Relevant bibliographies by topics / Criminal behavior (Prediction of)

Academic literature on the topic 'Criminal behavior (Prediction of)'

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Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Criminal behavior (Prediction of)"

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Peng, Yi-Ting, and Chin-Laung Lei. "Using Bidirectional Encoder Representations from Transformers (BERT) to predict criminal charges and sentences from Taiwanese court judgments." PeerJ Computer Science 10 (January 31, 2024): e1841. http://dx.doi.org/10.7717/peerj-cs.1841.

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People unfamiliar with the law may not know what kind of behavior is considered criminal behavior or the lengths of sentences tied to those behaviors. This study used criminal judgments from the district court in Taiwan to predict the type of crime and sentence length that would be determined. This study pioneers using Taiwanese criminal judgments as a dataset and proposes improvements based on Bidirectional Encoder Representations from Transformers (BERT). This study is divided into two parts: criminal charges prediction and sentence prediction. Injury and public endangerment judgments were used as training data to predict sentences. This study also proposes an effective solution to BERT’s 512-token limit. The results show that using the BERT model to train Taiwanese criminal judgments is feasible. Accuracy reached 98.95% in predicting criminal charges and 72.37% in predicting the sentence in injury trials, and 80.93% in predicting the sentence in public endangerment trials.
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Gottfredson, Don M., and Stephen D. Gottfredson. "Stakes and Risks in the Prediction of Violent Criminal Behavior." Violence and Victims 3, no. 4 (January 1988): 247–62. http://dx.doi.org/10.1891/0886-6708.3.4.247.

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Retributive and utilitarian goals for criminal justice decisions are in conflict. In part, this is because the retributive aim rejects prediction, while all utilitarian aims require it. In the context of this debate, we review research concerning the prediction of violence, and conclude that because such predictions are of low accuracy, they are only modestly useful for public policy formulation or for individual decision-making. Because we believe prediction, and utilitarian goals, to be important, this paper focuses on two issues that have potential for increasing the accuracy with which predictions may be made. One is the measurement of the seriousness of crime and ways to improve it. Second, we introduce the concept of societal stakes and suggest that this must be assessed as well. Finally, we propose a model that may be useful for lessening the conflict between retributive and utilitarian perspectives.
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Kaluzhina, Marina, Tamara Makarenko, Marina Spasennikova, and Tatyana Vedernikova. "The Methods of Digital Forecasting of Inmate Misconduct in Penal Institutions." Russian Journal of Criminology 13, no. 5 (October 31, 2019): 747–56. http://dx.doi.org/10.17150/2500-4255.2019.13(5).747-756.

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The authors use the analysis of existing research ideas regarding the structure and content of the criminological prediction methodology to examine modern approaches to predicting illegal activities in penitentiary institutions. They analyze and classify the objects of prevention — those inmates in places of confinement who need to be controlled while serving their sentence because they have a range of unlawful behavior. In the diagnostic sub-task the object is viewed as a source of information whose attributes and features are studied as they manifest its essence and condition. The authors present a large-scale review of Russian and foreign publications to research the evolution of scientific ideas regarding the contents of the hypothesis as a basis of criminological prediction. While recognizing the value of theoretical criminological achievements, the authors set the goal of analyzing the possible ways of integrating criminology and operative search activities for the identification of pre-criminal behavior in places of confinement. Using the theory of criminological prediction and empirical materials, the authors analyze the possibilities of designing a multi-factor prediction model for individual unlawful behavior by transforming this model into a system of corresponding indicators and risk factors of pre-criminal behavior. They conduct a systemic analysis of the contents of socio-demographic, penitentiary, psychological variables, as well as variables connected with the criminal past as an aggregate of risk factors of pre-criminal behavior. They also describe the essence of digital prediction methods - predictive analytics, analytical intelligence, initiative analytics, - which are used to build a system of indicators for studying and assessing the behavior of certain categories of inmates. The authors show the necessity of using digital analytical methods of making managerial decisions regarding the preventive measures of rapid response in cases of the destructive behavior of inmates. Using the regularities that form the basis of criminological prediction, the authors state that it is necessary to develop the methods of digital prediction and to adapt key features of the digital environment and newest information and telecommunication technologies to solving the tasks of preventing offences among inmates.
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Bridges, George S. "Predicting Criminal Behavior." Contemporary Psychology: A Journal of Reviews 36, no. 2 (February 1991): 152–53. http://dx.doi.org/10.1037/029435.

