This may be wrong for complex reasons. Similar minds making the same decision for the same reasons do not _causally_ influence each other, but that doesn't change the outcome, nor does it make it any less bad. For a fairly deep view into how one might formally describe this particular aspect of decision making, I strongly recommend: https://intelligence.org/files/TDT.pdf
If the receipts are anonymous, they can't prove how you voted, only that you were able to obtain a receipt. So what if we increase the supply of receipts enough to destroy the value of an individual receipt? For example, voting machines could drop duplicate receipts in a bucket that voters have access to.
Well, you can verify that the receipt says your vote was counted correctly. That assumes you both trust the receipt system and believe that whatever tampering was done to cause your Obama vote to become a Romney vote couldn't have possibly also resulted in the receipt providing incorrect information as well.
I find that while a neighborhood filter gets the job done, its usability isn't great, for a number of reasons:
* You have to roughly know the boundaries of all the neighborhoods for the list of names to be useful
* A one-dimensional arrangement of names doesn't correspond well to the 2D relationships you probably care about.
* Many neighborhoods don't map well to regions I care about. I often find myself selecting Bernal Heights, Mission District, and Noe Valley to capture the idea of "the area around 24th and Valencia", for example.
Is lifetime earning to cost a useful metric, though? There are costs outside of college that won't scale with that ratio. Extreme example: a 1000:1 ratio isn't much good if your lifetime earning is $1000, and a 2:1 ratio could mean you come out ahead by a few million dollars.
Using UDIDs for authentication isn't a good idea for a serious app. Even if it were unspoofable, devices aren't users. Users have iPhones and iPod Touches and iPads; they upgrade; they sell devices to other users.