Decision Uncertainty Research Articles

Exploring Structural Uncertainty in Cost-Effectiveness Modeling of Gestational Diabetes Screening: An Application Example from Norway.

Screening pregnant women for gestational diabetes mellitus (GDM) has recently been expanded in Norway, although screening eligibility criteria continue to be debated. We aimed to compare the cost-effectiveness of alternative GDM screening strategies and explored structural uncertainty and the value of future research in determining the most cost-effective eligibility criteria for GDM screening in Norway. We developed a probabilistic decision tree to estimate the total costs and health benefits (i.e., quality-adjusted life-years; QALYs) associated with 4 GDM screening strategies (universal, current guidelines, high-risk, and no screening). We identified the most cost-effective strategy as the strategy with the highest incremental cost-effectiveness ratio below a Norwegian benchmark for cost-effectiveness ($28,400/QALY). We excluded inconclusive evidence on the effects of screening on later maternal type 2 diabetes mellitus (T2DM) in the primary analysis but included this outcome in a secondary analysis using 2 different sources of evidence (i.e., Cochrane or US Preventive Services Task Force). To quantify decision uncertainty, we conducted scenario analysis and value-of-information analyses. Current screening recommendations were considered inefficient in all analyses, while universal screening was most cost-effective in our primary analysis ($26,014/QALY gained) and remained most cost-effective when we assumed a preventive effect of GDM treatment on T2DM. When we assumed no preventive effect, high-risk screening was preferred ($19,115/QALY gained). When we assumed GDM screening does not prevent perinatal death in scenario analysis, all strategies except no screening exceeded the cost-effectiveness benchmark. In most analyses, decision uncertainty was high. The most cost-effective screening strategy, ranging from no screening to universal screening, depended on the source and inclusion of GDM treatment effects on perinatal death and T2DM. Further research on these long-term outcomes could reduce decision uncertainty. This article analyses the cost-effectiveness of 4 alternative gestational diabetes mellitus (GDM) screening strategies in Norway: universal screening, current (broad) screening, high-risk screening, and no screening.The current Norwegian screening recommendations were considered inefficient under all analyses.The most cost-effective screening strategy ranged from no screening to universal screening depending on the source and inclusion of GDM treatment effects on later maternal diabetes and perinatal death.The parameters related to later maternal diabetes and perinatal death accounted for most of the decision uncertainty.

A business process network efficiency model for handling conflicting information

In today's big data era, where a significant volume of business data is generated daily, managing conflicting information within business process networks is crucial for maintaining operational efficiency. This paper addresses this challenge by proposing an efficiency model for business process networks tailored to handle conflict information, drawing on queuing theory and evidence theory. Firstly, we introduce a novel approach for measuring conflict information based on evidence theory and Pignistic probability transformation theory. Next, we tailor efficiency models for the four fundamental structures found in business process networks: sequential, selective, parallel, and loop structures, using queuing theory to manage conflict information effectively in each scenario. We further extend this approach by conceptualizing virtual business activities, allowing us to view the entire business process network as a sequential structure of virtual business activities, facilitating efficiency measurement across the network. Utilizing these measurements, we formulate the queuing service of the business process network as a nonlinear programming problem aimed at minimizing time, thus determining the optimal service rate for business process activities. Finally, we demonstrate the applicability and effectiveness of our proposed model through an experimental analysis focused on the railway intermodal transportation business process. The experimental results indicate that our model significantly reduces the impact of conflicting information, leading to a measurable improvement in the efficiency of the business process network. Specifically, the model achieves a notable enhancement in the coordination and execution of intermodal transportation activities, thereby streamlining operations and reducing decision-making uncertainties. This structured approach not only addresses the challenge of managing conflicting information within business process networks but also provides a clear framework for understanding and optimizing network efficiency.

Penerapan Metode Dempster Shafer Sistem Pakar Pada Penyakit Tuberkolosis

The Dempster-Shafer (DS) method is a method in trust theory that is used to overcome uncertainty in decision making. An expert system that utilizes the Dempster-Shafer method can be used to diagnose tuberculosis (TB). Application of the Dempster-Shafer method in an expert system for TB requires a good understanding of the medical domain and the ability to correctly combine evidence from multiple sources. In addition, collaboration with medical experts in developing this system will increase the accuracy and usefulness of the expert system. At the Wonokromo Community Health Center, the use of websites to diagnose tuberculosis is still lacking, therefore the aim of using this website is to help the community know more about tuberculosis. In the design process, this system uses a user interface, and is equipped with HTML, CSS, Boostrap, and Java Script. The result of this research is a website-based expert system application using the website-based Dempster Shafer method, and is able to overcome the problems that exist at the Wonokromo Community Health Center. The black box testing results show that the module configuration functions well and meets user needs.

