Matrix of Deceit: Election Myths, Logic and Probability of Fraud

29 Oct

Election Fraud: Uncertainty, Logic and Probability

Richard Charnin
Oct. 29, 2012

Everyone thinks about how to go about solve problems. But how can they be sure the methods used to solve them are valid? My new book Matrix of Deceit: Forcing Pre-election and Exit Polls to Match Fraudulent Vote Counts deals with uncertainty in our election systems. How do we know that the votes are counted as cast? If the information we are given is tainted, how do we know? We must distinguish between intuitive and logical reasoning. Decisions must be made where there are multiple solution methods.

Which make the most sense? Which is the most probable? If you flip a coin and it comes up heads five times in a row, is the next flip more likely to be tails? Is a baseball player with a .300 batting average who has not had a base hit in his last 10 at bats due to get one his next time up? In decision making, we always need to consider probabilities.

In mathematics we need unambiguous definitions and rules. In other words, we need logical thinking. Logic is defined as a systematic study of the conditions and procedures required to make valid inferences.

We start with a statement and infer other statements are valid and justified as a consequence of the initial statement. It is important to note that logical inference does not mean the statement is true, only that it is valid. If the starting statement is true, then a logically derived result must also be true.

For example, it is a statement of fact that Bush had 50.5 million recorded votes in 2000. Approximately 2.5 million Bush 2000 voters died prior to the 2004 election, so there could not have been more than 48 million returning Bush voters. But according to the 2004 National Exit Poll, there were 52.6 million returning Bush voters. This is clearly impossible.

Furthermore, since the 2004 National Exit Poll was impossible and adjusted to match the recorded vote, then the recorded vote must also have been impossible. This simple deductive reasoning proves 2004 Election Fraud. But the recorded 2000 vote was also fraudulent – as were all elections before that. None reflected true voter intent. The simple proof: there were 6-10 million uncounted votes in every election prior to 2004. Votes cast exceeded votes recorded by 6-10 million. And 70-80% of the uncounted votes were Democratic.

Each National Exit poll is forced to match the bogus recorded vote based on bogus returning voters from the prior bogus election. It’s a recursive process. The polls assume all elections are fair and accurate. The same returning voter logic applied to the 1988, 1992 and 2008 elections shows that they were also fraudulent; the National Exit Polls were forced to match the recorded vote by indicating there were more returning Bush voters than were alive to vote. The corporate media has never seen fit to explain these recurring impossibilities.

Science is “cumulative”. New developments may refine or extend past knowledge. There is no such thing as a foolproof system. What is needed is a probability-based system for many types of problems. It is the only rational way of thinking.

There is no way to eliminate all risk (error) in a system model (or election poll). The problem is to evaluate risk and measure it based on a probability analysis. Every important problem requires a comparison of the odds. Probability analysis supplements classical logical thinking but does not replace it. In fact, classical logic is required in every step in the development of probability theory.

Election Model Forecast; Post-election True Vote Model

2004 Election Model (2-party shares)
Recorded Vote: 48.3%, 255 EV
Election Model: Kerry 51.8%, 337 EV (snapshot)
State exit poll aggregate: 51.7%, 337 EV
Unadjusted National Exit poll: Kerry 51.7-Bush 47%
True Vote Model: 53.6%, 364 EV

2008 Election Model
Obama 53.1%, 365.3 EV (simulation mean);
Recorded: 52.9%, 365 EV
State unadjusted exit poll aggregate: 58.0%, 420 EV
Unadjusted National Exit Poll: Obama 61.0-McCain 36.2%
True Vote Model: 58.0%, 420 EV

2012 Election Model
Obama Projected: 51.6% (2-party), 332 EV snapshot; 320.7 expected; 321.6 mean
Adjusted National Exit Poll (recorded): 51.0-47.2%, 332 EV
True Vote Model 56.1%, 391 EV (snapshot); 385 EV (expected)
Unadjusted State Exit Polls: not released
Unadjusted National Exit Poll: not released


Posted by on October 29, 2012 in Uncategorized


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7 responses to “Matrix of Deceit: Election Myths, Logic and Probability of Fraud

  1. Scott

    October 29, 2012 at 3:02 pm

    Hi Richard,
    What is your comments on the vote flipping article that I summarize below:
    There seems to be vote rigging in presidential elections, including
    2004 and 2008, that can be seen by looking at statistics.
    Fraudulently, a computer program (possibly at the state’s central
    precinct tabulator level) automatically flips a percentage of votes
    from one candidate to another but that the percentage of votes flipped
    varies with the size of the precinct. The “rules” of the fraudulent
    vote flipping algorithm are the following:
    Very small precincts don’t have any votes “flipped” unless that
    precinct is larger than, for example, 350 voters.
    The percentage of votes that are flipped is small (such as .01%) for
    small precincts and large (such as 5%) for large precincts with a
    *gradual* change in percentage “flipped” dependent on the precinct
    voter population.
    The reason that the perpetrators don’t flip as many votes in small
    districts is because a recount will check a random number of precincts
    and a smaller precinct is *more* likely to be audited because there
    are more of them. So if fraudulent vote flipping flips 5% of votes
    from Democrats to Republicans, a random recount of precincts would
    show a smaller error – such as 2%.
    There is a smooth gradual change in the percentage of votes flipped
    between the small precincts and large precincts and is seen in the
    The authors of the paper show that the effect does not happen in data
    from some counties presumably because the perpetrators did not have
    access to those tabulating computers and it is not seen in democratic
    primaries. The authors of the study look at income and poverty rates
    which are highly correlated with voter choice but do not correlate
    with precinct size. This rules out more Republicans living in large
    precincts as being the cause of the anomalous data.
    To find the article, do a google on the following words:
    vote flipping small large precincts central tabulator
    Also, here is a link to the article,

    Click to access 2008_2012_ElectionsResultsAnomaliesAndAnalysis_V1.51.pdf

    • helenofmarlowe

      October 30, 2012 at 8:54 pm

      Scott, your link doesn’t work, at least not for me. The October 26 edition of Harper’s Magazine has a very good article entitled “How to Rig an Election”. Also, I’ve written up much of this on my blog, with info from Coursera (University of Michigan).

    • Richard Charnin

      October 31, 2012 at 1:43 am

      I have read the paper and am familiar with the the argument. I can say that to the best of my knowledge the analysis is thorough and the logic is sound. The key is that the flipping occurs over many different primaries in the same direction – always favoring Romney.


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