Videogames, economic predictions and gurus
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xi
Feb 21, 2009, 8:46:08 AM2/21/09
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A massive multiplayer videogame is a set of thousands of people whose
unpredictible and simultaneous actions have (or might have) inmediate
consequences on any other player, doll, character or whatever we want
to call it.
Mr. Volcker, former Fed chairman, confessed himself astonished of the
speed of cause-consequences during this crisis. Most economists and
policy makers are astonished. The global economy has changed in the
last years, we have to get used to it. Also we have to get used to see
that consequences can be amplified compared to causes. The usual
extreme example of the theory of chaos is that a tiny butterfly moves
its wings in Beijing and its consequence is a storm in New York
(reverse cities if you want). In the same way, using the appropiate
techniques, negative consequences could be cushioned or even avoided
if we can determine since the butterfly started to fly that it would
have consequences in New York and we can know which consequences they
could be.
In this message I will try to describe why, once this economic crisis
has been unleashed, many economic predictions from most gurus are not
accurate and why some techniques extensively used in Asian videogames
help economic predictions and decissions more than traditional linear
techniques.
In traditional linear way of thinking, based on formula and
algorithms, accuracy of raw data determines the accuracy of results as
you can find in my first cite below "The process of measurement is
central" (1). They work extremely well during quiet periods. While one
economy grows, for example, between 2% to 5% year after year. They can
predict easily anything, even a deep crisis. Even easier if they
predict a financial crisis. Until now, decission making and prediction
processes has been conducted in a linear way of thinking. Mostly based
on something called "quantitative methods" (2), in economics it is a
sequel of monetarism developed in Chicago and in other American
universities many years ago and even today. But what if they have to
analyse trillions of data? what if empiric data are changing fast, up
and down, constantly? what if the formula itself changes because
behaviours changes, inside or abroad, with dramatic consequences
inside our field of observation? (2) Their models, their algorithms,
their formula, their linear way of thinking do not work at all and
produce wrong results and predictions.
A tiny example, economic authorities rule against recession because
they receive a report that states that GDP has declined 0.2%. If that
report states that GDP decined 2% they would rule in a different way.
And if that report shows that GDP declined 20% their rules would be
completely different. But what if those data, 0.2% in the example, are
wrong? Or what if they are correct but the consequences from external
economies make that it turns from 0.2% to 2% or 20% in one month?
On the other hand, some decission-makers ask to have those new
concepts available right now for their actual decissions. For example,
one of the decissons of the G20 meeting in November or December was to
create an "early warning system" for the IMF. It means a system
designed according to something that we discussed here, a set of
indicators based not on "ceteris paribus" premises (3), but on "omnia
mobilis" (4) or, at least, on "beyond ceteris paribus" (5).
http://groups.google.com/group/world-thread/browse_thread/thread/a8189c944b38128a/aa50de230f3aed1d?hl=en&lnk=gst&q=omnia+mobilis#aa50de230f3aed1d
Finally, once we have results of our report, nowadays we cannot use
linear way of thinking. We should use "fuzzy logic" (6) or similar
techniques because the true goal of decission makers is not to know
whether growth will be exactly 0.1% or -0.1% in order to discern
whether technically we will be into a recession or not. Decission
makers want to know a set of actions that predictibly will have
extremely negative consequences on economies to avoid those
decissions, and their second goal to know a set of actions that
predictibly will have positive consequences on GDP growth to choose
one among them. The exact value of the consequence of their actions is
not very relevant (to create 4 millions jobs or 4,050,000 jobs is not
very relevant for US decission makers) if in order to know that value
exactly they might suffer a high price in terms of risk of error.
Unfortunately many gurus are educated just on traditional premises.
And even worse, some of them do not have an open mind to accept their
range of error. Fortunately, many others, although educated in
traditional techniques, openned their minds to new ones and, at least,
use them optionally for important analysis.
Peace and best wishes.
Xi
(1) Quantitative methods.
Quantitative research is the systematic scientific investigation of
quantitative properties and phenomena and their relationships. The
objective of quantitative research is to develop and employ
mathematical models, theories and/or hypotheses pertaining to natural
phenomena. The process of measurement is central to quantitative
research because it provides the fundamental connection between
empirical observation and mathematical expression of quantitative
relationships.
Quantitative research is widely used in both the natural sciences and
social sciences, from physics and biology to sociology and journalism.
It is also used as a way to research different aspects of education.
The term quantitative research is most often used in the social
sciences in contrast to qualitative research.
http://en.wikipedia.org/wiki/Quantitative_methods
A typical training of quantitave methods applied to decission making.
http://www.ebsglobal.net/programmes/quantitative-methods
(2) We have talked in this group about why consumer price index is not
accurate during crisis, people change their purchasing habits while
methodologies to gather date do not change so fast. Also, GDP is not
accurately gathered because societies change their behaviours too. For
example underground economy, black economy, hidden economy or whatever
you call it, blossom during crisis and we cannot measure it. In
economies based on farmery it is easier because we can watch from
satellites how fields develop, but industrial economies are more
difficult. Financial economies are completely impossible to be
measured accurately.
