Inconsistent Decisions Research Articles

The Unbefriended Patient, Their Professional Guardians, and Clinical Liaison Psychiatry: The Challenging Ethics of Changing Goals of Care.

AbstractUnbefriended patients are those with decisional impairments who lack family or friends to serve as healthcare surrogates. When such patients cannot make decisions, the court typically appoints a professional guardian to make choices aligned with the patient's values and preferences. However, this case report illustrates ethical challenges that can arise when professional guardians disregard the patient's authentic wishes. In this case study, the 38-year-old unbefriended African American male patient expressed fears about traumatic resuscitation efforts and ultimately desired de-escalation of care, which the guardian was hesitant to honor despite confirmed decision-making capacity. The guardian quickly reversed a new do-not-resuscitate order when the patient later changed his mind. Decisions about aggressive interventions like a colostomy were significantly delayed while awaiting final judgments involving the guardian's supervisors and the judicial system. The case highlights pitfalls with guardians defaulting to treatment escalation without sufficiently engaging with ethical standards or eliciting the patient's narrative identity, leading to inconsistent surrogate decisions. We propose that more robust reforms are needed, including enhanced training of guardians in ethical decision-making, and we present other means to facilitate best practices in proxy decision-making.

Understanding inconsistencies in risk attitude elicitation games: Evidence from smallholder farmers in five African countries

Recent empirical studies eliciting farmers’ risk attitudes through lab-in-the-field experiments have reported high levels of inconsistency in responses. We investigate inconsistencies in risk attitudes elicitation games using data from incentivized lotteries involving 2,319 smallholder farmers from Eastern Africa (Kenya, Uganda, Tanzania) and Northern Africa (Tunisia, Morocco). Our sample demonstrates high levels of inconsistent behavior, with 48% of the farmers exhibiting some type of inconsistency. Depending on the country, inconsistencies are explained by poverty, gender, and/or the interaction of gender and level of education. We find no significant impact (negative or positive) of education alone in all but one country model. Furthermore, we find session fixed effects to significantly explain inconsistencies in many cases, suggesting that session-specific circumstances, including inconsistencies across enumerators, play a crucial role in the successful implementation of these experiments. Our findings suggest that using risk attitude parameters without accounting for the presence and the potential causes of inconsistency may lead to unreliable results. This study may guide practitioners in identifying farmer typologies more prone to inconsistent decisions and inform policymakers about factors influencing operators’ choices.

Bird song comparison using deep learning trained from avian perceptual judgments.

Our understanding of bird song, a model system for animal communication and the neurobiology of learning, depends critically on making reliable, validated comparisons between the complex multidimensional syllables that are used in songs. However, most assessments of song similarity are based on human inspection of spectrograms, or computational methods developed from human intuitions. Using a novel automated operant conditioning system, we collected a large corpus of zebra finches' (Taeniopygia guttata) decisions about song syllable similarity. We use this dataset to compare and externally validate similarity algorithms in widely-used publicly available software (Raven, Sound Analysis Pro, Luscinia). Although these methods all perform better than chance, they do not closely emulate the avian assessments. We then introduce a novel deep learning method that can produce perceptual similarity judgements trained on such avian decisions. We find that this new method outperforms the established methods in accuracy and more closely approaches the avian assessments. Inconsistent (hence ambiguous) decisions are a common occurrence in animal behavioural data; we show that a modification of the deep learning training that accommodates these leads to the strongest performance. We argue this approach is the best way to validate methods to compare song similarity, that our dataset can be used to validate novel methods, and that the general approach can easily be extended to other species.

