Agent-based transport simulations rely on realistic representation of agents’ movements in the system, but also simulation choice making processes that resemble reality well. The MATSim framework uses scoring-based co-evolutionary algorithm to achieve this, based principle utility maximization. Discrete models are another common tool planning which same principle. Through a toy example, paper a...

We consider the estimation of dynamic binary choice models in a semiparametric setting, which per-period utility functions are known up to finite number parameters, but distribution shocks is left unspecified. This setup differs from most existing identification and literature for discrete models. To show we derive exploit new recursive representation unknown quantile function shocks. Our estim...

This paper is concerned with learning decision-makers’ preferences using data on observed choices from a finite set of risky alternatives. We propose discrete choice model unobserved heterogeneity in consideration sets and standard risk aversion. obtain sufficient conditions for the model’s semi-nonparametric point identification, including cases where depends some exogenous variables. Our meth...