The far Left wants the draft

Dan and I argued mightily over Kerry’s purported support of the draft. Besting me on technicalities, as usual, Dan had nothing substantive to say in the arguments, so I’ll just say I won. Dan supports ill-planned and unnecessary wars, and therefore the draft, and I support James Bond, and therefore deserve a beer. That aside, the energetic Kos and many of his readers have been calling for a draft for the past few weeks. Continued coverage of the “Hollow Army” talks about declining enlistments, raising bonuses, preventing loss and turnover, etc. The readers rightly criticize the armchair hawks of the right, who blog comfortably from their deckchairs in Aruba, sipping margaritas and no doubt wondering how much of their ill-gotten campaign kickbacks it would take to purchase the buxom serving girl from her Saudi banker / resort owner masters.

This causes me to wonder, what is their motivation? Greater equality? Get more of the white affluent campaign-contributing Christians into battle to lighten the load of the minority poor that are taking the brunt of insurgent attacks? Or are they hoping the draft would be so unpopular as to unseat the current Administration? Hard to say at this stage.

Isle of Wonders

Which, dear reader, is the most awesome cool aspect of Aruba? That fact that…

  • … it isn’t frikkin poor
  • … I was hassled by security, twice (see first point)
  • … the first store I saw was a Lincoln dealership (see first point)
  • … I visited the super-cool Nazi shipwreck

All, cool, indeed, but easily eclipsed by

the professional group swimsuit model photo shoot on the topless beach, right in front of my beach chair

Aruba? More like Awesomeruba

3.7 Genetic Algorithms

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

3.7 Genetic Algorithms

Genetic Algorithms (GAs) is a type of evolutionary algorithm (EA). Heitkotter describes EAs as “computer-based problem solving systems which use computational models of some aspects of known mechanisms of [evolution] as key elements in their design and implementation.” Like neural networks, EAs are based on biology. Indeed, a work from Brunel University states that the field was pioneered by John Holland who read a book on genetics while working on neural network research in the 1950s. EAs work by keeping a population of data structures. This population can change by deaths and reproduction.

There are two main differences between EAs in general and GAs in particular. First, GAs are always made up of chromosomes. Essentially, chromosomes are sets “of character strings that are analogous to the base-4 chromosomes that we see in our own DNA.” Like in DNA the pieces of data contained in chromosomes are called alleles. Thus chromosomes are no more than data structures, and GAs are simulations composed of data structures.

The second difference is that chromosomes are normally fixed-length in GAs. This allows for simple and mechanical manipulation of the alleles by basic operators. There are some GAs where the chromosomes are variable length, but Heitkötter and Beasley note that the majority off models are fixed. GA systems that do allow variables lengths are often “genetic programming” systems, which are discussed in the next section.

To visualize the genetic process for GAs, imagine two arrays of values. Say one had the values (1,2,3) and the other had (7,8,9). If they would breed, the children would inherit from both parents. So a possible child value is (1,2,9). Visually

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Figure 7. Genetic Algorithms Demonstration

GAs are based on the idea of evolution by selection where changes in a population result from the elimination of unfit members and the proliferation of fit members. This selection can be natural and caused by the environment, or intentional as ranchers and botanists artificially select for specific traits.

Specifically, new generations are created either by asexual reproduction (like when one cell splits into two identical cells) or sexual reproduction (like when children are the result of mixing of genetic material from their parents). In both of these cases, mutations (which are random changes in the genetic code) allow children to be different from their parents. Most of the time these mutations will be neutral or harmful. However, sometimes they will give the child a competitive advantage, allowing it to survive longer and have more children. Less fit members of the population may be killed off, which decreases the competitive pressures on more fit members. Thus over time, the population will be more fit for their environment. “Fitness” is a relative term because a change in the environment can cause many formerly fit members to die out. In a changing environment whole populations can become extinct.

