Maximizing Prediction Value in Complex Social Systems Analysis


The founding principle of Statistical Mechanics is the notion that an understanding of the evolution of a system produces predictive value. This would suggest confidence in the existence of normative function and a preference toward it. It would appear that normative function is a fundamental requirement for prediction; as discrete prediction in an entirely chaotic system would be improbable. In masses of interactions beyond that which humans can parse, probabilistic logic becomes a useful tool for producing predictive value. Statistical Analysis of Classical Systems has become commonplace for this reason. Data analysis is proving it’s worth in recent times.

Probability in Classical Systems:

The cognitive constraints of humans has been a focus of tool production as of late. Concerns over the issues that we face with finance, food production, energy production and environmental influence have become much more prevalent as the population increases. Data analysis has become a staple of not only scientific endeavors, but also for more general research.

The human brain can only reconcile about a dozen pieces of information. This falls far short of the number of significant interactions in complex systems. The most recent solution to this issue has been computer mediation. The combination of relevant information and narrow artificial intelligence has been very useful in sorting our thoughts on many issues. What it has also produced is axioms for consideration of complex systems. The value of the data that is analyzed after the fact, is the prediction value that it produces, as it demonstrates normative function. It’s the statistical analysis of the data that is producing the information that we are interested in. It’s our ability to make predictions about finance, food production, energy production and environmental influence that is the desired human payoff.

The ability to produce prediction value is in the ability to distinguish that which is interesting from that which is normative. This as an axiom, can produce a methodology for assigning probability to possible outcomes. This however requires discrete information concerning the morphology of normative function. This is where probability becomes a useful tool in analyzing systems with large numbers of variables; as the axioms can guide statistics toward significant findings.

Entropy, Normalization and Novelty:

Having some understanding of the morphology of complex systems is essential in producing axioms for statistical analysis. With a framework for classification and logic, the data can produce interesting and useful information. Much of this is already being accomplished with narrow artificial intelligence; however the data is being rendered into information that is useful and understandable to humans. It is important that the end product be humanly intelligible for obvious reasons. Producing an interface between large amounts of data and human cognitive ability seems an effective rout to a functional tool.

Existing theory is more than adequate; as the evidence demonstrates that social systems form with similar axioms. Motivation toward self-interest and self-preservation produce correlation between normative function and behavior. This is because the natural alternative to normalization is extinction. Entropy, being nature’s creative process, doesn’t produce normative function. That is the function of Normalization. Entropy may produce novel properties or systems that are non-destructive to normative function; however it’s normalization that sets the standard. It’s the combination of Entropy and Normalization that then produces Novelty. Entropy, Normalization and Novelty are the fundamentals of morphology and development in all systems.

Chaos and Emergence:

All natural systems are subject to Chaos and Emergence. This is probably because of the prevalence of Entropy. Chaos is of course produced with large numbers of interactions that are difficult to parse. Chaotic systems often do not produce discernible patterns in repetition. This is a large hurdle for prediction; as the changes in the patterning are also difficult to predict. This however can still be observed and expected. This is important when modeling, economizing and assigning probabilities. Emergence is of course the properties, components and systems that emerge to human surprise. Like chaos, it probably occurs due to large masses of interactions. It might also be a product of natural properties that we are not yet aware of. This too can be expected statistically; and can aid in assigning probabilities.


For the purpose of clarity, it’s important to define and give context to what is meant by prediction. In this article, it is a symbol of statistical significance. Complex Social Systems are the epitome of chaotic systems. The collection of data and the analysis of it can only result in the assignment of a probability. It’s not generally the intention to produce accurate predictions of the emergence of discrete facts at a specified time. For economic purposes, the intent would be to create models that have the highest probability of success as practical. By aligning the model with statistically predictive axioms, this can be achieved overall.

Considering the evidence concerning the Second Law of Thermodynamics, one would expect that Entropy be the most prevalent aspect of morphology. The second most prevalent aspect would then be extinction; as Normalization and Novelty would be far less frequent occurrences. One could then mathematically, logically and systemically deduce that the difference between Extinction and Entropy is the sum of Normalization and Novelty. One could also deduce that the third most prevalent aspect is Normalization; as Novelty over time becomes normal. That which is normal contains the properties that produce Normalization. Novelty that coordinates with a critical mass of normal properties has a high probability of becoming normal and amending Normalization. The understanding of the morphology of the system, in discrete processes, guided by the axioms allows the assigning of probabilities to the success of the discrete processes.

