Journal articles: '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 departmentsâ€™ 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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Kleymenov, Mikhail P. "Problems of criminal legal forecasting of individual criminal behavior." Law Enforcement Review 4, no. 2 (June 30, 2020): 99–108. http://dx.doi.org/10.24147/2542-1514.2020.4(2).99-108.

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The subject of the article is the problem of typical assessments of criminal legal risk by offenders. The purpose of the article is to confirm or refute the hypothesis that the attitude of various persons to the possibility of being punished for violating a criminal prohibition can be typologized, and the probability of being punished depends on the criminal's belonging to the corresponding type. The methodology includes dialectical, comparative legal, sociological, statistical, psychological methods, expert assessments, generalizing indicators. The main results, scope of application. The original criminal-legal aspect of predicting individual criminal behavior consists primarily in determining the probability of a potential criminal being brought to criminal responsibility for a possible offense and being punished. Foresight in this case is individual in the literal sense of the word – its subject is primarily a person who is inclined to commit a crime. The position of the researcher of criminal law forecasting of individual criminal behavior, who is obliged to put himself "in the place" of a socially dangerous person, to understand the nature and process of risk assessment, and to be brought to criminal responsibility, is also original. The attitude of different persons to the possibility of being brought to justice for violating a criminal law prohibition may vary widely, acquire a different character, and have specific features. In this regard, theoretically, we can distinguish the following groups of criminal risk: out of risk (“above the law”); habitual risk; "justified" risk; frivolous risk; emotional risk; situational risk; professional risk. The validity of this typology is confirmed by both empirical experience and materials of criminal-legal and psychological research. Conclusion. The magnitude of the criminal legal risk, of course, should be taken into account in the criminal law policy: both when assessing its purposefulness and effectiveness, and when solving the task of a comprehensive information and analytical support for it.

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Stankovic, Milos, Nikola Simonovic, Jelena Bulatovic, Jelena Stojiljkovic, Marina Hadzi-Pesic, and Milkica Nesic. "The prediction of criminal recidivism in male juvenile delinquents." Psihologija 52, no. 3 (2019): 285–301. http://dx.doi.org/10.2298/psi181002005s.

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Previous studies have demonstrated a strong association of criminal behavior of juvenile delinquents with delinquents? personality traits and family interactions. However, little is known about the extent to which family interactions and personality traits are associated with criminal recidivism. The present study aimed to examine these relationships, using the Velikih pet plus dva (Big Five Plus Two) ? short version (assessing Neuroticism, Extraversion, Openness to experience, Conscientiousness, Aggressiveness, Positive Valence, Negative Valence), The Quality of Family Interaction Scale (Satisfaction with family, Mother and Father Acceptance/Rejection), and official data from criminal records. The study included 61 institutionalized delinquents and 64 non-delinquents, 15 to 18 years of age. Neuroticism, Openness to experience, Conscientiousness, Negative valence, acceptance by father and rejection by mother are statistically significant predictors of criminal recidivism in juvenile delinquents. Delinquents showed higher Neuroticism, lower Conscientiousness and acceptance by mother compared to non-delinquents.

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Sukhodolov, Alexander, and Anna Bychkova. "Artificial Intelligence in Crime Counteraction, Prediction, Prevention and Evolution." Всероссийский криминологический журнал 12, no. 6 (December 24, 2018): 753–66. http://dx.doi.org/10.17150/2500-4255.2018.12(6).753-766.

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Crime prediction, prevention and counteraction with the use of modern technologies should, according to the authors, become a priority task for the state, along with the development of economy, education, medicine and the enhancement of defense capacity. The article describes the concepts of «artificial intelligence», «machine learning», «big data», «deep learning», «neural networks» from the standpoint of how they are used both by criminals and by law enforcement bodies and courts. The authors examine the application of technologies which use artificial intelligence, hi tech crime (fishing, drones, fake information, bots, and so on). They outline modern software solutions based on artificial intelligence and aimed at counteracting crime: software that analyzes big volumes of data, processing of stream videos, facial recognition, contextual searching platforms, etc. The authors also describe the existing resources for predictive analytics (in particular, inter-agency experimental software «Artificial Intelligence in Police Work and Investigation of Criminal Offences»; software for recognizing people based on fragments of their tattoos; facial recognition of people after plastic surgeries in pictures and stream videos, with the generation of variants of their original appearance; platform of contextual intelligence Nigel; system Mayhem and others) and how they can be used to predict both crimes in general and individual criminal behavior. The authors also outline ethical dilemmas connected with legal decisions made by artificial intelligence regarding specific people. They present examples of using artificial intelligence for crime prevention (software COMPAS, criminal community’s psychometric prediction system, Harm Assessment Risk Tool, analytical software complex CEG, crime prediction system PredPol, ePOOLICE system, Palantir software, Russian system «Artificial intelligence»). They also outline the indicators of the early crime prevention system: indicators of matching, lagging, cyclical and counter-cyclical indicators. The authors state that Russia is lagging behind other countries in its use of artificial intelligence in law enforcement and suggest adopting the Modern Strategy of Crime Counteraction, Prediction and Prevention. Possible directions of this strategy are described.

