Background: A difficulty with conditional markets is separating correlation with causation. For elections, being on the way to certain outcomes could influence the election itself, so a simple conditional market of (election winner) x (outcome) doesn't give you information specifically on which candidate would be more likely to cause certain outcomes. The solution is conditional markets that isolate outcomes that are relatively 'random'. In this case, we distinguish outcomes where the election is close (defined as: the counterfactual tipping point state was won by a margin of <1%), such that the result could easily have been determined by incidental circumstances like the weather on election day.

Gas prices will be measured the same as in this Trump-conditional market, which is based on the national monthly average gas price.

"Other" represents victory by any candidate other than Trump or Harris. If any candidate other than Trump or Harris begins to poll above 20% nationally, I will create new conditional options for them.

I will not bet on this market.