure
People also violate the 3rd law of rationality when they are placed with problems like group 1 by failing to follow normative models
WASON 4 CARD PROBLEM& evolutionary theory
Robert Trivers, evolutionary psychologist, has argued that reciprocal altruism is crucial to the social evolution of our species.
Additionally, reciprocity can only be spread if non-reciprocators are punished
WASON 4 CARD PROBLEM& evolutionary theory
In light of Triver’s theory of reciprocal altruism, Cosmides and Tooby interpret their findings as being indicative of an evolved mental capacity for recognizing when some one has cheated by violating a SOCIAL CONTRACT
WASON 4 CARD PROBLEM& cheat detection
Evolutionary strategy holds that individuals are controlled by behaviors that will serve to maximize the success of their OWN genes
THUS THE BEST STRATEGY WOULD BE TO CHEAT (getting all possible gains for oneself & profit from the good nature of others) AND NEVER RECIPROCATE
WASON 4 CARD PROBLEM& reciprocal altruism
THEORY: altruism/reciprocity gene and cheater gene are both FREQUENCY DEPENDENT.
Because if there were too many cheats, competition would override. But, eventually a random mutation for a set of ‘altruist genes’ in the cheater population would begin to have an advantage. Likewise, in an entirely altruist population, the best strategy is to be a cheat. and in a mixed population the best strategy is to be an altruist with cheater-detection, share with other altruists and punish cheats. So cheaters and
alrtuists hold a balance in our population…
WASON 4 CARD PROBLEM& cheat detection
…and in a mixed population the best strategy is to be an altruist with cheater-detection, share with other altruists and punish
cheats.
THUS, WE HAVE EVOLVED TO HAVE PREDISPOSITION TO GIVE THE NORMATIVELY CORRECT ANSWER TO SCENARIOS INVOLVING CHEAT DETECTION. AN
APOLOGIST/EVOLUTIONARY PSYCHOLOGIST WOULD SAY THE REASON FOR THE NON-NORMATIVE RESPONSE TO THE ABSTRACT PROBLEM IS BECAUSE WE HAVE NOT
EVOLVED IN ENVIRONMENTS THAT PROMOTE THE DEVELOPMENT OF THE RIGHT EQUIPMENT TO INTERPRET THE PROBLEM IN A WAY THAT ALLOWS US TO ANSWER IT
CORRECTLY.
PART II: PROBABILITY ESTIMATES
PROBABILITY ESTIMATES
THE LINDA PROBLEMLinda is 31, single, outspoken, and very bright. She
majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and participated in anti-nuclear and anti-war demonstrations. .What happened to Linda? Rank order the following possible outcomes:(a) Linda failed to graduate from college(b) Linda works as a bank teller(c) Linda works for Green Peace(d) Linda works as a bank teller and is active in the feminist movement
PROBABILITY ESTIMATES
The probability that Linda is a bank teller must be at least as large as the probability that Linda is a bank teller and active in the feminist movement, by shear odds
occurrence of ONE event is much more likely than the combined occurrence of TWO events
PROBABILITY ESTIMATES& The Linda Problem
WHY THE ERROR?
Evolutionarily, people have adapted to assume continuity in the environment. This
seems to have had a consequential effect on the human affinity for narrative.
We adopt a story of Linda from the snippet of info, and continue it in our estimates of
likelihood for her future narrative.
PROBABILITY ESTIMATES& The Linda Problem
This is adaptive because it served to give us appropriate responses to social environments. According to Geoffrey Miller, it is adaptive to assume people are generally consistent, because it serves our “cheat detection” and “trustworthy mate” concepts, helping us to better deem who is a safe reproductive partner—improving our reproductive success.
So, we may not answer the normative answer to the Linda problem, statistically speaking…but, we are answering with the most ecologically-appropriate response. (Panglossian)
PROBABILITY ESTIMATES& percentages vs.
frequenciesPROBLEM 1: You are a gynecologist who conducts breast cancer screening in your region using mammography. The probability that a woman in this region has breast cancer is 1%.If a woman has breast cancer, the probability she tests positive is 80% (sensitivity).If she does not have breast cancer, the probability she tests positive is 9.6% (false positive rate).A woman tests positive. What is the probability that she has breast cancer?
PROBABILITY ESTIMATES& percentages vs.
frequenciesPROBLEM 2: You are an experienced physician in a preliterate society. You have no books or surveys, only your accumulated experience. A severe disease is plaguing your people. You have discovered a symptom that signals the disease, but not with certainty. Over the years you have seen many people & most don’t have the disease. Of those who did have the disease, 8 had the symptom. Of those who did not have the disease, 95 had the symptom. Now you meet a patient who has the symptom. What is the chance he has the disease?
PROBABILITY ESTIMATES& percentages vs.
frequenciesWhich was easier to solve? IN PROBLEM 2, the solution is simple. Total
people=8+95=103. of that 103, only 8 had the disease—thus the likelihood of a person coming in and having the disease is 8 out of 103=VERY LOW(7.8 percent)
PROBLEM 1: I will go into detail on HOW to solve problem 1 in the next section. For now, know the answer here too is 7.8%, and more math is required.
