Let big data read the palms of individuals....
Can you predict your own future? The question sounds horribly sawed and straight out of a poorly written prophetic guide. But, let’s rephrase it in a
more specific way, “Where would you think you would be in a five years’ time?”
And most important, what should follow is “Can you predict it, or know
something about it in reality?” There are a few common responses to this
question, which say something about how people, in general, view fate or the course of events in their lives. Some of them which I get frequently, depending
on the type of person, are “No, future is not predictable”, “Yes, if you
believe in your dreams, you can be there" or if you are religious “Yes, only God knows it”. A more interesting segment, however
awaits when you ask them “Can you predict how many people would give the same
answer as yours, beside the other ones?”
Well, as it unfolds, this might not be a very difficult
task. In that, we can reasonably estimate how different people are going to respond to the first part.
The response obviously relies on many facets such as the amount of control one
might feel one has at a given time. But more important, it falls onto larger
scaled phenomenon, such as culture and socioeconomics. A person sitting in
West, where the cultural focus is on individual effort and ‘chasing your
dreams’, is likely to think that future is predictable while someone, who happens to
be born in a country plagued by poor governance would rather believe that
outcomes in life are unpredictable. It is not a random occurring that
someone has chosen to respond one way or the other to the questions above, but
instead it is embedded in a meta-statistical structure. Of course there are
outliers, and of course there is a margin of error, which is precisely what
differentiates statistics from prophecies, which can vary from a lot to
negligible depending upon what it is which we trying to predict. If you are
digging in to predict the response of a single individual, given that you know what
is there to know about that person, then the margin of error would be very
high. But if you are engaging to know the response of a particular group, or
population, the predictability would sharply jump. In this matter, the lives of
humans closely resemble the natural world, which, as you go at the smaller
levels, become more and more unpredictable, and as you slide towards larger
levels, let’s say at the Newtonian level, you begin to decipher patterns and
laws.
So where will you be in a five years’ time? Or, how much will
you be making at your job? Which life partner would you have? And how much happy
would you think you would be with them? These are no questions for palm readers
or crystal gazer, their answers clearly lay with good statistics and good
philosophy. For example, dating patterns show that the US millennials are getting
into relationships earlier than their ancestors were and are likely to go through an average of two long-term relationships and several mismatched dates and heartbreaks
before arriving at “the one”. So, it should not come as a surprise that some
would feel their relationship history is un-normal, when that is what most
people are playing around like. More interestingly however, on online dating platforms, people are more likely to hit those who belong to the same
race, despite answering “it does not matter” in their response boxes, and to
opposite sex, despite describing "bi-sexual" as their sexual orientation. It is easier
and more useful to analyze online dating data than to take surveys and
responses from people in the field, because the online dating show clicking and
email patterns which reveal what is really going on, instead of merely asking a
sample which often results in inaccurate responses. It seems like, that the fate happens to be rigged while arranging partners because it somehow always manages to set dates with those who fall near your class bracket, and also somehow with those who share your skin color.
The performance in education and the amount one makes at his
job are other facets of the larger scheme of things, which too seem to be rigged. This has been repeatedly
shown, when we see that middle-classers outperform lower-classers in schools
consistently no matter which country or culture. In South-Asian societies, the
cultural focus in academic achievement is primarily ‘the more you hard-work,
the more pleasant your grades will be’, something which is both disastrous and
wrong from the view of policy-making. Differences in income of the households
has profound impact on educational performance of the children in those
households, translated from cumulative factors which seem minor to ordinary
people. For example, research shows that lightening and temperature at the
place of study influences concentration and therefore learning. Proper
lightening and study place is recommended during focused study hours. Apart
from that, getting someone to monitor your performance significantly reduces
procrastination and improves lacking. Middle-class children are likely to have
educated parents who keep track of their children’s performance, visit
schools often, meet teachers, and are likely to hire coaches if their children
underperform. They are also, likely to closely guard their children’s
television time, computer games time, and are likely to encourage them to study beyond the scope of the syllabus. It seems like the “hard-work” phenomenon does
not work quite well, when you have a noisy household, where you are interrupted
every few minutes to do the house chores and where you don’t have any
authoritative agency to keep you motivated. There is no co-incidence that the
sprawling majority of the top colleges and universities are the middle and
upper-classers. The drumming whole of the values of hard-work which are
constantly bombarded into the popular ears, would work fine if everyone played
by the same rules.
There were just some of the major superficial factors which
contribute to educational performance, scratching the surface would uncover
more significant variables, for example psychological and cultural ones. In
households, where partners are in emotionally stable relationships, children
are likely to perform better in the educational pursuits than in households
with less stable partnerships. Children raised by single mothers who most often
belong to lower rungs of society or adoption homes, are not only going to significantly
underperform in schools, but have a high dropout rate and are also very likely
to engage in criminal activities, such as drugs, violence and abuse. This has
important implications for policies concerning abortion and birth control. The Donohue–Levitt hypothesis, shows
the impact of legalized abortion on reduction in crime rates. People who repeat
the ‘let the fate decide’ mantra should very well look at the probability of someone
from an economically meek and psychologically unhealthy surroundings ‘making
it’ around. What they don’t realize is that the fate which they so
earnestly assert, is basically the structural forces which decide the life
outcomes of individuals to a very large degree. This continues itself into the
work lives, and an even greater gap in the incomes of individuals.
