Existing Decision Rules Research Articles

Stepwise liability: Between the preponderance rule and proportional liability

There are two familiar decision rules: the binary, preponderance of the evidence and the continuous, proportional liability rule. This article proposes a thought experiment. Instead of all-or-nothing or a continuous rule, the law can utilise a middle ground—assigning liability stepwise, according to the procedural progression of the case—stepwise liability. Stepwise liability relies on the gradual design of civil procedure. Under the current system, the plaintiff has to pass several procedural thresholds with increasing evidentiary requirements in order to proceed to trial. Examples are a motion to dismiss and then a summary judgment. I propose that, corresponding to the procedural progression of the case, after surviving each step the plaintiff will be entitled to a gradually increasing share of the damages. Stepwise liability offers several advantages relative to the traditional rules. It provides partial compensation where the defendant's liability falls short of the 50% threshold, hence restoring incentives to take care. Unlike the proportional rule, this outcome can be achieved without major modifications to the existing decision rules. Unlike both rules, the proposal enables plaintiffs to cash in with some award before trial. I analyse the foregoing advantages together with the potential pitfalls, such as over-deterrence, larger legal expenses, and the day-in-court ideal.

Long-term English Learners: Untangling Language Acquisition and Learning Disabilities

AbstractStudents who have remained classified as English Learners (ELs) for more than six years are often labeled “Long-term English Learners” (LTELs). The present study examined the English Language Development (ELD) test scores and demographic information in a group of 560 students identified as LTELs. Despite assumptions that these students are still learning English, results showed many students who are labeled LTELs exhibited advanced English skills, especially on measures of expressive and receptive oral language (i.e., speaking and listening subtests). At the same time, ELD assessments showed many of these students struggled with literacy skills, especially reading. Perhaps due to these overlapping circumstances, we found many LTELs were also identified with learning disabilities. Based on these findings, we explored the impact of restricting domains needed for reclassification as English proficient on reclassification rates. Compared with existing decision rules in the students’ state, proposed models allow many more LTELs to reclassify as English proficient, and most LTELs not reclassifying are students in special education. Discussion focuses on interpreting ELD scores for students who have remained classified as ELs for more than a few years.

An approach for assessment of concrete deterioration by surface waves

The aging of concrete infrastructure facilities is a global problem. External and internal harmful factors such as water ingress, freezing and thawing, fire, chemical attack and forces lead to concrete degradation, frequently progressing from its surface. It is important to know how deep the deterioration process is expanded from the surface into the depth of the concrete bulk and how much the concrete degraded. The purpose of the study was to demonstrate the feasibility of an approach based on the application of pattern recognition methods to the analysis of ultrasonic signals to assess two variable factors of interest - the material degradation degree and the thickness of the conditionally weak surface layer on the strong concrete underlay. A series of specimens was made according to the 2-factor grid experimental design in order to build a mathematical model for the evaluation. Ultrasonic signals were acquired in the surface transmission at 2 frequencies, 50 to 100 kHz by stepped profiling of the specimens’ surface. 2D spatiotemporal waveform matrices acquired by surface profiling, where the coordinates were the propagation distance and ultrasonic time, served as the material for processing. The approach based on the methods of pattern recognition included steps of transformation the signals by the discrete Fourier transform, calculation of statistical criteria and creation of decision rules as bilinear mathematical approximation surfaces of the criteria. Verification of the method by substituting data on a control series of specimens into the mathematical model built on the grid of specimens of a training series allowed a realistic determination of the both factors of interest for a specimen from the control series. However, the use of only formal statistical criteria was found not completely sufficient for reliable recognition in all control cases. Further improvement of recognition can apply additional decision rules based on wave propagation in the time domain and refining of the existing decision rules by implementation of weights of influence.

Adapting Decision Rules to Estimate Occupational Metalworking Fluid Exposure in a Case-Control Study of Bladder Cancer in Spain.

