The role of personal mitigating factors in criminal sentencing judgments: an empirical investigation

Criminal sentencers must weight and integrate many different factors to reach a judgment, including aggravating factors that argue for a harsher sentence, and mitigating factors that suggest a more lenient sentence. Personal Mitigating Factors (PMFs) relate to the offender, rather than the offence (e.g., remorse or youth/immaturity). Research shows that discretionary sentencing produces inconsistency and bias and lacks the transparency needed to maintain public trust in justice. Although many jurisdictions have introduced more structured sentencing, the mitigation process remains largely discretionary. Structuring personal mitigation could help produce fairer sentences. Any structured approach must, however, be informed by empirical data, and little is known about how sentencers use PMFs, or how the public judges them. This thesis examined the role of three commonly occurring PMFs: remorse, good character, and addressing addiction. Study 1 examined sentencers’ use of PMFs in cases of assault and burglary through a statistical analysis of annual sentencing data from the Crown Court in England and Wales. Study 2 used a qualitative analysis of interviews with a small sample of Crown Court judges to further explore the findings of Study 1 and identify topics for future research. Studies 3 and 4 used experimental designs to measure how the three PMFs influenced public judgments about sentencing fairness and choice of sentence length. Study 4’s “idiographic” design permitted evaluation of the variation between individuals’ judgments about PMFs. The present thesis identified several issues with current sentencing practice, notably the underweighting of multiple co-occurring PMFs, and proposed some practical options for structuring the personal mitigation process. The thesis also identified conflicts between sentencers’ use of PMFs and public judgments, and suggested how the gap between sentencers and the public could be closed. Lastly, the thesis illustrates how methodology from psychology can be used to advance our understanding of criminal sentencing

Statistical analyses of court decisions: an example of multilevel models of sentencing

Quantitative empirical research into legal decisions must be conducted using statistical tools that are appropriate for the data involved. Court decisions are one example of a domain where the data is intrinsically hierarchical (i.e., multilevel), since decisions are made on individual cases by decision-makers in courts located across geographical (or jurisdictional) areas. Past research into court decisions has often either neglected higher level variables or incorrectly used single-level statistical models to analyze multilevel data. The lack of a clear understanding about when and why multilevel statistical models are required may have contributed to this situation. In this paper, we identify the problems of estimating single-level models on hierarchically structured data, and consider the advantages of conducting multilevel analyses under these circumstances. We use the example of criminal sentencing research to illustrate the arguments for the use of multilevel models and against a single-level approach. We also highlight some issues to be addressed in future sentencing studies

On getting inside the judge’s mind

According to the scales of justice, the judge, in an unbiased way and directed by law, attends to all of the available information in a case, weighs it according to its significance, and integrates it to make a decision. By contrast, research suggests that judicial decision-making departs from the cognitive balancing act depicted by the scales of justice. Nevertheless, the research is often dismissed as irrelevant, and the judiciary, legal policy-makers and the public remain largely unconvinced that the status quo needs improving. One potential rebuttal to the scientific findings is that they lack validity because researchers did not study judges making decisions on real cases. Another potential argument is that researchers have not pinpointed the psychological processes of any specific judge because they analyzed data over judges and/or used statistical models lacking in psychological plausibility. We review these two grounds for appeal against the scientific research on judicial decision-making, and note that it appears researchers’ choices of data collection methods and analytic techniques may, indeed, be inappropriate for understanding the phenomena. We offer two remedies from the sphere of decision-making research: collecting data on judicial decision-making using representative design, and analyzing judicial decision data using more psychologically plausible models. Used together, we believe these solutions can help researchers better understand and improve legal decision-making

Critical review of analytic techniques

In this paper, we classify 75 analytic techniques in terms of their primary function. We then highlight where across the stages of the generic analytic workflow the techniques might be best applied. Importantly, most of the techniques have some shortcomings, and none guarantee an accurate or bias-free analytic conclusion. We discuss how the findings of the present paper can be used to develop criteria for evaluating analytic techniques as well as the performance of analysts. We also discuss which sets of techniques ought to be consolidated as well as reveal gaps that need to be filled by new techniques

The "analysis of competing hypotheses" in intelligence analysis

The intelligence community uses ‘structured analytic techniques’ to help analysts think critically and avoid cognitive bias. However, little evidence exists of how techniques are applied and whether they are effective. We examined the use of the Analysis of Competing Hypotheses (ACH) – a technique designed to reduce ‘confirmation bias’. Fifty intelligence analysts were randomly assigned to use ACH or not when completing a hypothesis testing task that had probabilistic ground truth. Data on analysts’ judgment processes and conclusions was collected using written protocols that were then coded for statistical analyses. We found that ACH-trained analysts did not follow all of the steps of ACH. There was mixed evidence for ACH’s ability to reduce confirmation bias, and we observed that ACH may increase judgment inconsistency and error. It may be prudent for the intelligence community to consider the conditions under which ACH would prove useful, and to explore alternatives

