TIME-ORIENTED MODELS AND THE LEGAL PROCESS - REDUCING DELAY AND FORECASTING THE FUTURE

• NCJ Number: 61296
• Journal: Washington University Law Quarterly Volume: 1978 Dated: (SUMMER) Pages: 467-527
• Author(s): S S NAGEL; M NEEF
• Date Published: 1978
• Length: 61 pages
• Sponsoring Agency: Ford Foundation; Illinois Law Enforcement Cmssn (see Illinois Criminal Justice Information Authority); University of Illinois
• Corporate Author: Washington University
• Publication Format: Article
• Publication Type: Statistics
• Language: English
• Country: United States of America

Annotation

PRESCRIPTIVE AND DESCRIPTIVE MODELS FOR ANALYZING THE LEGAL PROCESS THAT EMPHASIZE SAVING TIME OR PREDICTING FUTURE EVENTS FROM PAST EVENTS ARE DETAILED.

Abstract

PRESCRIPTIVE OR OPTIMIZING MODELS ARE PRIMARILY CONCERNED WITH SAVING TIME. QUEUEING THEORY, DYNAMIC PROGRAMMING, CRITICAL PATH AND FLOW CHART TECHNIQUES, AND OPTIMUM LEVEL AND CHOICE ANALYSIS CONSTITUTE PRESCRIPTIVE MODELS. QUEUEING THEORY USES A SET OF MATHEMATICAL MODELS OR FORMULAS WHICH HAVE AS THEIR MAIN INPUTS THE NUMBER OF CASES ARRIVING IN THE LEGAL SYSTEM PER DAY AND THE NUMBER OF DAYS OR OTHER TIME UNITS NEEDED TO PROCESS EACH CASE. DYNAMIC PROGRAMMING SEEKS TO MINIMIZE THE TOTAL TIME CONSUMED BY ORDERING EVENTS IN THE MOST EFFICIENT SEQUENCE. CRITICAL PATH OR PROGRAM EVALUATION AND REVIEW TECHNIQUES ARE DESIGNED TO REDUCE TOTAL TIME BY REDUCING THE TIME OF STAGES THAT DIRECTLY INFLUENCE THE LEGAL SYSTEM. FLOW CHART MODELS ILLUSTRATE CRITICAL PATH TECHNIQUES, REPRESENTING STAGES OF THE LEGAL PROCESS. OPTIMUM LEVEL ANALYSIS OF TIME CONSUMPTION REVEALS THE LEVEL OF DELAY THAT WILL MINIMIZE THE SUM OF DELAY COSTS; OPTIMUM MIX ANALYSIS ALLOCATES A GIVEN BUDGET AMONG JUDGES, PROSECUTORS, AND DEFENSE COUNSEL TO MINIMIZE TIME CONSUMPTION; AND OPTIMUM CHOICE ANALYSIS IS VALUABLE IN ASCERTAINING WHAT JUDGES, PROSECUTORS, AND DEFENSE COUNSEL SHOULD DO TO ACHIEVE SETTLEMENTS AND REDUCE SERVICE TIME AND AVERAGE TIME CONSUMED PER CASE. DESCRIPTIVE OR PREDICTIVE MODELS INCLUDE MARKOV CHAIN ANALYSIS, TIME SERIES ANALYSIS, AND DIFFERENCE AND DIFFERENTIAL EQUATIONS. MARKOV CHAIN ANALYSIS PREDICTS SUBSEQUENT EVENTS BY DETERMINING THE PROBABILITY THAT ONE EVENT WILL FOLLOW ANOTHER. TIME SERIES ANALYSIS INVOLVES OBTAINING AND PROCESSING DATA ON ONE OR MORE VARIABLES FOR MANY POINTS IN TIME. DIFFERENCE AND DIFFERENTIAL EQUATIONS USE A DEPENDENT VARIABLE OR A VARIABLE TO BE PREDICTED AT CERTAIN POINT IN TIME, EXAMPLES OF BOTH PRESCRIPTIVE AND DESCRIPTIVE MODELS ARE CITED. SUPPORTING DATA, ILLUSTRATIONS, AND EQUATIONS ARE PROVIDED. A GLOSSARY AND MATHEMATICAL FORMULAS ARE APPENDED. (DEP)