This century seems to be characterized by
a dominant quantitative approach.
Most part of the observations,
discussions, surveys, debates, must be integrated by data and quantitative assessment.
If not, they look as if inconsistent not being supported by quantitative data
and for that reason “objective”. Subjectivities, characterizing the qualitative evaluations, are considered insignificant
and seem to have the right to citizenship, or almost exclusively, for "artistic"
considerations (but also in this case, as we shall see, sometimes quantitative logic
comes out) and, therefore, they appear questionable (and, so far, it is
legitimate) and, above all, lacking of substance and value.
If not reported
within its proper limits, the "escalation" of this quantitative approach is misleading
and can lead thinking and acting especially
for the younger generation.
The purpose of this brief
note, is not to underestimate the power of the quantitative approach, still essential
and often allowing the evolution of knowledge and stimulating in-depth research
and analysis, inventing, designing and employing equipment, instruments and
innovative applications. The aim is to propose a critical discussion on the
limits of the quantitative approach, showing that in some cases, not related to
engineering and/or scientific aspects and issues (on a common sense basis) are focused
on qualitative and subjective assessments and, therefore, the objectivity characteristic is completely
wrong.
Measurement, which is the basis of any quantitative approach, has always
represented a key factor in human life, especially for relationships with
others. Qualitative measures (i.e. strength, beauty, intelligence, dexterity, etc.)
which ensured a particular role in the community, have been replaced by
quantitative measures, linked either to very practical aspects (such as the
size of a plot of land and a building) or a speculation cognitive factors (consider
e.g. the astronomical sight for ancient civilizations), or, in many cases, both
possibilities (i.e. time measurement).
One of the most positive
aspects of the quantitative approach is that it allows a comparison for time
and space that qualitative approach doesn’t, because it is not only closely related
to personal assessments and therefore subjective, but also related to the
specific situation (hic et nunc - here
and now).
The use of tools and
algorithms is another key important factor that is not allowed by a qualitative
approach, conveyed according to ordinal numbers and not to cardinal ones (first-second,
better-worse and not 1,2,3 ...). On the other hand, within a qualitative
approach you cannot develop operations, only with logical rules and for
discussion and mutual relations, bound by strictly defined rules (it is obvious
that if A is greater than B and also C is greater than B, nothing can be said
of the relationship between A and C).
I do not know whether
it is natural or not, but it is easy to note that, almost always, by observing
children’s behavior, there is an inclination to compare, evaluate, “measure”.
Expressions lie more
beautiful, stronger, smarter, as well as more productive, easier to grow, etc.:
in everyday life, in the economy, in sports competitions, in wars, in literary
contests, and so on, evaluation is always related to measurement. Quite often
this kind of evaluation is leading to discover some elementary laws (as I recall,
Piaget said that the effect of the formula “speed = space / time” is sensed
differently by children. Space and speed
are perceptible faster and easier than space
and time) and this is linked to “hic et nunc” (here
and now). In different
conditions of space and time, the comparison is very difficult, if not
impossible. Unless measuring methods allow the comparison at different times
and in different environments. This is, in my opinion, the greatest strength
and importance of "quantitative logic" and also its main benefit. In many
fields of activity, in research, in knowledge improvement, in quality of life change, in medicine, in human relations, in art (the tekne
- Greeks) progress was, still is and will be, impossible without the adoption
of quantitative logic.
Together with quantitative
approach always coexisted the qualitative one, where subjectivity, context’s
situation, interrelation between contexts (there are no closed systems) and
discretion of the trial are the basics.
Consider the example of the proportions
of the Venus of Milo, that represented a landmark for a long time, but in any
case limited to a particular civilization, and within a determined time frame.
In my opinion the difference
of the two approaches is given by the method
that is adopted for the evaluations and comparison. The quantitative method
follows strict rules, though, changed and improved over time, and allows a
comparison data based on the same method. For the qualitative assessments, the
method, even when it is made explicit (e.g. school or vote given by the judges during
some competitions, such as diving, gymnastics, figure skating, boxing – unless there
is a knock-out - the foul in football and other sports, without mentioning more
relevant contexts such as judgments in court), is still highly subjective (the
teacher, the juror, the judge, etc.) and, therefore sometimes it is reasonably contested.
