Intercept of logit vs intercept of multinomial logit If I simulate the success probability for a Bernoulli random variable $t_{binary}$ conditional on some regressor $x$:
N <- 100000
x <- rnorm(N, 0, 1)
b0 <- 0
p <- plogis(b0 + 0.3*x)

the average probability (mean(p)) will be 0.5 as long as the intercept b0 is zero. Changing the slope of $x$ to something other than $0.3$ has no impact on this average.
But if I simulate the probabilities of a 3-category $t_{multi}$:
p0 <- exp(0 + 0.1*x)
p1 <- exp(0 + 0.2*x)
p2 <- exp(0 + 0.3*x)
p <- cbind(p0,p1,p2)
p <- t(apply(p, MARGIN = 1, function(h) h / sum(h)))

the average probability is split evenly (.33,.33,.33) only when the slope of x is the same for the three categories or if I fiddle with the intercepts. Is this correct (is this a property of the multinomial logit) or am I just simulating it wrong? The formula I have in mind is:
$P(t_{multi}=c|x)=\frac{exp(\beta_{0c}+\beta_cx)}{\sum_{k=0}^2exp(\beta_{0k}+\beta_kx)}$
Thanks.

This is a consequence of Jensen's inequality. Because the exponential function is convex, the expectation $\mathbb{E} \{ \exp(\beta_0 + \beta_1 x) \}$ is not the same as $\exp\{ \mathbb{E} (\beta_0 +  \beta_1 x) \}$. Instead, $\mathbb{E} \{ \exp(\beta_0 + \beta_1 x) \} = \exp(\beta_0 + \frac{\beta_1^2}{2} )$
For logistic regression, this doesn't matter because zeroes and ones are symmetric due to how the logistic function is defined. But in your multinomial case, you're using the softmax function to set the probabilities, and so using your example of 0.1, 0.2, and 0.3 for the coefficients:
$ p(X=1) = \frac{ \exp(0.1^2/2 ) }{\exp(0.1^2/2 ) + \exp(0.2^2/2 ) + \exp(0.3^2/2 )} = 0.327$