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Rajawat, Anand Singh, Pradeep Bedi, S. B. Goyal, Sandeep Kautish, Zhang Xihua, Hanan Aljuaid, and Ali Wagdy Mohamed. "Dark Web Data Classification Using Neural Network." Computational Intelligence and Neuroscience 2022 (March 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/8393318.

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There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online shopping. Understanding online criminal behavior is challenging because the data is available in a vast amount. To require an approach for learning the criminal behavior to check the recent request for improving the labeled data as a user profiling, Dark Web Structural Patterns mining in the case of multidimensional data sets gives uncertain results. Uncertain classification results cause a problem of not being able to predict user behavior. Since data of multidimensional nature has feature mixes, it has an adverse influence on classification. The data associated with Dark Web inundation has restricted us from giving the appropriate solution according to the need. In the research design, a Fusion NN (Neural network)-S3VM for Criminal Network activity prediction model is proposed based on the neural network; NN- S3VM can improve the prediction.

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Quinsey, Vernon L. "Improving decision accuracy where base rates matter: The prediction of violent recidivism." Behavioral and Brain Sciences 19, no. 1 (March 1996): 37–38. http://dx.doi.org/10.1017/s0140525x0004139x.

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AbstractBase rates are vital in predicting violent criminal recidivism. However, both lay people given simulated prediction tasks and professionals milking real life predictions appear insensitive to variations in the base rate of violent recidivism. Although there are techniques to help decision makers attend to base rates, increased decision accuracy is better sought in improved actuarial models as opposed to improved clinicians.

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Rao, V. Chandra Shekhar, Kallepelly Spandhana, C. Srinivas, M. Sujatha, Bojja Vani, and S. Venkatramulu. "An Adaptive Technique for Crime Rate Prediction using Machine Learning Algorithms." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8 (September 20, 2023): 271–75. http://dx.doi.org/10.17762/ijritcc.v11i8.7954.

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Any country must give the investigation and preventive of crime top priority. There are a rising amount of cases that are still pending due to the rapid increase in criminal cases in India and elsewhere. It is proving difficult to classify and address the rising number of criminal cases. Understanding a place's trends in criminal activity is essential to preventing it from occurring. Crime-solving organisations will be more effective if they have a clear awareness of the patterns of criminal behavior that are present in a particular area. Women's safety and protection are of highest importance despite the serious and persistent problem of crime against them. This study offers predictions about the kinds of crimes that might occur in a particular location using ensemble methods. This facilitates the categorization of criminal proceedings and subsequent action in a timely manner. We are applying machine learning methods like KNN, Linear regression, SVM, Lasso, Decision tree and Random forest in order to assess the highest accuracy.

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Åkerlund, David, Bart H. H. Golsteyn, Hans Grönqvist, and Lena Lindahl. "Time discounting and criminal behavior." Proceedings of the National Academy of Sciences 113, no. 22 (May 16, 2016): 6160–65. http://dx.doi.org/10.1073/pnas.1522445113.

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One of the most basic predictions of almost any model of crime is that individual time preferences matter. However, empirical evidence on this fundamental property is essentially nonexistent. To our knowledge, this paper provides the first pieces of evidence on the link between time discounting and crime. We use a unique dataset that combines a survey-based measure of time discount rates (at age 13) with detailed longitudinal register data on criminal behavior spanning over 18 y. Our results show that individuals with short time horizons have a significantly higher risk of criminal involvement later in life. The magnitude of the relationship is substantial and corresponds to roughly one-third of the association between intelligence and crime.

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P. Karthik, P. Jayanth, K. Tharun Nayak, and K. Anil Kumar. "Crime Prediction Using Machine Learning and Deep Learning." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 3 (May 4, 2024): 08–15. http://dx.doi.org/10.32628/ijsrset241134.

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The utilization of machine learning and deep learning methods for crime prediction has become a focal point for researchers, aiming to decipher the complex patterns and occurrences of crime. This review scrutinizes an extensive collection of over 150 scholarly articles to delve into the assortment of machine learning and deep learning techniques employed in forecasting criminal behaviour. It grants access to the datasets leveraged by researchers for crime forecasting and delves into the key methodologies utilized in these predictive algorithms. The study sheds light on the various trends and elements associated with criminal behaviour and underscores the existing deficiencies and prospective avenues for advancing crime prediction precision. This thorough examination of the current research on crime forecasting through machine learning and deep learning serves as an essential resource for scholars in the domain. A more profound comprehension of these predictive methods will empower law enforcement to devise more effective prevention and response strategies against crime.

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Boutwell, Brian B., Scott Menard, J. C. Barnes, Kevin M. Beaver, Todd A. Armstrong, and Danielle Boisvert. "The role of gene–gene interaction in the prediction of criminal behavior." Comprehensive Psychiatry 55, no. 3 (April 2014): 483–88. http://dx.doi.org/10.1016/j.comppsych.2013.11.005.

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Vitacco, Michael J., Craig S. Neumann, and Michael F. Caldwell. "Predicting Antisocial Behavior in High-Risk Male Adolescents." Criminal Justice and Behavior 37, no. 8 (June 30, 2010): 833–46. http://dx.doi.org/10.1177/0093854810371358.