Considering different time lapses,
benchmarking become meaningless. It was mentioned before the feminine beauty,
but what about the "quality of life" related to different factors
like poverty index, education and so on? We could reduce or even eliminate all
subjectivity and "inaccuracy" if we could move from quality to
quantity valuation-measure. That's exactly the aim of this discussion. In
common sense the number sets the objective.
And yet, is this
passage always legitimate? Doesn’t it hide perhaps
subjectivity broader than qualitative evaluation, broader because hidden and,
as such, leading to mistaken and inappropriate decisions?
The scope of this note is to
highlight and discuss the possibility of identifying a meta-method (what Edgar Morin called the "knowledge of
knowledge") allowing the identification of some rules for a proper
transition from qualitative to quantitative and confirm or disconfirm these
steps.
The reason for this debate
is due to the fact that the quantitative method is becoming more and more pervasive.
In fact as soon as you write a number it becomes true regardless of: It sometimes
generates decisions with substantial consequences, never emphasizing their
subjectivity and without considering and amending the initial error committed.
Big data, excellent for many
applications, are unquestioningly increasing significantly positive rating of
this approach since its early age. The spread of this approach through mass
communication tools make the quantitative culture increasingly pervasive. As
many artists, poets and writers have anticipated scientific discoveries in
their mind (i.e. Jules Verne) bringing also to practical experiments (one name above
all, Leonardo Da Vinci), on the issue we are trying to highlight now, perhaps
Orwell and Huxley were right, for example.
This might be an
overstatement, but sometimes
drama is useful.
To provide a starting point
for discussion/to start the discussion, we have to stimulate some
considerations on the quantitative method, analyzing two aspects: the metric
and the modeling first and then moving on to some examples where the question
on the legality and opportunity of the transition from qualitative to
quantitative seems still appropriate.
First of all we must clarify
the metric concept.
The metric, though little known, is the
most critical aspect of all quantitative assessments, that bases and qualifies
the evaluation as objective.
The conditions of this presumed objectivity
are based on the following principles:
-
First
of all we must be aware that any measure is conventional, and must be commonly accepted and shared by all those
who, in different roles and in different grades, participate to the same
measurement;
-
the unit of measure and its measurement
system should be defined and outlined (such as the metric system, well known,
but not universally adopted). For different systems you need to decode and
transform them from one system to another (e.g. from kilogram to pound, from
meter to yard). The definition and control of the correct unit of measure is
one of the most critical and most difficult aspects: for example time
measurement must constantly be controlled by sophisticated metrology institutes
(i.e. INRIM Institute in Italy, that has absorbed the former Galileo Ferraris
Institute, known to all elder people, since was giving the official "time
signal"). Whereas in case of money, its measurement unit has been
considered a problem/opportunity where all financial and economic policies of
different governments has been based (and still is). The devaluations (or revaluations)
are a clear example of conversion’s change from one currency to another: exchange
rate between the different monetary units. When coins were in use, the presence
of different amounts of precious metal brought to appreciation or depreciation
of the currency itself. Also in the primitive forms of barter the same problems
took place, although in a different portion. The examples are endless;
-
The equipment is the flip side of the measure’s
unit. Without established, certified and shared equipment (the standard meter,
the past "certified" scale, stopwatch, speedometer, etc.),
measurement and its comparison with the benchmark remain without any objective legitimacy;
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Another
element to consider is the procedure of
the measurement. In many measures in the physics filed it is well known
that, for example, the presence of the phishing detection system modifies the reality. Therefore the measure depends on the method you take
the measure itself. For instance, recently people opinions in surveys started
to become indicators and objective measures, even though affected by the way a question
is formulated. But, even without going to extreme cases like the one mentioned
before, the measurement procedure must be controlled and, above all, clarified
and communicated in an effective
and sheer manner. In the
scientific-technical field measure is considered unreliable without these elucidations.
-
Once
you take the measure, the issue is the interpretation
of the data. Each data reading is affected by what is technically defined noise.