We adapted previously developed decision rules from the New England Bladder Cancer Study (NEBCS) to assign occupational exposure to straight, soluble, and synthetic metalworking fluids (MWFs) to participants of the Spanish Bladder Cancer Study (SBCS). The SBCS and NEBCS are case-control studies that used the same lifetime occupational history and job module questionnaires. We adapted published decision rules from the NEBCS that linked questionnaire responses to estimates of the probability (<5, ≥5 to <50, ≥50 to <100, and 100%), frequency (in h week-1), and intensity (in mg m-3) of exposure to each of the three broad classes of MWFs to assign exposure to 10 182 reported jobs in the SBCS. The decision rules used the participant's module responses to MWF questions wherever possible. We then used these SBCS module responses to calculate job-, industry-, and time-specific patterns in the prevalence and frequency of MWF exposure. These estimates replaced the NEBCS-specific estimates in decision rules applied to jobs without MWF module responses. Intensity estimates were predicted using a previously developed statistical model that used the decade, industry (three categories), operation (grinding versus machining), and MWF type extracted from the SBCS questionnaire responses. We also developed new decision rules to assess mineral oil exposure from non-machining sources (possibly exposed versus not exposed). The decision rules for MWF and mineral oil identified questionnaire response patterns that required job-by-job expert review. To assign MWF exposure, we applied decision rules that incorporated participant's responses and job group patterns for 99% of the jobs and conducted expert review of the remaining 1% (145) jobs. Overall, 14% of the jobs were assessed as having ≥5% probability of exposure to at least one of the three MWFs. Probability of exposure of ≥50% to soluble, straight, and synthetic MWFs was identified in 2.5, 1.7, and 0.5% of the jobs, respectively. To assign mineral oil from non-machining sources, we used module responses for 49% of jobs, a job-exposure matrix for 41% of jobs, and expert review for the remaining 10%. We identified 24% of jobs as possibly exposed to mineral oil from non-machining sources. We demonstrated that we could adapt existing decision rules to assess exposure in a new population by deriving population-specific job group patterns.

Termination of resuscitation: Recognising futility

Purpose: To identify futile resuscitation attempts for adult out of hospital cardiac arrests (OHCA) of presumed cardiac origin, which are currently transported to hospital in the UK. Then to create a decision rule to allow for termination on scene. The results of applying the rule were compared with existing termination decision rules. Methods: A retrospective cohort study of OHCA data collected by a UK ambulance trust between 1 April 2011 and 29 June 2013 (n = 4870). Logistic regression was performed, which identified that return of spontaneous circulation (ROSC) (OR = 40.1; 95% CI: 25.7% to 62.5%) and initial shockable rhythm (OR = 10.7; 95% CI: 6.6% to 17.2%) had the strongest association with outcome. A decision rule derived from these required where a patient did not present with an initial shockable rhythm, and there was no ROSC on scene; resuscitation could be terminated, without the need for transport. The rule was applied to the dataset and compared against existing Basic Life Support (BLS) [1] and Advanced Life Support (ALS) [2] decision rules. Results: The rule identified all but five of the 514 survivors (0.2% of terminations) (Specificity 99.0%, 95%CI: 97.7% to 99.7%, sensitivity 53.1%, 95% CI: 51.6% to 54.6%, transport rate = 52.4%). The rule compared favourably with existing BLS and ALS rules which both identified more survivors (Specificity 99.4%, 95% CI: 98.1% to 99.8% and 99.6%, 95% CI: 98.4% to 99.9% respectively), but at the cost of higher transport rates (63.1% and 90.2% respectively). Conclusions: The NHS is under pressure to reduce spending, whilst improving standards and efficiency. Early decisions, which reduce the need for unnecessary, futile care, are in great demand. The decision rule derived here was able to identify survivors within previously determined thresholds [3]. Furthermore, it identified more futile transportations than existing decision rules.

Artificial neural networks: Predicting head CT findings in elderly patients presenting with minor head injury after a fall

Objectives: To construct an artificial neural network (ANN) model that can predict the presence of acute CT findings with both high sensitivity and high specificity when applied to the population of patients≥age 65years who have incurred minor head injury after a fall.Methods: An ANN was created in the Python programming language using a population of 514 patients ≥ age 65 years presenting to the ED with minor head injury after a fall. The patient dataset was divided into three parts: 60% for “training”, 20% for “cross validation”, and 20% for “testing”. Sensitivity, specificity, positive and negative predictive values, and accuracy were determined by comparing the model's predictions to the actual correct answers for each patient.Results: On the “cross validation” data, the model attained a sensitivity (“recall”) of 100.00%, specificity of 78.95%, PPV (“precision”) of 78.95%, NPV of 100.00%, and accuracy of 88.24% in detecting the presence of positive head CTs. On the “test” data, the model attained a sensitivity of 97.78%, specificity of 89.47%, PPV of 88.00%, NPV of 98.08%, and accuracy of 93.14% in detecting the presence of positive head CTs.Conclusions: ANNs show great potential for predicting CT findings in the population of patients ≥ 65 years of age presenting with minor head injury after a fall. As a good first step, the ANN showed comparable sensitivity, predictive values, and accuracy, with a much higher specificity than the existing decision rules in clinical usage for predicting head CTs with acute intracranial findings.

