Chapter IV. A New Model of the Behavior of Nations

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 IV. A New Model of the Behavior of Nations

4.1 Objects and Data Types

The system described in the thesis models the behavior of nations and how they change themselves, the populations that make them up, and the political systems that emerge from their actions. Therefore these aspects of nations, people, and politics, have to be represented quantitatively. A naiveapproach would start work immediately with the assumption that three class types corresponding to the above aspects would be used. However, a better method is possible.

In OO design a system is made up of things or “objects” which have properties (such as the weight of a rock and the sharpness of a knife) and methods (such as knives cutting or rocks falling). Joines and Roberts note that “[OO] systems view the world as a set of autonomous agents that interact or work together to solve some complex task.” As the only autonomous agents in this model are nations, nations is the sole object type used.

So then will the model just have many nations objects competing and cooperating, with populations and politics stored as a complex data type for each nation? A strict OO system might use this approach, but this is not how we think of populations and politics, and so it would merely work to sabotage the third benefit of OO design mentioned. The model therefore needs some approach to complement the OO method. Fortunately, the very common Entity-Relation (ER) design style fits this situation perfectly.

The ER model was created in the 1970 by E.F. Codd as a way of designing and organizing databases. Like OO design it attempts to view the model as humans do. Codd describes it as providing “a means of describing data with its natural structural only – that is, without superimposing any additional structure for machine representation purposes.” It does this by using entities, which are analogous to objects, and relations or relationships, which describe how entities pertain to each other. Entities represent the real-world object about which data is being collected. So populations and politics might be represented as entities. All entities are made up of attributes, so the hypothetical populations entity might have the number of people in it and their average wealth as attributes. Each important association between entities is shown as a relationship. Therefore the relation of populations to politicsmight be one-to-one (each population is associated with and only with one body politic), one-to-many, many-to-one, and many-to-many. The exact nature of the relation can be defined more precisely, but the basic types of relations have been listed above.

Unlike objects in OO systems, entities in ER systems do not have methods. The ER design is appropriate for determining which data can be stored, but it has no way to indicate what entities can do. For this system, this is a benefit because it clearly allows us to separate nations, which can act, from everything else that cannot.

Now that the design of the model has been finished using ER properties, how should it be set up? The best approach is to decide how one naturally thinks of this problem and model it accordingly. The fact that nations are often viewed as relating to land gives a hint. Using places as an entity type allows the model to easily describe how nations affect populations without viewing the world as an undifferentiated mass. Thus we have one object type, nations, which is related to the entity type places. Because we think of nations being in many places, and a place can host several nations, the relationship between nations and places is many-to-many. Because the number of possible relations between a specific nation and different places is variable, and the same is true of relations between a place and nations, this is known as an M-to-N relationship.

Figure 7. Conceptual E-R M-N Relation

For example, consider a model of Europe where there are a number of places and two nations: French and German. Clearly French will be very strong around Paris and German very strong around Berlin. But in Alsace-Lorraine which is on the border, there is a strong influence of both nations. So in this example every nation is in more than one place and some places are associated with more than one nation.

Figure 8. Instantiated E-R M-N Relation

The only concept left out so far is political entities. This concept is ambiguous, so a description of what this entity is and how it relates to other entities is needed. This political entity demonstrates how the actions of nations affect not only nations but also other institutions. Specifically, political institutions that appear to be important by themselves actually emerge entirely out of national behavior. Institutions entirely internal to a political unit such as form of government are out of the scope of this project, so political units can best be represented by states. States are the most obvious feature on any political map, and have been the focus of AI models in the past. For example, as far back as 1971 complex computer models were developed to model conflict between the People’s Republic of China and the United States (Clema and Kirkham). It thus seems appropriate to see if the model can correctly predict their behavior without allocating states any methods.

Because of the system’s ER approaches, states have to be directly related to either nations or places or both. Again the solution is found by asking how one normally thinks of states. For at least a hundred years the answer has been geopolitically, which means that states directly relate to places.