The step from instrumentations and analogic measures to digitization one is
closely related to noise reduction, associated with the signal reading. Again
it is necessary to clarify the terms of validity of the data itself because
nothing is free from noise. Furthermore we have to add the semantic noise,
linked to disorders of the noise and subjective interpretation. For example,
the same observation can be interpreted as a call affectionate or as an insult
that needs to be revenged. Any verbal message is conditioned by the tone of
voice, the punctuation, and so on. But also more
objective data (for example the relationship between the EU and Greece) depend
on the way they are communicated. Not to mention the data communication
face-to-face in which non-verbal communication conveys information as important
as the data itself. But also the data communication in table form or in
graphical form can convey information received in a radically different
way. The growth or decline of a market, GDP, employment and so on, represented
in a graphical form can lead to different interpretations. Therefore data interpretation
is closely linked to the terms of communication and presentation of the data
itself.
-
Finally,
it is necessary to specify and define the eligible term for the data manipulation
or, rather, which operations (arithmetic, for example), or comparisons, or associations,
are allowed.
These are the fundamental elements to deal with measures in the proper
sense and to use quantitative methods.
But how much of all above mentioned is clear
and respected when using data and quantitative methods to situations and
assessments that come out from situations not closely technically defined?
This not to mention pseudoscientific valuations
that are failing to comply the characteristics of science. Starting from
trivial cases and easily criticized as "post hoc ergo propter hoc"
considerations linked to ethical, ideological, political to explain, or even
worse, to provide some solutions and propose specific measures. Not to mention
areas called "scientific", also of major importance, but which have
nothing to do with science, even without citing the falsifiability principle which
is one of the key factors in any scientific approach. The most common examples,
in which you can open an strong argument, concerns many statements of economic
"rules". How many times have you considered the expected results with
the ones actually obtained and then evaluated the differences?
This is the main critical aspect this note would
like to highlight.
All of the above (and we have examined only the
most relevant features) is essential in order to speak of a proper quantitative
approach. In all other cases, it must be properly said that using numerical formulations
only because the writing is a quantitative way of extreme synthetic potential to
transfer a message. A formula can be written in a few lines, while his
explanation sometimes requires pages. If this is the main motivation, all
right, we have to understand each other, but when they are expected to give a
character of objectivity to any statement, just because you use a numerical formulation,
pseudo mathematics, then it makes a big mistake and mislead people. Apart from
the fact that, even in the most correct cases, some doubt (or not objectivity) still
remains.
Our hypothesis is that this serious error
occurs every time that people are involved, with their inevitable degree of subjectivity,
where you have to deal with the fundamental values of people, with the
objectives, more or less explicit, which is the basis of the data itself, with
prejudices and history of the individual or the entire communities, with more
or less accidental camouflage, of data and specific situations.
Hence the distortion that a predominantly
quantitative approach can lead not only in the daily life, but also in the most
important political decisions of public governments at various levels, in
strategies and business decisions in educational curricula.
With this regard we need to introduce another
argument related with what we mentioned before, that allows to better
illustrate the merits and defects of the quantitative approach.
First of all we must say something about modeling. Usually we have to build a
reality model to understand better a situation and, in particular, to make a
decision. It’s often unconsciously done, especially for simple situations and
for insignificant or repetitive decisions, but when we have a complex system,
then the construction of the (or a) model of reality becomes essential.
Now we look at the key features of a model,
illustrating some aspects that will provide additional elements to the purposes
of these notes.
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To
build a model must be clearly defined the purpose
you want to achieve. This feature is shared by the quantitative approach. You
can use geometry because you need to define the extension of a field, as well
as the number’s evaluation of the resident population, for example, because you
want to charge a tax; statistic was created to satisfy the knowledge of the
population and so on. But, of course, considering the technological or
scientific aspects, the preliminary identification of the aims is intrinsic to
the problem itself. The key, however, concerns the necessity or not that the
goal and the result should be red in quantitative approach. It is not said that
all models require a sorts of quantification.
