Systematic Political Science


A Survey of Algorithms, Ontologies and Parameters in Artificial intelligence
for the Human Behavioral Options of Conflict and Peace

by Dallas F. Bell, Jr.

1. Introduction

In the 1930's the mathematical logician, Kurt Gödel, made an ontological argument--modal logic--to discuss the infinite God. It could prove God's existence by the characteristic of omniscience or perfect awareness while providing the language of proof theory. Gödel also found the incompleteness of mathematics that established the nonexistence of algorithms capable of solving all problems. An algorithm, named for the Persian mathematician Al-Khwarizmi, is a set of mathematical or computer program instructions for accomplishing a task that terminates in an end-state. The result of Gödel's and other's findings indicated that computational complexity in artificial intelligence and so-called machine learning will never have the problem solving capacity of humans in all domains. Intelligence or problem-solving ability and self-learning emanates from the innate capability/motivation of awareness. Intelligence quotients measure the problem-solving ability with a chronological component that isn't applicable to machines. Artificial intelligence is the measurement of the ability of a device to organize and manipulate factual and heuristic imputed data which is often compared to human reasoning. Machines are storing and sorting tools. Those that attribute awareness to machines are reflecting their eschatological faith ako finite theological belief.

To be of use, artificial intelligence programs should identify the properties and problem-solving method as precisely as is possible. Algorithm complexity theory seeks to find the object of the solution with the shortest program. Genetic programming addresses a large statement and selects a program to generally solve the quest with parameterized topology. This means in a single run a graphic solution is generated. Genetic programming may include the use of genetic algorithms.

2. Genetic Algorithms

Genetic algorithms represent the solution to a problem as a genome or chromosome. The algorithm creates a population of solutions and applies genetic operators like mutation and crossover to narrow the solutions to a best option. An example is to solve the problem of balancing a baseball bat on a car hood. The car's position in relation to the bat must be known. As the bat moves, an appropriate movement of the car must be made to counteract the change. When the process is solved again and again the field of solutions will be narrowed over generations until the best one is adequately found.

Each problem to be solved by a list of parameters is called a genome or chromosome. They are strings of data like all car positions in relation to bat positions. Reproduction takes place when selected chromosomes from one generation are combined to form other chromosomes for the next generation called offspring. Each offspring is evaluated for fitness in the next generation using the genetic operators of crossover or recombination and mutation.

Crossover exchanges a pair of chromosome data that are added to the next generation's population. An additional operator, mutation, is required to change the value of genes of the selected chromosome to enter the population for a new generation. They combine with unaltered past solutions in an elitist selection strategy or in a pure replacement strategy where all the old population is replaced with the new. This may be accomplished using human evaluation or interactive genetic algorithm processes.

If progression is made it is determined by intelligent design. A rock crusher at a quarry may be observed smashing boulders. Often the resultant smaller stones make formations that are shaped into somewhat linear or semicircle groups or 1's and 0's. This doesn't mean the rock crusher is aware and trying to communicate in binary code. Patterns created by computer programs are no more than formations of stones. Since the product is data with word meanings, it is often wrongly attributed to intelligence or awareness.

3. Conflict and Peace

The human behavioral value of peace can be seen as a tangible balance like a baseball bat on the hood of a car. Conflict attempts to force the bat out of position and the car must be moved to counteract the change. In systematic political science, peace comes from working together for common behavioral goals. Those goals or levels of needs are individually and collectively pursued and achieved by compliance with or noncompliance with natural laws of freewill. The more compliant the behavior the less instance of conflict and greater opportunity for peace and vice versa. Meaning that less compliance produces conflict or pushes the bat out of position. Either the behavior is changed toward compliance, the bat is returned to a balanced position, or the behavior of others must be changed to accommodate the conflicting behavior, the car must be repositioned.

To solve the problem of anticipating conflict, the algorithms for systematic political science includes ontologies, parameters and solution options for individuals and groups, and institutions and nation-states. The esoteric symbols and word meanings of subset academic disciplines are necessarily superseded for their unification.

3.1 Ontologies of Systematic Political Science

An ontology studies the categories of things within a domain of relevance from the perspective of a specific language. When combined with systems of logic, they reflect the relationship of the entities in a domain. To remain current, merging and alignment are necessary and accomplished either manually or by automation.

T = theology, a superset of R = epistemology, a superset of B = individual behavior, a superset of W = societal behavior, a superset of E = eschatology, a subset of T

These domains are analyzed by known behavior, known T beliefs, interviews and polling, and corpora from relevant data bases considering deception strategies and intelligence categories of the object(s) of interest. Data bases are semantically searched for relationships between members of sets of objects and types i.e. ako, isa and likes, using a direct graph. The following is an example of a semantic network.