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Li, Muji. "Influencing Factors of Youths Antisocial Behavior." Lecture Notes in Education Psychology and Public Media 16, no. 1 (October 26, 2023): 87–93. http://dx.doi.org/10.54254/2753-7048/16/20231115.

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The causes behind the antisocial crimes young people encounter in life are discussed and analyzed in this review. Although the final manifestations of criminal behavior vary, most young peoples antisocial crimes are originated from their childhood experiences. This article discussed the causes of antisocial crimes from two perspectives: the anatomy of the root behavior through reflection because of the genetic and biological background and acquired environmental reasons (family factors: domestic violence, education methods, the relationship between parents) and individual psychological problems. Antisocial behaviors emerge during adolescence and young adulthood, which is different from personality. The aim of the article is to review the effect of negative healthy growth environment on youths antisocial behavior by outlining classical theoretical frameworks concerning the various pathways and processes that may place young people at higher risk of delinquency. The findings of influencing factors, with particular reference to family function and parenting, social environment and the mediating effect on individual psychological causes and why lead to the emergence of criminal behavior, understanding and prediction of antisocial criminal behavior through the prediction of innate and acquired environment.
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Caputo, Tullio C., and Thomas Gabor. "The Prediction of Criminal Behaviour." Canadian Journal of Sociology / Cahiers canadiens de sociologie 14, no. 3 (1989): 410. http://dx.doi.org/10.2307/3340621.

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Swadi Al-Janabi, Kadhim B. "A Proposed Framework for Analyzing Crime Data Set Using Decision Tree and Simple K-Means Mining Algorithms." Journal of Kufa for Mathematics and Computer 1, no. 3 (May 30, 2011): 8–24. http://dx.doi.org/10.31642/jokmc/2018/010302.

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This paper presents a proposed framework for the crime and criminal data analysis and detection using Decision tree Algorithms for data classification and Simple K Means algorithm for data clustering. The paper tends to help specialists in discovering patterns and trends, making forecasts, finding relationships and possible explanations, mapping criminal networks and identifying possible suspects. The classification is based mainly on grouping the crimes according to the type, location, time and other attributes; Clustering is based on finding relationships between different Crime and Criminal attributes having some previously unknown common characteristics. The results of both classifications and Clustering are used for prediction of trends and behavior of the given objects (Crimes and Criminals).Data for both crimes and criminals were collected from free police departments’ dataset available on the Internet to create and test the proposed framework, and then these data were preprocessed to get clean and accurate data using different preprocessingtechniques (cleaning, missing values and removing inconsistency). The preprocessed data were used to find out different crime and criminal trends and behaviors, and crimes and criminals were grouped into clusters according to their important attributes. WEKA mining software and Microsoft Excel were used to analyze the given data.
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Greely, Henry T., and Nita A. Farahany. "Neuroscience and the Criminal Justice System." Annual Review of Criminology 2, no. 1 (January 13, 2019): 451–71. http://dx.doi.org/10.1146/annurev-criminol-011518-024433.

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The criminal justice system acts directly on bodies, but fundamentally it cares about minds. As neuroscience progresses, it will increasingly be able to probe the objective, physical organ of the brain and reveal secrets from the subjective mind. This is already beginning to affect the criminal justice system, a trend that will only increase. This review article cannot begin even to sketch the full scope of the new field of law and neuroscience. The first workshop on the subject was held in 2003 ( Garland 2004 ), but the field already has its own casebook ( Jones et al. 2014 ) and the MacArthur Foundation Research Network on Law and Neuroscience (2018) shows more than 1,700 publications in the area between 1984 and 2017. Greely (2009) divided the implications of law into five different categories: prediction, mind-reading, responsibility, treatment, and enhancement. This article examines only three points: the current use of neuroscience to understand and explain criminal behavior, the possibilities of relevant neuroscience-based prediction, and plausible future applications of neuroscience to the treatment of criminals. But first, we discuss the human brain and how it works.
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Mills, Jeremy F., Daryl G. Kroner, and Toni Hemmati. "The Measures of Criminal Attitudes and Associates (MCAA)." Criminal Justice and Behavior 31, no. 6 (December 2004): 717–33. http://dx.doi.org/10.1177/0093854804268755.