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The study of the downward extension of psychopathy to adolescents is limited by a lack of research focusing on its predictive validity in high-risk samples. The current study presents data on a sample of 120 ethnically diverse male offenders released from a maximum-security correctional treatment facility and followed for an average of 5 years. Dichotomized follow-up criminal charges consisted of both misdemeanor and felony charges. Structural equation modeling results found that the Psychopathy Checklist: Youth Version accounted for a modest, but significant, amount of variance. Adding a measure that assessed previous instrumental violence increased variance accounted for in the criminal charges outcome factor, but the relationship was in an unexpected direction. Implications for predicting violent behavior with psychopathy and criminal conduct in adolescent male offenders are discussed.

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Cahyaningrum, Anindya Endah, and Margaretha Margaretha. "DOES EMOTIONAL CONGRUENCE AND COMPULSIVE SEXUAL BEHAVIOR INCREASE THE RECURRENCE RISKS OF CHILD SEXUAL ABUSE?" Jurnal Psikologi 19, no. 4 (November 14, 2020): 417–30. http://dx.doi.org/10.14710/jp.19.4.417-430.

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This research examines the influence of Emotional Congruence with Children and Compulsive Sexual Behavior towards Re-offend Prediction of Child Sexual Offenders. The research was conducted on 111 residents of correctional facilities for child sexual abuse in 5 correctional institutions in East Java, all of whom were males aged 18 to 81. The prediction of reoffending was assessed using an actuarial instrument, Static-2002 which was tested with Fleiss Kappa Inter-rater Reliability 89% (14 items). Emotional Congruence with Children was assessed by the adapted Emotional Congruence Scale of Children and Sex Questionnaire (EC-CSQ; 15 items, α = .88). Compulsive Sexual Behavior assessed by the Compulsive Sexual Behavior Inventory-13 (CSBI-13; 13 items, α = .85). The data was analyzed by using correlation and multivariate linear regression analysis. The research found that age and Compulsive Sexual Behavior, particularly Distress in Daily Functioning, significantly predicted reoffending among Child Sexual Crime Offenders (R 2 = .49). However, the inability to Control Compulsive Sexual Behavior was not predictive towards reoffending. The findings of this research are expected to provide input in the criminal justice and psychological correction-rehabilitation process for sexual offenders in Indonesia.

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Bovasso, Gregory B., Arthur I. Alterman, John S. Cacciola, and Megan J. Rutherford. "The Prediction of Violent and Nonviolent Criminal Behavior in a Methadone Maintenance Population." Journal of Personality Disorders 16, no. 4 (August 2002): 360–73. http://dx.doi.org/10.1521/pedi.16.4.360.24124.

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Cale, Jesse, and Patrick Lussier. "Merging Developmental and Criminal Career Perspectives." Sexual Abuse 24, no. 2 (July 25, 2011): 107–32. http://dx.doi.org/10.1177/1079063211403503.

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Currently, a majority of actuarial risk-assessment tools for sexual recidivism contain static risk factors that measure various aspects of the offender’s prior criminal history in adulthood. The goal of the current study was to assess the utility of extending static risk factors, by using developmental and criminal career parameters of offending, in the actuarial assessment of risk of violent/sexual recidivism. The current study was based on a sample of 204 convicted sexual aggressors of women incarcerated in the province of Quebec, Canada between April 1994 and June 2000. Semistructured interviews were used to gather information on the offender’s antisocial history prior to adulthood, and police records were used to collect data on the criminal career of these offenders in adulthood. For an average follow-up period of approximately 4 years, the violent/sexual recidivism rate for the sample was 23.7%. The results provided support for the inclusion of both developmental and criminal career indicators for the prediction of violent/sexual recidivism. More specifically, recidivists were characterized by an early onset antisocial trajectory and a pattern of escalation of antisocial behavior between childhood and adolescence. The findings suggest that risk assessors should look beyond broad adult criminal history data to include aspects of antisocial development to improve predictive accuracy.

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Saltos, Ginger, and Mihaela Cocea. "An Exploration of Crime Prediction Using Data Mining on Open Data." International Journal of Information Technology & Decision Making 16, no. 05 (September 2017): 1155–81. http://dx.doi.org/10.1142/s0219622017500250.

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The increase in crime data recording coupled with data analytics resulted in the growth of research approaches aimed at extracting knowledge from crime records to better understand criminal behavior and ultimately prevent future crimes. While many of these approaches make use of clustering and association rule mining techniques, there are fewer approaches focusing on predictive models of crime. In this paper, we explore models for predicting the frequency of several types of crimes by LSOA code (Lower Layer Super Output Areas — an administrative system of areas used by the UK police) and the frequency of anti-social behavior crimes. Three algorithms are used from different categories of approaches: instance-based learning, regression and decision trees. The data are from the UK police and contain over 600,000 records before preprocessing. The results, looking at predictive performance as well as processing time, indicate that decision trees (M5P algorithm) can be used to reliably predict crime frequency in general as well as anti-social behavior frequency.

