State Intrade prices imply a 98% probability of Obama Winning

Updated Simulation.R

library("hash")

# States treated as going democratic with probability 1
DemStates <- hash()
.set(DemStates, "WA"=12, "OR"=7, "CA"=55, "NM"=5,
                "MN"=10, "IL"=20, "MD"=10, "DC"=3, 
                "DE"=3, "NJ"=14, "CT"=7, "RI"=4, 
                "MA"=11, "VT"=3,"ME"=4,"HI"=4, "NY"=29)

# States treated as going republican with probability 1
RepStates <- hash()
.set(RepStates, "ID"=4, "UT"=6, "WY"=3, "MT"=3, "ND"=3,
                "SD"=3, "NE"=5, "KS"=6, "OK"=7, "TX"=38,
                "AR"=6, "LA"=8, "MS"=6, "AL"=9, "GA"=16, 
		"TN"=11, "KY"=8, "WV"=5, "SC"=9, "AK"=3)
     
# Swing States
# First entry in each value of the hash is the electoral 
# college vote
# Second entry is the intrade probability of the state 
# going democratic
SwingStates <- hash()
.set(SwingStates, "AZ"=c(11, 0.200), "NV"= c(6, 0.812), 
		  "IN"=c(11, 0.150), "WI"=c(10, 0.769), 
		  "CO"=c(9, 0.762), "MI"=c(16, 0.900), 
		  "IA"=c(6, 0.750), "VA"=c(13, 0.741), 
                  "MO"=c(10, 0.250), "NC"=c(15, 0.370), 
                  "FL"=c(29, 0.678), "PA"=c(20, 0.835), 
                  "NH"=c(4, 0.786), "OH"=c(18, 0.744))

# electoral college votes needed to win
kCountNeeded <- 538/2

Run.Simulation <- function () {
  # Returns TRUE if simulation predicts democratic win
  # ... and FALSE otherwise
  # treats each state as independent (I know - not true...)
 
  # the count of electoral votes that are "safely" democratic
  dem.count <- sum(values(DemStates))

  # runif(1) is a uniform random variable in (0,1)
  # it has a 75% probability of being less than 0.75
  # this is how we simulate the outcomes with the intrade
  # probabilities listed above
  for (i in keys(SwingStates)) {
    result <- runif(1)
    state  <- SwingStates[[i]]
    if (result < state[2]) {  # if runif(1) < intrade price
      dem.count <- dem.count + state[1] # then the dems get the votes
    }
  }
  if (dem.count > kCountNeeded) {
    return(T)
  } else {
    return(F)
  }
  
}

Run.Simulations <- function(count) {
  # returns portion of simulations that returned a
  # democratic win 
  i <- 0
  total.results <- c()
  while (i < count){
    single.result <- Run.Simulation()
    total.results <- append(total.results, single.result)
    i <- i + 1
  }
  return(sum(total.results)/count)

}

Run.Simulations(10000) #=> ~ 0.97