Numerous consumer choice situations involve the choice of multiple alternatives as opposed to a single alternative. Further, consumer choices typically involve continuous choice components of “how much to consume” along with the discrete choices of “what to choose”. Such multiple discrete-continuous (MDC) choices are pervasive in consumer decision-making and of empirical interest in a variety of social sciences, including economics, marketing, and transportation. Examples include individuals’ time-use choices (decisions to engage in different types of activities and time allocation to each activity), households’ vehicle fleet mix and usage (types of vehicles owned and miles traveled/fuel consumed on each vehicle), and grocery brand choice and purchase quantity decisions.

The past decade has witnessed notable advances in modeling multiple discrete-continuous choices. Recent innovations include:

·         The advancement of the Kuhn-Tucker demand systems to modeling MDC choices

·         The formulation of flexible and theoretically sound model specifications

·         The development of simple and computationally efficient techniques for prediction and welfare analysis with Kuhn-Tucker demand models

The workshop will bring together researchers working in this area to discuss the recent advances and challenges related to modeling MDC choices. Some of the items to be discussed include:

·         Theoretical foundations and recent modeling innovations based on the Kuhn-Tucker approach,

·         Alternative approaches to modeling MDC choices (e.g., indirect utility methods),

·         Current and new empirical applications,

·         Flexible model specifications, including non-additive utility forms, correlated utility structures, and alternative stochastic specifications,

·         Techniques to apply alternative model specifications for policy/welfare analysis,

·         Implied properties of alternative model specifications (e.g., substitution/complementarity),

·         Relevant data collection methods, and

·         Alternative estimation techniques.