Testing Distributional Dependence in the Becker-DeGroot-Marschak ...

would simply bid precisely that valuation in every round of the BDM phase, regardless of the
level of detail of instructions or the distribution of random prices in any given round. 11
If subjects are uncertain about their valuations they may learn from the varied distributional
information presented in successive rounds. Additional insight about the role of learning can thus
be gained if subjects are allowed to revisit and revise bids made in earlier rounds. In this setup,
all of these models would predict that if the subject were uncertain about her valuation and
learned about the item’s value from the distributional information presented, she would adjust
her valuation accordingly, and her last elicitations for the various rounds would be (weakly) more
closely clustered than her first elicitations. Note that the last elicitations of a rational expected-
utility maximizer from the standard model should be exactly the same across all 20 rounds.
The predictions of the framework presented in section 2.1 differ appreciably from those made
by the models above. Unlike the standard model (in particular) it predicts that even subjects’
last bids may well differ across rounds with different distributions of random prices. Contrary to
the models of Horowitz and Lusk, et al. it predicts that subjects’ bids will be biased toward the
direction of greater mass or longer range of the remaining support—not just for first bids, which
may be in the process of converging by reason of learning, but also for last bids.
The first and second principles of the framework predict that subjects given the same
remaining range of the support and the same mass in either direction from the subject’s
valuation should experience identical bias. For example, if the subject’s valuation were $4, the
first and second principles would predict that the seven distributions with high support (from $5
to $14.50) would all lead to the same bias and thus the same bid (at least for the last bids).
In contrast, the third principle predicts that the more distant mass is from a subject’s
valuation, the less bias it promotes in its direction. In the example from the preceding paragraph,
a subject with a valuation of $4 would be expected to bias his response upward more for the
11 This agent wouldn’t necessarily bid her certain valuation if it happened to lie above (below) the support of the
distribution of random prices for a particular round. In these instances, she would be indifferent over all the prices
above (below) the maximum (minimum) of the support.
10