-
The
model is always a selective adaptation of reality. This is perhaps the key to
all the modeling approach, because, beyond the fact that the selection criteria
are determined by the objective (and present obligations), the decision to
discriminate "relevant" aspects to those irrelevant is highly
subjective unless there are not conventional theories that provide a guideline.
This allows us to point out that the model is not reality: making an example of
food, looking at the menu (model) we choose the dish to eat based on a
hypothesis of transfer what is written on the menu to the plate that we are
served.
-
The
rules that connect together the many
aspects are an another fundamental aspect. The knowledge (or assumptions) of
the rules allows us to wonder what will probably happen if the action is taken
on a factor rather than another and the related effects .
-
We
can build the model to improve the knowledge, but also to make decisions. The
model’s formalization is so important as
long as the decisions are relevant. Therefore it is important to identify alternative lines of action. The
formulation of alternatives is closely related to individuals who are appointed
to build them. Then the experience, the knowledge, the imagination, comes out
as highly subjective factors. The options to solve the refugees’ problems is a
clear example. Thus if the alternatives are few and "poor" it is
useless to hope that the solution can be effective and efficient.
-
The
mode of the achievement of the best
considered alternative (and also in this case the evaluation of what can be
referred as best is left not always to a quantitative measure) or, when the
model was built for in-dept analysis, the way to disclose (or keep private) the
discovery is the same.
-
Finally
it must be highlighted and built the system
control. In other words the way to
verify if the model "works", if the alternative choice (in the case
of decision models) has produced the desired results or if the discovery made
is strong enough and thus can be considered not only an hypothesis. In both
cases you have to architect the verification and validation’s system and the
rules that allow you to rectify any flaw. The defects can be related to any one
of the above mentioned features and, therefore, the modifications must be able
to assist all major aspects, even by the choice of targets, as have proven to
be unrealistic (i.e. not reached with the present context situations) .
These two premises may seem extremely long, but
without these you are not able to figure out where a quantitative logic is
useful and essential or instead where it proves poorly suited or even
distorting.
Afterwards we will bring significant cases that
we hope lead to a debate in which there is no a priori nor bias.
A few cases will be examine where the
transition from qualitative to quantitative should generate at least some
doubt, and there are also other instance where the same manipulation of
quantitative data, correct from a formal point of view, can lead to
misjudgments.
Very often it can be observed, both in the
press and on documents or analysis made by qualified people, the emphasis of
percentages, sometimes detailed by graphics to make it better understand the
importance.
The percentage is surely an
"objective" data, allowing that the starting point is correct, but
can be misleading if not completely understood. For example by measuring the
variation of something (production, gross domestic product, margin, debt, etc.)
of a period (month, quarter, year, etc.) without at the same time show the
absolute of that information is not completely correct. It is likely to show a
phenomenon incorrectly, especially for those who are technically naive. You
cannot compare percentages among them. Let’s consider only two situations,
among the many possible. The increase of a percentage value compared to a
previous period says only that the trend line is positive, but says nothing
about the fact that the phenomenon which is referred became positive. If, for
example, the previous quarter had a reduction, related to the previous quarter,
of higher value in absolute terms of the percentage increase of the last period
referred to, the absolute value is still lower than that of two previous
periods and, therefore, the recovery is far from being achieved. And this is
absolutely obvious, but how often this aspect is emphasize? Therefore is more
important the percentage of comparison between different periods or the
absolute value? Surely this is a significant comparison and, as mentioned
before, it shows undeniably an important trend, a phenomenon to be aware of,
but at the same time doesn’t bring out the absolute value and this can be
misleading. Since often these data become references to the enormous part of
population, to achieve the favor (or not), you could say at worst, that it is a
misleading advertising.
Other times data are correct and comes out in
absolute, but it covers a number of subjects which diverges from case to case.
Remember when we talked about GDP growth of China that exceeded all other
nations, without specifying (which took place immediately after), that this
information, even if true, was not showing the actual state of the economy,
because non relate it to the existing population.
It has been shown that adopt a quantitative
method allows to compare different situations in time, in space and also from
the content’s point of view. For instance the specific school evaluation. We
judge students according to different numbers (from 1 to 10, 1 to 30 etc.).