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Recent research has demonstrated that antisocial attitudes and antisocial associates are among the better predictors of antisocial behavior. This study tests the predictive validity of the Measures of Criminal Attitudes and Associates (MCAA) in a sample of adult male offenders. The MCAA comprises two parts: Part A is a quantified self-report measure of criminal friends, and Part B contains four attitude scales: Violence, Entitlement, Antisocial Intent, and Associates. The MCAA scales showed predictive validity for the outcomes of general and violent recidivism. In addition, the MCAA significantly improved the prediction of violent recidivism over an actuarial risk assessment instrument alone. Discussion centers on the contribution that antisocial attitudes and associates make to risk assessment.
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Normandeau, André. "The Prediction of Criminal Behaviour: Statistical Approaches." Canadian Journal of Criminology 34, no. 1 (January 1992): 112–13. http://dx.doi.org/10.3138/cjcrim.34.1.112.

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Dissertations / Theses on the topic "Criminal behavior (Prediction of)"

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Rosky, Jeffrey W. "Examining the relationship between risk, protection, self-control and resilience." Pullman, Wash. : Washington State University, 2010. http://www.dissertations.wsu.edu/Dissertations/Spring2010/j_rosky_121409.pdf.

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Xu, Qiang. "FROM JUVENILE DELINQUENCY TO ADULT CRIMINAL BEHAVIOR: EXPANDING THE STATE DEPENDENCE PERSPECTIVE ON PERSISTENT CRIMINAL BEHAVIOR." Connect to this title online, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=bgsu1151021659.

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Min, Suhong. "Causes and consequences of low self-control: Empirical tests of the general theory of crime." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186809.

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This study operationalized and empirically tested the general propositions of Gottfredson and Hirschi's general theory of crime (1990). Specifically, the core concept of the theory, self-control, is operationalized using two data sets--Richmond Youth Project and Cambridge Study in Delinquent Development--and tested using criteria of reliability and validity. In this part of the study, a methodological question focuses on the pattern of validity change across types of data, namely, cross-sectional and longitudinal data. In the following tests, causes and consequences of low self-control are tested using Richmond Youth Project data. Child rearing as early socialization and individual traits are tested as sources of self-control. Then the measure of self-control is related to crime, delinquency, and analogous behaviors that are, according to the theory, manifestations of low self-control. A research question here focuses on the generality of self-control theory. Overall, the test results support the claims of the general theory of crime. Findings from the validity tests of the self-control index show theoretically expected relations with important individual variables such as gender, race, and delinquent status. In particular, findings from two differently designed data sets are very similar. Test results also show that boys low on self-control are more likely than others to have committed crime, delinquency, and various analogous behaviors. One possible research problem based on the theoretical assumption was also tested and empirically supported. Theory implies that respondents low on self-control are more likely than others to fail to answer questions in self-report survey. Empirical tests support this theoretical implication, revealing that respondents dropped from the index due to missing data are more likely than others to be delinquents. Further research implications are also discussed.
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Spencer, David James. "Predicting early failure on probation using survival analysis and psychological predictor variables /." Digital version accessible at:, 2000. http://wwwlib.umi.com/cr/utexas/main.

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Zager, Mary Ann. "Explicating and testing a general theory of crime." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186570.

