The model simulates European nations during the 1960s. The chose of setting was heavily influenced by the need to reliable census data, which made Europe a great fit. However, political scientists have been able to estimates of populations in a variety of areas. It therefore would be possible and profitable to model different theaters or areas that are currently of greater concern than Europe. The Middle East, for example, with its mixture of national identities, would be a prime candidate for consideration as a new theater.

A related exploration would be into ethnic groups within the United States. This would require a reworking of definitions, as the simulation typically treats groups with a shared language as the same ethnicity. Nonetheless, ample information is available. Very detailed ethnological information is available from the U.S. Census. It would be fascinating to move the simulation from a world of sovereign states and wars to the United States and a peaceful democracy.

Running the simulation as it currently stands is processor intensive. Several times algorithms were modified to allow for quicker execution. Additionally, a local firm donated the use of a four-CPU Intel Xeon system with 2 GB of RAM, which allowed the runs to be completed in a reasonable amount of time as several simulations could execute simultaneously. Even then there were issues relating to processor usage.

A potential solution is moving away from monolithic code. The current programming of the simulation assumes there is only one CPU to work on the problem. Distributed computing allows several CPUs to be involved. While a high performance server may be difficult to reserve, a larger of number of less powerful computers may be readily available. The code currently evaluates the next decision of each nation sequentially. Instead, it could offload computations to a collection of computers. This distribution of work could occur in a computer lab that is not currently being used, but as “Seti@Home” and “Seventeen or Bust” have demonstrated, this can also occur over the internet.

Bauer reports that “genocide” was first defined by Raphael Lemkin as the “destruction of a nation or ethnic group” and that “[generally] speaking, genocide does not necessarily mean the immediate destruction of a nation.” This model’s relevance to genocide studies is obvious, especially as the willingness to commit genocide by this definition is very close to the simulation’s definition of “aggressiveness.” With respect to genocide and holocaust studies, three avenues of research are immediately available.

First, the parallel between this model and existing research could be embraced, and the simulation could be further explored to better inform existing research. Robert Melson views genocide as of basically two types: “total domestic genocide” which are complete annihilation of a subgroup by their countryman, and “genocide in general” which can exist without state planning and little bloodshed. This broad range, from mass murder on the one-hand to multicultural assimilation on the other, is not distinguished in the current code. By adding details to allow better examination of how a nation is reduced, the study of genocide by the model is possible.

Second, differences could be explored. Especially in the context of the nation-centric concept of genocide, Bauer’s claim that “One can change one’s religion or one’s political color. One cannot change one’s ethnicity or nationality or ‘race’” (page 11) is striking. An alternate simulation could be used. The population of places could be disaggregated into age categories. Working under Bauer’s assumption, national identity of a population could be fixed after a certain age. These changes would help this view of genocide.

Third, Appendix D discusses anomalous results for the Jewish nation in the simulation. The voluntary relocation of a substantial number of Jews into the former mandate Palestine is one of the most visible national movements of the 20th century. Expanding the theater into the Middle East and North Africa would allow more research into this finding.

Genetic algorithms are used in the simulation to model the genetic relationship between nations. Allowing nations to inherit not only attributes but even logic and ways of dealing with problems would make the model more realistic.

As discussed in Appendix B, acquiring a complete and internally consistent data set for the model was a challenge. However, new developments present an opportunity. The Minnesota Population Center at the University of Minnesota is currently funding IPUMS, the “Integrated Public Use Microdata Series.” From a project web page,

Large machine-readable census microdata samples exist for many countries, but access to these data has been limited and the documentation is often inadequate. Even where such microdata are available for scholarly research, comparisons across countries or time periods are difficult because of inconsistencies in both data and documentation. IPUMS-International addresses these issues by converting census microdata for multiple countries into a consistent format, supplying comprehensive documentation, and making the data and documentation available through a web-based data dissemination system.

Such a resource would be invaluable. The database of census information supporting this model came from numerous sources and often there were gaps in the data. Additionally, the lack of census data in other theaters narrowed the study being conducted. More data and better data would help make the simulation’s output more reliable. IPUMS is still very incomplete, but as more information comes online these areas of the study can be strengthened.

Computer Science Thesis Index