Hard Practical Questions? Handle Them or Dodge Them! Here's How. *

edited 4/24/21

If you can't solve a problem, then there is a easier problem that you can solve: find it.¨ -- George Polya, How to Solve It (cited in Kahnemann, p. 98)[1]

Don't go 'roun' flippin' cowpies! -- American Common Sense(?)

An often useful, cost-saving heuristic. Does watching violence on TV make kids more violent? The answers to this question, it appears, could have important practical consequences. But the question itself is problematic: it is highly ambiguous. And it risks generating time-consuming argument and more.

It used to be all-too-common, although somewhat bizarre, to observe even "professionally trained" people spending a lot of time and energy vehemently debating this question without taking the trouble to first determine whether they all understood the terms to mean the same (or similar) things.

Getting practical answers to vague questions can often be addressed by operationalization, that is, the specification of vague terms, e.g., "watching," "violence," by providing replicable, observable procedures for their determination.[2] The down side is that it might require expensive investigation to answer. All we might turn up, in any case, is a lot of dissensus, an impractical impasse!

Our original question about the effects of TV on children's propensities to violence generates even more problematic sub-questions:

1. What do you mean by "watching TV"? Need a child be paying close attention to it, or would just having it on in the background count? How do we determine how much TV a child is watching?

2. What counts as violence? Football? Mighty Mouse? A dramatization of an assault? Documentary footage from a war?"

3. How are we to determine if kids have become more or less violent? From their play-acting? From their actual fighting? From their arguments or threats?

In most situations, people would find this detailing process tedious. It distracts from the entertainment purposes of many a "debate," normally a sort-of quasi-intellectual arm wresting, or a contest in one-upmanship. Easy questions are usually thought to be "more interesting" than hard ones if only because they can be appreciated by a wider, "technically-challenged" audience (i.e. anybody having to weigh in on something beyond the reach of their experience).

So it is that even though the rituals of open discussion seem to satisfy our commitments to "democratic" participation, they muddy the flow of decision-making. Operationalization with its consequent query-reformulation process takes much more patience, with no guarantees of high-consensus outcomes to enable a push to the point of practical application.[2a]

Daniel Kahneman calls the reformulation of a query the Question-Substitution Heuristic. Developing heuristics often, if not always, involves operationalizing vague, general terms into something more specific and apprehendable without intricate processes. In his book, Thinking, Fast and Slow, Kahneman gives a chart of comparisons between what he calls Target Questions (hard) and Heuristic Questions (easy).[3]

Target Question	Heuristic Question
How much would you contribute to save an endangered species?	How much emotion do I feel when I think of dying dolphins?
How popular will the president be six months from now?	How popular is the president right now?
This woman is running for the primary. How far will she go in politics?	Does this woman look like a political winner?

Chart 1: Abridged from Kahneman (2011) pp. 98-99

Note that working out answers to the target questions, especially when money is involved, is often a laborious undertaking. By contrast, heuristic questions can be pretty much answered off the top of the head: they reduce the information-base for answering to what the questioning persons can easily access in memory. How likely this procedure is to provide good solutions to real problems one might judge by the example of participants of TV talk shows and no small number of our political and social pundits. (The accuracy of an expert, much less a pundit, in futurology tends to randomness. [3a])

But professional scientists, even, have been criticized for providing answers to questions they seem to have reformulated as easy.[4] Congressional response in 1958 to the question, "Why did the United States fail to beat the Russians into space?" was reformulated in the easier "who's-to-blame" mode, and ultimately responded to with the passage of the National Defense Education Act.[5]

The Theory-To-Practice Gap. An example. Public-supported institutions in our democratic society are often under severe pressure to widen participation, little matter whether those to be involved are even minimally informed or not. Consequently, questions that the most practiced and learned professionals might disagree on are usually decided by the least practiced and least knowledgeable of people.

What is mathematics, in its essence? Professional mathematicians can and do disagree. Likewise professional historians, on history. And professional political scientists, on political science. But, for example, educational institutions or systems, have (often elected) local governing boards, who must act to distribute (often, partially) tax-funded budgets. These governors, often with minimal knowledge, if any, about subject matter content, resource-needs and age-appropriate teachability, tend to decide all such questions as matters of budget. Consequently, matters of mathematics and history course contents become fodder for political brewhahas!

The operative heuristic for cost-chary governing boards dealing with broad, general questions is an interrogatory[6] that looks somewhat like this:

a. Are there any foreseeable, imminent and severe repercussions to our ignoring this question? If not, "table" it.

b. Is there any demand for any of these subject matters from Influential Constituencies (ICs) ? If not, "table" it.

c. What costs would different programs, say, of mathematics education, entail, were we to decide to implement one of them in the schools? Get those estimates. (Don’t rush. "Table" it.)

d. Will our ICs likely support us in our decisions? If not, "table" it.

