Chapter III. Overview of Methods for Building a Simulation

Note: This is an excerpt from a draft of my thesis, A Computer Model of National Behavior. The introduction and table of contents
are also available

Chapter III. Overview of Methods for Building a Simulation

3.1 Introduction

The following sections overview different methods that were considered for this simulation. Ultimately, object-oriented programming, fuzzy logic, and genetic algorithms were accepted. Conversely, game theory, cellular automata, neural networks, and genetic programming were rejected. Lastly, the issue of simple and complicated data is discussed.

Computer Science Thesis Index

2.5 Computerized Simulations with Agents

Note: This is an excerpt from a draft of my thesis, A Computer Model of National Behavior. The introduction and table of contents
are also available

2.5 Computerized Simulations with Agents

By combining the world of previous models of group behavior with the principles of the new field of AI, many social scientists began crafting social science models employing both. In Simulation by the Social Scientist, Gilbert and Troitzsch note “Using computer simulation in the social sciences is a rather new idea – although the first examples data from the 1960s, simulation only began to be used widely in the 1990s…”

Quoted by plum, Troitzsch states, “Computer simulation in the social sciences had a difficult birth.” The models were all based on discrete event simulations or systems dynamics. Further, “The system dynamics approach makes use of large systems of difference equations to plot the trajectories of variables over time.”

The first major use of this socio-historical approach was the Club of Rome simulation of the global economy (Franz). However, the Club of Rome simulation gave the entire field a poor reputation, and its prediction of “global environmental catastrophe” revealed shortcomings of this approach.

“This early work also suffered in another respect: it was focused on prediction, while social scientists tend to be more concerned with understanding and explanation. This is partly due to scepticism [sic] about the possibility of making social predictions, based on both the inherent difficulty of doing so and also the possibility, peculiar to social and economic forecasting, that the forecast itself will affect the outcome.”

However, by the 1980s agent-based modeling had opened up new doors. Agents are self-contained programs that control their own actions based on their view of the world around them. Gilbert and Troitzsch note that agency models are increasingly influenced by the social sciences.

An impetus for the adoption of agent-based simulations by the social sciences was the need to provide more evidence for theories. Writing about the development of civic traditions in Italy, Bhavnani notes “Historical processes are notoriously difficult to study, and their findings equally difficult to validate empirically.” Historical analysis is a valuable tool, but without being able to “rerun” the past except in one’s mind any analysis is necessarily limited. Agent-based simulations provide a way around this.

Davidsson calls this emerging field Agent-Based Social Simulation and defines it as the intersection of social science, agent-based computing, and computer simulation. Davidson gives several justifications for this new field, including:

“[Social sciences] are very messy. They have ill-defined or unknown boundaries and individuals comprising the systems face constraints that are beyond their information processing capacities to define or to use in reaching decisions to act.

“The formal structure of agent based computing clearly provides a supportive environment for the application of logical formalisms and the formalisms developed with a view to a new social theory are frequently found useful in specifications of agents for purposes of engineering multi-agent systems.

“Social scientists have begun to convert social theories to computer programs. It is then possible to simulate social processes and carry out “experiments” that would otherwise be impossible “

Computer Science Thesis Index

2.4 Artificial Intelligence-based Approaches

Note: This is an excerpt from a draft of my thesis, A Computer Model of National Behavior. The introduction and table of contents
are also available

2.4 Artificial Intelligence-based Approaches

Smith states that the roots of Artificial Intelligence (AI) go back to Aristotle, who first tried to formalize “right thinking.” Smith notes that “[Aristotle's] syllogisms (three-part deductive reasoning) provided patterns for argument structures that always give ‘true‘ conclusions given ‘true‘ premises. [sic]” Mohsin, Amine, and Majeed note that Thomas Hobbes’ Leviathan is one of the earliest proponents of artificial life:

For seeing life is but a motion of limbs, the beginning whereof is in some principal part within, why may we not say that all automata(engines that move themselves by springs and wheels as does a watch) have an artificial life? For what is the heart but a spring, and the nerves but so many strings, and the joints but so many wheels giving motion to the whole body such as was intended by the artificier [sic]?

and further:

By ratiocination, I mean computation. Now to compute, is either to collect the sum of many things that are added together, or to know what remains when one thing is taken out of another. Ratiocination, therefore, is the same with addition and subtraction.

