Víctor Hugo Masías

Predicting sentencing outcomes with centrality measures

Despite their importance for stakeholders in the criminal justice system, few methods have been developed for determining which criminal behavior variables will produce accurate sentence predictions. Some approaches found in the literature resort to techniques based on indirect variables, but not on the social network behavior with exception of the work of Baker and Faulkner [ASR 58: 837–860, 1993]. Using information on the Caviar Network narcotics trafficking group as a real-world case, we attempt to explain sentencing outcomes employing the social network indicators. Specifically, we report the ability of centrality measures to predict a) the verdict (innocent or guilty) and b) the sentence length in years. We show that while the set of indicators described by Baker and Faulkner yields good predictions, introduction of the additional centrality measures generates better predictions. Some ideas for orienting future research on further improvements to sentencing outcome prediction are discussed.

Transparency-Driven Business Process Management in Healthcare Settings [Leading Edge]

Around the world, CEOs and senior managers of healthcare systems are asking themselves how best to deal with the challenges of today?s rapidly changing healthcare landscape. The solutions they opt for must be solidly grounded on fundamental values that will guide future practices, decisions, and standards.

Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice.

Using self-organizing maps to model turnover of sales agents in a call center

Abstract This paper proposes an approach for modeling employee turnover in a call center using the versatility of supervised self-organizing maps. Two main distinct problems exist for the modeling employee turnover: first, to predict the employee turnover at a given point in the sales agents trial period, and second to analyze the turnover behavior under different performance scenarios by using psychometric information about the sales agents. Identifying subjects susceptible to not performing well early on, or identifying personality traits in an individual that does not fit with the work style is essential to the call center industry, particularly when this industry suffers from high employee turnover rates. Self-organizing maps can model non-linear relations between different attributes and ultimately find conditions between an individuals performance and personality attributes that make him more predisposed to not remain long in an organization. Unlike other models that only consider performance attributes, this work successfully uses psychometric information that describes a sales agents personality, which enables a better performance in predicting turnover and analyzing potential personality profiles that can identify agents with better prospects of a successful career in a call center. The application of our model is illustrated and real data are analyzed from an outbound call center.

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers–Logistic Regression, Naïve Bayes and Random Forest–with a range of social network measures and the necessary databases to model the verdicts in two real–world cases: the U.S. Watergate Conspiracy of the 1970’s and the now–defunct Canada–based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.

Exploring the prominence of Romeo and Juliet’s characters using weighted centrality measures

Why are Romeo and Juliet prominent characters in Shakespeare’s play of the same name? Contrary to what common sense might suggest, the academic literature does not provide a unique answer to this question. Indeed, there is little agreement on who the main character is and which elements of a script contribute to establishing a character’s leading role. The objective of this article is to explore and compare the prominence of characters in Romeo and Juliet by using social network analysis. To this end, we calculate the centralities of several characters in Romeo and Juliet using a method based on Social Network Analysis. Comparing the scores generated by this analysis, we found that Romeo’s centrality is more stable than Juliet’s while hers is lower and supported by the ‘strength of the bonds’ she develops with other characters. Thus, the comparison of different centrality rankings and clusters provides new knowledge about the plays of Shakespeare. We show that the ‘strength’ of the relationships affects the prominence of the characters. This finding opens new directions for analyzing Shakespeare’s scripts and determining who the main character is using weighted centrality measures. Finally, we discuss some theoretical and practical implications of the method used in this study.