Taking Conjoint Analysis to Task

Taking conjoint analysis to task.

Taking Strategies to Task

In this issue of Neuron, Uka and DeAngelis report a neural signature of the strategy that monkeys' brains developed to solve a visual discrimination task by training them on one version of the task and testing them on another. Extensive training on one version caused decision networks in the animals' brains to ignore certain classes of neurons whose signals would have been useful on the modifie...

A Checklist for Conjoint Analysis Applications in Health: Report of the Ispor Conjoint Analysis Good Research Practices Task Force

Conjoint analysis applications in health--a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force.

BACKGROUND The application of conjoint analysis (including discrete-choice experiments and other multiattribute stated-preference methods) in health has increased rapidly over the past decade. A wider acceptance of these methods is limited by an absence of consensus-based methodological standards. OBJECTIVE The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Res...

A Machine Learning Approach to Conjoint Analysis

Choice-based conjoint analysis builds models of consumer preferences over products with answers gathered in questionnaires. Our main goal is to bring tools from the machine learning community to solve this problem more efficiently. Thus, we propose two algorithms to quickly and accurately estimate consumer preferences.