Charles Darwin gave us insight into the nature of change in species that is arguably a powerful tool for gaining understanding into other other areas of study, as well. Universal Darwinism aims to formulate a generalized version of the mechanisms of variation, selection and heredity proposed by Darwin, so that they can apply to explain evolution in a wide variety of other domains.
This document explores the use of Universal Darwinism to search for new ways of understanding politics. The goal is not to make use of genetic/biological discoveries of Darwinian evolution, but to develop a complement to the field of Psephology, a branch of political science which deals with the study and scientific analysis of elections.
Darwin's algorithm adorns a very basic truth of the universe with regard to competition for "slots" of survival. The world is a perpetually changing, finite environment in which a more general version of [the mechanisms described by Darwin] are enforced at many levels. If nothing else, a study of politics through this lens may shed light on traits that support the fitness of candidates to win elections.
In her TED Talk, "Memes and Temes",
Susan Blackmore makes good use of a variation of Darwin's insight
to describe how ideas evolve in human society.
Note: This video is presented here for reference. It's not necessary for an understanding of the following content, so long as you accept that the concepts behind Darwin's basic algorithm are useful in understanding much more than the evolution of species.
Darwin's Algorithm
Here's how Blackmore breaks down the basics of Darwinism:
If, said Darwin, you have creatures that vary, and most of them die, and the survivors pass on whatever it was that helped them survive to their offspring, then you must get evolution. ~Susan Blackmore
Here are the key ideas:
- [Creatures that vary]
- + [Most of them die].
- + [Survivors pass on traits]
- = [increase in fitness], or "evolution".
We accept as self-evident that creatures have various traits which help and hinder their survival to various degrees. Darwin recognized that those whose traits tend to promote their survival will live to pass those traits to their offspring, while those that don't will tend to fail to procreate and eventually they (and their less useful traits) will recede or disappear.
Generalization of Darwin's Algorithm
Darwin's algorithm, as presented, has proven helpful in understanding the evolution of species. If reduced to the most general terms, the algorithm may also be useful in describing other systems in which fitness determines success.
These variables represent generic substitute terms:
| ENV | Environment. Any finite environment, real or theoretical. Environment provides positions for which entities (OBJ) compete, and enforces the conditions that determine the fitness value of any given entity's aptitude (APT) in such a way that the most fit entities tend to gain and keep positions (POS). |
|---|---|
| POS | Position. The position is a place in the environment. In natural (Darwinian) terms it's simply "the world", "nature", or some "niche". In more algorithmic terms, position can be seen as an array of zero or more slots to be filled by the most fit objects, entities, ideas, or whatever is represented by OBJ. |
| OBJ | Object. Any real or theoretical object (animal, man, algorithm, idea, etc...) that competes to find and/or keep a position (POS) in a specific environment (ENV). Any object will have a certain aptitude (APT) that determines its fitness to occupy a given position in a particular environment. |
| APT | Aptitude. Aptitude is a set of attributes and/or qualities of that affect the ability of an object (OBJ) to maintain a position (POS) in the relevant environment (ENV). Note that aptitude is one set of multiple qualities/attributes assigned to any given object for the purpose of determining how each affects fitness, alone or in combination with other qualities/attributes. |
| SRC | Source. The source of new instances of OBJ. In nature, SRC is supplied by the reproductive systems of creatures. In purely theoretical terms, this is not necessarily (and probably not usually) the case. The source of objects must be defined as dictated by the realities of the envrionment being studied. |
In these terms, SRC provides individual instances of objects (OBJ), each of which possess unique sets/values of attributes/qualities (APT) that determine fitness for filling positions (POS) in the envrionment being studied (ENV).
-
When you have, in any theoretical environment (ENV)...
- a finite set of [vacancies/slots/positions] (POS)
- being filled by [objects/entities/ideas] (OJB)
- with varying [traits/attributes] (APT)
- provided by [a prolific suplier] (SRC)
...then those OBJ with the APT most fit for gaining/keeping POS in ENV will gain/keep POS. Further, when SRC creates OBJ with APT sets more fit for POS in ENV, such OBJ will will replace a less fit OBJ currently existing in POS within ENV.
Competition and Selection
Clearly, competition is a key feature of Darwin's algorithm and it's obvious that any generalization would be useless without some form of competition driving the selection process. The most obvious factor would be competition for POS in ENV based on the APT of OBJ, so any ENV must have a finite, positive range of POS instances.
Less clear is the mechanism of the passage of traits. While it's true that dynasties seem to occur frequently even in modern politics, the similarity to Darwin's passage of traits ends there. However, a huge variation of traits exists in the general gene pool, and perhaps much more significantly, within the "meme" pool as (for example) prevalent politically relevant ideologies and philosophies.