3.2 Parameters of Behavior and Epistemology

Levels of common behavior
1 = survival
2 = economic security
3 = love and affection
4 = status and self-esteem
5 = self-actualization

Levels of epistemology calibration
Goals of compliance with 10 of 10 NLF = R1-
Goals of compliance with 9 of 10 NLF = R2+
Goals of compliance with 6 to 8 NLF = R2
Goals of compliance with 5 NLF = R2-
Goals of compliance with 4 NLF = R3+
Goals of compliance with 1 to 3 NLF = R3
Goals with compliance with 0 NLF = R3-

(A listing of NLF and their subsets are on Attachments A and B of the paper "The Basic META Corpora and Semantic Taxonomy of Systematic Political Science" by Dallas F. Bell, Jr.)

3.3 Solution Options for Individuals and Groups of Like Individuals, and Institutions and Nation-States

Solutions for individuals and groups
1B1, 1B2, 1B3, 1B4, 1B5 = compliance with 10 of 10 Natural Laws of Freewill, NLF
2B1, 2B2, 2B3, 2B4, 2B5 = compliance with 5 to 9 NLF
3B1, 3B2, 3B3, 3B4, 3B5 = compliance with 0 to 4 NLF

Solutions for institutions and nation-states
1W = First World (A listing of solutions for institutions and their subsets are on the "Expanded First World Model" by Dallas F. Bell, Jr.)

1W1, 1W2, 1W3, 1W4, 1W5 = potential compliance for the majority of 10 of 10 NLF
1W3, 1W4 = historical compliance for the majority of 5 to 9 NLF
1W5 = historical compliance for the majority of 0 to 4 NLF

2W = Second World (A listing is at "Expanded Second World Model.")
2W1, 2W2 = potential compliance for the majority of 5 to 9 NLF
2W2 = historical compliance for the majority of 0 to 4 NLF

3W = Third World (A listing is at "Expanded Third World Model.")
3W3 = potential and historical compliance of 0 to 4 NLF

ST = Societal Transition
STr, STrr, STrw = beginning of 1W1
STir, STird, STirr, STirc = beginning of 2W1 or 3W1

(The majority or 51% or more can be represented by the symbol of P1.)

4. Conclusion

Heuristic algorithms find solutions fast. However, they are approximations and not considered accurate. The traveling salesman problem seeks a solution to finding the best route to all the cities on the salesman's route. If there were more than twenty cities it would take thousands of years to compute the solution. If the solution is tied to spending the least, an algorithm could approximate the most frugal route considerably quicker. An approximation algorithm is often a tool for game theory solutions. It seeks to find the best solution in a region which has a min or max value of the objective function. The objective function determines how good a solution is as seen in the total cost of edges in the traveling salesman problem. An edge is the connection between two vertices of a graph. Weighted graphs have edges with a number or weight. Directed graphs have an edge from one vertex or source to another or target connecting in only one direction. Informatics algorithms and others may by be used.

One may also consider the logarithm which is any function that is constant times the logarithm of the argument such as execution time or memory space. It is bounded by the logarithmic function of the problem size in complexity theory mentioned in the introduction of this paper. Polylogarithmic is any function which is the sum of constants times the logarithm argument. The big-O notation is a measure of the execution of an algorithm like time or memory needed given the problem size or number of objects.

How the rules are ordered is of obvious importance. A set of rules may be established to work back from the conclusion to the evidence and is known as backward chaining. Forward chaining, of course, would be rules that work in the opposite manner. Conflict resolution decides which rules to use and in which order.

The purpose of this paper is not to dictate the best algorithm or system for analyzing conflict and peace. Instead, it is to be an interdisciplinary introduction to social computing concepts using the parent structure of systematic political science. The key to which is compliance with or noncompliance with NLF. Jesus was the only person recorded by his behavior, beliefs, and corpora to have been in perfect compliance with NLF. That perfect awareness or omniscience garnered him the name Prince of Peace. Many have claimed to come in peace but their noncompliance with NLF eventually gave rise to conflict exposing their deception. If one such world leader imposed a seven year peace treaty on the nation-state of Israel, maintaining that it would create lasting world peace, would in time also be revealed as a deceiver.

In closing, the standard of analysis for conflict and peace is not one of perfect awareness but one that may reach solutions similar to experts referred to as an expert system.