Systematic Political Science

Epistemological Context in Social Simulation:
Merging Empirical Realities and Data for Validation Strategies
Using Supercomputers, DNA Microarray Analysis, and Case-Based Reasoning

by Dallas F. Bell, Jr. 

Abstract:  Social simulation is increasingly being determined to be useful for human decision making by facilitating the sorting of possible behavioral options into credible probabilities.  The effort to make simulation more accurate has brought focus to the epistemological foundations of decision making.  Locke, Berkeley, Hume, and other philosophers help to provide the historical solutions to questions concerning how we know.   With their reasoning tools of induction and deductive confirmation, the societal universal center of gravity can be reflected much like Galileo's principle involving motion and matter.  Beginning at the highest societal level of a nation-state, we may examine the nation-state's institutions and the institutions' demographics.  Then, the common need levels of each individual can be observed as being pursued by behavior or motion that indicates their epistemological rationality based on the core theological belief.  That structure or matter provides the simulation parameters of potential for both the human brain and supercomputers. The recent examples of merging empirical realities and data in DNA microarray analysis and case-based reasoning illustrate the success of the validation strategies.   Thereby, epistemology in social simulation can be seen in its proper context.

Keywords:  epistemology, social simulation, supercomputers, systematic political science 

1. Introduction

Social simulation can obviously be useful to human decision making reality by facilitating the sorting of possible behavioral options into likely courses of action.  Conversely, the actual occurrences of the simulated behavior can validate the accuracy of the reality of the inputted decision making foundations.  Understanding the foundations of decision making has correctly brought due focus to epistemology.  Galileo played a key role in the history of science and philosophy.  He provided a principle that encompassed both the motion and nature of matter (Galilei 1638/2000).  Social sciences must have the same universal center of gravity for accurate simulations.  Simulators must ask how the subject or simulators can know the object of the simulation.  They must also determine the motives of the subject and the object.  

2. Epistemology 

2.1 General Context 

If a car is observed to be the color of blue, it is not in reality blue.  The car is absorbing the spectrum of all colors except blue which is being reflected and perceived by the observer.  Simulation must reflect reality and not just mere perception.  If humans are observed fleeing from an office building which is filling with smoke, it would appear to reflect panic.  Those office workers in reality are not inherently panicky.  They have processed the input of smoke and expressed an output to flee the building based on inherent decision making abilities.   

The philosopher David Hume said we do not see the cause of the universe but only the effect.  We know that if there is an effect that there must have been an antecedent cause.  Years earlier, John Locke stated that correspondence meant truth comes from reality and not human imagination contrary to Plato, Augustine, and other rationalists (Sproul 2000).  Rationalists knew that small children innately understood the law of noncontradiction or the idea that something can't both be here and there and/or this and that simultaneously. Aristotle, Thomas Aquinas, and other empiricists acknowledge that all possible examples cannot be known and the senses can be deceived.  Therefore, the standard of 100 percent certainly is not a viable goal for simulation.  Later, George Berkeley added that truth is correspondent to reality as perceived by the infinite God.  Thus, reality is objective and not subjectively based on finite perceptions.  Then simulation accuracy requires more than inclusion of just materialism or things that can be experienced through the senses.  Simulations must also realistically consider the nonmaterial (e.g., love and justice).  If the standard required empirical verification, the standard itself couldn't be verified.  

Love and justice, two examples of nonmaterial reality, can't be seen but their effects on behavior make their existence a factor for behavioral motivations.  The alternative of "no" love and "no" justice is untrue because humans innately compare via negationis, meaning love or "not" love and just or "not" just.  This example of Boolean logic serves to reinforce linguistic efforts currently being undertaken to mine and analyze related corpora which is providing valuable input for simulations. Additionally, the truth of a cause may be questioned but that query process should not consider that a violation of the law of causation has occurred if the effect's cause is not seen or exactly known.   

In the 1700's, Immanuel Kant merged rationalism and empiricism into the understanding that we know through the senses and experience with a priori knowledge in intuitive categories in the brain.  That balanced idea was biblically expressed by the writings of David who said that the heavens declare the glory of God (David 1004-965 B.C.) and Paul's words that the invisible things of Him (God) from the creation of the world are clearly seen (Paul 55-56 A.D.).  David and Paul realized the senses and experience confirm the innate knowledge of the infinite God, unlike Kant, ironically named Immanuel which is Hebrew for God with us, who thought the seen could not be mixed with the unseen.  Kant's agnostic view led Georg W. F. Hegel to merge or synthesize the seen and unseen with a dialectical approach where a thesis that generates an antithesis is synthesized into a higher truth.  Simply said, those ideas of a subject and its object are thought to be evolving into the highest truth that all things are made up of the parts of a finite god.  