Lets mimic Kahneman’s chart 1. The target question group, in the left column, will contain the kinds of questions encountered, for example in educational governors' meetings (or, even, in casual public discussions). In the right column well put the heuristic questions, the kinds of questions likely to be substituted by governing boards' committees.

Target Question	Heuristic Question
What are the aims of our institution's educational programs?	How can we use our institution to satisfy the demands of different community and political constituent groups?
Should beginning (pre-college or undergraduate) students be required to take introductory classes of a general nature?	Can we make room in the budget for such new courses without threatening the sacred cows of influential people?
Should all K-12 students be prepared to go to college?	Who can we overlook without raising a din that threatens our tenure as governors?

Chart 2

From Chart 2 and the preceding interrogatory we can see why, despite much lip-service to the contrary, academic programs will likely continue to be kept in distant second place by educational decision makers in comparison to their personal or political concerns. With a bit of adjustment the processes shown in Chart 2 can be adjusted to many different kinds of institutions and the problems they are faced with.

Why Is It Wise to Consider Using the Substitution Heuristic? The heuristic can be useful for transforming idealistic, but vague claims into something that can be tested for implementation, but ... nonetheless, it's use is somewhat of a distraction from the original hard problem. In fact it may lead quite far from the original problem if cleverly transformed by sloganeering supported by enthusiastic promotion. For example, instead of dealing with the difficulties of admitting underprepared students to higher education, we focus on "expanding" opportunities by reducing entrance requirements. This may address the issue of underfilled classrooms, but exacerbates the problem of second- or third-year dropouts.

The drop-out problems might, in turn, be addressed by giving credit for "experience" or watered-down course work so as to satisfy public perceptions of equity in admissions, instead of funding pre-admission courses in remedial training for academic demands.

A decision-maker ought to learn and retain the Substitution-Heuristic for unforeseen necessities. Why is that? It's because honesty is not always the best policy if one wants to keep one's position or reputation in one's institution. Inverse correlations between celebrated "high expections" demanded and resources available are not uncommon. Just as a stupid question is said to merit a like answer, so does a complex and difficult problem, masqueraded as a simple, "common-sense" inquiry merit calling up an intricate process of analysis, starting with the temporizing of the Substitution Heuristic. Why? Ultimately, for the sake of one's rationality and personal mental-emotional health.


Cordially,
----EGR
12/5/17

* The above blog developed from my essay, "A Pathological Heuristic: dodging hard practical questions/ published in New Educational Foundations Volume 2, Spring 2013

ENDNOTES

[1] Kahneman, Daniel Thinking, Fast and Slow (2011) New York: Ferrar, Strauss and Giroux.

[2] Rozycki, EG available at Operationalization

[2a] Clabaugh, GK & Rozycki, EG available at "Getting It Together." The nature of consensus.

[3] Depending on circumstances, heuristics often work and are much more economical than alternative processes. See Gigerenzer, G & Todd, P Simple Heuristics That Make Us Smart (1999) NewYork: Oxford U Press

[3a] Tetlock, P (2005) Expert Political Judgment: How Good Is It? How Can We Know? Princeton University Press ISBN-10: 0-691-12871-5. pp/ 161-162 and throughout.

[4] See Moyer, M "Person of the Year Nomination for Higgs Boson Riddled with Errors" Scientific American Blog 11-29-12 at http://blogs.scientificamerican.com/observations/2012/11/29/person-of-the-year-nomination-for-higgs-boson-riddled-with-errors/?WT_mc_id=SA_CAT_BS_20121130

[5] See Rozycki, EG (2008) at Illogic and Dissimulation in School Reform

[6] See Rozycki,EG (12/5/17) Available at Developing Interrogatories to Aid Analysis

Artificial Intelligence Weirdness. Need categorizing relate to visual cues?

... humanity shouldn’t assume our machines think as we do. Neural nets sometimes think differently. And we don’t really know how or why. -- David Berreby[1]

New Memories Never Formed. I have a neighbor in my retirement community[2] who had a stroke three years ago that seems to have impaired her ability to retain recent memories. I have conversed with her on several occasions. On each new occasion we meet she asks me, “Who are you? Have you been here long?”

When I bring an old (pre-stroke) friend of hers with me to visit her, see recognizes the friend immediately and then asks him or her, “Who is this person you brought with you?” And so it continues, although I do not dress or look different so far as any others in my community are concerned.