This is a magistral simplification and very helpful.
It allows to compare outcomes in completely
different disciplines, at different times, in different schools, as it allows
the use of elementary operations such as sums, averages, even subtractions and
divisions. But the question remains on the deeper meaning of such system/but
which is the deeper meaning of this system? Obviously it is hard to think in a
different ways, but this depend on the variety skills of the evaluators such as
time, as places. However, for example, for different territories the final
evaluations of a certain cycle of studies have a high value, while, using other
parameters, it is evident that the data, probably absolutely correct in the
comparison of students belonging to the same school or at the same territory,
are hardly comparable when the universe is extended? This can work only if the
evaluators (or assessment system) is absolutely uniform and controllable.
Otherwise, the quantitative data is not true. When those who have to use the
school "product" is not subject to public entities procedures, in
addition to the final grade, like private institution, they evaluate from where
you are coming, without considering that, for positions with strong commitment
and responsibility, the final assessment depends on individual interviews and
other evidence. In fact in many countries you can even find the belonging
school name on the business card. If the quantitative data were
"objective" would you need this information? Not to mention that even
the single vote is the attempt to shift from a quantitative to a qualitative,
where in the algorithm is totally subjective. There are some teachings and some
methods of examination that lend themselves better than others in this
measurement, but in other cases it can arise many doubts. And what about the
"average" of the votes in which all disciplines are considered
"equal", but they are required different skills and different
commitment to fully understand some topics? And what about the critical skills
that should be a fundamental element of the student’s growth: how do you
quantify it? Just because the comparison it is simple and the data can be
easily manipulate, then the quantitative assessment has its own reason for
being. The simplicity and not the objectivity is the real reason. That's why a
good teacher prefer the interview to the written test, to understand the way of
thinking of the candidate.
We have dwelt at length, perhaps too much, on
the criteria for school evaluation not only for the long years of draftsman’s
work in the schools, but because, since childhood, quantitative logic is
introduced as an element of safety and objectivity and then it remains as a
term of reference.
Remaining at school field, are we sure that the
valuation rules for the academic career, based on a logical and
quali-quantitative, are really effective? Now it cannot be made a motion of
ecological salvation of the plants, since the printed paper is reduced (but not
always and not for all institutions), but the amount of writings is sufficient
to overcome any human capacity for critical reading. Write less to write
better, this should be an absolute principle. But this goes against the
quantitative logic.
It is not convenient to discuss the evaluation
criteria for the universities, the subject of controversy and fierce
discussion, and even systems of evaluation of different schools from the
university, not to mention the teachers. The motivations and aims are right:
those who might oppose/face on a substantial assessment? But too often the
executive procedure betray the purposes and basic principles. By “ope aegis” we all have many years of
experience and, unfortunately, the skepticism of many, it is well worth. On the
valuation of educational institutions, precisely on the universities, we will
talk later, not much for specific application cases, but mainly discussing
scoreboards and similar systems.
Today we propose a logic which has never been
considered. One of the most effective ways to assess whether a product is
quality or not, is based on the judgment of those who use the product. A
company can say whatever he wants on the products, unless it is an obvious
fake, and advertising is very important to influence potential customers: otherwise
the whole industry would disappear with large problems of employment and GDP.
But if then, there is no touch with reality, in the end, for sure, the results
could not be positive.
Promote or encourage is correct and required,
but the verdict is to the customer, including the assessment of the promotion.
I definitely don’t want to compare a school to a company, but there are some
common aspects. The evaluation of the "product" by the final customer
seems to be one of these. One assessment factor, not the only one, is the user
of the school itself. And users are manifold: next school (or class), the job
market (excluding the public administration where is licit to doubt the ability
of evaluation), the student (better after a few years), the family.
I have always said that the Italian university
exceeds for a positive evaluation as long as their students, are accepted or
even demanded by the leading foreign universities and successful companies. The
quality and success of a school is mainly given by the success of its
graduates, PhDs and not by other criteria like self-certification and so on.
