Markets vs. polls as election predictors: An historical assessment

Prediction markets have drawn considerable attention in recent years as a tool for forecasting elections. But how accurate are they? Do they outperform the polls, as some scholars argue? Do prices in election markets carry information beyond the horserace in the latest polls? This paper assesses the accuracy of US presidential election betting markets in years before and after opinion polling was introduced. Our results are provocative. First, we find that market prices are far better predictors in the period without polls than when polls were available. Second, we find that market prices of the pre-poll era predicted elections almost on par with polls following the introduction of scientific polling. Finally, when we have both market prices and polls, prices add nothing to election prediction beyond polls. To be sure, early election markets were (surprisingly) good at extracting campaign information without scientific polling to guide them. For more recent markets, candidate prices largely follow the polls.     

Forecasting the 2015 British general election: The 1992 debacle all over again?

This article introduces and reviews a set of twelve academic forecasts of the 2015 British general election. Along with the vast majority of others including journalists and betting markets, they failed by a big margin to predict that the Conservatives would emerge with an overall majority of seats. Several suffered from the 1992 scale inaccuracies of the vote-intention opinion polls. Forecasts based on other data sources typically did a bit better, but also fell short. Nonetheless, this was not 1992 all over again. The dramatic collapse of the Liberal Democrats and rise of the SNP, UKIP and Greens were successfully anticipated. Also this collection includes numerous methodological advancements, with several new methods and developments to established approaches.     

Using prediction market data for measuring the expected closeness in electoral research

This article analyzes the effect of the expected closeness on turnout for 56 direct-democratic votes held in Switzerland between 2012 and 2015. It is the first study to measure the expected closeness by using data obtained from prediction markets. It clarifies empirically the relation between the expected closeness and the levels of turnout in direct democratic votes showing that the expected closeness of the result exerts a positive effect on participation levels.     

A multivariate statistical approach to for the evaluation of the biogeographical ancestry information from traditional STRs

The capability to achieve biogeographic ancestry (BGA) information from DNA profiles have been largely explored in forensic genetics because of its potential usefulness in providing investigative clues. For law enforcement and security purposes, when genetic data have been obtained from unknown evidence, but no reference samples are available and no hints come out from DNA databases, it would be extremely useful at least to infer the ethno-geographic origin of the stain donor by just examining traditional STRs DNA profiles.Current protocols for ethnic origin estimation using STRs profiles are usually based on Principal Component Analysis approaches and Bayesian methods. The present study provides an alternative approach that involves the use of target multivariate data analysis strategies for estimation of the BGA information from unknown biological traces. A powerful multivariate technique such as Partial Least Squares-Discriminant Analysis (PLS-DA) has been applied on NIST U.S. population datasets containing, for instance, the allele frequencies of African-American, Asian, Caucasian and Hispanic individuals. PLS-DA approach provided robust classifications, yielding high sensitivity and specificity models capable of discriminating the populations on ethnic basis. Finally, a real casework has been examined by extending the developed model to smaller and more geographically-restricted populations involving, for instance, Albanian, Italian and Montenegrian individuals.     

Predictors of criminal recidivism among male batterers

The present study examined recidivism risk factors in a sample of 320 male batterers attending community treatment. Recidivism was assessed by new charges for violence or for any offence during a 5-year follow-up period. The variables associated with recidivism among male batterers were similar to those found for other criminal populations (e.g. young, unstable lifestyle, substance abuse, criminal history). There was no evidence that potential offenders were deterred by expectations of negative consequences, either social (e.g. friends would disapprove) or official (e.g. being arrested, losing job). Maintaining positive relationships with treatment providers was associated with reduced recidivism.     

Predicting Short-Term Institutional Aggression in Forensic Patients: A Multi-Trait Method for Understanding Subtypes of Aggression

Accurately predicting inpatient aggression is an important endeavor. The current study investigated inpatient aggression over a six-month time period in a sample of 152 male forensic patients. We assessed constructs of psychopathy, anger, and active symptoms of mental illness and tested their ability to predict reactive and instrumental aggression. Across all levels of analyses, anger and active symptoms of mental illness predicted reactive aggression. Traits of psychopathy, which demonstrated no relationship to reactive aggression, were a robust predictor of instrumental aggression. This study (a) reestablishes psychopathy as a clinically useful construct in predicting inpatient instrumental aggression, (b) provides some validation for the reactive/instrumental aggression paradigm in forensic inpatients, and (c) makes recommendations for integrating risk assessment results into treatment interventions.