Quasirational models of sentencing

Cognitive continuum theory points to the middle-ground between the intuitive and analytic modes of cognition, called quasirationality. In the context of sentencing, we discuss how legal models prescribe the use of different modes of cognition. These models aim to help judges perform the cognitive balancing act required between factors indicating a more or less severe penalty for an offender. We compare sentencing in three common law jurisdictions (i.e., Australia, the US, and England and Wales). Each places a different emphasis on the use of intuition and analysis; but all are quasirational. We conclude that the most appropriate mode of cognition will likely be that which corresponds best with properties of the sentencingtask. Finally, we discuss the implications of this cognition-task correspondence approach for researchers and legal policy-makers

Oviposition of Culex pipiens in water at different temperatures

At 20C air temperature, female <i>Culex pipiens</i> L. laid the greatest number of egg rafts at water temperatures between 20 and 25C. They laid very few at 15 or 35C even when given no alternative site. The possibility is discussed of manipulating temperature in a mosquito control programme

Using scenarios to forecast outcomes of a refugee crisis

The Syrian civil war has led to millions of Syrians fleeing the country, and has resulted in a humanitarian crisis. By considering how such socio-political events may unfold, scenarios can lead to informed forecasts that can be used for decision-making. We examined the relationship between scenarios and forecasts in the context of the Syrian refugee crisis. Forty Turkish students trained to use a brainstorming technique generated scenarios that might follow within six months of the Turkish government banning Syrian refugees from entering the country. Participants generated from 3-6 scenarios. Over half were rated as ‘high’ quality in terms of completeness, relevance/pertinence, plausibility, coherence, and transparency (order effects). Scenario quality was unaffected by scenario quantity. Even though no forecasts were requested, participants’ first scenarios contained from 0-17 forecasts. Mean forecast accuracy was 45% and this was unaffected by forecast quantity. Therefore, brainstorming can offer a simple and quick way of generating scenarios and forecasts that can potentially help decision-makers tackle humanitarian crises

The value of experiments in futures and foresight science as illustrated by the case of scenario planning

An already pressing need to evidence the effectiveness of futures and foresight tools has been further amplified by the coronavirus pandemic, which highlighted more mainstream tools’ difficulty with uncertainty. In light of this, the recent discussion in this journal on providing futures and foresight science with a stronger scientific basis is welcome. In this discussion critical realism has been proffered as a useful philosophical foundation and experiments a useful method for improving this field’s scientific basis. Yet, experiments seek to isolate specific causal effects through closure (i.e., by controlling for all extraneous factors) and this may cause it to jar with critical realism’s emphasis on uncertainty and openness. We therefore extend the recent discussion on improving the scientific basis of futures and foresight science by doing three things. Firstly, we elaborate on critical realism and why the experimental method may jar with it. Secondly, we explain why the distinction between a conceptual and a direct replication can help overcome this jarring, meaning experiments can still be a valuable research tool for a futures and foresight science underpinned by critical realism. Thirdly, we consider the appropriate unit of analysis for experiments on futures and foresight tools. In so doing, we situate the recent discussion on improving the scientific basis of futures and foresight science within the much longer running one on improving the scientific basis of business, management and strategy research more broadly. We use the case of scenario planning to illustrate our argument in relation to futures and foresight science

Criminal sentencing by preferred numbers

Criminal sentencing is a complex cognitive activity often performed by the unaided mind under suboptimal conditions. As such, sentencers may not behave according to policy, guidelines and training. We analyzed the distribution of sentences meted out in one year in two different jurisdictions (i.e., England and Wales, and New South Wales, Australia). We reveal that sentencers prefer certain numbers when meting out sentence lengths (in custody and community service) and amounts (for fines/compensation). These ‘common doses’ accounted for over 90% of sentences in each jurisdiction. The size of these doses increased as sentences became more severe, and doses followed a logarithmic pattern. These findings are compatible with psychological research on preferred numbers and are reminiscent of Weber’s and Fechner’s laws. Our findings run contrary to arguments against efforts to reduce judicial discretion, and potentially undermine the notion of individualized justice, as well as raise questions about the (cost) effectiveness of sentencing