For sure criteria and methods (such as PISA), are useful in providing a
guideline, but only affect the acquired skills, which are important but not
unique. The school should also provide other tools: curiosity, critical
thinking, research and study, responsibility, risk-taking, the capacity to work
with others, respect for different opinions, the ability to analyze and
synthesis, matching "two cultures", the capability to use the
technology, without being overwhelmed. These are the main factors that should
indicate the quality of a school.
Are we sure that criteria and assessment
procedures ensure these features? Perhaps are these more qualitative than
quantitative?
With reference to the choice of indicators
these arguments lead to a very broad issue which could open a very large source
of debate.
For instance, what does it means "quality
of life"? Over time the number of studies, research, investigations have
been increased. For example some of them lead to the ranking of cities,
countries, regions, according to their "quality" of life for the
populations. But no one ever asked why, in some or many cases, there is a
transmigration to sites with higher quality? (We exclude the migrants situation
for whom quality simply means survival and think only to the citizens who have
no constraints or regulatory barriers for changing citizenship or residence).
Are we quite sure that the indicators are correct or are they important only to
enable drafting reports and articles in the newspapers? Referring then to what
was previously written about the conditions under which a metric can be
considered correct, many doubts arise about the validity of units’ choice, the
equipment used, the measurement mode, the way they were weighed the various
factors. For each of them a careful examination allows us to conclude that, in
most cases, these indices appear to be, at least, refutable. What about
periodic surveys about the livability in a city over another? Are we confident
that people of a city considered "less livable" prefer to move
independently of the available job, services, and other factors all
"quality"?
Another example that we want analyze is the
wealth (or poverty) rank of a specified region. It's very difficult if not
almost impossible to define wealth and poverty of a population, territory,
community except in extreme cases where poverty is so obvious as is not to
require special surveys; you could probably say that poverty and wealth should
be related to the ability to satisfied the needs, starting from basic ones. In
that regard, you must consider the aspects linked to the two basic premises of
any quantitative logic: the metric and modeling.
In this case, which are the relevant variables
related to the model? Are they true for every situation or, on the contrary,
are they related to the specific context, since the needs are those perceived
and these depend on the particular situation in which you find yourself?
Poverty and wealth are then measured only in terms of money? And the unit of
measure is not affected by the conditions of life, for example living in the
cities or in the countryside, where you can have a vegetable garden, an
orchard, a small domestic breeding, are perhaps the same thing? Even after
work, instead of watching television, dig a vegetable garden, collect firewood,
make preserves seems to be an acceptable alternative? And, in such case, to
have a power equivalent, the required amount of money is equal? At one point,
not so remote, were abolished the so-called "wage cages" through
which they tried to establish, even approximately, the different cost of living
according to the territories considered and, therefore, you could be
established a salary equivalent to the different types of employment and
commitment. Surely there were many flaws, but were the goals so wrong?
This is an example, and perhaps not the most
significant "macro" indicator. Similar considerations can be advanced
for most of macroeconomics’ indicators. When these indicators are based on the
perception of respondents (chosen, of course, with "objective"
criteria) then the question of a lack of significance of the data at the
conclusion of the survey becomes almost certain. Without going to these extreme
cases, when you lead to the quantitative explanation of certain phenomena, most
of the time is not mentioned how you reach to these results (metric and model
adopted), except perhaps to insiders. In other words, do you have the
opportunity to criticize the basic method adopted? It would be a matter of
seriousness, not to mention about science. But then it says clearly that the
data is far from objective. This should make us reflect on the limits of
validity and the usability of the data itself. For example, the proportion of
graduates in the population says very little: an improvement occurs when you
specify the type of degree earned (and where), but in any case, even when the
data are more significant, is not enough to indicate the real capacity economic
development, technological, entrepreneurial of a particular territory or
country.
The GDP and important indicators such PRODUCTS
must be questioned. But when you rely on such indicators to define the
economic, industrial, investment, then caution should be even greater and the
doubts should be deepened. This leads to think about issues that are subject to
economic and political constraints of our days. I think it is legitimate to ask
how precise quantitative constraints such as the 3%, the maximum debt of 60%
and others, have been defined. Which models are based? What are the
"laws" that, respecting these parameters, a country could be promoted
by the international community? The forecast at the macroeconomic level have
not always given good results. Recall, for the sake of completeness, that
forecasts often are based on the story and that, in this case, the hypothesis
is that there is no change of laws. But that the laws are invariant, regardless
of dynamic, unpredictable, context, it seems to be overly apodictic statement.