Read Some Plato! So what’s new? No recall, no recognition. No recognition, no identification! Even Plato knew that! And wrote about it, too. Read up!

Not so fast! That’s a quick response, but an inaccurate one. It’s only part of the story, because recognition needn’t depend on recall. An example: however hard you looked, you can’t recognize (in 2015) the 46th President of the United States. Why? Because he or she hadn’t been elected and sworn in , even though he or she, as a person, existed then. There was not even a likely candidate for the position down the road to guess about.

But once someone is sworn in on January 20th, 2025 as POTUS, you will, little doubt, -- barring a brain stroke -- recognize him or her as such, whether or not you have seen him or her in person. [3]. Such recognition requires neither memory (the first time) nor visible presence -- eg. a TV picture will do.

Weirdly Inhuman Artificial Intelligence. David Berreby in his article "Artificial Intelligence is Already Weirdly Inhuman gives some surprising examples of how neural-net computers consistently "miscategorize" visual input data. Two almost identical (to humans) pictures of a dog are classified as a dog and a giraffe. Two relatively random patterns of speckles, discernably quite different to humans, are categorized as a starfish and a cheetah. Berreby comments that no one has yet an explanation to offer for these results.

It is important to understand that not even the neural network computers jump directly from light-input-patterns to category-outputs. The “visual” inputs are processed through various algorithms which, depending upon certain conditions, yield categorical outputs. As the examples of the dog, giraffe, starfish and cheetah show, there appears to be something going on in either the inputs, or the algorithmic processing that does not “replicate (?)” or “parallel(?)” the human processes. (We can’t even assume there are some well-defined isometrical relations[3B] between the human and the computer processes: thus the bracketed question-marks. -- my comment, not Berreby’s.)

Let's check some possible ways of dealing with this “weirdness.” Are the computer visual-inputs sensitive to light frequencies that human eyes are not? Are there internal interference phenomena that are different in humans than in computers? Clearly there are algorithms (actually, heuristics) that humans learn to use for identification that might not yet be available to computers, especially those that depend upon conditions of social interaction or on multisensory input coordination. (This is difficult stuff. I really don’t begrudge AI researchers the personification of their apparatus, i.e. as thinking, seeing, etc., if it keeps up their enthusiasm.)

Some Human Identification Algorithms. For many, many English categories of objects that are visually discernibly different, we have hosts of subcategories. These subcategories enable us to practically recognize two objects as “the same,” i. e. “time-pieces, ” which have quite different appearance and functioning parts, e.g. a sundial, an hour-glass, and IPad. Meta-categories such as “purpose” or “typical use” and the like help us to sort-out discernably different time pieces into practical categories.

It’s important to note that “recognition” is not a unique process. We use the term to indicate both

a. our experience of visual -- more generally, sensory -- familiarity based on previous experience, i.e. recall; and,

b. an act of participation in a social practice of equal treatment, i.e. acknowledgement: this thing shall be treated, under certain circumstances, as a “time piece.”
(See Two Senses of 'Recognize'.)

It is important to understand that categorization for adult humans, except for the most basic uses, e.g. learning to identify paradigmatic examples, is not based substantially on recall, but on processes that establish recognition-equivalence.[4].

This requires one or more meta-sets of algorithms (or heuristics) that are called into play to work on items pre-processed by lower level algorithms.

The following categories, which could be used as algorithm names, are meta-set indicators: aging, larval, decrepitating, disguised, worn-out, broken, in-process, unintentional, illegal, shrunken, decayed, inebriated, etc. Paradigm objects subjected to the algorithmic meta-set processes, may be identified as recognition-equivalent to the paradigm members despite substantial deviation in appearance. [5].

Addendum 8/15/16: for an interesting new article see AI's Language Problem MIT Technology Review 8/9/16

Cordially
--- EGR


-----------------------------------------------
FOOTNOTES

[1] Berreby, D Nautilus 8/8/2015 Artificial Intelligence is Already Weirdly Inhuman

Many AI enthusiasts use the verb, “think,” somewhat over-enthusiastically -- perhaps for promotional purposes. But is this only a stretched metaphor? Why is it important to stretch it? Do computers “think” in any way recognizably as do humans, or animals? For some discussion on this issue see "Thinking" Like Computers Do

[2] See Foulkeways at Gwynedd

[3] see Part 3: Recognition and Knowing

[3B] see Isomorphism: Program, Structure, and Process -- a catalog

[4] See Recognition-Equivalence

[5] See Dimensions of Individuation. Such dimensions can be used to identify the algorithms applicable to the superset of individuals under consideration. So, for example, when considering a professional football team, we might ask How can we tell the half-backs from the full-backs? Or, How can we identify individual contract-holders?