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Ruibyte, Laima, Evelina Viduoliene, and Birute Balseviciene. "PERCEPTION OF CRIMINALS: WHAT STEREOTYPES HOLD FUTURE LAW ENFORCEMENT OFFICERS?" SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference 1 (May 26, 2016): 515. http://dx.doi.org/10.17770/sie2016vol1.1520.

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The main reasons for why stereotypes of police officers about criminals are dangerous and affecting the legal system are: firstly, having stereotypes and misconceptions about typical criminals is damaging witness's ability to correctly identify and/or remember the offender’s features; secondly, stereotypes determine the peculiarities of interrogations; thirdly having stereotypes hinders the identification of individuals who actually commit crimes. 270 university students of Law and Police Activity program participated in the study and gave their opinion on the portrait and likely activities of a potential perpetrator. The Criminal Stereotype Questionnaire-Revised (Sparks & MacLin, 2011) was used to evaluate students’ judgment concerning the potential perpetrator’s socially desirable/undesirable personality traits, early years of family life history and childhood and adolescence activities. The results of this study revealed that future law and public security officers refer to delinquent activities during childhood and adolescence as well to adverse parental family life circumstances when predicting criminal behavior rather than personality traits. Furthermore, they have some preconceptions about gender, race and criminal behavior in advance.

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Međedović, Janko, and Uroš Kovačević. "Sadism as a Key Dark Trait in the Link Between Football Fandom and Criminal Attitudes." Journal of Individual Differences 42, no. 1 (January 2021): 9–18. http://dx.doi.org/10.1027/1614-0001/a000325.

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Abstract. Football (soccer) fans, particularly those with active involvement in supporting clubs, are especially prone to violence and criminal behavior. However, there is a gap in literature regarding individual characteristics which lead to criminal behavior in this group. In the present research, we examined the Dark Tetrad traits (narcissism, Machiavellianism, psychopathy, and sadism) and criminal attitudes (Violence, Entitlement, Antisocial intent, and Associates) in a group of active football fans ( N = 128) and a control group ( N = 118). We were led by a hypothesis that psychopathy and sadism as the dark traits with highest associations with violence are more pronounced in football supporters and key traits to predict criminal attitudes. The results showed that football fans had higher scores in all criminal attitude scales as well as in trait sadism. Furthermore, membership in a football supporters’ group was the most important predictor of all four measures of criminal attitudes. However, Dark Tetrad traits contributed to the prediction as well: sadism was the most important predictor of criminal attitudes, followed by psychopathy, and Machiavellianism, while narcissism had the fewest associations with the criteria measures. Finally, sadism was the only dark trait which significantly mediated the link between club supporting and criminal attitudes. Study findings help in understanding personality profiles of football supporters and provide new knowledge of the role that the Dark Tetrad traits (especially sadism) play in violence and criminal involvement in general.

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Medjedovic, Janko, Daliborka Kujacic, and Goran Knezevic. "Personality-related determinants of criminal recidivism." Psihologija 45, no. 3 (2012): 277–94. http://dx.doi.org/10.2298/psi1203277m.

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The goal of this study was to explore personality-related determinants of recidivism, with recidivism being defined as a) the number of lawful sentences a person had (criminal legal recidivism), and b) the number of prison sentences pronounced (penal recidivism). The study was carried out in two independent samples: a) convicts from the Correctional Institution of Belgrade - Penitentiary of Padinska Skela (N=113), and b) convicts from the Special Prison Hospital in Belgrade (N =112). The variables of the Five-Factor Model of Personality (Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness) were measured, together with two additional basic personality traits: Disintegration (a broad dimension of psychosis-proneness), and Amorality (three factors representing a disposition to amoral forms of behavior). In addition, psychopathy (Manipulative and Antisocial tendencies) - a psychological entity expected to most successfully predict criminal recidivism - was measured as well. The efficiency of prediction of the two criteria of recidivism was assessed separately in each of those two samples. The results revealed differences in the orchestration of predictors depending on the kind of recidivism as the criterion and the severity of offense. The most important predictors of both forms of recidivism in the sample of convicts with lower intensity of criminal behavior were psychopathic traits. However, in the sample of convicts with higher intensity and variety of criminal behavior, the most important predictors of the number of sentences were Antisociality and Amorality Induced by Frustration, while the most important predictors of the number of prison sanctions were Amorality Induced by Brutality and Disintegration.

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DeLisi, Matt, Kevin M. Beaver, Michael G. Vaughn, and John Paul Wright. "All in the Family." Criminal Justice and Behavior 36, no. 11 (October 19, 2009): 1187–97. http://dx.doi.org/10.1177/0093854809342884.

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A range of Gene × Environment interactions is associated with antisocial phenotypes, and the evidence is clear that the etiology of antisocial behavior is strongly heritable and that environmental liabilities are important. However, the precise ways that genetic and environmental pathogens interact to predict antisocial behavior are underspecified. The present study shows that the interaction between a polymorphism in a dopamine receptor gene (DRD2) and a criminal father predicts five antisocial phenotypes among African American females ( n = 232) in the National Longitudinal Study of Adolescent Health. Genetic risk (as measured by the A1 allele) and a criminal father interacted to predict serious and violent delinquency at Wave 1, serious and violent delinquency at Wave 2, and number of police contacts. The current investigation represents the first study to show Gene × Environment interactions in the prediction of antisocial phenotypes using criminal justice system status as an environmental pathogen.

