Consider my home county:
- Number of voters
- (Corollary: probability of swinging an election: )
- GDP: $400B
- Amount by which GDP might go up or down depending on a single election: 0.1%
- Probability I support the better side: 60%
Expected GDP increase from my voting
... = (fraction of GDP at stake) * P(I swing election) * (P(I'm good) - P(I'm bad))
... = ($400B * 0.1%) * (1/800) * (60% - 40%)
... = $100k
...which seems absurdly large! And it just gets crazier as you look at larger areas, since GDP goes up like while P(swing) only goes down like . For the United States, the same calculation yields a benefit of $300k.
What's going wrong here? (Or, is nothing going wrong? In which case, I guess I'll stop donating to charity and devote that time and energy to Getting Out The Vote instead.)
Could you explain where P(I swing election) = 1/sqrt(nVoters) is coming from?
Sure! I'm modeling the election as being nVoters coin flips: if there are more Heads than Tails, then candidate H wins, else candidate T wins.
If you flip n coins, each coin coming up Heads with probability p, then the number of Heads is binomially distributed with standard deviation √(nVoters)(p)(1−p), which I lazily rounded to √nVoters.
The probability of being at a particular value near the peak of that distribution is approximately 1 / [that standard deviation]. ("Proof": numerical simulation of flipping 500k coins 1M times, getting 250k Heads about 1/80... (read more)