The defocalizing effect of international courts: Evidence from maritime delimitation practices

AbstractCan international courts influence state policies and facilitate interstate cooperation? Existing literature argues that they can. Courts can make cooperative outcomes easier for states by formulating or endorsing rules around which state expectations and practice can converge. While it is widely assumed that court rulings may become focal points and play a role in harmonizing state practices, we know little about the conditions under which they have such an effect. We suggest that court rulings can often have an opposite, defocalizing effect, which may durably harm the prospects of convergence around what the law requires. We introduce defocalization as a process and discuss its possible types and implications. We argue that defocalization may be driven by incongruence of court rulings with existing treaty law and state practice and inconsistency of rulings over time. We illustrate our argument by examining the effect of key judicial rulings on the convergence of state views about the appropriate maritime delimitation rules by relying on an original dataset. Our findings show how defocalization unfolds and suggest that complexity can accumulate over time through legal rulings that are incongruent with existing state practice or treaty law, and can be maintained through inconsistent court decisions.

Legal Analysis of the Decision on the Request for Guardianship by the State for Christianity

General Background: Marriage in the Christian religion fundamentally differs from other traditions, notably in the absence of a legal requirement for a marriage guardian. Specific Background: Instead, the presence of a priest to officiate the ceremony is deemed sufficient. This distinction is often misunderstood, leading to legal ambiguities and misapplications. Knowledge Gap: Despite clear doctrinal guidelines, there persists a lack of awareness regarding the role of state-appointed guardians in Christian marriages, as evidenced by legal cases requesting such appointments. Aims: This study aims to analyze the necessity and implications of appointing a state guardian in Christian marriages through the examination of relevant legal cases in the Jombang District Court. Results: The study highlights two pivotal cases: Request Number 20/Pdt.P/1994/PN Jmb, which was granted, and Request Number 119/Pdt.P/2023/PN Jbg, which was withdrawn. Novelty: By juxtaposing these cases, the research underscores the inconsistencies and the resultant need for greater legal and doctrinal clarity. Implications: The findings advocate for enhanced educational efforts within both the Christian community and the broader public to mitigate legal misunderstandings and to streamline the judicial approach to marriage guardianship in Christian contexts. This research contributes to the discourse by elucidating the legal boundaries and ecclesiastical stipulations of Christian marriages, promoting informed judicial decisions and societal understanding. Highlights: Legal Ambiguity: Inconsistent court decisions on state guardians in Christian marriages. Educational Need: Urgent need for educating communities on marriage guardianship in Christianity. Judicial Clarity: Importance of clear legal guidelines to prevent misapplications. Keywords: State Guardian, Decision on Request, Christian Marriage, Legal Analysis, Religious Doctrine

A Blueprint for Multi-use Disease Modeling in Health Economics: Results from Two Expert-Panel Consultations.

The current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs. We elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings. In total, 54 respondents contributed to the panel results. The term 'multi-use disease models' was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with comparing alternative policies and supporting resource allocation decisions as the top 2), while 20 issues (with model transparency and stakeholders' roles as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores (regular updates and revalidation after updates were the top 2). MUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerningwhich diseases and policy decisions they will be helpful for.

Algorithmic Harms in Child Welfare: Uncertainties in Practice, Organization, and Street-level Decision-making

Algorithms in public services such as child welfare, criminal justice, and education are increasingly being used to make high-stakes decisions about human lives. Drawing upon findings from a two-year ethnography conducted at a child welfare agency, we highlight how algorithmic systems are embedded within a complex decision-making ecosystem at critical points of the child welfare process. Caseworkers interact with algorithms in their daily lives where they must collect information about families and feed it to algorithms to make critical decisions. We show how the interplay between systemic mechanics and algorithmic decision-making can adversely impact the fairness of the decision-making process itself. We show how functionality issues in algorithmic systems can lead to process-oriented harms where they adversely affect the nature of professional practice, and administration at the agency, and lead to inconsistent and unreliable decisions at the street level. In addition, caseworkers are compelled to undertake additional labor in the form of repair work to restore disrupted administrative processes and decision-making, all while facing organizational pressures and time and resource constraints. Finally, we share the case study of a simple algorithmic tool that centers caseworkers’ decision-making within a trauma-informed framework and leads to better outcomes, however, required a significant amount of investments on the agency’s part in creating the ecosystem for its proper use.