However, little practical work would come of Hobbes’ dream until the dawn of the computer age. The first decades of Computer Science saw steady advance in the field and application of AI. According to Smith, McCulloch and Pitts described artificial neurons in 1943, and eight years later Minsky and Edmons built SNARC, the first neural computer. Also in the 1950s LISP, a popular language family for AI programs, was written, as were checkers and chess playing programs. The emergence of board-game playing programs was a harbinger of future AI-society work, but one not built on for years.

Monolithic AI systems developed from the 1960s to the 1980s, but failed to progress. Weizenbaum states that by 1966 the AI program ELIZA could pass for a human. It proved useful enough that specialized versions of ELIZA were created since, according to Whitby. MYCIN, a domain specific system to diagnosis blood disorders, was running successfully by the late 1970s. Many practical applications were developed, and some some researches focused on developing social simulations. However, the effort to create a broadly credible AI system was not successful.

Computer Science Thesis Index

2.3 Computer Games

Note: This is an excerpt from a draft of my thesis, A Computer Model of National Behavior. The introduction and table of contents
are also available

2.3 Computer Games

Advances in computer technology since the 1970s have allowed simulations for fun to be increasingly complex yet still popular. A thorough review of the electronic entertainment field is outside the scope of this thesis. However, a few games covering community control and empire building will be described.

Many computer games have modeled political history by allowing the player to control a state, and therefore implicitly assume that states are the only acting agents. Hammurabi is the earliest example of this. Ahl relates that, though originally written for the Digital Equipment Corporation PDP-800 and made to fit in 400 bytes of memory, the game modeled a city-state and let the player manage land and food. Empire was another such game to be written, by Walter Bright in 1977 for the VAX/VMS computer system. Empire modeled the entire flow of history, from a first small village to global domination. According to Wikipedia, Empire is still popular and modifications for it are still sold.

SimCity was another early computer game that took a different view of history. Unlike Empire, it was consciously designed to use simulation theory. Only half in jest, its creators described it as a “System Dynamic CA [Cellular Automata] Hybrid Discrete Stochastic Monte Carlo Thing” in a book on the work by Dargahi and Bremer. Some of these concepts will be explored later in this thesis; for now, it is enough to say that SimCity assumed that cities were the drivers of history. The model mostly ignored larger affects, viewing growth and chance as being caused by the suitability of a local environment for different types of jobs.

A 1993 article in Computer Games Magazine by Tom Chick states that Sid Meier and a team under his direction wrote Civilization in 1990 to merge the models of Empire and SimCity, and a previous railroad management simulation. Combining SimCity’s complex economic trade engine with Empire’s world-scope, Civilization is considered to be one of the best strategy games ever. However, the statism of previous efforts remained.

Computer Science Thesis Index

Chapter II. Applicable Models

Note: This is an excerpt from a draft of my thesis, A Computer Model of National Behavior. The introduction and table of contents
are also available

Chapter II. Applicable Models

2.1 Introduction

This section covers avenues of research that were considered as potential models. Games are looked at, from primitive board games played for thousands of years to new single players games that unfold on a computer. The mathematical area of game theory is examined for what it contributes. Lastly the increasing interrelated disciplines of artificial intelligence and simulation are examined.

Computer Science Thesis Index

2.2 War Games

Note: This is an excerpt from a draft of my thesis, A Computer Model of National Behavior. The introduction and table of contents are also available

2.2 War Games

The simulation of opposing forces representing different groups of people is as old as humanity. War games are a good example of this activity. James F. Dunnigan notes that “Given the lethal nature of actual warfare and man’s penchant for self-preservation, it is quite possible that some form of war game occurred before the first organized war.” Greg Lastowka elaborates that “The very first time a ‘battle plan’ was discussed, debated, and refined, a crude form of war game took place.”

