Survey Statistics: quantifying uncertainty in ranked choice voting polls

We’ve talked about uncertainty in polls (see Margin of Error, Total Margin of Error, Total Margin of Error II) and we’ve talked about ranked data (see exploded logit !). A new paper, Rosenman & Liang 2026, looks at uncertainty in ranked choice voting (RCV) polls. Recall the multinomial logit model that Train (2009) Chapter 7 calls the exploded logit: P[ranking Other then Left then Right] = exp(f_Other) / sum_c’ exp(f_c’) * exp(f_Left) / (exp(f_Left) + exp(f_Right)) Without covariates, it has only 3 parameters: f_Other, f_Left, f_Right. It makes the independence from irrelevant alternatives (IIA) assumption to go from these 3 parameters to rank probabilities. In contrast, the multinomial model in Rosenman & Liang 2026 does not make the IIA assumption and has 14 parameters, one for each of 15 possible rankings minus one so they sum to 1: P[ranking Other then Left then Right] = pi_{Other, Left, Right} Rosenman & Liang 2026 note that in RCV the election outcome is not expressable as one…