Considering the evidence concerning General Systems Theory, discrete systems are influenced by overarching systems; as systems that are higher in the hierarchy produce initial conditions. The initial conditions are often normative and influence discrete systems via Normalization. Where Novelty is present, one might consider it’s economic advantages and weaknesses. One might also consider the influence it might have on that which is normal. This may maximize long term predictive value.

Considering the evidence concerning the behavioral sciences, types of behaviors in individuals, and large and small groups are somewhat predictable. Being chaotic systems means not only that patterns can be difficult to demonstrate, but also that initial conditions are the focus of influence. Where the behaviors are not congruent with the initial conditions, one might consider that some form of Entropy is at play. This might come in many forms. This would also help to determine the predictive value of the success of the behavior. By the rigor of consideration with multiple axioms, the most predictive information as practical is gathered. With the application of each axiom the information is refined producing more accurate probabilities.


The difficulty in producing predictive value in Complex Social Systems Analysis can be overwhelming to the human psyche. It however isn’t just the complexity of social systems that produces the response. The observer effect appears to play a large roll in the responses. The fact that Behavioral Science is still socially in it’s infancy; and hasn’t yet had time to penetrate common knowledge may be one factor. The frustration that comes with argumentation between individuals may be another. There is also the constant natural perception of our hunter, gatherer ancestry influencing our thoughts. We are intrinsically poorly suited to Social Science; as we are evolved and conditioned to the life of a hunter gatherer. This however is not where we are. The difficulties that we face are the product of Entropy; and the solution, by natural processes, will likely either be the normative coordination with initial conditions that leads to transcendence, or abrupt extinction.

Considering the probable outcomes that are likely to be either our future or our end may insight one to be adamant toward public awareness. This however isn’t part of the model. Public awareness is much more likely to produce Entropy than Normalization. The issue is with the state of human understanding in general. The issues in the paragraph above are the root. Human behavior is not necessarily a result of initial conditions. It’s more of a perception of initial conditions. A path toward normative function would likely be more effective for risk management. This means that a perception of initial conditions that produces normative behaviors would be the desired condition. This isn’t likely to result as the current perception of initial conditions for human society in general is still entropic: and thus the vast majority cannot distinguish between normative and entropic behaviors. This doesn’t necessarily mean that humans cannot survive this phase. There are environmental factors that would likely produce normative, impulsive responses via self-preservation. This seems the most likely source of Normalization; as artificial perceptions are so prevalent. This also appears to be the most effective axiom for the purpose of economics; as it is dictated by the initial conditions.

Personal note;

It is likely that normative behaviors will result from the current state of the biosphere. What is in question is whether or not humanity or the whole of biological life as we know it will be a part of it. This article is in a sense optimistic; in that the impulsive responses that are likely to bring normative behaviors will be accompanied with the dangerous results of entropic behavior. There is no precedent for social movements creating significantly normative behaviors in recorded history. This is because polarization is a part of our social paradigm; and has been since the dawn of civilization. The notion that society would all of a sudden wake up and start behaving in a normative fashion is just pure fantasy. Until the initial conditions are so dire that they have much greater influence than the polarizing entropy and the ineffective rituals that are associated with it, no significant normative behavior should be expected. It is probable that we will put ourselves and the rest of the biosphere at great risk before change will occur. Many are concerned that it will be too late; however the arguments for it are weak. This is not the first time that humans or even the rest of the biosphere has been in a similar or even worse situation. Many scientists have stated that this phase of development (from type 0 to type 1) may be the most dangerous; however all phases appear to be wrought with extinction and existential risk for a wide variety of reasons. This type of danger appears to be as natural and as prevalent as at any other point in time. There appears to be nothing historically special about the dangers that lay before us. I think that this notion is probably rooted in a misunderstanding of how we interact with our environment. We seem to have a bloated account of our influence on others, society, the biosphere and beyond.


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