UC Berkeley

This dissertation offers three studies on criminal behavior and criminal risk forecasting. The first two chapters present theoretical models and empirical evidence on the nature and motives of criminal behavior among high risk and high rate offenders and the implications these models have for criminal justice policy. The third chapter is a study of judicial and administrative decision-making with regard to assessments of crime and violence risk.

Chapter 1 gives an account of the 1970s and 80s rise and dominance of the policy of incapacitation through incarceration. The chapter describes the deterministic model of criminal behavior underpinning the theory and policy of incapacitation, and the policy implications of recent re-conceptualizations that view criminal behavior much more like other human choices - a question of contingencies and opportunities. The chapter focuses on Franklin Zimring's (2011) account of the New York City crime decline as evidence against the notion that criminal propensities are fixed and predictable over periods of years. Rather, it appears, relatively modest and superficial changes in circumstances and environmental features can lead to vastly different rates of criminal engagement.

The second chapter turns to a particular aspect of the New York City crime decline and the question of future criminal risk prediction. Specifically, the chapter examines the more than two decade drop in the rate of prison return for a new felony among New York City offenders. The chapter assesses whether the declining prison return rate is indeed an indicator of significant behavior change, or is a reflection of changes in criminal justice system actor practices. Further, to the extent that the statistics are an indicator of behavior change, the chapter evaluates whether this can interpreted as the result of the changing New York City crime environment over the last two and a half decades, or is better understood as a reflection of changes in the individual criminal propensities of those leaving prison over this period. To tease apart these competing accounts, the chapter analyzes a unique dataset involving individual records of four cohorts of prisoners from New York City released in the years 1990, 1995, 2000, and 2008. The analysis suggests a mixed picture - all three accounts are at work.

Finally, the third chapter of the dissertation turns from models of criminal behavior and criminal decision-making, to judges and administrator decisions regarding criminal risk. The chapter uses machine-learning procedures for prediction and causal estimation to analyze release decisions in California Parole hearings for inmates serving life sentences with the possibility of parole. Using an original dataset generated from all parole suitability hearings conducted since 2011 (over 8,000 transcripts), the chapter offers an empirical analysis of the current system, evaluating the rationality, uniformity, and defensibility of the criteria and decision-making applied to each claim for release. Finally, using the parole analysis as proof of concept, the chapter considers the promises and pitfalls of algorithmic assisted decision-making in the criminal justice system.