Design of Near Optimal Decision Rules in Multistage Adaptive Mixed-Integer Optimization

In recent years, decision rules have been established as the preferred solution method for addressing computationally demanding, multistage adaptive optimization problems. Despite their success, existing decision rules (a) are typically constrained by their a priori design and (b) do not incorporate in their modeling adaptive binary decisions. To address these problems, we first derive the structure for optimal decision rules involving continuous and binary variables as piecewise linear and piecewise constant functions, respectively. We then propose a methodology for the optimal design of such decision rules that have a finite number of pieces and solve the problem robustly using mixed-integer optimization. We demonstrate the effectiveness of the proposed methods in the context of two multistage inventory control problems. We provide global lower bounds and show that our approach is (i) practically tractable and (ii) provides high quality solutions that outperform alternative methods.

Effects of technical interventions on flexibility of farming systems in Burkina Faso: Lessons for the design of innovations in West Africa

African farmers have always been exposed to climatic and economic variability and have developed a range of coping strategies. Such strategies form part of flexible farm management, an ability that may prove very valuable in the face of future climate change and market dynamics. The generally low productivity of African smallholder farming systems is usually addressed by research and development institutions by a variety of solutions for improving farm performance. However, changes to the system may affect the flexibility of farms and thus their ability to cope with variability. We quantified the added value of being flexible and how this flexibility is affected by technical changes, such as composting and cattle fattening recurrently proposed and promoted by research and development institutions and projects. The study was conducted in two villages of the agro-pastoral area of Burkina Faso, where livestock, cereals and cotton are the main farming activities. A whole-farm simulation model was developed based on information gathered during focus group meetings with farmers and detailed individual monitoring of farmers' practices. The model simulates farmers' decision rules governing the management of the cropping and livestock farm components, as well as crop and livestock production and farm gross margin. Using the existing decision rules, current farm performance was simulated by assessing the cereal balance, the fodder balance and the whole farm gross margin. Then, by comparing the mean and the coefficient of variation of these indicators resulting from (a) the existing decision rules (baseline scenario) and (b) a set of less flexible rules (rigid scenario), the added value of flexible management was revealed. The adoption of composting practices allowed a slight increase in gross margin associated with a decrease in its between-year variability in comparison with conventional practices. Cattle fattening only led to a higher gross margin in the years with high rainfall and low input prices when no management practices were used to limit dependence on external input. This kind of technical change thus requires increased management agility by farmers to deal with climatic and economic variability. We conclude that assessing the impact of technical interventions not only in terms of productivity but also in terms of changes in flexibility is useful for a better understanding of potential adoption of technical changes.

Use of electronic data and existing screening tools to identify clinically significant obstructive sleep apnea.

To assess the ability of electronic health data and existing screening tools to identify clinically significant obstructive sleep apnea (OSA), as defined by symptomatic or severe OSA. The present retrospective cohort study of 1041 patients referred for sleep diagnostic testing was undertaken at a tertiary sleep centre in Calgary, Alberta. A diagnosis of clinically significant OSA or an alternative sleep diagnosis was assigned to each patient through blinded independent chart review by two sleep physicians. Predictive variables were identified from online questionnaire data, and diagnostic algorithms were developed. The performance of electronically derived algorithms for identifying patients with clinically significant OSA was determined. Diagnostic performance of these algorithms was compared with versions of the STOP-Bang questionnaire and adjusted neck circumference score (ANC) derived from electronic data. Electronic questionnaire data were highly sensitive (>95%) at identifying clinically significant OSA, but not specific. Sleep diagnostic testing-determined respiratory disturbance index was very specific (specificity ≥95%) for clinically relevant disease, but not sensitive (<35%). Derived algorithms had similar accuracy to the STOP-Bang or ANC, but required fewer questions and calculations. These data suggest that a two-step process using a small number of clinical variables (maximizing sensitivity) and objective diagnostic testing (maximizing specificity) is required to identify clinically significant OSA. When used in an online setting, simple algorithms can identify clinically relevant OSA with similar performance to existing decision rules such as the STOP-Bang or ANC.

