Proof Discontinuities and Civil Settlements
Abstract
This Article explores settlement incentives under three different burden
of proof rules. The conventional burden of proof is a discontinuous
step-function, jumping from no damages to full damages at the 0.5
jury confidence level. Continuous burdens of proof, by contrast, would
permit sanctions to steadily increase as juror confidence rises from
0 to 1, with no discontinuity. Linear burdens, which have received
extensive attention in prior literature, escalate sanctions steadily
across the whole range of confidence levels, while the logistic burden
takes a nonlinear form.
Using a data simulation approach guided by the empirical realities of
American civil litigation, I consider the incentives that each of these
rules creates for parties contemplating settlement, using a model in
which parties make divergent forecasts of their expected outcomes
at trial due to optimism bias. Based on this analysis, I conclude that
a linear burden would likely raise our settlement rate by a modest
amount, except in very large cases and in “easy” cases, in which
an unbiased person would predict that a trial factfinder would have
a level of confidence in liability quite close to either zero or one. I
also compare the expected error rate of the settlements that each rule
produces, and fnd that the linear rule modestly lowers the expected
error rate of settlement overall, although this benefit does not hold
for easy cases or those with very high damages. Lastly, I conduct
a similar analysis for the logistic burden, finding that it induces a
similar quality and quantity of settlements as we currently achieve
using conventional burdens.
of proof rules. The conventional burden of proof is a discontinuous
step-function, jumping from no damages to full damages at the 0.5
jury confidence level. Continuous burdens of proof, by contrast, would
permit sanctions to steadily increase as juror confidence rises from
0 to 1, with no discontinuity. Linear burdens, which have received
extensive attention in prior literature, escalate sanctions steadily
across the whole range of confidence levels, while the logistic burden
takes a nonlinear form.
Using a data simulation approach guided by the empirical realities of
American civil litigation, I consider the incentives that each of these
rules creates for parties contemplating settlement, using a model in
which parties make divergent forecasts of their expected outcomes
at trial due to optimism bias. Based on this analysis, I conclude that
a linear burden would likely raise our settlement rate by a modest
amount, except in very large cases and in “easy” cases, in which
an unbiased person would predict that a trial factfinder would have
a level of confidence in liability quite close to either zero or one. I
also compare the expected error rate of the settlements that each rule
produces, and fnd that the linear rule modestly lowers the expected
error rate of settlement overall, although this benefit does not hold
for easy cases or those with very high damages. Lastly, I conduct
a similar analysis for the logistic burden, finding that it induces a
similar quality and quantity of settlements as we currently achieve
using conventional burdens.