Current Issues in Discrete Choice Modeling

Until recently, computational constraints forced researchers in thediscrete choice area to limit themselves to very simple statistical models,such as the multinomial logit (MNL), in which choice probabilities could beevaluated quickly on a computer. But the MNL only makes sense as abehavioral model under very special circumstances. Recent advances incomputation make it possible to estimate richer behavioral models thatgenerate very complex choice probability expressions. This paper discusses anumber of possible avenues for future research in the discrete choice areain light of these developments.