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Xiaomin Li, Lixuan Zhao, Qi Wu, Wei Du, Shangxuan Jiang, Shuo Wen,. "Machine Learning-Based Prediction Methods for Home Burglary Crimes." Journal of Electrical Systems 20, no. 2 (April 4, 2024): 123–30. http://dx.doi.org/10.52783/jes.1106.

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In today’s rapidly evolving society, as technology continues to advance, various new forms and methods of crime emerge incessantly. It becomes particularly crucial to accurately predict future criminal behaviors. This paper delves into the study of forecasting home burglary crimes in the realm of property-related offenses. Utilizing a dataset of criminal cases, relevant variables with high correlation to crime prediction are selected as features. Through employing diverse machine learning algorithms, the likelihood of the occurrence of home burglary crimes is forecasted. Consequently, a crime prediction model specifically tailored for home burglary cases is constructed, and the accuracy of the model is evaluated. By using the accuracy of the model as the benchmark, the optimal crime prediction model is chosen, and a system is implemented for building and evaulating the model. Experimental results demonstrate that the developed crime prediction model is capable of effectively foreseeing home burglary crimes, thereby providing valuable support and scientific evidence for the prevention and handling of such criminal cases.

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Sánchez-Teruel, David, and Mª Auxiliadora Robles-Bello. "Model "Big Five" personality and criminal behavior." International Journal of Psychological Research 6, no. 1 (June 30, 2013): 102–9. http://dx.doi.org/10.21500/20112084.709.

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It reflect on the theoretical issues that currently versa Personality Psychology in general and antisocial or criminal behavior in particular. It discusses how the model can be used personality "Big Five" applied to the field of crime, and shows the variables that the literature presented as more predictive, through one of the most widely used assessment instruments at present. It currently advises finding, meeting points between the various existing theories, for that personality does not become a field of study restricted exclusively to researchers and scholars. It discuss the most important results in the application of the "Big Five" personality of the offender, and posess some limitations, as future research for practitioners and researchers.

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Lessing, Benjamin. "Logics of Violence in Criminal War." Journal of Conflict Resolution 59, no. 8 (June 4, 2015): 1486–516. http://dx.doi.org/10.1177/0022002715587100.

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What kind of war is Mexico’s drug war? The prominent “criminal insurgency” approach helpfully focuses attention on cartel–state conflict, but unnecessarily redefines insurgency as “state-weakening,” eliding critical differences in rebels’ and cartels’ aims. Whereas rebels fight states, and cartels fight with one another, to conquer mutually prized territory and resources, cartels fight states “merely” to constrain their behavior and influence policy outcomes. This distinction yields a typology with theoretical consequences: decisive victory plays an important role in most models of civil war but is impossible or undesirable in wars of constraint. Theories of criminal war must therefore explain how ongoing coercive violence can be preferable to pacific strategies. I distinguish two such coercive logics of cartel–state conflict: violent lobbying and violent corruption. Lobbyings' more universalistic benefits elicit free riding, so turf war among cartels should make it rarer than violent corruption. This prediction accords with qualitative and quantitative evidence from Mexico, Colombia, and Brazil.

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Kroner, Daryl G., Jeremy F. Mills, and John R. Reddon. "A Coffee Can, factor analysis, and prediction of antisocial behavior: The structure of criminal risk." International Journal of Law and Psychiatry 28, no. 4 (July 2005): 360–74. http://dx.doi.org/10.1016/j.ijlp.2004.01.011.

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Cihan, Abdullah, and Charles R. Tittle. "Self-Control, Sanction Threats, Temptation, and Crime: Examining Contingencies of Self-Control in a Cross-National Context." Crime & Delinquency 65, no. 4 (January 16, 2019): 555–80. http://dx.doi.org/10.1177/0011128718824939.

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Using a representative household survey data, we examine the generality of self-control, the predictive strengths of sanction threats, and the interaction between criminal propensity and sanction threats in explaining criminal probability. Although the data confirm the generality of self-control predictions of deviant/criminal behavior in the Turkish cultural context, the effects appear quite modest and contingent on fear of informal sanctions and temptation. Consistent with the findings of recent studies, a small interaction between self-control and sanction threats suggests that deterrence is greatest among individuals with weak self-control. However, there is no interaction between sanction threats and temptation, suggesting that sanction fear is equally likely among individuals regardless of their level of temptation.

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Glenn, Andrea L., and Adrian Raine. "Neurocriminology: implications for the punishment, prediction and prevention of criminal behaviour." Nature Reviews Neuroscience 15, no. 1 (December 11, 2013): 54–63. http://dx.doi.org/10.1038/nrn3640.

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Duckitt, John H. "The Prediction of Violence." South African Journal of Psychology 18, no. 1 (March 1988): 10–16. http://dx.doi.org/10.1177/008124638801800102.