By this logic, traits for selection are provided largely by the gene and meme pools, rather than by direct ancestral inheritence of politicans. The appearance of dynastic political lines even in representative government only serves to support this reasoning.
A Simple Scenerio for Darwinian Politics
Imagine a hypothetical political example: a small democratic city-state. The government consists of a single leader elected from among the 5000 citizens. A survey has indicated that 1% of the citizens are apparently qualified and interested in running for office.
Here's a breakdown of the variables for this scenerio:
- ENV is the city-state, its rules regarding political representation, and any other (potentially external) factors that might affect the internal environment.
- SRC is a set of 50 citizens apparently qualified and interested in running for office.
- POS represents the single representative position available.
- OBJ is a single candidate; there are 50 OBJ instances, one for each candidate.
- APT is a set of traits, one set per OBJ, that determines the fitness of each OBJ to win office (POS).
Discounting any unknown or "apparently random" variables, the member of SRC that possesses the top configuration of APT will end up being selected. It's important to note that the set of APT consists of traits not related to successfully fulfilling the requirements of POS, but for successfully winning the position. Potentially, the best candidate for the job might actually be the worst-suited to win the office, while the worst-suited to rule may be the best-suited to win.
Now selection can be understood by two factors:
- The traits that are most appropriate for winning. Broken down, this set of traits may include charisma, public perception of "honesty", proclaimed policy ideology, etc... but probably are more likely a result of some social mechanism based on voter perception of the candidates.
- The rules of the election as they are enforced. As the 50 candidates were defined (for the purpose of this hypothetical situation) as apparently qualified, it may be that some or all of them are disqualified before the actual election. The mechanism by which qualification is investigated, assessed, and enforced plays a potentially huge role in who actually gets to run for office.
Selection may also be affected by pseudo-random unknowns. Note that the traits encompassed by aptitude (APT) are not limited to working within the rules. It's conceivable (and potentially likely) that the most aggressive of technically unqualified candidates may successfully hide any self-disqualifying factors while arranging for fully-qualified candidates to be perceived as unqualified or at least unfit.
In addition to willful subterfuge by candidates and/or their agents, there may also exist pseudo-randomness hidden in unknown attributes of any given candidate or the environmtent in general.
Election Prediction
There's a strong potential for pitfalls presented by hidden, incorrect, and undiscovered values in the environment and in aptitudes. Development of a prediction algorithm may require much time and effort, and will likely require constant refinement so as to be kept current with changing trends.
To use the Darwin Algorithm as a predictive tool, Environment (ENV) and Position (POS) might be thought of as algorithms to a function intended to represent "reality" (the rules of environment as we understand them) so as to return the best possible guess as to which OBJ (based on their individual APT) are most fit to fill the available POS within ENV.
Arguments to such a function might be given as an array of candidates (OBJ) in which each candidate object contains another array of value pairs [trait names = a value assignment]. If all relevant traits were specified correctly for each candidate (OBJ) and all algorithms for environment and position were correctly framed, the results would be perfect.
Unfortunately, this is an impractical expectation in the real world. To predict the winner of a political race would require a thorough understanding of the environment and of the traits that affect fitness for winning the election. These two factors, if perfectly and completely understood, would lead to a 100% success rate in prediction. As a 100% perfect understanding of reality is impossible, it must be accepted that only a partial success is possible. The level of success in such a prediction would depend on the honesty and thoroughness by which aptitude (APT) traits are selected and values assigned, as well as on the accuracy of the algorithms used to assess the fitness such traits suggest.
It may, however, be possible to run specify certain ranges for tests within the algorithms, along with certain ranges of value assessments, in such a way that intersections of correct matches to historical data may be encountered. Such an analysis might reveal changing trends over multiple elections, and such trends might reveal a connection to socioeconomic, capitalistic, moral, or other ideological trends. This may appear more to be more art than science, but this too can be useful and may even lead to a sort of political calculus that encompases real world conditions as variables that affect the liklihood that certain apparent traits (APT) are more or less fit than others.
Notes and Document Status
This seventh draft brings what I feel to be a complete outline. Please give any feedback, kind or cruel. I'm looking for holes in both the ideas and their presentation. Even spelling/grammar corrections are greatly appreciated!
A couple of things I'm wrestling with at the moment are:
- How readable is this document? Is it easy to understand? Where are my points unclear? Where does the reasoning seem illogical?
- Who can give me some insight and ideas into the validity of my descriptions of algorithms in the last section?
- I'm wondering whether "Darwin's Algorithm" is the best description for this generalization of his idea. I'm not too sure whether this idea meets the criteria for being considered under Universal Darwinism.
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