But these were the ultra bolded lines on
a map, which many would feel are obvious to highlight (even though that’s not
the case). What about the thin lines or subdivisions? In other words, how come
inter-class, inter-group factors lead to differences in the incomes earned?
Well, as it might unweave, these too are not that random. HBR often publishes
studies showing salary differences between managers in the same occupation, in
the same industry and even with same qualifications. There is of course, a
whole complex web of factors feeding into one and another, but they are not
non-traceable. On individual level, for
example, person-job fit can be an important factor in one’s job success which
is the fit of personality traits of an individual with the requirements of the
job. Now, whose fault is this that your genes happened to express themselves in
a certain way to make your personality fit or unfit for the job you happened to
find. You cannot possibly trace the genetic and phenotypical characteristics of
individuals to predict whether they have the ability to smile most of the time,
if they chose to be frontline managers. It might seem absurd, as it bears
witness to a previous analogy, that on smaller scales such as at individual
level, things become very unpredictable. There is still, however, a probability
which determines the chance that your genes happened to be arranged in a
certain way, to make up your personality traits both naturally and by training,
which happened to be suitable for your job, and which now earns you way above
mean of your industry, occupation and geography, but it would be ridiculously difficult
to calculate.
Gleaning at the big picture, the crime
and health statistics, in a similar way, help us look at the wider inferences. For
example, measuring and comparing weight patterns across the world show that obesity
has a high incidence rate in developed and developing countries. But this is
not the end of story. In rich nations, people who are poor financially and
socially, are more likely to be obese. Not only that, but weight is also found
to be unevenly distributed among sexes, with women typically being more obese
than men. There is also a correlation between higher gender inequalities with
higher obesity of females. Now, all of this does not quite fit into the
over-consumption of food theory because there is a stark difference between the
quality of food consumed by different brackets of society. Lurking beneath are
the high levels of stress, which cause hormonal disturbance which affect fat
storage and metabolism. The incidence of diseases or crimes for that matter,
are not population variant but also, more important, historic and structural.
The incidence of violence and terrorism, as is now popular knowledge, a
characteristic of poor and ill developed societies, but it is too unevenly
distributed among areas and groups. The likelihood of violence is a lot more,
within the same country, in areas historically disputed. Those areas are also
likely to be least well off than the rest of country. Now, the cause and effect
can work both ways, whether poverty leads to violence or violence perpetuate
poverty, but the overall message is that no problem is isolated, one problem
intricately feeds into the other creating a vicious cycle of all-time misery.
Now, let’s dissect the portion which this
post is originally about. The statistics of fate. The probability hits which,
many would say, you cannot control such as being born in a particular
nationhood, race, or bracket, which is supposed to ‘just happen’. Well, by
control, one should mean that an individual cannot control these occurrences,
because when one wear telescopic glasses and affords a bird eye of view of the
statistical terrain, the ‘controllable’ deem obvious. The probability of you
being born in a country which are ranked high on development index, is
basically the children born in those countries every year over the children born
all around the world every year. And since, high ranked countries have low reproduction
rates, the probability would be quite low. This might be an inaccurate measure
because children being born are not equal to children who survive, and in
countries who rank low on development index, the mortality rates are high but the
population growth rates too are high despite more children dying, so there is
more likelihood of being born in medium and low ranked countries.
As you might
have already guessed, this probability depends on the sprawling global
inequality. In a world with more equal distribution of wealth, the probability
of a child being born in downcast surroundings would be droopingly low. Yes, it
is not the child’s fault in where he or she happens to be born, but it is very
much the fault of the larger world dynamics which clearly determines the happens-to’s.
Of course, it does not end there, there
are numerous systematic gaps which pronounce the decision of one fate over the
other. Subsequently, it trickles down to thinner and less visible lines of the
map, as the class, race, status, gender and ethnic gaps are bridged on. However,
many would throw the query that what about the incidences which are not
apparently the result of structural gaps, such as earthquakes or tsunamis, which
are totally unpredictable and thus out of control. Well, of course that is
true, but that is not how we are looking at the statistics. The problem does not
lie in how many disasters hit the population, but actually how they impact
it. The disasters are likely to do more damage in resource stricken areas
because their rehabilitation take much longer, and sometimes is never initiated.
The trick here, is not to never expect bad things, but to have a strong coping
mechanism against those occurrences.
The underlying power of statistics is truly
unthinkable, and touch the secluded grooves of information where ordinary eye
cannot intrude. Maybe one day, we get to the position of predicting the
unpredictable, as both the sources and quality of data improves. A time when
big data is used to read and predict the fates of masses, a juncture where
science does replace prophecy. The whole
anagram of ‘fate’, as some veiled plan, has consistently been used as excuses
against bad governance, bad policies, bad economics, bad socials and most
important, bad information and bad philosophy.