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Gottfredson and Hirschi's A General Theory of Crime (1990) motivated much research on the concept of self-control and its relationship to crime, delinquency, and deviant behavior. Although researchers are aware of this theory's contribution to criminological research, some confusion about the exact nature of the relationship between self-control and criminal behavior (as specified by Gottfredson and Hirschi) remains. To clarify this relationship, the assumptions most vital to the theory are explained. One theorem derived from these assumptions regards the role of opportunity in deviant behavior. Gottfredson and Hirschi clearly posit opportunity as a necessary but not sufficient condition for criminal (and analogous non-criminal) behavior to occur. The precise role of opportunity in self-control theory, however, is somewhat unclear in Gottfredson and Hirschi's work. The present work clarifies this element of opportunity, searches for a measure of self-control that is opportunity free, and addresses the relationship between this type of measure and delinquent behavior using data from the National Youth Survey. The role of opportunity in this theory is clarified using gender differences in delinquent behavior as a tool for separating the components of opportunity. Using gender differences in several delinquent behaviors, the existence of the two components of opportunity (one inherent in the act and one inherent in the actor) is confirmed. After establishing these elements of opportunity, gender differences are used to facilitate the search for a measure of self-control that is distinct from both. This attitudinal measure raises the issue of the role of attitudes in Gottfredson and Hirschi's theory. The final analysis focuses on the relationships between attitudes (both children's and parent's) and children's delinquent behavior. Log-linear models are used to specify the structures of these relationships, which are complimentary to Gottfredson and Hirschi's assumptions regarding social norms, parental influence on children's value systems, and an individual's ability to engage in behaviors that they realize are inappropriate.
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Lovetere, D'Arcy N. "Criminality and the life course : a study of the influence of age graded transitions and offending patterns /." Abstract and full text available, 2009. http://149.152.10.1/record=b3079644~S16.

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Thesis (M.S.) -- Central Connecticut State University, 2009.
Thesis advisor: Stephen M. Cox. "... in partial fulfillment of the requirements for the degree of Master of Science in Criminal Justice." Includes bibliographical references (leaves 40-41). Also available via the World Wide Web.
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Peterson, Tracy L. "The father/child relationship and its influence on criminal behavior." Online version, 2001. http://www.uwstout.edu/lib/thesis/2001/2001petersont.pdf.

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Ronis, Scott T. "Criminal behavior patterns of adolescents and young adults who have histories of sexual offenses." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5524.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on July 29, 2009) Includes bibliographical references.
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Burraston, Bert Owen. "The career criminal debate : comparing finite mixture modeling with growth mixture modeling /." view abstract or download file of text, 2003. http://wwwlib.umi.com/cr/uoregon/fullcit?p3095238.

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Thesis (Ph. D.)--University of Oregon, 2003.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 95-103). Also available for download via the World Wide Web; free to University of Oregon users.
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Britt, Chester Lamont III. "Crime, criminal careers and social control: A methodological analysis of economic choice and social control theories of crime." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185168.

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This study tests the validity of two theories of crime: economic choice (as manifest in the criminal career paradigm) and social control. The test of these two theories is primarily methodological, in that four types of crime data (official and longitudinal (Uniform Crime Reports), official and cross-sectional (Bail Decisionmaking Study), self-report and longitudinal (National Youth Survey), and self-report and cross-sectional (Seattle Youth Study)) and a variety of graphical and statistical techniques are used to compare findings on (1) the stability of the age distribution of crime, (2) the prevalence of offense specialization, and (3) the differences in the causes of participating in crime compared to the causes of frequency of criminal activity among those individuals committing crimes. The findings on the relation between age and crime show the general shape of the age-crime curve is stable across year of the data or curve, type of data, cohort, and age group. The tests for offense specialization reveal that offenders are versatile. An individual's current offense type is not predictable, with much accuracy, on the basis of prior offending. Again, the lack of offense specialization held across type of data, but age, race, and gender distinctions also failed to alter significantly the observed pattern of versatility. Findings on the causes of participation in crime and frequency of criminal activity among active offenders showed only trivial differences in the set of statistically significant predictors for each operationalization of crime and delinquency. Two distinct operationalizations of frequency also showed no substantial difference in the set of statistically significant predictors. Similar to the findings on age and crime, and offense specialization, the pattern of results for the participation and frequency analyses held across type of data. In sum, the results tended to support the predictions of social control theory over those of the economic choice-criminal career view of crime.
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Books on the topic "Criminal behavior (Prediction of)"

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Bartol, Curt R. Criminal behavior: A psychosocial approach. 5th ed. Upper Saddler River, N.J: Prentice Hall, 1999.

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Bartol, Curt R. Criminal behavior: A psychosocial approach. 2nd ed. Englewood Cliffs, N.J: Prentice-Hall, 1986.

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Bartol, Curt R. Criminal behavior: A psychosocial approach. 7th ed. Upper Saddle River, N.J: Pearson, 2005.