Site Selection of Medical Waste Disposal Facilities Using the Interval-Valued Neutrosophic Fuzzy EDAS Method: The Case Study of Istanbul

In recent years, as a result of the increasing demand for health services, medical waste (MW) generated from health facilities has increased significantly. Problems that threaten the environment and public health may arise as a result of inadequate medical waste management (MWM), especially in densely populated metropolitan areas. Therefore, it is very important that the disposal process of waste is carried out in a way that minimizes harm to human health and the environment. MW disposal site selection is among the most important decisions that local governments make. These decisions have many conflicting and similar criteria and alternatives. However, decision-makers may experience significant uncertainty when evaluating the alternatives. This study adopts the interval-valued neutrosophic (IVN) fuzzy EDAS method for the evaluation of MW disposal facility siting alternatives in Istanbul. This approach is used to evaluate potential sites based on a comprehensive, hierarchical criteria framework designed to address data uncertainty and inconsistency common in multi-criteria decision-making (MCDM) scenarios. Within the scope of the study, six main criteria (distance settlement area, social acceptance, costs, environmental impacts, infrastructure facilities, and disaster and emergency) and nineteen sub-criteria are meticulously analyzed. Considering the geographical location and dense urban texture of Istanbul, the study emphasizes the criteria related to distance to residential areas, logistics costs, and potential disaster risks. Among the identified criteria, land costs, topographical features, proximity to landfills, and distance to high-voltage lines are emphasized as the least important criteria. This study, which evaluated various alternatives, identified Pendik, located on the Anatolian side of Istanbul, as the most suitable site for MW disposal due to its minimal risk. The study also compares the four main alternatives and highlights their relative strengths and weaknesses.

Cost-effectiveness analysis of CTZ/TAZ for the treatment of ventilated hospital-acquired bacterial pneumonia and ventilator-associated bacterial pneumonia in Japan

BackgroundResistant bacterial infections, particularly those caused by gram-negative pathogens, are associated with high mortality and economic burdens. Ceftolozane/tazobactam demonstrated efficacy comparable to meropenem in patients with ventilated hospital-acquired bacterial pneumonia in the ASPECT-NP study. One cost-effectiveness analysis in the United States revealed that ceftolozane/tazobactam was cost effective, but no Japanese studies have been conducted. Therefore, the objective of this study was to assess the cost-effectiveness of ceftolozane/tazobactam compared to meropenem for patients with ventilated hospital-acquired bacterial pneumonia/ventilator-associated bacterial pneumonia from a health care payer perspective.MethodsA hybrid decision-tree Markov decision-analytic model with a 5-year time horizon were developed to estimate costs and quality-adjusted life-years and to calculate the incremental cost-effectiveness ratio associated with ceftolozane/tazobactam and meropenem in the treatment of patients with ventilated hospital-acquired bacterial pneumonia/ventilator-associated bacterial pneumonia. Clinical outcomes were based on the ASPECT-NP study, costs were based on the national fee schedule of 2022, and utilities were based on published data. One-way sensitivity analysis and probabilistic sensitivity analysis were also conducted to assess the robustness of our modeled estimates.ResultsAccording to our base-case analysis, compared with meropenem, ceftolozane/tazobactam increased the total costs by 424,731.22 yen (£2,626.96) and increased the quality-adjusted life-years by 0.17, resulting in an incremental cost-effectiveness ratio of 2,548,738 yen (£15,763.94) per quality-adjusted life-year gained for ceftolozane/tazobactam compared with meropenem. One-way sensitivity analysis showed that although the incremental cost-effectiveness ratio remained below 5,000,000 yen (£30,925) for most of the parameters, the incremental net monetary benefit may have been less than 0 depending on the treatment efficacy outcome, especially the cure rate and mortality rate for MEPM and mortality rate for CTZ/TAZ. 53.4% of the PSA simulations demonstrated that CTZ/TAZ was more cost-effective than MEPM was.ConclusionAlthough incremental cost-effectiveness ratio was below ￥5,000,000 in base-case analysis, whether ceftolozane/tazobactam is a cost-effective alternative to meropenem for ventilated hospital-acquired bacterial pneumonia/ventilator-associated bacterial pneumonia in Japan remains uncertain. Future research should examine the unobserved heterogeneity across patient subgroups and decision-making settings, to characterise decision uncertainty and its consequences so as to assess whether additional research is required.