On a decision rule supported by a forecasting stage based on the decision maker’s coefficient of optimism

The paper contains a description of a new approach (called the SF + AS method, i.e. the scenario forecasting + alternative selection method) that can be used in decision making under uncertainty when pure optimal strategies are sought-after. This procedure takes into consideration the level of decision maker’s coefficient of optimism (or coefficient of pessimism) and consists of two stages: the true scenario forecasting (on the basis of the decision maker’s preferences) and the appropriate alternative selection by taking into account the payoffs of the appointed true scenario or the most probable scenarios. In contradiction to existing decision rules, this procedure assumes that the decision making process should involve only a part of the payoff matrix because only one state of nature will occur in the end. The second essential difference between the SF + AS method and other decision rules is that in the first one there is an attempt to appoint globally the best and the worst scenario (regardless the alternative considered). Meanwhile, other procedures determine the status of a given event depending on the decision.

Heuristics: foundations for a novel approach to medical decision making.

Medical decision-making is a complex process that often takes place during uncertainty, that is, when knowledge, time, and resources are limited. How can we ensure good decisions? We present research on heuristics-simple rules of thumb-and discuss how medical decision-making can benefit from these tools. We challenge the common view that heuristics are only second-best solutions by showing that they can be more accurate, faster, and easier to apply in comparison to more complex strategies. Using the example of fast-and-frugal decision trees, we illustrate how heuristics can be studied and implemented in the medical context. Finally, we suggest how a heuristic-friendly culture supports the study and application of heuristics as complementary strategies to existing decision rules.

A framework to assess the added value of subgroup analyses when the overall treatment effect (TE) was significant

Although statisticians have already awared and discussed the problem, it is still not clear whether we should perform and how to perform such subgroup analyses when the overall TE is significant in both single clinical trials and systematic reviews. So a framework is needed to assess and compute the long term effect of different strategies to perform subgroup analysis. We propose two performance measures to evaluate the average post-study TE for patients in all studies (E) and fraction of patients with a negative TE in the positive studies (P). Nine existing decision rules are applied to different assumptions of subgroup specific and individual TE. Optimistic, moderate and pessimistic scenarios are assumed for true TE. A hierarchical structured simulation study is set up with the fixed power of 90% and an assumed constant TE for each study in particular case of binary outcomes with TE presented as risk difference, a constant sample size 280 is required. Patients were allocated in 1:1 ratio to two hypothetical treatment subgroups. 10000 simulation runs applied to each scenario. We demonstrate that there are decision rules for subgroup analysis which decrease P and increase E simultaneously comparing to the situation of no subgroup analysis. These rules are much more liberal than the usual significance testing, since there is a high risk to decrease E using the latter.

Common and un‐common sense in managerial decision making under task uncertainty

PurposeThe purpose of this paper is to explore the concept of “common sense” and to distinguish it from uncommon sense as it applies to managerial decision‐making under conditions of task uncertainty. The paper enunciates the definition of common sense decision‐making and develops the concept of “uncommon” sense making. A typology of common sense is put forth, and a case study is used to illustrate it in practice.Design/methodology/approachThis is a conceptually developmental paper, which explores and develops the concept of uncommon sense through literature review and typology development. A mechanistic, or internally driven, decision approach is compared with an organic, externally driven one, and the question of how common sense is related to these approaches in varying task uncertainty conditions is explored. A short case study is used as an illustration of the practical managerial implications under low task certainty conditions.FindingsThe concepts of MCS (common sense) and MUS (uncommon sense) are established, as well as O and M errors. It is suggested that within a mechanistic approach MCS is most appropriate in conditions of high task certainty. Within an organic approach MUS is most appropriate in conditions of low task certainty. An O error occurs when a mechanistic approach is taken, using common sense, under low task certainty. Lack of appropriate resources deems such an approach unsuitable. An M error occurs when a more risky organic approach (MUS) is taken when resources exist to make a common sense decision.Practical implicationsIncreasing globalization, work ambiguity, and task complexity, introduce a need for greater managerial adaptability and speed. MUS may therefore play an increasingly greater role in organizational decision‐making as managers seek answers to questions generated by new and unique situations where existing decision rules may not apply.Originality/valueThe theoretical framework offered in this paper looks at the increasingly common situations in which “thinking outside the box” is essential for sound decision making. It suggests a taxonomy that allows for exploration as well as application of “uncommon sense”‐based theory in situations that require such innovative thinking. It takes a significant step in the theoretical and practical understanding of the very relevant issues of inspiration, adventure and creativity in managerial decision making today.

