Roger Mexico’s Poisson Equation

“His little bureau is dominated now by a glimmering map, a window into another landscape than winter Sussex, written names and spidering streets, an ink ghost of London, ruled off into 576 squares, a quarter square kilometer each. Rocket strikes are represented by red circles. The Poisson equation will tell, for a number of total hits arbitrarily chosen, how many squares will get none, how many squares will get one, two, three, and so on” (Pynchon 56).

This passage is referring to the statistical method by which Roger Mexico “prophesizes” where the bombs will hit throughout London. It is also used as a statistical tool to explain the locations of Slothrop’s sexual rendezvous later in the novel. When reading this portion of Pynchon’s novel, his reference to the Poisson equation struck me. Now, being more of a book nerd than a math nerd myself, I had struck a prime investigative focus. I decided to further explore this Poisson equation in an attempt to better understand Roger’s occupation and character.

First, I addressed the Gravity’s Rainbow Wiki as I pulled up the Wikipedia page on the Poisson equation. I was hoping the Wiki could somehow put it into layman’s terms for me, as the formula itself looked VERY confusing. The Wiki provided me with this definition, “54; a probability density function reflected in where the bombs hit London and the locations of Slothrop’s (fantasized?) sexual-encounters, 55, 85-86; 171; and babies born during the blitz, 173; “erotic Poisson” 270.” So, the Wiki kind of outlined what I had already deducted from the novel. The equation is used to figure out probability density. But what it did highlight for me was the connection between Roger and Slothrop’s maps, and the relation of Slothrop’s fascination with bombs to his fascination with women. I also thought it was interesting that this definition provided by the Wiki underlying highlighted the connection between what can be ordered and what houses orderlessness in the novel. One can use an equation to figure out where bombs are dropped. But can one truly come up with an accurate number for Slothrop’s encounters?

But I wanted to dive a little deeper than this – I wanted to figure out how one actually uses this equation to figure out probability density; i.e. what numbers go into the equation? etc. I turned to the University of Massachusetts resource website  for help on the subject, and they provided me with what they referred to as “The Classic Example”. In 1898 the formula was used in order to figure out how many Prussian cavalry members would get killed via kick of a horse. X number of army corps were observed over Y number of years, Z number of deaths were recorded, and so on and so forth. So after finding the average deaths for one corps per year, this number is then plugged into the Poisson equation. A table is used to figure out which values to use for certain other variables of the equation. The equation is then set to equal anticipated results (0,1,2,3…) in order to test which is the most likely to occur; that is, one death by horse, two, three, and so on. So the equation then explains to us what the most likely result is.

So essentially what I’d figured out from this exploration is that the formula sort of goes over my head, and that for my purpose in reading this book, the Poisson equation should exist as a function within the text. The function itself displays the probability of the events as independent of one another, which is vital when understanding what Roger says to Jessica on page 58 in response to her statement, “Well, it isn’t fair”:

“It’s eminently fair,” Roger now cynical, looking very young, she thinks. “Everyone’s equal. Same chance of getting hit. Equal in the eyes of the rocket” (Pynchon 58).

The Wiki explains this statement a bit further, “even if a given square kilometer of London has already received 100 rocket strikes today, it is still just as likely to be hit again as any other square kilometer of London.”

Let’s relate this back to Slothrop then. If Slothrop has a cluster of stars on his map representing his sex life, the Poisson equation would tell us that though he may have already slept with someone in that region, it is just as likely to happen again in that location as it is in any other. Thus again connecting the bomb strikes to Slothrop’s sexual exploitations.

 

Works Cited

“Gravit’ys Rainbow Wiki.” Thomas Pynchon Wiki. N.p., 6 Apr 2012. Web. 20 Mar 2014. <http://gravitys-

rainbow.pynchonwiki.com/wiki/index.php?title=Pages_53-60>.

Pynchon, Thomas. Gravity’s Rainbow. New York: Penguin, 1973. Print.

“The Poisson Distribution.” University of Massachusetts Resources . N.p., 24 Aug 2007. Web. 20 Mar

2014. <http://www.umass.edu/wsp/resources/poisson/

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3 Responses to Roger Mexico’s Poisson Equation

  1. moniquebriones2014 says:

    With Pitt’s statistics department being so understaffed that it can’t even guarantee offering required courses every term, I wouldn’t be too hard on yourself for not completely understanding the Poisson distribution.

    I like your point about the importance of statistical independence and how it’s used to make Roger’s point about how this distribution proves to be “eminently fair” and to show how “everyone’s equal” because it removes statistical bias. One question always hits me whenever the Poisson equation is brought up: what is fair about randomness? The same question was posed during a law school orientation exercise someone I know attended, during which he and a group of students had to choose, in a hypothetical hospital situation, who would get life-saving treatment and who wouldn’t–some groups randomized the lives they chose to save. Is it fair when you’re not taking into account people’s dues? I feel that Jessica, Pointsman, and a few others within the novel try to make this point to Roger, particularly in the section you chose. And if it is so “fair” and equality-promoting, why wouldn’t we use it more often in other institutions? For instance, while our lives may not be as directly on the line as those experiencing the Battle of London, we wouldn’t use randomness to determine our political leaders. What would make Roger think that doing the same with human lives is any different? What’s so bad about keeping some statistical bias in the equation? It’s these questions that Roger either doesn’t think about or refuses to answer (“It’s just an equation . . .”) that seem to be why Pirate calls his nihilism “cheap.”

  2. jcm93pitt says:

    Great point, Monique. Randomness, in many instances, is not fair. But I think you’re right in that Roger is refusing to answer that question. I think his function as a character demands that he refuse to answer that question. But you posed a great point – I’m just not sure what the alternative to this equation could possibly be in order to make it more “fair”. Thoughts?

    • moniquebriones2014 says:

      There are a few ways that people have gone about trying to find more “fair” alternatives (though I wouldn’t say I completely subscribe to any of them).

      There’s the utilitarian method of choosing who gets saved. Under that method, we would save whoever “maximizes utility,” or rather whoever provides the greatest overall benefits to a society. Doctors and scientists and such.

      The second method I can think of is saving those who can provide services that no one else, or not many others, can. Working single mothers with multiple kids under working age, for instance.

      I think you can see where the faults in both these methods lie. They’re quite subjective, and I guess a better question would be: what about the rest of us? Is this what we should ultimately measure our worth by? In the law school exercise my boyfriend participated in, most groups chose to not save the model or the major league baseball player. What’s so bad about them when we can probably assume that they’ve paid their dues and followed their lives accordingly to the norm like the rest of us?

      I actually do like the above methods I’ve mentioned a little more than plain randomness, however. I’m probably oversimplifying a lot of statistical concepts by giving this example, but if we took ten people from the FBI’s Most Wanted list and ten of the country’s biggest philanthropists, and could only save ten of the twenty, would randomness really be the best way to execute how we choose?

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