Model for percent resource allocation between groups I have a panel data set indicating each entities percent resource allocation among a number of different options. I have been using a logistic regression model to predict the share of allocation for each option for each entity over time. This, however, allows for the possibility of the sum of one entities allocations being greater or less than 100%.
Does a softmax model (from a dnn perspective only a single layer) fitted on these percent allocations solve my problem or is there a better methodology to use?

Yes, a softmax model is essentially a multi-output version of a logistic regression model.
The inputs are sent through a linear transformation, then each of those outputs are sent through an exponential activation function (which necessarily outputs a positive number), then those outputs are just normalized. You recover a logistic regression model when there are only two possible outputs. Since they sum to 1, so you only need one output to determine the other.
See https://en.wikipedia.org/wiki/Softmax_function.