Physicians who typically solve problem 2 correctly give estimates of problem 1 of roughly 70-80% -- nearly 10 times too high!
PROBABILITY ESTIMATES& percentages vs.
frequenciesWhy is the normatively correct answer only intuitive in problem version 2?
The difference between the problems is the use of percentages in version 1 and frequencies in version 2
In fact, Cosmides and Tooby (1996) found that when they converted relevant info from probability to frequency formats in an experiment, their subjects’ performance improved in parallel
SO WHY DID WE ADAPT TO PREFER FLAT RATES INSTEAD OF PERCENTAGES?
PROBABILITY ESTIMATES& percentages
Why frequency preference adaptive?1. Probabilities and percentages were not an everyday
encounter until the 20th century2. Formal percentages began as scientific notation
during the 19th century3. Mathematical probability arose in the mid-17th
century Thus, the Environment of Evolutionary
Adaptiveness didn’t have selection pressures involving these mathematical structures BECAUSE
THEY DID NOT EXIST IN THE EEA.
PROBABILITY ESTIMATES& percentages
Instead, the EEA built our probability estimates on naturally occurring phenomena.
Therefore, we base our conclusions on NATURAL SAMPLING– taking census of events in the environment and
judging likelihoods based
on past encounters
PROBABILITY ESTIMATES& percentages
Using natural sampling, we set an event counter each time some event occurs. Humans seem to spontaneously count events, and because this is so automatic to us, our cognitive processing is more conducive to problems that suit this type of format.
So, as the apologists say, we are doing the best we can with the equipment we have evolved.
PART III: SOLVING PROBLEM # 1:BAYESIAN INFERENCETo find the normative answer to problem 1, we
need to use BAYESIAN INFERENCE.
This is a formula for using base rates, likelihoods/probabilities of 2 events to come to a final likelihood estimate for one occurrence, given some evidence.
BAYESIAN INFERENCE
Suppose we have two events, C and T, with probabilities P(c) and P(t)There are two conditional probabilities, P(U|K) and P(c|t)We define P(c|t) = P(c)*P(t/c) / P(c)*P(t/c)+
P(not c)*P(t/not c)This tells us how to go from one conditional probability
to the otherIf we know P(c|t), P(c), and P(t), we can calculate P(t|c)
** assume c is the unknown state (a hypothesis that the patient has cancer)t is the known information (i.e., evidence a positive on the mammogram )I.e., we use t to update our probability of c
BAYESIAN INFERENCE
By solving the formula with the given values, we can reason that the probability of cancer is .078=7.8%
BUT THIS IS NOT HOW WE REASON…WHY?- fast and fruegal heuristics are more evolutionarily
adaptive than normative reasoning skills- We haven’t evolved the capacity to be sensitive to
base rates, in the way that we would need to be to if we had intuitive guide towards using bayes’ theorem
BAYES THEOREMwhy fast and frugal
heuristics?Gigerenzer points out that we use our impression of what is representative or what is more familiar to us in order to solve problems, even when that is not going to produce normatively correct responses.
This is because ‘FAST AND FRUGAL HEURISTICS’ are much more effective at dolving real-world problems quickly with minimum information
BAYES’ THEOREMheuristics/evolution
when you look at it from the point of view of evolution, this makes sense. The adaptive value of saving in the EEa time was very high. Using utility theories, Bayes theorem, and doing the math to find the probability of attack from a wild animal given the evidence that you see him approaching quickly, but you are unsure of how long it has been since he has eaten could cost you your life. It is better to use a HEURISTIC—and err on the side of saftey: an over-generalized false positive is much less detrimental in these circumstances than a false negative. So, it is suggested that we have adapted to have less sensitivity to the occurrence of false-negative rates and we hone in on false positives.
BAYES’ THEOREM& base rate neglect
TVERSKY AND KAHNEMAN consider the breast cancer example to include a type of cognitive bias called BASE RATE NEGLECT
In this specific example, the overall rarity of breast cancer is being ignored.
Gigerenzer references back to our bias for frequency data as to why we may neglect base rates: because in the EEA, a concept such as base rates would not have existed. Our minds have evolved algorithms that can only work on the sort of input that would’ve been available in the EEA, so input such as base rates are commonly ignored.
EVOLUTION AND RATIONALITYOur cognitive processes were designed by selection to solve
problems our ancestors faced in the EEACognitive errors arise from rules made based on typicality & natural
sampling that do not fit probability scenarios and contemporary mathematics
Other cognitive errors arise from our predisposition to favor reasoning that promotes sociality, rather than normative logic
Additional problems occur because of our tendency to conserve effort and time by using heuristics
EVOLUTIONARY THEORISTS ACT AS APOLOGISTS CLAIMING THAT ALL OF THESE ERRORS ARE THE RESULT OF
MAKING THE BEST USE OF OUR COGNITIVE CAPACITIES GIVEN OUR LIMITATIONS
EVOLUTION AND RATIONALITY
the end
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