Hegel's method was adopted by those like Karl Marx, as evidenced in dialectical materialism which formulated communism from its socialist utopian eschatology.  Communism and socialism have thus far resulted in a minimum of 100 million murders (Courtois 1997/1999).  This deadly eschatological view continues to be held by many today.  Their general atheist premise is that anything that works, no matter how temporary, is to be considered the truth.  That truth is thought to relate chaotically with other truths and are not considered to be subsets of a total truth.  History is replete with the destruction of both individuals and societies that have implemented those failed theological beliefs.  The emergence of a prevalent world view which is also unanchored to natural law will cause chaos from the corresponding conflict.  This should eventually produce a world leader to fill the vacuum of peace.  Such an opportunist could garner rapid popular support by declaring a utopian end to conflict.  Though he may offer the hope of world peace, his hidden nefarious agenda would be revealed in time as all other despots before him.         

2.2 Social Simulation Context 

The universal center of gravity for social simulation may be found by inducing the structure of societies beginning at the level of nation-states and their dominate eschatology.  Nation-states are composed of the descending institutions of government, business, church, and family.  These institutions are made up of subset categories of individuals.  Individuals must meet their common ascending needs of survival, economic security, love and affection, status and self-esteem, and self-actualization.  Those individual needs are pursued from the innate epistemological process of storing input based on what is considered good or rational and what is considered evil or irrational.  These epistemological values are calibrated by the individually chosen divine authority and standard for what is considered good and/or evil.  This reflects the universal center of gravity for social science and its philosophers--theology. 

Systematic political science confirms the previously discussed structure of societal induction by charting its conclusion with deduction beginning from either one of three tracks of theological choices.  The natural laws of freewill (NLF) provide the anchors for the Manifold Equation of Theological Asymmetry (META) game theory process.  The theological tracks correspond to the epistemological track which corresponds to the individual and subsequent nation-state track formed from the eschatological belief of the originating theology (Bell 2002-2006).   

On a nonspecific micro societal level, the theological categories of people fleeing an office building filled with smoke may not be known but estimates can be made from appropriate sample data that should not be considered stable.  It would be useful for crisis managers and risk analysts to know if the office workers had a theology like Soren Kierkegaard, who promoted an epistemology of self-sacrifice, which would make them likely to assist their fellow workers injured by smoke or fire.  On the other hand, it would be equally as important to identify the office workers that had a theology like Friedrich Nietzsche, the clinically diagnosed madman, and his followers such as Adolf Hitler, Sigmund Freud, Carl Jung, Alfred Adler, William Yeats, and George Bernard Shaw (Wicks 2004).  Nietzsche dialectically reasoned that man must become a Superman and embrace the will to power. This involved epistemologically rejecting the self-sacrificing attributes of pity, mercy, grace, etc. and would make such believers very unlikely to assist co-workers in any situation that is not self-serving.   

There are groups, such as college fraternities, whose membership can be specifically known and the data considered stable.  On a macro societal level, more data is available and provides more stability for nation-state simulation.  For example, the likelihood or probability of a government either rejecting or becoming an ally of terrorists could be reasonable determined. 

3. Supercomputers and the Human Brain

The human brain contains billions of neurons that are connected to around 10,000 synapses depending on specific neuroanatomy.  That cluster of neurons forms a parallel information processing system.  A person can simultaneously write a letter while listening to a class lecture and smell the cologne of another student as they scratch their back on their chair.  That ability contrasts with computers which execute a single series of instructions on a single processor.   

The number of action potentials and synaptic potentials in the human brain are generated by a combination of morphology, imaging during activity, and the energy consumption.  It is difficult to calculate its floating points of operations per second (flops).  Because, according to the National Center for Supercomputing Applications (NCSA), the coding is analog which means the coding modality is frequency of action potentials instead of having the information coded in each one (Jakobsson 2006).  Estimates of human processing ability may range from 100 teraflops (Tflops) or 100 trillion flops to 100 petaflops (Pflops) or 100 quadrillion flops.  Studies conducted on the intellectual ability of identical twins raised in different environments indicate that their inherited problem solving ability (IQ) remained the same relative to their ages (Garlick 2002).  That IQ range of potential may be achieved by stimulation causing the neurons to adapt referred to as neural plasticity.   

In the future supercomputers are expected to reach the processing ability of humans.   Artificial intelligence (AI) systems will never be grounded in reality which requires the human quality of self-will that motivates behavior.  However, supercomputers will be useful and need to epistemologically react with the incomplete input of their finite human programmers by probabilities.  Those probabilities may be used in a Bayesian sense to enhance decision making.  The theological inputted values of the programmer used for simulation will determine its accuracy and benefit. The more the programmer is aligned with the realities of natural law the higher the probability of accuracy.  Just as a well known chess program assigns a processor to each of the 64 squares on a chess board, an ultimate supercomputer for social simulation at the highest levels would need to assign a processor section, minimally equal to a human brain, to each of the 512 subsets of the systematic political science model.  Of course isolated or limited microsimulations, such as modeling the traffic patterns in an office building, could operate with a much smaller system. 

