Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models. Machine learning can push the boundaries of discrete choice modeling for policy-based preference elicitation by adopting a data-driven approach...

This study compares individual preferences across incentives (i.e., hypothetical vs. real incentives) and over time (i.e. elicitation at two different points in time) in a choice experiment involving charitable donating decisions. We provide evidence of hypothetical bias but little evidence of...

This study compares individual preferences across incentives (i.e., hypothetical vs. real incentives) and over time (i.e. elicitation at two different points in time) in a choice experiment involving charitable donating decisions. We provide evidence of hypothetical bias but little evidence of...

This paper shows how anticipated impacts of environmental projects and policies can be valued in terms of money as a common denominator, and costs and benefits assigned in an acceptable distribution. To that effect, a new mechanism design of situational contracting is introduced that generates...