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Except, as [[David Viniar]]’s immortal words remind us, the events these numbers represent — the territory for which they are a map — are wont to have other ideas.  
Except, as [[David Viniar]]’s immortal words remind us, the events these numbers represent — the territory for which they are a map — are wont to have other ideas.  


{{quote|{{viniarquote}}}}
{{quote|{{viniarquote}}<ref>explaining why the [[vampire squid]]’s flagship hedge funds lost over a quarter of their value in a week, in 2008.</ref>}}


But there is a great difference between rolling dice and a stock market. A die is a “[[nomological machine]]”: a carefully constrained, sealed environment, designed to yield a specific theoretical outcome. The dice-rolling map is, as far as engineering permits, ''identical'' to the dice-rolling territory. We can, indeed, generate an indistinguishable outcome purely by running the model with a random number generator. The machined dice, the flat, constrained surface — these are a representation of the reality, which is the hypothetical model, and not the other way around.  A loaded die is a ''flawed'' machine. You don't chuck out the theory: you chuck out the equipment.
But rolling dice are not like the stock market.  
 
Mr Viniar’s model, he hoped, would tell him something about the behaviour of the market. The model is the map, the market is the territory. We judge the success of the model by how close its prediction is to historical experience. You would not expect a “twenty-five sigma” day once in several lifetimes of the universe. The model was in effect saying, this kind of event ''will not happen''. That there were several such days in a row — in a market history measured in decades, not universe lifetimes — must mean the model was wrong.<ref>It was, for reasons we explore elsewhere.</ref>
 
Rolling dice to ''determine'' an outcome is is quite
different. We do not build a statistical model that predicts a ⅙ probability: we build the dice to yield the that outcome. The dice are what [[Nancy Cartwright]] calls a “[[nomological machine]]”: a carefully designed, constrained, hermetically-sealed device, designed to generate a specific theoretical outcome. If over time the dice don’t yield a ⅙ outcome we don't chuck out our statistical model: we chuck out the ''dice''.
 
The “map” and territory ” are transposed: the dice are the map, the theoretical ⅙ probability is the territory. The map is, as far as engineering permits, ''identical'' to the territory. We could, indeed, generate the outcome we wanted without dice, by running the model with a random number generator.  
 
The machined dice, the flat, constrained surface — these are a representation of the reality, which is the hypothetical model, and not the other way around.  A loaded die is a ''flawed'' machine. You don't chuck out the theory: you chuck out the equipment.


Likewise, if, inside your nomological machine there is a mischievous imp who catches and places the die as it sees fit, the conditions for your probabilistic calculation do not prevail. There is an interfering causal agent.  
Likewise, if, inside your nomological machine there is a mischievous imp who catches and places the die as it sees fit, the conditions for your probabilistic calculation do not prevail. There is an interfering causal agent.  
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“Nomological machines” are highly constrained, artificial environments. If all their conditions are not satisfied, we can expect the world to behave differently without validating the machine. This is how, as [[Nancy Cartwright]] put it “the laws of physics lie”.
“Nomological machines” are highly constrained, artificial environments. If all their conditions are not satisfied, we can expect the world to behave differently without validating the machine. This is how, as [[Nancy Cartwright]] put it “the laws of physics lie”.


In any case, these are the circumstances in which the rules of probability prevail. Should the universe misbehave
In any case, these are the circumstances in which the rules of probability prevail. Should the universe “misbehave” then the conditions required for the nomological machine cannot be present.
 
Boy did I get sidetracked.
 
Normal distributions standard deviations, and confident probabilities require a complete nomological machine where all potential events are known, are independent, and there is no intervening agency that can upset the observed behaviour of the system. If you have all that all risks can be calculated and probabilities assigned.
 
Markets, in the abstract, look just like such a machine. There is a bounded environment, a finite trading day and a limited number of market participants and financial instruments which one can buy or sell. In the modern days of computerised trading everything is very clean, tidy observable, unitary and discrete.


====Derivatives trading====
====Derivatives trading====