The oldest existing simulations involving popular conflict are only a few thousand years old, but they may be much older. Chess, through its ancestor Chaturanga, is often considered the first example, but there were older similar board games in both Asia (in China and India) and the Mediterranean Littoral (Greece, Rome, and Egypt). In particular, Kowlawski reports Latrunculi was probably played during the Trojan War and had a striking resemblance to Go, which is a now a popular game in east Asia.

It was not until the 17th century, however, that more detailed simulations developed. According to Lastowa, Koenigspiel was developed in 1664 by Christopher Weikhmann. Weikhmann defended this “king’s game” as “a compendium of the most useful military and political principles.” Later variations of Koenigspiel would expand to 1,666 distinct land areas, each with its own terrain and other attributes.

There would be further developments in this field, including Kriegspiel and Tactics. “Kriegspiel” means “war game,” and it was devised by Prussia and played widely during the Wars of German Unification. Tactics was developed by Charles S. Roberts and simulates a conflict between Post World War II superpowers. As modern militaries grew in complexity most simulations shifted focus to supply and distribution of armies in the field. In the words of Dunnigan:

“Much of the “gaming” that took place at the behest of the military after World War II was more operations research (OR) and systems analysis than the study of history. The study of past military operations, and history in general, which had formed the basis of the earlier wargames [sic], was very much neglected.”

Slowly the board game simulation of history fossilized. However, as this research began to peter out, computer scientists and psychologists working on artificial intelligent systems were approaching the model from a different direction.

Computer Science Thesis Index

Chapter I. Introduction

Note: This is an excerpt from a draft of my thesis, A Computer Model of National Behavior. The introduction and table of contents are also available

Chapter I. Introduction

This thesis seeks to use genetic algorithms and fuzzy logic to build an object-oriented model of national behavior. Nations will be shown to be autonomous actors that perform on the international stage in the
same way that individuals go about their daily lives. This model attempts to simulate observed history, especially the development of nations and states, according to simple rules. It assumes that the only agents are nations that can evolve, have descendants, and die. Everything else is viewed as a resource that cannot act on its own.

The following definitions are important. A place is a region of land that can border other regions of land. Depending on the scale, “Minnehaha County” or “Lower Saxony” are example places. A nation is collection of people that share a language, culture, and ethnicity. “French,” “German,” and “Occidental” are nations in western Europe. Finally, a state is political subdivision usually possessing sovereignty. The geographical borders of states can closely coincide with places and nations. States can sometimes be subdivisions of other states. Lower Saxony, the Federal Republic of Germany, and the European Union are all examples of states.

This thesis first covers research applicable to modeling this type ofpolitical behavior. Later, the specifics of these approaches areexplored, such as programming techniques like genetic algorithms andneural networks,
and the types of information to store. The thesis then identifies the specifics of the model as well as enumerating ways of verifying the model. Conclusions are researched and areas of future research are explored.

Lastly,this document contains four appendices. Appendix I gives an overviewof the Simulation Design, including the programming tools used,custom tools written by the author to assist in creating thesimulation. These range from data entry utilities to the automatedprocess by which objective and subjective reports were created. Appendix II discusses the subjective tests used and the expert reaction to them. Appendix III is concerned with the objective tests and their findings. Appendix IV contains the source code for the model and some tools.

Computer Science Thesis Index


Note: This is an excerpt from a draft of my thesis, A Computer Model of National Behavior. The introduction and table of contents
are also available


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Computer Science Thesis Index

A Challenge for Aaron (and Mark, and Bill, and Larry, and C…)

Hola de Puerto Rico

One of Thomas P.M. Barnett’s main theme is that they “do know better” — that enemies are not stupid, but understand the world and see where the current trends are leading. That is why they are enemies — they do not like that future.

When I watched it, I thought one of the weaknesses of “The Power of Nightmares” was its cartoonish claim of a secret alliance between “Neoconservatives” and Evangelical Christians. A throw-away line in Niall Ferguson’s “Colossus” has made me re-evaluate that criticism. I need to think it through, but after that one sentence the naturalness of a “neocon” and “religious right” alliance is clear as it never was before.

I will blog on that (with diagrams!) when I get back. But in the meantime: what is the current relationship between “neoconservatives” and religious conservatives? Is there anything natural about that relationship?

-Dan (en San Juan, Puerto Rico)

Update: For answer see here