Selection is the termination of unfit members. This culling can be done in one of two ways. One way is to calculate the fitness value for each member and allow the members with the highest fitness value to live on and multiply. This may be probabilistic to keep up a level of diversity and allow for the vagaries of chance. Another way is “tournament selection.” Tournament selection is more realistic and relies on direct competition between members of the population. In reality in any model there has to be some abstraction, and every model is a combination of these.

This model relies on genetic algorithms for the following reasons.

Firstly, genetic algorithms do not distort the model. They do not suffer from the problem of neural networks, where cognition is assumed to occur somewhere. Instead, nations are treated as simple creatures that can behave complexly. Just as bacteria act and evolve without any cognition, Beer shows nations play on their stage with just the tools available to them. “Successful nations are selfish… There is an environment. Units must adapt to it.” Occam’s Razor states that we should make no more assumptions than needed, and genetic algorithms allow us to do this.

Secondly, genetic algorithms provide all the functionality needed by the model. Nations have attributes which are their genetic code. New nations can splinter off from old nations (asexual reproduction) or as the result of mixing of many old nations (sexual reproduction). There is uncertainty when the attributes of the child are being formed, which is mutation. Some nations die off and some nations leave no descendants, so there is a selection mechanism at work. In short, necessary features of nations clearly exist in any genetic view of nations.

Lastly, genetic algorithms provide a population-environment feedback mechanism. As nations evolve and the population becomes more fit, the environment itself will change. Life as a nation among unfitnations is much safer than life as the same nation among morecompetitive nations. Fit nations would be more effective atgathering resources (places, magnitude, etc.),and so new strategies would be needed. A nation could merely keepoptimizing for the old environment, or could become a “predator”and attempt to gain by the loss of others by becoming moreaggressive. As nations optimize for this new environment, however,countermeasures will be developed and the situation will change again. Genetic algorithms provide a clean way to allow for this dynamic without adding new elements to the model. A cause of this fragility is the unscientific nature of GP development.

For these reasons, nondistortion, functionality, and feedback, GAs provide the best approach to modeling nations.

Computer Science Thesis Index

3.6 Neural Networks

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

3.6 Neural Networks

Clark describes neural networks as models of cognition based on the neurons in the human brain. In this model there are many layers of neurons which can affect each other through forward and backward propagation. A neuron receives inputs through dendrites, decides whether or not to pass a signal by comparing its inputs to a threshold in the nucleus, outputs through its synapses. Neural nets are most appropriate when no clear algorithm is available to perform the task, and when the model will need to adapt itself to new situations quickly.

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Figure 6. Neuron with Component Parts

Neural networks have been shown to explain the behavior of insects, animals, and in some areas humans, so perhaps they might explain the behavior of national populations of humans. Because there are many neurons involved, the calculations will be necessarily parallel, opening the door to optimizations. Additionally, the proven ability for biological neural networks to learn concepts and plot strategies have obvious benefits for nations.

However, despite these advantages, neural networks for modeling nations’ behavior.

First, a neural model would lose explanatory power. One of the potential justifications for the model of this thesis is that it can be used as a teaching aid in explaining how nations behave. However, a neural model would result in a method that would be too complex for students to understand. Any attempt to give a summation of the model would degenerate into a meaningless listing of weights

Relatedly, a solution that presumes that there is no clear algorithm is not wanted. Like a wide variety of other political science models, such as those by Gamble, Johnston, and Olsen, the model adopted in the thesis should be deterministic and attempt to explain behavior with reference to simple rules. Therefore, even if a neural net model produced the correct results, the system will not achieve its goal.

Finally, one of the major advantages of the neural net model is not needed. The system will not have to deal with suddenly different situations. For instance, neural nets are well suited to determining if two shots from two angles show the same person. The network can handle the abrupt change in perspective. But the ebb and flow of nations is far smoother. The same small number of rules are always working on nations, and only relatively few values are changing. This situation is different from image recognition, where an astonishing set of factors have to be considered. Whereas the first two objections show neural nets to be deficient, the final objection furthers the point that a neural network is simply inappropriate for the task at hand.

Computer Science Thesis Index