(3) Cēterīs paribus is a Latin phrase, literally translated as "with
other things the same." It is commonly rendered in English as "all
other things being equal." A prediction, or a statement about causal
or logical connections between two states of affairs, is qualified by
ceteris paribus in order to acknowledge, and to rule out, the
possibility of other factors which could override the relationship
between the antecedent and the consequent.
A ceteris paribus assumption is often fundamental to the predictive
purpose of scientific inquiry. In order to formulate scientific laws,
it is usually necessary to rule out factors which interfere with
examining a specific causal relationship. Experimentally, the ceteris
paribus assumption is realized when a scientist controls for all of
the independent variables other than the one under study, so that the
effect of a single independent variable on the dependent variable can
be isolated. By holding all the other relevant factors constant, a
scientist is able to focus on the unique effects of a given factor in
a complex causal situation.
Such assumptions are also relevant to the descriptive purpose of
modeling a theory. In such circumstances, analysts such as physicists,
economists, and behavioral psychologists apply simplifying assumptions
in order to devise or explain an analytical framework that does not
necessarily prove cause and effect but is still useful for describing
fundamental concepts within a realm of inquiry.
http://en.wikipedia.org/wiki/Ceteris_paribus
(4) I do not know any accurate definition in English and I could not
find any decent definition over the net. But it can be translated to
something like: "Based on the Theory of chaos, "omnia mobilis" is the
assumption that every single entity (material or non-material) changes
and produce actions non-stop. Those actions produce consequences on
any other entity."
(5) To create useful models under omnia mobilis assumption is not
feasible nowadays and computers could not work with so many data.
Therefore, most researchers work on "beyond ceteris paribus". "Beyond
ceteris paribus" do not handle all existing entities and their
actions, but states a set of indicators that are meaningful to warn on
external actions that might have meaningful consequences on the field
that we work with. As far as I know, these techniques are used just on
Asian videogames and by Chinese decission makers as one of their
source of information. As it is also studied in Japan and in South and
Southeastern Asia, I guess that it can be used in those countries for
somedecission making processes, but I cannot be sure because my lack
of information. Regarding actual use of it in economies, as far as I
know, China is the only country that uses it in some actual local
economies nowadays and always in experimental purposes.
(6) Fuzzy logic is a form of multi-valued logic derived from fuzzy set
theory to deal with reasoning that is approximate rather than precise.
http://en.wikipedia.org/wiki/Fuzzy_logic
unpredictible and simultaneous actions have (or might have) inmediate
consequences on any other player, doll, character or whatever we want
to call it.
Mr. Volcker, former Fed chairman, confessed himself astonished of the
speed of cause-consequences during this crisis. Most economists and
policy makers are astonished. The global economy has changed in the
last years, we have to get used to it. Also we have to get used to see
that consequences can be amplified compared to causes. The usual
extreme example of the theory of chaos is that a tiny butterfly moves
its wings in Beijing and its consequence is a storm in New York
(reverse cities if you want). In the same way, using the appropiate
techniques, negative consequences could be cushioned or even avoided
if we can determine since the butterfly started to fly that it would
have consequences in New York and we can know which consequences they
could be.
In this message I will try to describe why, once this economic crisis
has been unleashed, many economic predictions from most gurus are not
accurate and why some techniques extensively used in Asian videogames
help economic predictions and decissions more than traditional linear
techniques.
In traditional linear way of thinking, based on formula and
algorithms, accuracy of raw data determines the accuracy of results as
you can find in my first cite below "The process of measurement is
central" (1). They work extremely well during quiet periods. While one
economy grows, for example, between 2% to 5% year after year. They can
predict easily anything, even a deep crisis. Even easier if they
predict a financial crisis. Until now, decission making and prediction
processes has been conducted in a linear way of thinking. Mostly based
on something called "quantitative methods" (2), in economics it is a
sequel of monetarism developed in Chicago and in other American
universities many years ago and even today. But what if they have to
analyse trillions of data? what if empiric data are changing fast, up
and down, constantly? what if the formula itself changes because
behaviours changes, inside or abroad, with dramatic consequences
inside our field of observation? (2) Their models, their algorithms,
their formula, their linear way of thinking do not work at all and
produce wrong results and predictions.
A tiny example, economic authorities rule against recession because
they receive a report that states that GDP has declined 0.2%. If that
report states that GDP decined 2% they would rule in a different way.
And if that report shows that GDP declined 20% their rules would be
completely different. But what if those data, 0.2% in the example, are
wrong? Or what if they are correct but the consequences from external
economies make that it turns from 0.2% to 2% or 20% in one month?
