A new generalized statistical model for continuous decisions under stochastic constraints and bounded rationality

• This paper develops a statistical modeling approach for continuous choice problems. • The choice set is a continuous space characterized by stochastic constraints. • We propose continuous choice distributions to describe decision-makers’ behavior. • The distributions reflect the underlying mechanism that drives decision-makings. This paper develops a new generalized statistical modeling approach for choice problems where decision-makers are faced with a continuous set of alternatives. In the existing literature, decision-making behavior is usually analyzed in the context where there are only a few discrete alternatives from which decision-makers may choose. This paper generalizes this approach and investigates the scenario where the choice set of decision-makers is a continuous space characterized by stochastic nonlinear constraints. We develop a family of choice distributions to describe decision-makers’ choice behavior for continuous decision-making problems under stochastic constraints and bounded rationality. The proposed choice distribution family provides a generic statistical modeling and prediction approach based on the underlying mechanism that drives the decision-making process to reflect a trade-off between conflicting decision criteria and resource constraints. Finally, two case studies are used to illustrate the developed method.