Mathematical models of criminal careers: deriving and testing quantitative predictions

David P. Farrington, John F. MacLeod, Alex R. Piquero

Research output: Contribution to journalReview ArticleResearchpeer-review

12 Citations (Scopus)

Abstract

Theories in criminology rarely make exact quantitative predictions that can be tested empirically. This article reviews mathematical models of criminal careers, which are simple theories that fit a wide variety of empirical data. It focuses on the work of Blumstein and his colleagues in the 1980s and on the more recent research of MacLeod, Grove, and Farrington. Criminal career data can be fitted by simple assumptions specifying that the frequency of offending and the probability of recidivism are constant over time and that there are two or three categories of offenders who differ in these parameters. These theories also predict future offending. It is useful to build on these simple mathematical models to predict a wider range of criminological results and convert criminology into a more predictive and accurate science.

Original languageEnglish
Pages (from-to)336-355
Number of pages20
JournalJournal of Research in Crime and Delinquency
Volume53
Issue number3
DOIs
Publication statusPublished - May 2016
Externally publishedYes

Keywords

  • age–crime curve
  • criminal careers
  • mathematical models
  • offending frequency
  • recidivism probability

Cite this