Human behavior like any other group of similar phenomena can be studied in different ways. In today’s world, advanced marketing uses computer technology which furtively follows our smartphones from aisle to aisle in the grocery to itemize preferences and then ads and coupons are directly targeted at us when we hop online. This requires enormous databases of various inputs that characterize our purchasing behavior. This is known as “data mining” (more below) and from this intensive calculation, inferences that characterize each individual’s pattern of consumption can be roughly made by linear regression analyses.
Now consider the way this was typically done up to the present day.
Our studies of psychology, advanced psychology and even medical psychiatry and neurology were never meant to be classified using keywords or fields of data since those familiar disciplines are learned and practiced differently from one person to the next, often by case study. Moreover, these studies typically carry over into the way a person is able, and more often, unable to interact with society in perhaps the best ways. These are known as maladaptive behaviors and by way of interpolation are where our definitions of what “normal” come from.
Essentially several generalizations are required. It’s easy to “define” or create behavioral archetypes even stereotypes, yet those are still the types of statements researchers today use to inaugurate almost any discussion or research program that would deal with something un about what we do and why we do it.
Notice the observations and definitions in the massive data storage we record with smart gadgetry and that such a system is on the opposite end of the spectrum from more “old-fashioned” methods–overtly complex and confusing, fields and sub-fields that develop. These do have some use. Upon reflection these traits have barely any interconnection at all with our real lives.
Current studies are based upon actions and intentions. In short, it means we are researching the patterns of action, inaction and reaction from our day-to-day existence though not in any consistent and useful long term manner unto ourselves but rather to sellers in the marketplace.
How often do we ask, “Now why on earth did I do that, again?” And we don’t mean buying a certain kind of candy bar. Bouncing around as bits of matter as if at random we can predict purchasing preferences and other banalities like A versus B gets more mouse clicks. But we have no firm grasp on the underlying idea. It is insufficient to base important decisions in our lives and resort to vague terms like temperament or genetics or advertising, as towhy we do the things we actually do and how that pattern continues throughout a lifetime.
Invariably that kind of research of the past boils down to the way people act and react to each other and whether those interactions are socially appropriate or not. We’ve never before however, implemented aspects of pattern recognition (in the computational sense) into either of these methods, and we have never explored these patterns by calculation to give any practical output other than preferences for one brand over another. There are few heuristics involved. This is an incomplete approach.
Calculating almost anything these days is trivial. We have all the technology we need at our disposal, we just need scientific principles and research to solidify their significance. Where did we go wrong with human behavior to the extent that we can safely assume or pretend to understand it? What did we miss, and how can we reassess what we know about human activity?
Our classifications are limited to bits and pieces of data–really fragments of knowledge that are incomplete. You may be thinking that’s the best that can be done. Somehow we remain unable to interpret motivations; we do not adequately define human phenomena, and we haven’t been capable of conducting pure research to make precise predictions. The usefulness of science lies in its predictive value.
Where This Discussion Is Headed
When it comes to interpersonal relationships, psychology has so-called Personality Disorders in which you can define people by vague categories…usually by abnormal or aberrant behavior. We have inherent prejudices in these matters and are easily deceived or distracted by such information. I will offer a simple example.
Several different systems are used in the study of psychology and people are classified into types. There are at least 4 to 16 different types of personalities on this planet of billions of individuals. So what we actually do is divide humans into just 16 “zones.” The more often one engages in those behaviors, the more characterized one becomes. That was and is the limit of our knowledge for now.
What ever happened to “we are all different and unique”?
Thinking critically, we all differ in that we each think differently and have different minds. However, through these processes, we don’t act exactly the same at different times or in different situations. The actions are known; and we can define and categorize almost all of them because they are observable and easily described.
Some basic examples: He turned the light on. She felt good about herself. He is going to shop for something. She is going to workout. He is going to call her. She is going to launch a new project. She went to the cafe to order her favorite drink, and so forth. Simplistic yet deeply complex, even if we neglect the ethics behind these particular behaviors (another matter).
Those are not actions of the kind that should be used in research of human behavior, whether or not it interferes with “social and occupational functioning for a period of 6 months or longer” as psychologists frame maladaptive behavior. Few have considered trying to gather any statistical data beyond epidemiological studies (counting the prevalence of something) because of the overwhelming complexity of living matter. We are not just the organic chemicals that we are composed of. It turns out we are quite predictable.
