If it comes to a N:N+1 vote split, with M people not voting even though they could, then

▪ Any of the N+1 voters was pivotal, as you correctly note. That’s because if any of these voters abstained, the result would instead have been a tie.

▪ However, none of the N voters were pivotal. That’s because if any one of these N voters didn’t vote, the result would still be exactly the same (the candidate who’s winning still wins).

▪ As for the M voters, say x would’ve voted for the losing candidate and M-x would’ve voted for the winning candidate. Then by similar reasoning, any of the x voters would’ve been pivotal had they cast their vote, because they would’ve created a tie. In contrast, none of the M-x voters would’ve been pivotal had they cast their vote, because even with their vote, the same candidate would still win.

Altogether then, only N+1 voters were pivotal. And if you want to count also those voters who could’ve voted but didn’t, then N+1+M-x voters were pivotal.

Note: The analysis is slightly different if it comes to a N:N vote. In that case, everyone is indeed pivotal.

]]>Approximate numbers I’ve seen are something like 4000 crimes per 100,000 people per year in the general population, and 20/100000/yr among concealed carry holders.

if you slice it differently (based on say violent crimes, or gun related crimes, or whatever) you can probably wiggle the estimates around, but as a baseline 4000/20 = 200 times more likely to have a random general population member commit a crime than a random carry holder. Even if you somehow slice this along violent non-drug-related crimes causing injury or something and you could maybe make the ratio as low as 50:1 or something (I just made that number up) it’s still way more likely that the crime was committed by a non-carry-holder. Also, carry holders are about 6% of the population… so

p(carry holder | crime)/ p(non carry holder | crime) = p(crime | carry holder) p(carry holder)/p(crime) / ( p(crime | non carry holder) p(non carry holder)/p(crime))

the p(crime) cancels. p(carry holder) ~ 0.06 and p(non carry holder) ~ .94

p(crime | carry holder)/p(crime | non carry holder) we are estimating at 1/50 to 1/200

so the overall ratio is 1/50 * .06/.94 = 0.00128 down to 1/200 * .06/.94 = .00032 or 1/ those numbers = somewhere in the range 781 to 3125 times more likely to be a non carry holder than a carry holder.

]]>If a single honest vote could decide an election — then a single dishonest/phony vote could decide an election.

The mundane voting process matters greatly.

The emphasis here upon a single vote being valuable seems primarily subjective wheedling to bolster the reflexively assumed legitimacy of “elected” American government. The underlying story line:

‘Your vote really counts (or could really count)– and since every vote really counts — then elections are really important & really represent the will of the people… and we all should therefore really support our government that is ultimately delivered by that really good election system’.

Doubts/questions about that election system’s practical accuracy /legitimacy must be promptly dismissed as unreasonable, insignificant gray areas, or contrary to theory.

]]>I have more trouble with pivotality wrt courts, which would be triggered when it’s too close. Though I suppose it must be true, the hard threshold must exist, I have real trouble accepting that, say, there is a world in which it goes to the courts but it wouldn’t have gone to the courts if only the margin had been greater by 1 vote.

And then there’s the stuff Daniel Lakeland has brought up above.

It’s very hard to even conceive of the event of being decisive in a huge election.

]]>I agree that the numbers in the 1e-9 range are speculative and model dependent. But I think the 1e-6 numbers are pretty solid, as you can derive them in other ways just by counting the number of realistic possibilities for the election outcome in any state.

]]>http://models.street-artists.org/2015/05/27/99-999999999-durability-of-objects-over-a-given-year/

The adequacy of a model that is based purely on vote tallies and not on things like the motivations of key Romanian, Russian, Chinese etc online spying and fraud organizations, or how many Al Qaida cells are specifically targeting terrorism attacks at the american election process, or whether certain space junk might deorbit over florida, or if there are concerted efforts in certain cities in Alabama or Missisippi to intimidate black voters, or if we have a snowstorm in Maine or whether there will be a large power outage along the west coast, or if a nuclear power plant will have a thermal excursion causing it to go offline, or if the Cascadia fault will slip wiping out Seattle, or there is a conspiracy of high powered people of the sort mentioned in the Panama papers to use Trump to undermine the legitimacy of the Republican party and he is secretly working for Clinton…

I mean we’re talking about your plot containing estimates of between 1 in a million to 1 in a billion chance of swing vote. I’m not sure I’m willing to dismiss the Illuminati theory at negligible compared to 1 in a billion

We’ve had 5 mass extinctions in 4 billion years, and you’re graphing plots of events with a 1 in a billion chance?

This is the kind of stuff that gets power plants built along the coast at Fukushima.

It’s plausible that your numbers are in the right order of magnitude for the swing states, but it rests squarely on your prior that the effect of space junk and Romanian hackers and mass food poisoning events related to a WalMart distribution center with a bad refrigeration unit is zero. Estimates from Bayesian models are … model based. Many of us have strong priors that the effect of all that other stuff you are ignoring is non-negligible compared to 1 in a million, certainly compared to 1/billion.