Human-AI collaboration to mitigate decision noise in financial underwriting: A study on FinTech innovation in a lending firm

Financial institutions have recognized the value of collaborating human expertise and AI to create high-performance augmented decision-support systems. Stakeholders at lending firms have increasingly acknowledged that plugging data into AI algorithms and eliminating the role of human underwriters by automation, with the expectation of immediate returns on investment from business process automation, is a flawed strategy. This research emphasizes the necessity of auditing the consistency of decisions (or professional judgment) made by human underwriters and monitoring the ability of data to capture the lending policies of a firm to lay a strong foundation for a legitimate system before investing millions in AI projects. The judgments made by experts in the past re-emerge in the future as outcomes or labels in the data used to train and evaluate algorithms. This paper presents Evidential Reasoning-eXplainer, a methodology to estimate probability mass as an extent of support for a given decision on a loan application by jointly assessing multiple independent and conflicting pieces of evidence. It quantifies variability in past decisions by comparing the subjective judgments of underwriters during manual financial underwriting with outcomes estimated from data. The consistency analysis improves decision quality by bridging the gap between past inconsistent decisions and desired ultimate-true decisions. A case study on a specialist lending firm demonstrates the strategic work plan adapted to configure underwriters and developers to capture the correct data and audit the quality of decisions.

Logical Inconsistencies in the Health Years in Total and Equal Value of Life-Years Gained

ObjectivesThis study aimed to assess whether recently proposed alternatives to the quality-adjusted life-year (QALY), intended to address concerns about discrimination, are suitable for informing resource allocation decisions. MethodsWe consider 2 alternatives to the QALY: the health years in total (HYT), recently proposed by Basu et al, and the equal value of life-years gained (evLYG), currently used by the Institute for Clinical and Economic Review. For completeness we also consider unweighted life-years (LYs). Using a hypothetical example comparing 3 mutually exclusive treatment options, we consider how calculations are performed under each approach and whether the resulting rankings are logically consistent. We also explore some further challenges that arise from the unique properties of the HYT approach. ResultsThe HYT and evLYG approaches can result in logical inconsistencies that do not arise under the QALY or LY approaches. HYT can violate the independence of irrelevant alternatives axiom, whereas the evLYG can produce an unstable ranking of treatment options. HYT have additional issues, including an implausible assumption that the utilities associated with health-related quality of life and LYs are “separable,” and a consideration of “counterfactual” health-related quality of life for patients who are dead. ConclusionsThe HYT and evLYG approaches can result in logically inconsistent decisions. We recommend that decision makers avoid these approaches and that the logical consistency of any approaches proposed in future be thoroughly explored before considering their use in practice.

The U.S. needs a National Biodiversity Strategy

AbstractThe U.S. needs a National Biodiversity Strategy (hereafter Strategy). As a nation that places a high value on domestic biodiversity and on being a global leader in biodiversity conservation, the U.S. is failing to live up to its expectations regarding a national Strategy. A Strategy should suggest both legislation and policies and guide our country toward achieving stated objectives. We first propose a process to create a Strategy that involves establishing a government task force and supporting expert advisory panel. We then identify five key elements of a Strategy. First, a Strategy should identify priorities among legislation that impacts biodiversity. At present, conflicting rules lead to administrative uncertainty and inconsistent decisions. Second, a Strategy should create a comprehensive vision for societal participation in biodiversity protection. Current practices comprise a mixture of different levels of commitment to stakeholder participation and social justice. Third, a Strategy should mandate periodic review to better provide the American public a synopsis of how we are faring with our treasured biodiversity. Fourth, a Strategy should be as comprehensive and broad as biodiversity itself, spanning organizational ranges from genes to ecosystems. Finally, a Strategy should provide a structure for achievement that incentivizes public and business and that stimulates innovation, particularly in nature‐based solutions to complex environmental challenges.