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Behaviour prediction is an important applied goal of psychology and the prediction of violent behaviour, in particular, has attracted considerable attention. Although the ability of mental health professionals to predict violence adequately was widely accepted till the late 1960s, a number of important studies then seemed to establish irrefutably the conclusion that clinical assessments of dangerousness, or violence proneness, were hopelessly inaccurate. Renewed attempts to predict violent behaviour, particularly in criminal populations, however, have recently culminated in the development of empirically based actuarial systems, which have shown a dramatically improved capacity to predict violent behaviour. These systems have already begun to have important impacts on parole and institutional classification policies. It is argued that these new systems involve not merely a methodological, but also an important conceptual shift in the enterprise of violence prediction, and that actuarial strategies may have been unjustifiably neglected by psychologists. Some suggestions for the integration of such actuarial approaches with contemporary theoretical developments in personality and social psychology are discussed.

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Görgülü, Tuğba, and Vedat Işıkhan. "The Effects of Personality, Family Functioning and Perceived Social Support on Suicide: Suicide Risk Among Individuals in Turkish Probation System." Bulletin of Legal Medicine 24, no. 2 (October 13, 2019): 122–30. http://dx.doi.org/10.17986/blm.2019254303.

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Each year, an average of one million people end their lives by suicide. This rate is higher for disadvantaged groups such as the criminal population and substance users. Psychiatric problems and certain social factors increase suicide risk. Although the criminal involved and substance users have been studied in Turkey, research on suicide risk factors are limited. Therefore, the aim of this study is to examine the effects of personality traits, and psychosocial variables such as family functioning, and perceived social support on suicide risk in individuals directed to Turkish probation services. Participants were 403 males, of which 183 were substance users and 220 were criminals. Results, revealed a significant relationship between being single, low income, criminal behavior at an early age, substance use behavior, multiple drug use, and suicide risk. Additionally, personality traits of neuroticism and psychoticism, as well as family members’ interest in each other were found to be best predictive variables of suicide risk R2= 0.551, F (12, 389) = 39.79, p<.001; Adjusted R2= .537, and the explained variance ratio was 55%. These results indicate that inclusion of social support factors such as family support, in suicide prevention programs may decrease suicide risk.

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Shittu, Adebisi Kolawole, and Oshotoye Adedayo Bradford. "Organizational Ethic, Locus of Control, Self-Esteem as Predictors of Criminalities among Employees in Tertiary Institutions, Southwest, Nigeria." International Journal of Research and Innovation in Social Science VIII, no. VI (2024): 2707–17. http://dx.doi.org/10.47772/ijriss.2024.806206.

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This study aimed to investigate the predictive relationship between organizational ethic, locus of control, self-esteem, and criminal behaviors among employees in tertiary institutions in Southwest, Nigeria. A sample of 416 employees from various institutions participated in the study. Data was collected using self-report questionnaires, while correlation analysis, and structural equation modeling (SEM), were used to analyze the relationships between the variables and predict criminal behavior. Specifically, employees with lower levels of organizational ethic, external locus of control, and low self-esteem were found to be more likely to engage in criminal activities. These findings highlight the importance of promoting ethical values, fostering internal locus of control, and enhancing self-esteem among employees to reduce likelihood of criminalities behaviors in tertiary institutions in Southwest, Nigeria. Recommendations for organizational interventions and future research are discussed.

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Lie, Nils, and Edwin E. Wagner. "Prediction of Criminal Behavior in Young Swedish Women Using a Group Administration of the Hand Test." Perceptual and Motor Skills 82, no. 3 (June 1996): 975–78. http://dx.doi.org/10.2466/pms.1996.82.3.975.

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2,074 Swedish girls and adolescents were administered a group version of the Hand Test. About ten years later, 80 of these subjects had committed crimes serious enough to be entered on the General Police Register. These young women offenders were then matched by age with nonoffenders and the two groups compared on 17 quantitative Hand Test variables. Significance was found for the Tension and Pathology scores, suggesting that these young female lawbreakers were characterized as youngsters having scores indicating anxiety and other forms of psychopathology rather than innate hostility.

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Notaro, Domenico. "Scientists and Earthquake Risk Prediction: “Ordinary” Liability in an Extraordinary Case?" European Journal of Risk Regulation 5, no. 2 (June 2014): 159–67. http://dx.doi.org/10.1017/s1867299x00003573.

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This article aims to consider whether classic criminal offences (such as manslaughter) are adequate to reprove the scientists’ behaviour when major calamities are being judged to have caused the death of people and wide destructions. The fundamental problem hinges on the role of risk-assessment and consultancy carried out by the scientists, as well as on the unknown state of major risks. Then, to establish a link of causality between the defendants’ behaviour and the death-events affecting the victims, it must be proved that: a) the scientists “psychically” influenced the victims to leave any safety precaution in relation to the risk; b) the deaths of the inhabitants are not to be considered an “extraordinary” circumstance, even by experts. The difficulties faced by the Judge to fulfil these tasks prompt us to wonder whether other types of criminal charges would be more appropriate for sanctioning scientists who are found to be derelict in their duty of risk-assessment to authorities and citizens.