4. DNA Microarray Analysis

To insure emails sent over the Internet do not become changed in the transmission a "check sum" protocol was developed.  Bits of information beginning as a 0 or 1 value may become garbled during transmission by being inverted to the opposite value during the email process.  The communications is now checked by counting the number of 1s in the message.  If the number is odd the last bit would be set to 1.  Otherwise, it is set to 0.  By comparing the number of 1s from the sender with the value of the last bit on the receiving end, the receiving computer can determine if the message was accurate.  If it wasn't, the receiver's computer can request the sending computer resend the email.   

Similarly, DNA microarray data points in a time series are now being compared against a summary of the temporal response (Bar-Joseph 2005).  If the two sets of results are equal, the DNA microarray time series is considered real.  If not, a gene's activation is considered to have been missed.  This method can be applied to social simulation.  A time series of human institutions can be sampled and compared to the static systematic political science template.  If there are inconstancies, an inquiry would be needed and adjustments made.  This process will also aid in overcoming synchronization loss.  Like large groups of living cells, humans with similar points in time eventually become asynchronized in their activity.  If specific data is known, all relevant individuals can be appropriately tagged and their asynchronization observed. 

5. Case-Based Reasoning

Microarray data sets have been documented to improve prediction accuracy by approximately 10 percent (Arshadi, Jurisica 2005) using case-based reasoning (CBR).  The problem paradigm of CBR does not just rely on the general knowledge of a problem domain, or making an association between descriptors and conclusions.  As many know, CBR can use the specific knowledge of previously experienced problems or cases.  The new problem may be solved by finding a similar problem and then reusing that solution.  Each time a problem is resolved that data is available to be reapplied to future problems. 

If a government's ambassador was negotiating a difficult situation a previous case may be applied and a decision reached.  That problem solving and learning feature of CBR should also incorporate all types of knowledge within the domain of systematic political science.  Thereby, post hoc tendencies, or the fallacy of arguing from a temporal sequence to a false causal relation, may be avoided.  A common humorous example is to wrongly surmise that a rooster causes the sun to rise since the sun is observed rising each morning after the rooster has crowed. 

6. Conclusion

The introduction of this paper briefly reviewed the philosophy of how we know.  The societal model for simulation was demonstrated by the systematic political science template.  It was followed by a description of the potential of knowing for humans and supercomputer AI.  DNA microarray analysis was demonstrated to have been enhanced by seemingly unrelated computing methods and CBR.  In turn, they may improve the art of social simulation.   It is becoming increasingly clear that when empirical realities and data are properly merged they can serve to validate simulation strategies which would be wholly inaccurate without their epistemological context. 


Arshadi, Niloofar; Juriscia, Igor (2005) Data Mining for Case-Based Reasoning in High-Dimensional Biological Domains.  IEEE Volume 17, 1127-1137. 

Bar-Joseph, Ziv (December 2005) Carnegie Mellon-Led Research Team Transforms DNA Microarray Analysis with Ideas from a Standard Internet Communications Protocol.  A press release by Carnegie Mellon University. 

Bell, Dallas F., Jr. (2002-2006) Parts I-IX.  A series of papers on systematic political science. 

Courtois, Stephane (1997/1999) editor, Le Livre Noir du Communisme: Harvard University Press. 

Galilei, Galileo (1638/2000) Dialogues Concerning Two New Sciences, Toronto: Wall and Emerson. 

Garlick, Dennis (2002) Understanding the Nature of the General Factor of Intelligence: The Role of Individual Differences in Neural Plasticity Explanatory Mechanism.  Psychological Review 109, 116-136. 

Jakobsson, Eric (February 2006). An email exchange initiated by Dallas F. Bell, Jr. and facilitated by Thom Dunning the director of the National Center for Supercomputing Applications (NCSA). 

King James Version Bible (David 1004-965 B.C.) Psalms chapter 19 verse 1. 
(Paul 55-56 A.D.) Romans chapter 1 verse 20. 

Robinson, Thomas (1992) The Bible Timeline, Nashville: Thomas Nelson Publishers. 

Sproul, R. C. (2000) The Consequences of Ideas: Understanding the Concepts That Shaped Our World, Wheaton, Illinois:  Crossway Books.  

U.S. Census Bureau (2005) Population Division, International Programs Center. 

Wicks, Robert (2004) Friedrich Nietzsche, Stanford Encyclopedia of Philosophy. 

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