In any forecast, then, even the most reasonable, given the available knowledge,
is given a fork of reliability. Instead you have established a rigid and
precise value of the parameters to be followed, it means that, according to the
forecasts of those who called themselves the parameters, you should conclude
that only by respecting these constraints, you can have a development and if
even you leave, there will be a catastrophe, sanctioned, for another, the
political community in order to accentuate the negative. The naïve question
could be: "why 3% instead of 3.3, for example?”
Other two aspects should be highlighted. The
first is the lack of data synchronization that should be coordinated. The
recent data controversy, on the one hand to the IGC and the other to the level
of unemployment, shows a lack of harmonization for collecting data that should
be easily comparable and provide a reliable indication to figure out whether
you are moving or not on the right path. The second aspect concerns the
reliability of the data. Recently we analyzed the expenses of foreign tourists
in different Italian regions. At first glance it was possible to note an
evident discrepancy with respect to what might be expected. The level of income
of southern regions has been well below the forecast and in some cases lower
than some northern regions that have less appeal for foreign tourism. Luckily
for the Lombardia area there is the EXPO effect, but the question is: could be
that for most of the expenses the payment off the books are much more
widespread in the South than in the North? Also here it would be interesting to
bring out and understand the metric used. It can goes on and on with
macroeconomic examples, from percentage of investment in R & D to GDP, on
training at various levels on the index of corruption, the efficiency of
justice (especially civil), on territorial competitiveness, not to mention the vexed quaestio of so-called rating
agencies on which the controversy and allegations are wide-domain and on which
you do not want to discuss here. Now is the time to move on to some examples of
micro-economic.
The basic example is represented by the balance
sheet.
It’s clear that the budget is a model of
business operation and, like all models (v. above) is a simplification of
reality, which are only consider certain factors and not others. Then when you
build indicators from the financial statements and these indicators are in turn
the basis for other algorithms, this means that you are using a "template
model" and there are no limits to this succession. It then becomes clear
that there is detaching more and more from reality and that, although they
represent phenomena and significant aspects, you are not able to understand
reliable which will be the future development of the company. What risk
acceptance have vertices (and the property, if not spread)? How to behave with
respect to innovation? What is the business climate? What are the core
competencies on which you can play in the future? What is the perception that
suppliers, competitors, customers have about company? All these are essential
issues, along with the balance sheet, in order to judge a company. But how many
of these elements are evaluated by financial analysts? How many bubbles in the
stock market we have seen in the past, and the last few days? But they are
strictly implemented all regulations, national and international, to certify
the accounts? We have not had to adopt increasingly sophisticated methods of
risk analysis, with relevant quantitative parameters?
But, then, what is the meaning of subdivision
quarterly, in memory of an old peasant culture based on the seasons? In some
cases, see for example the management of complex projects, even in the annuity
division is against the structure of the project, which has its own intrinsic
timing, so you have to "jump" to adjust the actual performance of the
project to the balance logic.
-tech market, is represented by the staff. How
do you consider this aspects? Ultimately, the whole system based on budgets and
indicators of financial character, (to evaluate for example the convenience of
an investment) is based on the fact that, in most cases, the goal, as claimed
authoritative studies, is the maximization for shareholder, which means the
prevalence of a vision on the short term. But should the company be the main
aim of any management? And is the life of the company that should be preserved
and not the benefit to shareholders. Perhaps for this reason healthy and
profitable companies have decided not to go public, probably (is my guess) not
to be subject of financial analysts, whose "assessment" (?) influence
the value of the company. But what should you say about the stock market
fluctuations, in which the media give explosive titles: burned tot billion in
one day (but almost never is called "generated" tot billion in a
stock market bubble). But where was the true wealth? Only paper and bets by
investors, while it’s hardly based on the actual production of a good or
service effectively.