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Harrison, Lana, and Joseph Gfroerer. "The Intersection of Drug Use and Criminal Behavior: Results from the National Household Survey on Drug Abuse." Crime & Delinquency 38, no. 4 (October 1992): 422–43. http://dx.doi.org/10.1177/0011128792038004002.

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In 1991, questions on involvement in criminal behavior and being arrested and booked for a crime were added to the National Household Survey on Drug Abuse (NHSDA) to ascertain the relationship between drug use and criminal behavior. Analysis shows that drug use is a strong correlate of being booked for a criminal offense, but age is the more important correlate of criminal involvement. There were few differences in models predicting violent as opposed to property crime, although minority status was a more important predictor of violent crime, and poverty was a more important predictor of property crime. Cocaine use was the most important covariate of being booked for a crime in large metropolitan areas that were oversampled in the 1991 NHSDA.

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Shukla, Arpita, and Dr Jitendra Sheetlani. "A study on criminal behaviour and pattern prediction using machine learning techniques." International Journal of Computing and Artificial Intelligence 3, no. 1 (January 1, 2022): 47–49. http://dx.doi.org/10.33545/27076571.2022.v3.i1a.46.

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Heath, Linda, Candace Kruttschnitt, and David Ward. "Television and Violent Criminal Behavior: Beyond the Bobo Doll." Violence and Victims 1, no. 3 (January 1986): 177–90. http://dx.doi.org/10.1891/0886-6708.1.3.177.

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This study builds on the research concerning television viewing and aggression by extending the external validity, or generalizability, of the dependent variable. We assess the relationship between self-reported television viewing at 8, 10, and 12 years of age and the subsequent commission of a violent criminal act. This study is based on interview data from 48 males incarcerated for violent crimes and 45 nonincarcerated, nonviolent males matched on age, race, and neighborhood of residence during adolescence. Results show that the extent of a respondent’s reported television viewing was not, in and of itself, predictive of violent criminal acts. Instead, it was the interaction of heavy doses of television viewing and exposure to either maternal or paternal abuse that related to violent crime. These findings support the efforts of some recent scholars in their attempts to understand why television has a negative effect on only some viewers. The results are discussed in light of the cognitive formulations of neoassociationism, encoding specificity, and the double-dose effect.

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Wallinius, Märta, Thomas Nilsson, Björn Hofvander, Henrik Anckarsäter, and Gunilla Stålenheim. "Facets of psychopathy among mentally disordered offenders: Clinical comorbidity patterns and prediction of violent and criminal behavior." Psychiatry Research 198, no. 2 (July 2012): 279–84. http://dx.doi.org/10.1016/j.psychres.2012.01.005.

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Cohn, Ellen S., and Kathryn L. Modecki. "GENDER DIFFERENCES IN PREDICTING DELINQUENT BEHAVIOR: DO INDIVIDUAL DIFFERENCES MATTER?" Social Behavior and Personality: an international journal 35, no. 3 (January 1, 2007): 359–74. http://dx.doi.org/10.2224/sbp.2007.35.3.359.

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The purpose of this study was to determine the role of individual differences (i.e., belief in a just world and authoritarianism), and attitude (i.e., attitudes toward the criminal legal system), in predicting delinquent behavior. High school students (412 males, 423 females) completed questionnaires that measured demographics, belief in a just world (BJW), authoritarianism (RWA), attitudes toward the criminal legal system (ATCLS), and delinquent behavior. Two models were assessed in this study. The first was a direct model, which assessed whether individual differences or attitude best predicted rule-violating behavior. The second was an integrating model, which assessed the role of both factors, individual differences and attitude, as predictors of rule-violating behavior. For male adolescents, the direct model best predicted delinquency, suggesting negative ATCLS was the sole significant predictor of rule-violating behavior. In contrast, for females, the integrating model best predicted delinquency, as negative ATCLS mediated the negative relation between BJW and delinquency, and partially mediated the negative relation between RWA and delinquency. The implications of gender differences in predicting delinquent behavior are discussed.

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Fernando, Zico Junius, Rosmanila, Laily Ratna, Achmad Cholidin, and Bhanu Prakash Nunna. "The Role of Neuroprediction and Artificial Intelligence in the Future of Criminal Procedure Support Science: A New Era in Neuroscience and Criminal Justice." Yuridika 38, no. 3 (September 1, 2023): 593–620. http://dx.doi.org/10.20473/ydk.v38i3.46104.

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Recent developments in the field of neuroimaging in the world of neuroscience, when combined with artificial intelligence and, more specifically, with the use of mechanical engineering, have resulted in the creation of brain reading technology that may soon be widely used in scientific fields in the world including detecting, for example, criminal lies. When used in forensic psychiatry, this approach can increase the precision of risk assessment and help determine areas where intervention can be most effective. Neuro prediction with artificial intelligence can be called AI. This neuroprotection is a method of predicting criminal behaviour by finding biomarkers of brain function that can indicate that someone is committing crimes in the future. Normative legal research or library legal research (library research), with a statute, conceptual, comparative, historical, or futuristic approach. The nature of the research used in this research is descriptive-prescriptive and uses content analysis. The result of this research is to dissect the development of AI Neuroprediction in forensic psychiatry and criminal justice with attention to this technology's legal and ethical implications and potential applications. In the future, AI neuroprediction may play an important role in integrating forensic psychology into the criminal justice system. Through in-depth analysis of neurological data, AI neuroprediction could assist in identifying behavioral patterns or tendencies that might influence a person's criminal propensity, thus enriching traditional forensic psychological evaluations. It may also contribute to creating more precise and personalized intervention strategies to prevent repeat crimes.