A formal analysis of inconsistent decisions in intertemporal choice through subjective time perception

The framework of this paper is subjective time perception in the context of intertemporal choice, that is to say, the process of making decisions on dated outcomes (monetary or not) by an individual or a group of individuals. In this setting, the Discounted Utility model and, more specifically, the exponential discounting have been the paradigmatic methodology used to measure the preferences on delayed outcomes. However, this model can only be applied to consistent choices in which individuals do not change their preferences when the involved rewards are delayed the same time interval. Unfortunately, this is not the case of several decision scenarios where time is viewed as a subjective variable. The objective of this paper is to formally analyze the consistency of intertemporal choices governed by a discount function, derived from the exponential, where time has been distorted according to certain psychological traits of the subjects involved in the decision-making. More specifically, the different types of decreasing impatience will be characterized by focusing on the distortion derived from the subjective perspective of time. The findings of this research are very relevant in order to explain the time-related behavior of decision-makers in some noteworthy fields such as finance, psychology, marketing or sociology.

‘We’ll have the bill (of costs), please’

Abstract If a beneficiary of an estate or trust is unhappy with the level of legal fees that the personal representatives (PRs) or trustees have incurred and paid out of the fund, what, if anything, can he do? Over the past decade or so, several cases have grappled with this apparently simple question. The result seems to be a patchwork quilt of subtly inconsistent decisions. The latest instalment is Kenig v Thomson Snell & Passmore LLP, which provides yet another curve in the winding road to an answer, with potentially far-reaching implications, both for beneficiaries and for solicitors instructed by PRs and trustees.

Context-aware Big Data Quality Assessment: A Scoping Review

The term data quality refers to measuring the fitness of data regarding the intended usage. Poor data quality leads to inadequate, inconsistent, and erroneous decisions that could escalate the computational cost, cause a decline in profits, and cause customer churn. Thus, data quality is crucial for researchers and industry practitioners. Different factors drive the assessment of data quality. Data context is deemed one of the key factors due to the contextual diversity of real-world use cases of various entities such as people and organizations. Data used in a specific context (e.g., an organization policy) may need to be more efficacious for another context. Hence, implementing a data quality assessment solution in different contexts is challenging. Traditional technologies for data quality assessment reached the pinnacle of maturity. Existing solutions can solve most of the quality issues. The data context in these solutions is defined as validation rules applied within the ETL (extract, transform, load) process, i.e., the data warehousing process. In contrast to traditional data quality management, it is impossible to specify all the data semantics beforehand for big data. We need context-aware data quality rules to detect semantic errors in a massive amount of heterogeneous data generated at high speed. While many researchers tackle the quality issues of big data, they define the data context from a specific standpoint. Although data quality is a longstanding research issue in academia and industries, it remains an open issue, especially with the advent of big data, which has fostered the challenge of data quality assessment more than ever. This article provides a scoping review to study the existing context-aware data quality assessment solutions, starting with the existing big data quality solutions in general and then covering context-aware solutions. The strength and weaknesses of such solutions are outlined and discussed. The survey showed that none of the existing data quality assessment solutions could guarantee context awareness with the ability to handle big data. Notably, each solution dealt only with a partial view of the context. We compared the existing quality models and solutions to reach a comprehensive view covering the aspects of context awareness when assessing data quality. This led us to a set of recommendations framed in a methodological framework shaping the design and implementation of any context-aware data quality service for big data. Open challenges are then identified and discussed.

Nature-Based Offsets in Russia: Key Challenges and Conditions for Success

The article discusses the key problems and conditions for the successful development of the nature-based offset projects (NBOPs) in Russia. The conditions for the successful entry of verified carbon units from Russian nature-based offset projects into the global market were determined considering the state and prospects for development of global carbon markets. The key limitations for the national carbon market for offsets are presented: the lack of internal economic incentives for companies to buy carbon units and to invest in Russian NBOPs; the lack of national methodologies for the NBOPs; legal restrictions for land-use; lack of available tools for assessing costs and profitability of NBOPs on given land plots; failures of carbon market regulation under the Sakhalin experiment. The limitations of the narrow agency-based approach to the development of NBOPs are shown. These limitations result in inconsistent decisions that do not fit real conditions in the global market. It is necessary to create a competence center to solve the issues of the NBOPs from specific methodologies of individual projects to science-based assessments of their total potential in Russia. Two scenarios of the use of NBOPs for decarbonization of the Russian economy are analyzed. Scenario 1 assumes large-scale sale of carbon units generated in Russian in foreign markets in order to maximize mid-term profit. Scenario 2 assumes the use of carbon units generated in Russian NBOPs mainly by Russian companies to achieve net zero by 2060. A realistic and balanced strategy assumes that the key buyers of carbon credits from Russian NBOPs in the first stage should be export-oriented Russian companies that can use these credits to reduce the carbon footprint of their products and implement corporate climate strategies.

Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints in Cancer Clinical Trials-Innovative Medicines Initiative (SISAQOL-IMI): stakeholder views, objectives, and procedures.

Patient-reported outcomes (PROs), such as symptoms, functioning, and other health-related quality-of-life concepts are gaining a more prominent role in the benefit-risk assessment of cancer therapies. However, varying ways of analysing, presenting, and interpreting PRO data could lead to erroneous and inconsistent decisions on the part of stakeholders, adversely affecting patient care and outcomes. The Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints in Cancer Clinical Trials-Innovative Medicines Initiative (SISAQOL-IMI) Consortium builds on the existing SISAQOL work to establish recommendations on design, analysis, presentation, and interpretation for PRO data in cancer clinical trials, with an expanded set of topics, including more in-depth recommendations for randomised controlled trials and single-arm studies, and for defining clinically meaningful change. This Policy Review presents international stakeholder views on the need for SISAQOL-IMI, the agreed on and prioritised set of PRO objectives, and a roadmap to ensure that international consensus recommendations are achieved.

Modeling teachers’ reactions to unexpectedness

BackgroundEven experienced teachers make inconsistent classroom decisions in unexpected situations. From the cognitive load theory perspective, the effective handling of the novel, unexpected events by teachers depends on the cognitive load of the task, the teaching context in which the unexpectedness appears, and the available cognitive capacity. AimsTeachers’ reactions to unexpected mathematical events, in particular the unexpectedness of the arithmetic calculation, was modeled, investigated experimentally, and explained within the theoretical framework of cognitive load theory. Sample64 mathematics teacher trainees took part in the experiment. MethodsIn a dual-task arrangement, participants verified alternative answers to simple mathematical questions while memorizing task-irrelevant information. The answers represented low (schematic good responses), and high (unexpected good responses) processing load conditions, and control condition (incorrect responses). The memory load was low or high representing levels of extraneous load. The participants’ cognitive capacity was estimated by a complex working memory span task. ResultsThe verification of unexpected but correct answers was slow and more error-prone as compared with the processing speed and accuracy of schematic answers, presumably due to elevated processing (intrinsic) load. The increase in memory load resulted in slower and more inaccurate verifications. However, working memory capacity was found to mediate the extraneous load effect. ConclusionsThe results stress the importance of well-organized schemas for effective reactions to unexpected classroom events. Furthermore, it highlights the importance of accurately understanding and being aware of the impact of cognitive load on teachers to improve teaching practice.

OGC P08 A machine-learning approach to predict Upper Gastrointestinal multidisciplinary team treatment decisions

