I put together a post on ranked choice random coefficients logit, which comes up a lot in market research. Filing it here to make it searchable.

http://khakieconomics.github.io/2018/12/27/Ranked-random-coefficients-logit.html

Jim

I put together a post on ranked choice random coefficients logit, which comes up a lot in market research. Filing it here to make it searchable.

http://khakieconomics.github.io/2018/12/27/Ranked-random-coefficients-logit.html

Jim

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Thanks for sharing. Can this be used for MaxDiff? I use Sawtooth and R right now, but will read your post in greater detail and comeback with questions.

Sree.

Yeah, you could implement Sawtoothâ€™s version of MaxDiff by modifying the best choice code. Remember that `softmax(V)`

describes the probability each element of the vector `U = V + e`

(where `e`

is Gumbel distributed) is the maximum. If we want the probability that an element of U is the worst, then you might think that we could take the softmax of the negative of `V`

, but thatâ€™s actually wrong, because `e`

is not symmetrically distributed. In the Sawtooth whitepaper they say that it â€śworks well in practiceâ€ť but Iâ€™d be cautious. This little code snippet will show you how wrong it can be.

```
rgumbel <- function(n, mu = 0, beta = 1) mu - beta * log(-log(runif(n)))
V <- rnorm(10)
n_rep <- 10000
U <- sapply(1:n_rep, function(i) V + rgumbel(10))
max_choice <- apply(U, 2, which.max)
min_choice <- apply(U, 2, which.min)
max_proportions <- unlist(lapply(1:10, function(i) sum(max_choice == i)))/n_rep
min_proportions <- unlist(lapply(1:10, function(i) sum(min_choice == i)))/n_rep
softmax <- function(x) exp(x)/sum(exp(x))
# Maximum probabilities
plot(as.numeric(max_proportions), softmax(V))
abline(0, 1)
# The minimum probabilities given by softmax(-V) versus the (more correct) simulated minimums.
plot(as.numeric(min_proportions), softmax(-V))
abline(0, 1)
```

In any case, to implement the Sawtooth version of MaxDiff, which you shouldnâ€™t, you can do the following. First, add another dummy vector for `worst_choice`

describing worst choices in each task, then modify the model chunk:

```
model {
// create a temporary holding vector
vector[N] log_prob;
vector[N] log_prob_worst;
// priors on the parameters
tau ~ normal(0, .5);
beta ~ normal(0, .5);
to_vector(z) ~ normal(0, 1);
L_Omega ~ lkj_corr_cholesky(4);
to_vector(Gamma) ~ normal(0, 1);
// log probabilities of each choice in the dataset
for(t in 1:T) {
vector[K] utilities; // tmp vector holding the utilities for the task/individual combination
// add utility from product attributes with individual part-worths/marginal utilities
utilities = X[start[t]:end[t]]*beta_individual[task_individual[t]]';
log_prob[start[t]:end[t]] = log_softmax(utilities);
log_prob_worst[start[t]:end[t]] = log_softmax(-utilities);
}
// use the likelihood derivation on slide 29
target += log_prob' * choice;
// increase the log probability for worst choices
target += log_prob_worst' * worst_choice;
}
```

thanks a lot for the detailed response. I will examine and get back with any questions.

Hello.

Do you have a better way to model the *worst* choice, rather than the way Sawtooth does it?

Cheers~

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