On the other hand, some decission-makers ask to have those new
concepts available right now for their actual decissions. For example,
one of the decissons of the G20 meeting in November or December was to
create an "early warning system" for the IMF. It means a system
designed according to something that we discussed here, a set of
indicators based not on "ceteris paribus" premises (3), but on "omnia
mobilis" (4) or, at least, on "beyond ceteris paribus" (5).
http://groups.google.com/group/world-thread/browse_thread/thread/a8189c944b38128a/aa50de230f3aed1d?hl=en&lnk=gst&q=omnia+mobilis#aa50de230f3aed1d
Finally, once we have results of our report, nowadays we cannot use
linear way of thinking. We should use "fuzzy logic" (6) or similar
techniques because the true goal of decission makers is not to know
whether growth will be exactly 0.1% or -0.1% in order to discern
whether technically we will be into a recession or not. Decission
makers want to know a set of actions that predictibly will have
extremely negative consequences on economies to avoid those
decissions, and their second goal to know a set of actions that
predictibly will have positive consequences on GDP growth to choose
one among them. The exact value of the consequence of their actions is
not very relevant (to create 4 millions jobs or 4,050,000 jobs is not
very relevant for US decission makers) if in order to know that value
exactly they might suffer a high price in terms of risk of error.
Unfortunately many gurus are educated just on traditional premises.
And even worse, some of them do not have an open mind to accept their
range of error. Fortunately, many others, although educated in
traditional techniques, openned their minds to new ones and, at least,
use them optionally for important analysis.
Peace and best wishes.
Xi
(1) Quantitative methods.
Quantitative research is the systematic scientific investigation of
quantitative properties and phenomena and their relationships. The
objective of quantitative research is to develop and employ
mathematical models, theories and/or hypotheses pertaining to natural
phenomena. The process of measurement is central to quantitative
research because it provides the fundamental connection between
empirical observation and mathematical expression of quantitative
relationships.
Quantitative research is widely used in both the natural sciences and
social sciences, from physics and biology to sociology and journalism.
It is also used as a way to research different aspects of education.
The term quantitative research is most often used in the social
sciences in contrast to qualitative research.
http://en.wikipedia.org/wiki/Quantitative_methods
A typical training of quantitave methods applied to decission making.
http://www.ebsglobal.net/programmes/quantitative-methods
(2) We have talked in this group about why consumer price index is not
accurate during crisis, people change their purchasing habits while
methodologies to gather date do not change so fast. Also, GDP is not
accurately gathered because societies change their behaviours too. For
example underground economy, black economy, hidden economy or whatever
you call it, blossom during crisis and we cannot measure it. In
economies based on farmery it is easier because we can watch from
satellites how fields develop, but industrial economies are more
difficult. Financial economies are completely impossible to be
measured accurately.
(3) Cēterīs paribus is a Latin phrase, literally translated as "with
other things the same." It is commonly rendered in English as "all
other things being equal." A prediction, or a statement about causal
or logical connections between two states of affairs, is qualified by
ceteris paribus in order to acknowledge, and to rule out, the
possibility of other factors which could override the relationship
between the antecedent and the consequent.
A ceteris paribus assumption is often fundamental to the predictive
purpose of scientific inquiry. In order to formulate scientific laws,
it is usually necessary to rule out factors which interfere with
examining a specific causal relationship. Experimentally, the ceteris
paribus assumption is realized when a scientist controls for all of
the independent variables other than the one under study, so that the
effect of a single independent variable on the dependent variable can
be isolated. By holding all the other relevant factors constant, a
scientist is able to focus on the unique effects of a given factor in
a complex causal situation.
Such assumptions are also relevant to the descriptive purpose of
modeling a theory. In such circumstances, analysts such as physicists,
economists, and behavioral psychologists apply simplifying assumptions
in order to devise or explain an analytical framework that does not
necessarily prove cause and effect but is still useful for describing
fundamental concepts within a realm of inquiry.
http://en.wikipedia.org/wiki/Ceteris_paribus
(4) I do not know any accurate definition in English and I could not
find any decent definition over the net. But it can be translated to
something like: "Based on the Theory of chaos, "omnia mobilis" is the
assumption that every single entity (material or non-material) changes
and produce actions non-stop. Those actions produce consequences on
any other entity."
(5) To create useful models under omnia mobilis assumption is not
feasible nowadays and computers could not work with so many data.
Therefore, most researchers work on "beyond ceteris paribus". "Beyond
ceteris paribus" do not handle all existing entities and their
actions, but states a set of indicators that are meaningful to warn on
external actions that might have meaningful consequences on the field
that we work with. As far as I know, these techniques are used just on
Asian videogames and by Chinese decission makers as one of their
source of information. As it is also studied in Japan and in South and
Southeastern Asia, I guess that it can be used in those countries for
somedecission making processes, but I cannot be sure because my lack
of information. Regarding actual use of it in economies, as far as I
know, China is the only country that uses it in some actual local
economies nowadays and always in experimental purposes.
(6) Fuzzy logic is a form of multi-valued logic derived from fuzzy set
theory to deal with reasoning that is approximate rather than precise.
http://en.wikipedia.org/wiki/Fuzzy_logic
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