A Distributed Consensus-Based Cooperative Spectrum-Sensing Scheme in Cognitive Radios

In cognitive radio (CR) networks, secondary users can cooperatively sense the spectrum to detect the presence of primary users. In this paper, we propose a fully distributed and scalable cooperative spectrum-sensing scheme based on recent advances in consensus algorithms. In the proposed scheme, the secondary users can maintain coordination based on only local information exchange without a centralized common receiver. Unlike most of the existing decision rules, such as the or-rule or the 1-out-of-N rule, we use the consensus of secondary users to make the final decision. Simulation results show that the proposed consensus scheme can have significant lower missing detection probabilities and false alarm probabilities in CR networks. It is also demonstrated that the proposed scheme not only has proven sensitivity in detecting the primary user's presence but also has robustness in choosing a desirable decision threshold.

Evaluation of Decision Rules for Identifying Serious Consequences of Traumatic Head Injuries in Pediatric Patients

We evaluated the existing decision rules and developed our own decision rule for use with pediatric patients having head injuries to see how good they are for identifying serious complications. In a retrospective survey covering 5 years, we tested 3 decision rules on all the 485 children with head trauma admitted to the Department of Pediatrics at Oulu University Hospital in Finland. Four hundred two (83%) of our 485 patients had uncomplicated head trauma, 55 (11%) had complicated trauma, and 28 (6%) had severely complicated trauma. We found the 3 existing decision rules to be applicable with 96% to 99% sensitivities in identifying patients with at least complicated head trauma and 100% sensitivity in identifying severely complicated cases, but the specificities were low, from 5% to 21%. Use of the best National Emergency X-Radiography Utilization Study (NEXUS) II decision rule would have resulted in 89 (18%) less hospital admissions, 216 (14%) less days in the hospital, and annual savings of 30,600 dollars without compromising the prognosis for our patients. A decision rule developed on the basis of our own series had a sensitivity of 94% for patients with at least complicated head trauma and 96% for patients with severely complicated head trauma and a specificity of 29% in both groups. All the 3 existing decision rules tested had high sensitivities for at least complicated head trauma, but rather low specificity. Because the most optimal decision rule based on the present data was not superior to the existing ones, we conclude that it is difficult to develop a rule that would be markedly better than that of NEXUS II. Use of the NEXUS II rule would have resulted in reduced hospitalization and imaging rates in our hospital, where no decision rules are currently used.

Transportability of a Decision Rule for the Diagnosis of Streptococcal Pharyngitis

Use of existing decision rules could improve management of streptococcal pharyngitis, but the validity of such rules outside their original patient population is not known. We applied a four-item decision rule derived at the Medical College of Virginia, Richmond, to 516 patients at the University of Nebraska, Omaha, to test how accurately it would predict outcome of throat culture. After correction for differences in the prevalence of a positive culture (17% in Virginia, 26% in Nebraska), the rule closely predicted the frequency of positive cultures in five subgroups based on the presence of clinical findings. We conclude that this rule transported well to a different patient population and would have been useful in identifying patients with pharyngitis who had a high likelihood of throat cultures positive for group A streptococci.

RATIONALISM VERSUS INCREMENTALISM IN CRIMINAL SENTENCING

Rationalism in management science tends to mean attempting to determine the benefits and costs of the alternative decisions under consideration, and then picking the one that is best on benefits minus costs. Incrementalism tends to mean determining the decisions that actually get made under various circumstances, and then working with those existing decision rules as the basis for making adjustments to consider special or changing circumstances. Those two approaches can be well illustrated with the problem of attempting to determine what criminal sentences should be legislated to cover given crimes and prior records. The analysis tends to show that a rationalist approach is more effective in achieving societal goals when (1) alternative policies can be meaningfully related to those goals, and (2) existing decisions reflect individual goals which are generally in conflict with societal goals. An incrementalist approach is more effective when either of those criteria is absent, which is so when seeking to arrive at legislatively determined nondiscretionary criminal sentencing.