Quantification of Uncertainty Cost Functions for Controllable Solar Power Modeling

Navigating the secheduling of generation resources of energy in power systems marked by a significant presence of renewable generation involves intricate optimization challenges. The conventional tools for resolving such challenges include programming techniques and heuristic approaches, both contingent upon a precisely articulated target function for optimization. Traditional optimization tools rely on precisely defined target functions, but the evolving landscape of power systems introduces complexity, especially with unpredictable behaviors of renewable sources. The research specifically quantifies penalty costs associated with photovoltaic (PV) generators, employing probabilistic methods for a robust mathematical analysis. The developed analytical model enhances adaptability in economic dispatch problems, considering uncertainty in decision-making. Validation using Monte Carlo simulation emphasizes uncertainty in PV generation and highlights the advantages of the proposed analytic model. The quadratic form of the model aligns coherently with simulation outcomes, contributing significantly to understanding uncertainty quantification in solar power modeling. The research aims to refine controllable solar power models, establish robust uncertainty cost functions, and improve the accuracy of economic dispatch strategies. Ultimately, this work promotes the seamless integration of solar energy into diverse and dynamic energy grids.

Anti–Vascular Endothelial Growth Factor Drugs Compared With Panretinal Photocoagulation for the Treatment of Proliferative Diabetic Retinopathy: A Cost-Effectiveness Analysis

ObjectivesThis study aimed to evaluate the cost-effectiveness of anti–vascular endothelial growth factor drugs (anti-VEGFs) compared with panretinal photocoagulation (PRP) for treating proliferative diabetic retinopathy (PDR) in the United Kingdom. MethodsA discrete event simulation model was developed, informed by individual participant data meta-analysis. The model captures treatment effects on best corrected visual acuity in both eyes, and the occurrence of diabetic macular edema and vitreous hemorrhage. The model also estimates the value of undertaking further research to resolve decision uncertainty. ResultsAnti-VEGFs are unlikely to generate clinically meaningful benefits over PRP. The model predicted anti-VEGFs be more costly and similarly effective as PRP, generating 0.029 fewer quality-adjusted life-years at an additional cost of £3688, with a net health benefit of −0.214 at a £20 000 willingness-to-pay threshold. Scenario analysis results suggest that only under very select conditions may anti-VEGFs offer potential for cost-effective treatment of PDR. The consequences of loss to follow-up were an important driver of model outcomes. ConclusionsAnti-VEGFs are unlikely to be a cost-effective treatment for early PDR compared with PRP. Anti-VEGFs are generally associated with higher costs and similar health outcomes across various scenarios. Although anti-VEGFs were associated with lower diabetic macular edema rates, the number of cases avoided is insufficient to offset the additional treatment costs. Key uncertainties relate to the long-term comparative effectiveness of anti-VEGFs, particularly considering the real-world rates and consequences of treatment nonadherence. Further research on long-term visual acuity and rates of vision-threatening complications may be beneficial in resolving uncertainties.

SocialCVAE: Predicting Pedestrian Trajectory via Interaction Conditioned Latents

Pedestrian trajectory prediction is the key technology in many applications for providing insights into human behavior and anticipating human future motions. Most existing empirical models are explicitly formulated by observed human behaviors using explicable mathematical terms with deterministic nature, while recent work has focused on developing hybrid models combined with learning-based techniques for powerful expressiveness while maintaining explainability. However, the deterministic nature of the learned steering behaviors from the empirical models limits the models' practical performance. To address this issue, this work proposes the social conditional variational autoencoder (SocialCVAE) for predicting pedestrian trajectories, which employs a CVAE to explore behavioral uncertainty in human motion decisions. SocialCVAE learns socially reasonable motion randomness by utilizing a socially explainable interaction energy map as the CVAE's condition, which illustrates the future occupancy of each pedestrian's local neighborhood area. The energy map is generated using an energy-based interaction model, which anticipates the energy cost (i.e., repulsion intensity) of pedestrians' interactions with neighbors. Experimental results on two public benchmarks including 25 scenes demonstrate that SocialCVAE significantly improves prediction accuracy compared with the state-of-the-art methods, with up to 16.85% improvement in Average Displacement Error (ADE) and 69.18% improvement in Final Displacement Error (FDE). Code is available at: https://github.com/ViviXiang/SocialCVAE.