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Claire, Fitzpatrick, and Francis Brian, eds. Understanding criminal careers. Cullompton, Devon: Willan, 2009.

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Bartol, Curt R. Criminal behavior: A psychosocial approach. 4th ed. Englewood Cliffs, N.J: Prentice Hall, 1995.

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Bartol, Curt R. Criminal behavior: A psychological approach. 9th ed. Boston: Prentice Hall, 2011.

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Bartol, Curt R. Criminal behavior: A psychosocial approach. 8th ed. Upper Saddle River, N.J: Pearson Prentice Hall, 2008.

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Bartol, Curt R. Criminal behavior: A psychological approach. 9th ed. Upper Saddle River, NJ: Pearson Education/Prentice Hall, 2011.

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Bartol, Curt R. Criminal behavior: A psychosocial approach. 6th ed. Upper Saddle River, N.J: Prentice Hall, 2002.

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Bartol, Curt R. Criminal behavior: A psychological approach. 2nd ed. Englewood Cliffs, N.J: Prentice-Hall, 1986.

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Book chapters on the topic "Criminal behavior (Prediction of)"

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Bonta, James, and D. A. Andrews. "Prediction and Classification of Criminal Behavior." In The Psychology of Criminal Conduct, 197–239. 7th ed. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003292128-13.

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Stephan, Egon. "The Prediction of Drunken Driving Offences." In Criminal Behavior and the Justice System, 53–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-86017-1_4.

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Farrington, David P. "Long-Term Prediction of Offending and Other Life Outcomes." In Criminal Behavior and the Justice System, 26–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-86017-1_2.

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Monahan, John. "Prediction of Criminal Behavior: Recent Developments in Research and Policy in the United States." In Criminal Behavior and the Justice System, 40–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-86017-1_3.

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Lussier, Patrick. "26 Sex Offending and Developmental Criminology: A Research Agenda for the Description, Explanation, and Prediction of Juvenile Sex Offending." In The Development of Criminal and Antisocial Behavior, 413–30. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08720-7_26.

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Ozgul, Fatih, and Zeki Erdem. "Resilience of Criminal Networks." In Social Computing, Behavioral-Cultural Modeling, and Prediction, 400–404. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16268-3_50.

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Huddleston, Samuel H., Matthew S. Gerber, and Donald E. Brown. "Geographic Profiling of Criminal Groups for Military Cordon and Search." In Social Computing, Behavioral-Cultural Modeling and Prediction, 503–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37210-0_55.

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Megargee, Edwin I. "Criminal Behavior." In Clinical Psychology, 231–58. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4757-9715-2_9.

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Brown, Shelley L., Shannon Gottschall, and Craig Bennell. "Criminal behavior." In APA handbook of forensic psychology, Vol. 1: Individual and situational influences in criminal and civil contexts., 219–56. Washington: American Psychological Association, 2015. http://dx.doi.org/10.1037/14461-008.

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Goode, Erich. "Criminal Behavior." In Deviant Behavior, 140–72. Twelfth edition. | New York, NY : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9780429202902-6.

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Conference papers on the topic "Criminal behavior (Prediction of)"

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Wu, Xiaoquan, Biying Yan, Feng Chen, Yaqi Cao, Siqing Zhang, Guanyu Chen, Rong Cao, and Mengjun Xia. "Suspicious Criminal Behavior Prediction Based on Roaming Trajectory Characteristics." In 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2020. http://dx.doi.org/10.1109/iccasit50869.2020.9368561.

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Banerjee, Kakoli, K. G. Harsha, P. Vinooth, Ajay Kumar, Pradeep Kumar, Kinjal Sharma, Nandini Roy, and Kendra Bansal. "Prediction of Criminal Behavior Based on Genetic Data - A Review." In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2022. http://dx.doi.org/10.1109/ic3i56241.2022.10073062.

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Dipakkumar Pandya, Darshanaben, Geetanjali Amarawat, Abhijeetsinh Jadeja, Sheshang Degadwala, and Dhairya Vyas. "Analysis and Prediction of Location based Criminal Behaviors Through Machine Learning." In 2022 International Conference on Edge Computing and Applications (ICECAA). IEEE, 2022. http://dx.doi.org/10.1109/icecaa55415.2022.9936498.

