In an increasingly complicated world, how do individuals make choosing manageable by restricting the options they consider before making a choice? How do individuals’ goals affect the options they consider when making a choice? How should we measure and model individual choices?
Researchers at I4C are engaged in developing a range of choice methods and models.
- Discrete choice experiments
Early members of I4C developed and tested DCEs, which are now a widely accepted method in choice modelling. DCEs continue to be an active area of research, including testing their external validity.
- Combining DCEs with revealed preference (actual choice) data to increase predictive validity. Our research seeks to understand the conditions under which choice experiments predict well to real-world choices, as well as to understand what leads them to fail.
- Predicting market share and consumer willingness to pay from DCEs.
- Goal-based representations of behaviour
Antecedent volition (AV) refers to higher-level decision processes that direct evaluative and selection processes; these include such diverse phenomena as what goals to activate and pursue, what information to use, what products to eliminate and what decision rule(s) to employ to support identification of the preferred alternative in a decision instance.
- Choice information
Consumers regularly face complex decision making scenarios with many choice alternatives, from what wine to buy from a liquor store, to what suburb to live in. Choice set formation refers to the process, either internal or external to the individual, which constrain or restrict the number of options which are considered before making a choice. Accounting for choice set formation means we more accurately describe these constraints in our representation of choice.
- Hybrid choice models
Hybrid choice models incorporate the influence of latent psychological, sociological and biological constructs, like attitudes, beliefs and desires, to explain choice behaviour.