Now there is almost a complete OO and ER system to describe the model. The only object type is nations. The only three entities are nations, places, and states. Places directly relate to both nations and to states, meaning there is a relation between places and nations and a relation between places and states. Both of these relations are many to many, as a nation can be in many places, a place can have many nations, a place can be owned by many states (as in a federal system), and a state can own many places.

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4.2 Nations-in-Places

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

4.2 Nations-in-Places

The relation between nations and places is complex. It has its own attributes can perform its own actions. In some ways it is closer to an “entity” or “object” than a relation. In the ER model this is valid. As importantly, such a complex relationship is the best approach for this model. Through the rest of this paper this relationship will be referred to as a nation-in-place. For convenience, in the remainder of this document a nation-in-place will be written “NP,” and the plural will be “NPs.”

NPs play several vital roles. They rationalize the apparent influence of regionalism in a world governed by nations. Collections of NPs determine the overall characteristics of nations themselves. NPs are the agents that can directly shape the attributes of a given place. Also, NPs present a critical method for the birth of new nations and so are vital in the implementation of a genetic approach.

A possible objection to a nation-based model is that the definition is nation is arbitrary. Even in a place where national feeling is completely shared among the people there will be differences in outlook depending on where one goes. Some places will be more inward looking, others might dislike the mannerism of some of their fellow countrymen, etc. To model this, one might have to create a new nation for each region of a country, even though these nations would have mostly identical attributes. NPs provide an alternate approach, which views this apparent dissonance as a necessary part of a national world view.

Modeling the force of regions, such as the impact that Bretons and Alsatians have in France, as the relation between nations and places is attractive for several reasons. First, it simplifies the model. There is no reason to add a new entity type if some behavior can be explained using existing entities and relations. Second, it matches a human view of the world in that the behavior of everything is affected by the land. This belief is far older than the concept of geopolitics, and the inclusion of NPs allows the model to retain this concept while still viewing history as nation-driven.

NPs are used to model the effect that places have on nations. Time is modeled as essentially a series of discrete moments where nations can act. However, nations have to be updated so that they continue to reflect facts “on the ground.” Implementation of NPs make this step much easier. A nation is recalibrated by averaging out the values of its constituent NPs; these averages become the new values that make up a nation. The weighted average is determined by the magnitude and density of the NP and the power, population, and wealth of a place, over all places where a nation exists. The specifics of the formula will be determined during the construction of the model. The values of the coefficients for these factors are a detail of implementation and before the model can be run, no precise values are better or worse than any others.

NPs also provide a feedback mechanism that affects places themselves. Though some changes are made purposefully by nations, and some changes are the result of momentum, places are nonetheless obviously affected by nations passively as well. The values of places can thus be altered by the averages of a place’s NPs, which can be calculated by their magnitude and density. Unlike the above related calculations for recalibrating nations, the recalculation of places concerns only NPs that reside in that one particular place.

NPs provide the “splintering” mechanism that gives birth to new nations. The attributes of all things, but especially nations and places, are constantly changing. This allows for the natural evolution of nations and also introduces uncertainty. Differences in the environments in different places help rend nations apart. For example, a definite Dutch nation existed in both the Netherlands and southern Africa. However, a series of shocks lasting from the late 18th to early 20th centuries split off an Afrikaner nationality. Another famous example of this asexual reproduction is the revolution that created the United States of America from thirteen very British colonies.

NPs also give rise to births, which allow for national mixing and multi-parented new nations. The mechanism for this is as follows. Between each round where nations make decisions, the model calculates the new state of all entities as described above. As the national state is updated, new nations are created by essentially averaging the values of NPs in regional groupings of places. These new nations are much weaker than their parent nations, and so normally cannot compete – they would be absorbed back into their parents or starved. However, if the parent nation is undergoing significant stress then one or more child nations might be able to survive.

In this way, NPs yield a never ending stream of new generations of nations that are tossed into the mix. The young may begin as weak new variants that have a difficult time surviving. Just as with life, the selection process can be harsh and cause many useful features might die out due to unfair circumstances. This constant opportunity for development will never end until change itself stops and so the fitness of nations will be ever changing. NPs provide the lynch pin in this process and so are very appropriate in this genetic attempt to model national behavior.

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