Testing Preference Formation in Learning Design Contingent Valuation Using Advance Information and Repetitive Treatments

Policymakers have largely replaced single-bounded discrete choice valuation by the more statistically efficient repetitive method: double-bounded discrete choice and discrete choice experiments. Repetitive valuation permits classification into rational and irrational preferences: (1) a priori well formed; (2) consistent nonarbitrary values "discovered" through repetition and experience;, (3) consistent but arbitrary values as "shaped" by preceding bid level, and (4) inconsistent and arbitrary values. Policy valuations should demonstrate behaviorally rational preferences. We outline novel methods for testing this in double-bounded discrete choice experiments applied to renewable energy premiums in Chile.