Criminal Recidivism Among Juvenile Offenders: Testing the Incremental and Predictive Validity of Three Measures of Psychopathic Features

We studied the predictive, comparative, and incremental validity of three measures of psychopathic features (Psychopathy Checklist: Youth Version [PCL:YV]; Antisocial Process Screening Device [APSD]; Childhood Psychopathy Scale [CPS]) vis-à-vis criminal recidivism among 83 delinquent youth within a truly prospective design. Bivariate and multivariate analyses (Cox proportional hazard analyses) showed that of the three measures, the CPS was most consistently related to most types of recidivism in comparison to the other measures. However, incremental validity analyses demonstrated that all of the predictive effects for the measures of psychopathic features disappeared after conceptually relevant covariates (i.e., substance use, conduct disorder, young age, past property crime) were included in multivariate predictive models. Implications for the limits of these measures in applied juvenile justice assessment are discussed.     

Methodologies of election forecasting: Calling the 2010 UK “hung parliament”

Election forecasting has experienced considerable growth as a subfield within political science. Research work on United Kingdom elections has been cutting edge here. The recent 2010 general election afforded the opportunity for a trial of different forecasting methodologies. These efforts are showcased in this volume, and include standard, and not-so-standard, statistical models. Overall, these models perform well, foreseeing the unprecedented outcome of a “hung parliament”, as most pollsters and pundits did not. Moreover, they achieved this accuracy with forecasts well in advance of the election itself.     

A Comparative Test of Clinical Judgment Versus Actuarial Prediction of Future Violence

This study tested how graduate level psychology graduate students (n = 20) using the Adapted Violence Risk Appraisal Guide (Adapted VRAG) would do relative to practicing psychologists/psychiatrists (n = 16) using clinical judgment when predicting violence in 10 narratives from the MacArthur Violence Risk Assessment study (Monahan et al., The MacArthur violence risk assessment study. . Retrieved 10 Oct 2005). Results indicated that the practicing psychologists/psychiatrists made significantly more correct predictions than the master’s level students. The professional group demonstrated sensitivity levels of 77.7% and specificity of 96.3%. For the use of the adapted VRAG method by the graduate student group, specificity levels were modest at 54.0%. Sensitivity levels, however, were lower than earlier demonstrated levels at 58.0%. These findings are at variance with earlier reports comparing clinical and actuarial methods. The results may reflect the short amount of time the master’s level students were trained using the Adapted VRAG as well as the small number of participants in this study. Additional research comparing other professions is recommended, as well as examining if experience in the forensics field would affect one’s ability to predict violence.     

Model for the prediction of mechanical asphyxia as the cause of death based on four biological indexes in human cardiac tissue

Determination of mechanical asphyxia as the cause of death has always been difficult for forensic pathologists, particularly when signs of asphyxia are not obvious on the body. Currently, depending on only physical examination of corpses, pathologists must be cautious when making cause-of-death appraisals. In a previous study, four biomarkers—dual-specificity phosphatase 1 (DUSP1), potassium voltage-gated channel subfamily J member 2 (KCNJ2), miR-122, and miR-3185—were screened in human cardiac tissue from cadavers that died from mechanical asphyxia compared with those that died from craniocerebral injury, hemorrhagic shock, or other causes. Expression of the markers correlated with death from mechanical asphyxia regardless of age, environmental temperature, and postmortem interval. However, a single biological index is not an accurate basis for the identification of the cause of death. In this study, receiver operating characteristic curves of the ΔCq values of the four indexes were generated. The diagnostic accuracy of the indexes was judged according to their area under the curve (DUSP1: 0.773, KCNJ2: 0.775, miR-122: 0.667, and miR-3185: 0.801). Finally, a nomogram was generated, and single blind experiment was conducted to verify the cause of death of mechanical asphyxia.