Naive, Biased, yet Bayesian: Can Juries Interpret Selectively ...

268 The Journal of Law, Economics, & Organization. V12 N1
2.5
1.5
0.5
Heads
Tails
0.2 0.4 0.6 0.8
0.8
true p
jury's p
jury's prior
0.6
0.4
0.2
0.2 0.4 0.6 0.8
Figure 1. Effects on evidence when jury has uniform pnors: {a = 1, b = 1).
favors the plaintiff. Here the plaintiff "free rides" on the jury's stronger prior,
and doesn't begin flipping until a much higher value of p. This means that
there is a larger range over which the jury's estimator is biased toward the prior
mean |, but, as above, when both parties produce evidence, bias disappears. 34
34. Note that, in the cases illustrated in Figures 1 and 2, a corner solution is reached (i.e., one
of the parties produces no evidence). When p is close to zero or one, there is bias, but not in
cases where p is "close" to ,5. According to the Priest and Klein (1984) model of litigation versus
settlement, the selection of cases for litigation will be biased toward cases where p is close to .5.