More valid judgments were given about the
empire paper based on nothing. Too bad that these observations are not
considered, and re-emerge only in the periodic meltdowns. Is the used model
relevant to the reality? And how can they still boast those who have attested
to the validity of financial statements (fake) corporate and even whole states
(compare the declaration of validity of Greece's budget during its EU
accession!)
These considerations lead to the mentioned
before matter: the lack of feedback: what are the achieved results in relation
with those expected? And is good the model
that is being adopted ?
I don’t want to talk about the forecast, almost
never nailed since they don’t consider (and could not) behavior of individuals
and their reaction to trends, real, imagined, or artfully emphasized for the
economic situation of a country, a bank, a company. It’s too easy to say this
forecast could happen exactly in that way only after the fact. I almost never
saw eminent "scientists" who explain the reason why the forecasts
have been dramatically refuted: It's too easy to show that it could not happen
other than what actually happened. Let's still confidence in these
"predictions"? On what are based? (v. as mentioned in the
introduction).
Let’s no longer deal with the economic word,
both macro and micro, to avoid dwell excessively.
I remember that when I was a student
(engineering, whose design is based, necessarily, on the approach and
quantitative methods) I was lucky enough to have very good teachers and a broad
culture. One of their favorite aphorisms was as follows. "One of the laws
always effective is the data torture law” which could be summed up as follows:
a data, tortured enough, tells you exactly how much you want to say." Is
it just a joke?
Another case is worth considering, because it
moves away from a lot of what we learned so far: a few years ago, when you went
to a doctor, he was, in the majority of cases, auscultated, palpated, you had
to provide a thorough history (unless it was the family doctor) and the doctor
gave his diagnosis requiring further examination or not through appropriate
analysis. Now, sometimes, the patient hardly exists: we examine analysis of all
types and are more numerous and specific is much better. The analyzes were
required to verify the correctness of a diagnosis, to check the progress, which
was obtained according to a certain therapy. Today, often, the analyzes are
required in order to make a diagnosis. But the patient is not shaped through
laboratory testing. The culture of the quantitative data means that it is the
patient that asks the doctor to prescribe analysis: in some cases if the doctor
places limitations on the analysis, the patient changes doctor because not
"scientific" enough. The number, really impressive, of medical
analysis related to patient, is a clear demonstration. But how much does it
cost this perverse behavior?
There is one final aspect which necessarily must
dwell. Having stated that the referred examples want to be only illustrations
of quantitative distortion, and this aspect is tremendously dangerous, which is
only mentioned here since this require a long and depth coverage: the
democracy.
What matters in democracy is the majority, that
is always a quantitative figures. But this approach is valid always and for
each topic or you can or should be some limits?
Let’s stop here.
At the end, you must reiterate the objective of
the beginning: the note has the only primary aim to open a discussion. The
quantitative approach is a powerful method that, in many branches of knowledge
and in different applications, has been the method par excellence. But in
numerous other cases it may be limiting, and sometimes absolutely distorting.
The humanities, ethics, the ability to synthesize. Creativity, intellectual
curiosity, the innovation, beauty, represent an obstacle to correct this
over-expansion. In many cases you are not able, often, to propose today
alternative routes, but consider the problem is already one step ahead.
Wherever man interferes, with its passions, and
its values, with his sensitivity, his intelligence, his mood also contingent,
it can hardly speak a quantitative and effective approach. The art, philosophy,
innovation, feelings, values, ways of being, are not attributable to a model,
they are not measurable. That is why it is dedicated space to clarify what it
means to measure and model, to verify that, in many cases (one could say in all
cases in which it intervenes man) as it has already been said, not only are not
useful or used, but they can also be highly harmful.
The culture is definitely moving more and more
towards a quantitative approach, designed as "objective" and
therefore better than any other way of behavior. Is it an unstoppable trend, or
can you stop? We talk about, not always in the way, of humanoid robots, but are
we thinking of robotic men? This sort was envisaged for the science fiction,
but was also showing the dangerous distortions.
This is being definitely overdramatic, but perhaps probably best dramatize.
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