The Big Picture

Chapters 4 and 5 diverge into very interest ideas that support an objective interpretation of evolution and animal behavior. The first introduces the idea of genes programming our acting much like a human may program a computer program to play chess. The last introduces some concepts of mathematical balance and ESS, introducing enlightening interpretation of animal behavior in regards to territory. The similarity between computer programming and the role genes have of predisposing their survival machines to work on their own based on certain rules and hints given shocked me. The true working conditions of computer programs, such as chess games, were new to me in its integrity. I used to think that the computer had every possible play inscribed and the best possible move was always given in a list, from where the move to follow would come in accordance to the level chosen, matching the number of logical moves in the list. As of today, I understood how this was pretty much impossible, breaking apart my previous idea of computer programmed activities being far better than human equivalents.

Later in the chapter, Dawkins mentions negative feedback as a process where “the largest the discrepancy, the harder the machine works. In this way the machine will actually tend to reduce the discrepancy – this is why it is called negative feedback- and it may actually come to rest if the desire state is reached (50).” The discrepancy here refers to a specific objective or purpose intended for the machine, but it gets a little more complex when taken into human context. A machine will have a definite purpose and a stable formula for a specific way to get close to the objective by implementing x action as result of y condition. Humans lack that basic formula, or else life would be completely empty of mystery. For the purpose of the text, we shall consider the ultimate human intention to be the protection of its genes. Then what’s the formula? Clearly we have visualized an objective but lack a flawless method to reach such.

A mathematical interpretation of evolutionary stable strategies, or EES, comes handy in chapter 5. Through an example of behavior of doves and hawks we are carried around accustomed behaviors. The context is the following: “Hawks always fight as hard and as unrestrainedly as they can, retreating only when seriously injured. Doves only threaten in a dignified conventional way, never hurting anybody. If a hawk fights a dove the dove quickly gets away, and so does not get hurt. If a hawk fights a hawk they go on until one of them is seriously injured or dead (70).” To a simple mind, the dove seems to be in great disadvantage. To the current ideals, no strategy is superior because they are both evolutionarily stable when reached an interdependent balance. The dove has evolved into having the mechanism that provides for the best average survival and so has the hawk (notice how in this idea we are talking as species as a whole and not individuals, for the concept of individual behavior on all members of a group would need another book of its own). Species evolve to obtain the higest survival rate as a species in accordance to others, reaching a certain point where the strategy is stable. There may be more than one such point, but evolution is always headed to one of these as to produce a balance. As understood from this chapter, evolution is a complex concept. It is important to understand individual genetic matters as a component, but not to be ignored is species evolution as a whole.