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Petrovic, B., J. Medjedovic, M. Savic, and J. Zeleskov-Djoric. "Predicting multiple criteria of criminal behavior with HEXACO domains and facets." Personality and Individual Differences 60 (April 2014): S40. http://dx.doi.org/10.1016/j.paid.2013.07.105.

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Sukhodolov, Alexander, Marina Kaluzhina, Boris Spasennikov, and Viktor Kolodin. "Digital Criminology: the Mmethod of Digital Profiling of an Unidentified Criminal's Behavior." Russian Journal of Criminology 13, no. 3 (July 4, 2019): 385–94. http://dx.doi.org/10.17150/2500-4255.2019.13(3).385-394.

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The authors examine the specific features of the digital environment to analyze contemporary approaches to the system of instruments of studying illegal actions in cyberspace. They state that law enforcement bodies should adapt key characteristics of the digital environment to the accomplishment of investigation tasks. The authors analyze the possibilities offered by digital profiling and modeling of the digital profile (portrait) of an unidentified criminal through mathematical methods of modeling and prediction in investigating and solving serial crimes, including cybercrimes. An extensive review of Russian and foreign publications is used to study the evolution of scientific ideas regarding the profiling method, which is the basis for the digital profiling of the behavioral model of an unidentified criminal in the digital environment. It is stated that none of the branches of criminal law, including criminology and criminalistics, could alone solve the interdisciplinary problem of the investigation and detection of crimes in the digital environment. The authors prove that it is necessary to integrate the knowledge of these branches and to conduct interdisciplinary research involving experts, i.e. to duly streamline the organization of those activities that together make up the investigation and detection of crimes. Based on the content of the concept «modus operandi», which lies at the heart of building an abstract model of criminal behavior, they conclude that it could be used to investigate and solve crimes in the digital environment and determine the specific features of the content of its structural elements. The comparative analysis of the contents of the key stages of profiling is used to prove the expediency of employing the whole range of logical and mathematical methods of analysis to process and analyze criminological information, which leads to the necessity of both critically reviewing them and finding ways to go beyond the traditional approaches. The authors describe the essence of the mathematical extrapolation method, which is most commonly used in criminology for the quantitative analysis of knowledge regarding objects, phenomena, processes, as well as the possibility of using it in digital profiling. As a result of this research based on the systemic approach, the authors state the objective character of links between the traditional and the digital profiling, point out the existing links and regularities, which allow them to reduce the essence of the examined phenomena to building a model through the recreation, in the process of investigation, of the mental trace pattern and then using it to find the guilty person.

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Hagan, Michael P., and Sandra L. King. "Accuracy of Psychologists' Short-Term Predictions of Future Criminal Behavior Among Juveniles." Journal of Offender Rehabilitation 25, no. 1-2 (July 1997): 129–41. http://dx.doi.org/10.1300/j076v25n01_09.

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GENDREAU, PAUL, CLAIRE E. GOGGIN, and MOIRA A. LAW. "Predicting Prison Misconducts." Criminal Justice and Behavior 24, no. 4 (December 1997): 414–31. http://dx.doi.org/10.1177/0093854897024004002.

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A meta-analysis was conducted on 39 studies that generated 695 correlations with prison misconducts. Predictors of prison misconducts were grouped into 16 domains as follows: (a) personal characteristics ( n = 9), (b) situational factors ( n = 3), and (c) actuarial measures of antisocial personality and risk ( n = 4). Personal and situational variables were similar in their ability to predict prison misconduct. Within these two categories, antisocial attitudes and behavior (e.g., companions, prison adjustment), criminal history, and institutional factors were the strongest predictors. Among actuarial measures, an interview-based risk protocol produced the highest correlations with prison misconducts. The prediction of violent misconducts was associated with greater effect sizes than nonviolent misconducts. Despite the limitations of the database, several recommendations for assessing prison misconducts appear warranted.

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Farrington, David P. "Early Predictors of Adolescent Aggression and Adult Violence." Violence and Victims 4, no. 2 (January 1989): 79–100. http://dx.doi.org/10.1891/0886-6708.4.2.79.

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The Cambridge Study in Delinquent Development is a prospective longitudinal survey of 411 London males from ages 8 years old to 32 years old. This article investigates the prediction of adolescent aggression (ages 12-14 years old), teenage violence (ages 16-18 years old), adult violence (age 32 years old), and convictions for violence. Generally, the best predictors were measures of economic deprivation, family criminality, poor child-rearing, school failure, hyperactivity-impulsivity-attention deficit, and antisocial child behavior. Similar predictors applied to all four measures of aggression and violence. It is concluded that aggression and violence are elements of a more general antisocial tendency, and that the predictors of aggression and violence are similar to the predictors of antisocial and criminal behavior in general.

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

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