]]>Perhaps a bit late for 2016 now but you could park it in your research plans for 2020 as it should be a good bet for newsworthiness, TED talks etc.

]]>No. Pr(your vote is decisive | you don’t die tomorrow) is 1e-6 (if you live in a swing state). Pr(you die tomorrow) = 3e-5. So Pr(your vote is decisive) is (1 – 3e-5) * 1e-6, which is essentially the same as 1e-6.

]]>Suppose there’s a 1-in-1000 chance of one of those things happening (hacking, etc). Then Pr(your vote is decisive) becomes, say, 0.999e-6 instead of 1e-6. Even if you allow for dependence, sure, maybe it becomes 0.9e-6. Not much of a big deal.

Let’s put it another way. Suppose a voter’s chance of dying this year is 1/80 (based on a rough calculation that avg lifespan is approx 80). So the chance of dying tomorrow is (1/365)*(1/80) = 3e-5, which is quite a bit higher than the probability of your vote being decisive. Fine, you might die tomorrow. But you might not, and in that case your vote can make a difference.

]]>so we have a new predictive distribution taking into account the cheating effect

normal_pdf(0.5,.01) * (1-.999*exp(-1/2*((x-.5)/0.00005)^2)) * Z where Z is a number very close to 1 that renormalizes everything. Now it looks like a normal curve with a very tight notch taken out of it at the center.

That is, the density of outcomes very near the even split under pure voting is reduced by 3 orders of magnitude under realistic conditions because of the active “push” away from even split by cheaters.

Is this a terrible model for how things work? It’s hard to know. Cheating exists for sure, we hear about it every year. The question is, does it actually act to push the vote away from even split, or does it mostly cancel out just causing even more entropy and hence a little wider uncertainty in the outcome than the 1%? How strong is the effect? I don’t know, but I do know that if you don’t model it at all, and you start to throw out numbers like 1/10M or 1/100M of decisive vote, you’re estimating something that is so unlikely that the effect of ignoring the small amount of cheating might well swamp the estimate.

]]>Nonononononono. Your 1 vote can be the one that triggers the recount or the lawsuit or whatever. See the appendix on the last page of this article for a full explanation.

]]>http://election.princeton.edu/2016/08/21/sharpening-the-forecast/

http://election.princeton.edu/code/

http://election.princeton.edu/code/matlab/EV_estimator.m

http://election.princeton.edu/code/matlab/EV_prediction.m

Thanks. Is he really only using state polls? That seems just weird to me.

]]>He also has a recent post that goes into decisions he made for the model for this year, which goes into some detail: http://election.princeton.edu/2016/11/06/is-99-a-reasonable-probability/#more-18522 ]]>

BTW, I love Montana and Montanans. ]]>

I’m glad Wang did this because it’s good to have lots of different people looking at the polls. But it’s hard for me to evaluate his method because I didn’t see a single description of his procedure and his code. It might be somewhere on his website but I didn’t see it. It did seem to say that he’s only using state polls. If so, I think that’s a mistake because then he’s leaving lots of information on the table. But maybe I was missing something there.

I like what Kremp is doing because it’s all in one place and all open. Thus if Wang or anyone else can suggest ways of improving Kremp’s method (which, again, is a variation on Drew Linzer’s model which is itself based on the political science literature), they can tell Kremp or even implement it directly for themselves.

As far as I’m concerned, the future is in using more information.

]]>https://80000hours.org/2016/11/why-the-hour-you-spend-voting-is-the-most-socially-impactful-of-all/

]]>1. The probability that your vote will determine the popular vote winner is on the order of 1 in 10 million. 1 in 10 million is not zero. All of us live in the real world. And in the real world, a 1 in 10 million chance is not trivial, if it’s multiplied by a large enough gain.

2. It is well known that voters vote for what they think is the best for the country. This has nothing to do with a collectivist ideology. For example, suppose you voted for Ronald Reagan in 1980, not because you thought he’d give you a personal tax cut, but because you thought that his anti-collectivist economic policies would be good for the U.S. economy and world peace and freedom. You’d be voting for what you thought was good for the country and the world. Nothing collectivist about that!

3. Regarding margins of error, recounts, etc.: I discuss this in the Slate article which they unfortunately don’t seem to have posted yet. This question comes up a lot and it turns out it’s not an issue at all. See the appendix on the last page of this article for a full explanation.

]]>Sorry, that can not be true. The odds of a single vote accomplishing that are zero in the real world. None of your discussion contradicts that directly.

Also, your “charity” rationale for personal voting presumes a generally collectivist political ideology toward society and government. There are other ideologies among many Americans.

What is your estimate of the overall margin of error in actually counting/recording all the popular votes cast for President across America?

IMO it is easily 1-2% and likely higher. The abstract ideal of democratic voting differs much from its practical reality on Election Day. Voting, counting votes, and officially reporting votes is a very complex process in national elections… with many sources of possible error & opportunities for malicious interference.