Can it be Irrational to prize Rationality? What is Rationality?

http://en.wikipedia.org/wiki/Melancholia

I am a big fan of Scott Plous's The Psychology of Judgment and Decision Making, because not only does it call out cognitive failure modes, but it also suggests remedies.  The book is written in a non-technical style, so uses the conventional language of modes of thought being "Rational" or "Irrational", and "biases" leading to "Irrational" decisions.

I found a blog post "Is postdecisional dissonance functional?" that takes exception to calling "Post-decision dissonance" irrational (post-decisional dissonance is where the self-judged value of a chosen item increases, and the value of a declined item decreases, compared to the self-judged values before the choice is made: http://en.wikipedia.org/wiki/Cognitive_dissonance#Post-decision_dissonance ).

"Is postdecisional dissonance functional?" seems like a yes/no question, but the answer can change from situation to situation.  We can construct a situation where this bias is "Irrational/dysfunctional", or is "Rational/functional".

Example: If postdecisional dissonance is the way that one "stops" the decision process, instead of endlessly revisiting a decision and wasting time and energy, then postdecisional dissonance is functional (this is the point raised by Konrad Talmont-Kaminski).  If postdecisional dissonance keeps you from switching decisions when later you are offered the alternate choice along with a small but real payment, because you deny yourself the additional payment even though the options were judged to be identical in value, postdecisional dissonance is dysfunctional.

Which is the most likely scenario?  What is the cost of a more rigorous and rational analysis?  Different answers from subtle changes to these questions...

All of these biases, because they are manifest in humans today, cannot absolutely prevent reproductive success or success in cultural transmission of ideas, obviously.  So you are on very shaky ground calling these biases non-adaptive.  And if you cannot call them non-adaptive, what is the exact basis for calling them "Irrational/dysfunctional"?

Modeling, instead of using the language of Bias and Rationality and Functionality

George Mason University,
Dept of Statistics,
Gallery of Great Statisticians,
George E. P. Box
http://statistics.gmu.edu/pages/famous.html

That is why it is not always wise to use the culturally defined notion of rationality, or assume an implied sound situationally defined notion of rationality, and why *sometimes* there is benefit to specifically stating:

(1) the failure mode of decision that you are trying to avoid and

(2) how you are modeling the
(2A) cost of falling victim the failure mode and the
(2B) cost of remedy

(3) how you are modeling the likelihood of different scenarios taking place.

And different models will give different answers.  As George E. P. Box says "All models are false but some models are useful."

[Edit 7/29/2010]

Very helpful reply [ http://deisidaimon.wordpress.com/2010/07/25/is-postdecisional-dissonance-functional/#comment-1675] from academic Konrad Talmont-Kaminski, but my profound ignorance prevents me from getting much from it.  I am self-taught exclusively from an engineer's perspective of decision making from Decision Analysis texts [ term coined in 1964 by Ronald A. Howard ].

I fixed the post above, to add

1) specific examples as to how postdecisional dissonance can be functional or dysfunctional,

2) why one is unjustified to call manifest biases non-adaptive, and

3) the need to model the likelihood of different situations arising, or else the the analysis is nonsensical.

[Edit #2 7/29/2010]


Konrad Talmont-Kaminski recommends the writings of Herbert Simon, Nobel Prize winner in Economics 1978.

Statistics versus Causality - A predictable impasse

Image by Cedric Favero via Flickr

My undignified reply to Andrew Gelmans's take on Causality and Statistical Learning

http://www.stat.columbia.edu/~cook/movabletype/archives/2010/03/causality_and_s.html
follow-up here:
http://www.stat.columbia.edu/~cook/movabletype/archives/2010/03/no_comment.html

The causality people and the statistics people are talking past each other, your [Andrew Gelman's] 12 page magnum opus included.

Point 0) Sense of responsibility → decision → commitment to action/inaction → action/inaction ⇒ implies you possess a general description of reality, unless you are limiting yourself to a very narrow sphere of responsibility.

Point 1) Statistics cannot be the basis for a general description of reality because of Simpson's Paradox.  When it arises, the paradox can only be eliminated by an appeal to plausible causality, directly or indirectly.  Also, no statistical test exist, for a static situation, to make a prediction of what relationships would prevail if conditions change -- again, only an appeal to causality can do such.  (See Judea Pearl's book Causality, chapter 6)

Point 2) Causality cannot be the basis for a general description of reality because reality violates the assertion of independent variables needed for effective causal analysis ("no true zeroes" as you put it).  Reality doesn't even adhere to the laws of conditional probability [ http://www.stat.columbia.edu/~cook/movabletype/archives/2009/09/the_laws_of_con.html ] much less the structure of independence needed for causal analysis.

Image via Wikipedia

Point 3) There are no other contenders for general descriptions of reality besides statistics or causality.

Conclusion) SOL

So people, under the burden of responsibility, must maintain several models of reality, over smaller and larger domains of applicability, some statistical, some causal, some based on symmetry & curve fitting, some based on the laws of probability, some based on scientific laws, some based on economic laws, some based on rules of thumb, some based on multiple simulation runs, some hybrids.  These models compete against each other, at the cost of maintenance, data collection, computation, and comparison, with the benefit of correct probabilistic predictions of consequences of action/inaction, or the benefit of demonstrations of broad range of uncertainty that swamps discernment of effects between decisions.

And the sense of responsibility is made of shifting sands, and human values and goals are not static.  So you could pay all the costs for a model, just to dispense with it.

Image via Wikipedia

But all this *still* can be done for individuals or small groups.  Once you get past 30 members, what is rewarded are techniques for rubber stamping decisions already taken by the politically powerful, under the name of "objective analysis" for political cover.

So "small" decisions can be made quite well, with effort.  And "large" decisions are made quite poorly, because evidence of a cold calculated analysis would be blood on the hands of the politically powerful (besides, the ability to perform such analysis is in opposition to dumb loyalty, which is the most prized character trait of the privileged in-group).  But these "large" lousy decisions possess notoriety, and thus human appeal.  So a thousand pages each over describing a thousand theories chase after a relative small number of very poor decision making processes.

The consequences of all this may dim my sparkling optimism, so I must leave that as an exercise for others.

Math - could slack off, and still get good grades

To be honest, what I liked best about math was that I could slack off and still be at or near the top of the class. The harder the math got, the better I did, relative to the rest of the class.

Image via Wikipedia

Ultimately, I got a pretty decent undergrad math education from UCLA, took some graduate level algebra as an undergrad. I never had to rise above a loping slacker's pace.

Lacked and would have appreciated: category theory (zip zero zilch taught), better differential equations (I got a decent grade with no need to understand the subject, which is lame), continuous distributions and how they relate to the Fourier transform (if this was taught to me, I don't remember it).

It is shocking how much I need to learn now was invented/developed after I graduated from college. Before 1993 - No distributed revision control, no distributed operating systems, no decent theory of just-in-time compiling, no decent published real-world examples of Bayesian probability, Judea Pearl's theory of causality wasn't invented yet, no decent cryptographic hashes, no Bloom filters, no decent introductory texts on decision analysis. My wife says that is why she doesn't want our daughter to go into computers - you have to keep learning and forget the crap that just doesn't matter anymore. Whatever. I think it is inescapable that you have to keep learning just to keep somebody from eating your lunch.

Image by hyperboreal via Flickr

Frankly, I am glad I didn't do well enough, overall, to attempt a post-graduate degree in math or computers, back in 1993. A lot of what passes in academia today is pretty weak sauce, in applied math and computer science. God bless the IntarWebs. I can just grab the info I need and go. One problem, the tempo has increased.