This Demonstration attempts to parameterize the Mathews v. Eldridge framework. It does so by considering a situation in which the law bars official action against a person unless their behavior or situation is believed to have fallen below some cutoff level. The actual behavior or situation cannot be perfectly detected, however, at least not without significant cost. Rather, it can be measured imperfectly using different procedural mechanisms, each of which has different costs. The frequent legal issue is which set of procedural mechanisms must the government employ before taking away a constitutionally protected interest.

You select from among 10 datasets, each of which provides information on 100 sample people. The people have three scores (each measured by three procedures) that provide information on the actual behavior in which they engage. They also each have an actual level of behavior that some omniscient being or perfect test could determine. For convenience, the results of all tests and the actual behavior lie between zero and one for all persons. You select for comparison two of the possible procedure sets the government could employ. Each of these procedure sets is a non-empty subset of the set of all three possible procedures. The procedure sets correspond roughly to the second Mathews v. Eldridge factor, since they determine the risk of an erroneous characterization of the person's behavior. You select the cost of type I (false positive) and type II (false negative) errors that result from mistaken determinations as to whether the person's behavior falls short of a cutoff value that you determine using a locator. The magnitude of a type I error may be thought of as parameterizing the first Mathews v. Eldridge component. You likewise choose the cost of running each test, which, when coupled with the cost of a type II error, may be thought of as parameterizing the third Mathews v. Eldridge factor relating to the costs of operating a procedure set.

The Demonstration uses linear regression to determine for each of the two user-selected procedure sets a reasonable mapping between the scores of a person on those tests and their predicted behavior. It then computes "contingency tables" for each such mapping and for varying prediction cutoff scores. The contingency tables classify the people in the dataset according to whether they are "truly positive" (i.e., their behavior falls below the actual cutoff) and according to whether they are "test positive" (i.e., whether the mapping predicts their behavior will fall below the prediction cutoff score). From these contingency tables, the Demonstration computes the total costs of operating the procedure set for each prediction cutoff score.