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Gubanov, Nikolay N., Nikolay I. Gubanov, Evgeny Yusipov, and Andrey Volkov. "Determination of Criminal Behavior." In Proceedings of the 4th International Conference on Contemporary Education, Social Sciences and Humanities (ICCESSH 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/iccessh-19.2019.399.

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Majumder, Prattoy, A. R. M. Fahim, Nusrat Haque Supti, and Md Manzurul Hasan. "Criminal Behavior Analysis for Questionable Vehicle Detection." In ICCA 2020: International Conference on Computing Advancements. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377049.3377126.

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Pillai, Ramesh Kumar Goplala, and P. Ramakanth Kumar. "Simulation of Human Criminal Behavior Using Clustering Algorithm." In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.88.

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Wang, Hong, Zhao-wen Wang, Jian-bo Li, and Qiu-hong Wei. "Criminal behavior analysis based on Complex Networks theory." In Education (ITIME). IEEE, 2009. http://dx.doi.org/10.1109/itime.2009.5236213.

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Montes, Jesús, Alberto S´nchez, and María S. Pérez. "Grid Global Behavior Prediction." In 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, 2011. http://dx.doi.org/10.1109/ccgrid.2011.17.

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Li, Jiexun, and G. Alan Wang. "Criminal identity resolution using social behavior and relationship attributes." In 2011 IEEE International Conference on Intelligence and Security Informatics (ISI 2011). IEEE, 2011. http://dx.doi.org/10.1109/isi.2011.5983994.

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Litvishkov, V. M. "Socio-Pedagogical Prevention Aspects Of Criminal Behavior Among Minors." In Pedagogical Education: History, Present Time, Perspectives. European Publisher, 2020. http://dx.doi.org/10.15405/epsbs.2020.08.02.56.

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Reports on the topic "Criminal behavior (Prediction of)"

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Paul, Satashree. The Criminal Behavior of Genes. Science Repository OÜ, November 2020. http://dx.doi.org/10.31487/sr.blog.14.

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Identifying the crucial role of genetics in criminal behavior implies there must be something known as a “Crime Gene”. Genes come out as the strongest predictor of whether a person has predisposition towards crime or any criminal behavior.
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Anderson, D. Mark, Resul Cesur, and Erdal Tekin. Youth Depression and Future Criminal Behavior. Cambridge, MA: National Bureau of Economic Research, December 2012. http://dx.doi.org/10.3386/w18656.

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Delavande, Adeline, Dana Goldman, and Neeraj Sood. Criminal Prosecution and HIV-related Risky Behavior. Cambridge, MA: National Bureau of Economic Research, February 2007. http://dx.doi.org/10.3386/w12903.

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Haga, William J. A Survey of Economic Models of Criminal Behavior. Fort Belvoir, VA: Defense Technical Information Center, October 1987. http://dx.doi.org/10.21236/ada206520.

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Jacob, Brian, Lars Lefgren, and Enrico Moretti. The Dynamics of Criminal Behavior: Evidence from Weather Shocks. Cambridge, MA: National Bureau of Economic Research, September 2004. http://dx.doi.org/10.3386/w10739.

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Prescott, J. J., and Jonah Rockoff. Do Sex Offender Registration and Notification Laws Affect Criminal Behavior? Cambridge, MA: National Bureau of Economic Research, February 2008. http://dx.doi.org/10.3386/w13803.

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Visser, Michael, William Harbaugh, and Naci Mocan. An Experimental Test of Criminal Behavior Among Juveniles and Young Adults. Cambridge, MA: National Bureau of Economic Research, September 2006. http://dx.doi.org/10.3386/w12507.

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Cesarini, David, Erik Lindqvist, Robert Östling, and Christofer Schroeder. Does Wealth Inhibit Criminal Behavior? Evidence from Swedish Lottery Winners and Their Children. Cambridge, MA: National Bureau of Economic Research, December 2023. http://dx.doi.org/10.3386/w31962.

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Linn, R. R. A transport model for prediction of wildfire behavior. Office of Scientific and Technical Information (OSTI), July 1997. http://dx.doi.org/10.2172/505313.

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Kimagai, Toru, and Motoyuki Akamatsu. Human Driving Behavior Prediction Using Dynamic Bayesian Networks. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0305.

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