Additional notes on the current status and limitations of research into human behavior:
– We rarely implement time…only stages of life (infancy, childhood, the teens, young adulthood, middle age, etc).
– We implement only select extrinsic factors (stressors, relationships, incidents…) but not all of them.
– We are not using the classification entries that we should actually use.
– We do not connect these data in a logical way.
– Our statistics have too few parameters and the limitation still depends on definitions of behavior.
The point is we cannot actually connect such data since before that can take place, we set out first to try to explain and understand, rather than simply to record and connect them. This is contrary to the way statistics and science is done.
We want to give definition and make distinctions among 7 Billion people and cluster each type, without using mass recognition technology because we assume human behavior is too complicated or complex.
In Truth, We Are Not That Complex.
Over the preceding century we’ve come to understand how powerful and precise statistics is at both the quantum and macroscopic levels. We now have tremendous computational ability. Why not conduct research in ways we understand from other scientific disciplines? Namely, using statistics and advanced mathematics to move the study of human behavior a bit closer to physical science.
Machine Learning, Data Mining and Artificial Intelligence
Google, Facebook and other large social networks, even the medical establishment, use advanced computing systems to sort people as points of data. And the sorting part of it has more fields than you can possibly imagine.
The aspect of Time is crucial. For example this person is 42 years old, this person “liked” a particular type of event 7 times on 12.02.2014 15:36 PM GMT +1.
Location is also crucial: He activated his GPS and traveled from New York to New Jersey. He is sunning himself on the Mediterranean in Greece. Behavior depends not only on what we do but also where we are.
Data mining uses more advanced statistical systems than psychology which is around 40% pure statistics at best (measuring internal validity, epidemiology etc.). It is flawed by being free of considerations about time and space.
Recording and Processing is the way data is converted into output by machines and also how it is “understood” by a machine. Statistics, is mathematically principled and is quite similar to machine understanding. To a purely analytical engine, it is all a matter of recording and processing events.
A Bit More About Statistics
The statistics we take part in can be quite interesting and it’s always like data mining in mathematics and can be easily compared to it by analogy. We input fields we want to record and then we process them.
Sometimes we do add time inputs, fields and data clusters. Sometimes there are even more advanced parameters, like financial records or health records which have around 130 different measurable pieces of data.
Again, we can easily define an action in terms of human behaviors. Many philosophers believe that the mind can be reduced to just behavior. This individual killed 2 two others, he is going to kill again with 89% probability. Just as easily, one individual is willing to help someone else because that person helped him 89% of the time and thus reaching his threshold to do so, he will reciprocate in kind.
My point is we have the statistical tools necessary to implement all we need, but we have not thought of an intelligible way to calculate it. Since there is a crucial function of time, we are hopelessly confused in defining human action in time and it is reasonable only for reasons mentioned above. We simply lacked the means.
Statistics on the other hand and computation in general, shouldn’t necessarily be defined by humans. We know how limited such endeavors are. Psychology has come a long way. But fields of so many things actually can be connected and defined by a greater and unified process referred to as data mining can give us a more accurate picture of who we are.
Data Mining and Calculation
Data mining (the analysis step of the “Knowledge Discovery in Databases” process or KDD), an interdisciplinary sub-field of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use elsewhere. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, level-of-interest metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
The answer boils down to mathematical formulae which can compute large quantities of statistical data by applying general logic from computers. The machine then gains understanding or learns.
This type of system actually can sort data into clusters and fields tantamount to a machine’s discovery of its own logic and to sort inputs (tremendous numbers of different aspects) into different fields. The resulting computational output if it were accurate would be absolutely useful. Can you imagine?
Our psychology undoubtedly develops in and around time and space. By researching social behaviors using computation, one can separate and define periods in a human lifespan. We are at the precipice of research based on mood, biometrics and instantaneous detection. Such computation can be accomplished even with everyday technology like mobile smartphones. It is possible not tomorrow or some day, but now. The future is here and we are attempting to do that. The science of data mining human behavior in the way that is what we predict to be most accurate is “Astrosy Celestatistics.”
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