Abstract Background The complexity of the Upper Gastrointestinal (UGI) multidisciplinary team (MDT) is growing, leading to rising clinician workload, time pressures and demands. This increases heterogeneity or ‘noise’ within decision-making for patients with oesophageal cancer (OC) and may lead to inconsistent treatment decisions. In this context, machine learning (ML) approaches offer the potential to standardize, automate and produce more consistent, data-driven decisions. Such models benefit from incorporating information-rich, diverse datasets to increase predictive model accuracy. The aim of this study was to develop a model capable of predicting UGI MDT treatment decisions for OC patients (whether the patient should receive surgery alone (S), chemotherapy prior to surgery (C+S) or chemoradiotherapy prior to surgery (CRT+S)) using only variables available at the time of the first treatment decision. Methods We conducted a retrospective analysis of patients who underwent oesophageal cancer resections between 2010–2016. Twenty pre-operative clinical variables available to the MDT were used to develop a predictive machine learning model using a penalised Multinomial Logistic Regression (MLR) classifier with 10-fold cross-validation and 5 repeats. Results A total of 399 cases were identified with a male: female ratio of 3.6:1, and median age of 66.1yrs (range 32–83 yrs). The overall model accuracy was 61.4% (Kappa 0.399). A Receiver Operator Characteristic analysis produced an Area Under Curve of 87.7% for Surgery vs. CRT+S, 86.9% for Surgery vs C+S and 66.1% for C+S vs CRT+S when trialled on a validation set. Conclusions These results suggest even basic ML modelling techniques offer the potential to model and predict current UGI MDT treatment decisions when selecting patients for surgery or neoadjuvant therapy options. Such models may allow prioritization of caseload, improve efficiency, potentially reduce waiting times, and offer data-driven decisions where more complex cases pose challenges. While this study is the first step in such a process, future work will need to incorporate additional data modalities such as medical imaging and histopathology as well as expansion to other ML classifier algorithms to determine the best performing models in order to improve overall predictive accuracy.

Deepening and widening the gap: The impacts of the COVID-19 pandemic on gender and racial inequalities in Brazil.

Shaped by inconsistent policy decisions, the COVID-19 pandemic in Brazil has made structural gender and racial inequalities more acute. Black and low-income women are overburdened with unpaid domestic work, increased domestic violence, and more vulnerable due to informal and exploitative working regimes. These structural aspects are intensifying, since the pandemic has broadened inequalities at the intersection of gender, race, labor market, and social class. We examine pre- and during pandemic inequalities on three dimensions: (a) unpaid domestic and care work, (b) women's labor market participation, and (c) domestic violence. We link the care diamond model and racial stratification forwarding a feminist perspective by examining how the interlocking of race and gender in Brazil renders different socioeconomic dynamics to the detriment of Black and low-income women. Based on this evidence, we stress that a more equal future requires a better social protection and policies targeting the articulation of gender, race, and class.

Hearsay Evidence Admissibility: Due Process and Evidentiary Rules in Muslim Marriage Legalization (Isbat nikah)

The validity of hearsay evidence remains a contentious issue in Indonesian civil procedural law, resulting in inconsistent judicial decisions regarding religious civil cases reliant on "hearsay witnesses." For instance, the Samarinda Religious Court accepted hearsay evidence in a marriage legalization case, while the Samarinda Religious High Court rejected it, citing the unsuitability of hearsay in contentious matters. This paper analyzes the judges' reasoning in these decisions to assess whether their considerations align with appropriate legal arguments and the principles of justice and legal certainty. Findings reveal that the Samarinda Religious Court's acceptance was based on exceptional circumstances surrounding the marriage event, whereas the High Court's rejection lacked clear legal justification. The evolving jurisprudence of procedural law in Indonesia offers opportunities to refine the application of hearsay evidence, ultimately enhancing the pursuit of justice and legal certainty for litigants.

Establishing the Medical Actionability of Genomic Variants.

Actionability is an important concept in medicine that does not have a well-accepted standard definition, nor is there a general consensus on how to establish it. Medical actionability is often conflated with clinical utility, a related but distinct concept. This lack of clarity contributes to practice variation and inconsistent coverage decisions in genomic medicine, leading to the potential for systematic bias in the use of evidence-based interventions. We clarify how medical actionability and clinical utility are distinct and then discuss the spectrum of actionability, including benefits for the person, the family, and society. We also describe applications across the life course, including prediction, diagnosis, and treatment. Current challenges in assessing the medical actionability of identified genomic variants include gaps in the evidence, limited contexts with practice guidelines, and subjective aspects of medical actionability. A standardized and authoritative assessment of medical actionability is critical to implementing genomic medicine in a fashion that improves population health outcomes and reduces health disparities.