Learning Efficient and Robust Multi-Agent Communication via Graph Information Bottleneck

Efficient communication learning among agents has been shown crucial for cooperative multi-agent reinforcement learning (MARL), as it can promote the action coordination of agents and ultimately improve performance. Graph neural network (GNN) provide a general paradigm for communication learning, which consider agents and communication channels as nodes and edges in a graph, with the action selection corresponding to node labeling. Under such paradigm, an agent aggregates information from neighbor agents, which can reduce uncertainty in local decision-making and induce implicit action coordination. However, this communication paradigm is vulnerable to adversarial attacks and noise, and how to learn robust and efficient communication under perturbations has largely not been studied. To this end, this paper introduces a novel Multi-Agent communication mechanism via Graph Information bottleneck (MAGI), which can optimally balance the robustness and expressiveness of the message representation learned by agents. This communication mechanism is aim at learning the minimal sufficient message representation for an agent by maximizing the mutual information (MI) between the message representation and the selected action, and simultaneously constraining the MI between the message representation and the agent feature. Empirical results demonstrate that MAGI is more robust and efficient than state-of-the-art GNN-based MARL methods.

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.

Multi-Criteria Decision under Uncertainty as Applied to Resource Allocation and Its Computing Implementation

This research addresses the problem of multi-objective resource allocation or resource deficits, offering robust answers to planning decisions that involve the elementary question: “How is it done?”. The solution to the problem is realized using the general scheme of multi-criteria decision-making in uncertain conditions. The bases of the proposed scheme are associated with the possibilistic approach, which involves the generalization of fuzzy sets from the classical approach to process the uncertainty of information to produce robust (non-dominated) solutions in multi-criteria analysis. Applying this general scheme makes it possible to reduce regions of decision uncertainty through the maximum use of available quantitative information. In the case where quantitative information analysis is insufficient to obtain a unique solution, the proposed approach presupposes the appropriation of qualitative data extracted from experts, who express their opinions considering their knowledge, experience, and intuition. The information on the qualitative character can be represented in diverse preference formats processed by transformation functions to provide homogeneous information for decision procedures used at the final decision stage. The presented results have been implemented within the system of multi-criteria decision-making under uncertain conditions described in the paper. Its functioning is illustrated by solving the typical problem in investment planning activities.

Corporate Investment and Economic Policy Uncertainty (EPU) Index Evidence from India

The author examined the relationship between the capital expenditure to total assets (CETA) ratio and the economic policy uncertainty (EPU) index, as well as a few firm-specific financial and two macroeconomic variables in the Indian context. Logistic Regression and quantile regression are used to evaluate secondary data from 50 listed companies on India's National Stock Exchange (NSE) from 2014 to 2023 (January to December). The author found that as uncertainty increases, the sample companies' investment in assets decreases and vice versa. The study's originality is the use of EPU index data in an Indian context, which has rarely been done in previous studies. The findings have consequences for the promoter in understanding the impact of uncertainty in investment decisions, as well as for the government in reducing uncertainty in order to create a more stable economic environment.

A vision for implementing equitable early mental health and resilience support in pediatric primary care: A transdiagnostic, developmental approach.

Primary care is at the forefront of addressing the pediatric mental health (MH) crisis due to its broad reach to young children and prevention and health promotion orientation. However, the promise of the delivery system for population impact remains unrealized due to several barriers, including pragmatic screening, decisional uncertainty, and limited access to evidence-based services. This article lays the conceptual foundations for the articles in this Special Section on Mental Health, Earlier in Pediatric Primary Care, which all apply a translational mindset to proposed strategies and solutions to overcome the barriers that have limited the potential of pediatric primary care for improving the MH and wellbeing of all children. Valid, pragmatic, transdiagnostic, developmentally-based screening measures to identify children at heightened risk are needed. Risk screening for MH problems should assess and empirically weight socioecological risk and protective factors, as well as the child's own assets for resilience to determine probabilistic risk. Pediatric clinicians require clear clinical cutoffs and guidelines for action when risk for MH problems is identified. These strategies-a developmentally-based screener with associated risk calculator that offers clear guidance to pediatric clinicians-address decisional uncertainty regarding when to worry and when to act. The communication of probabilistic risk requires additional client-centered communication skills to overcome different types of biases (e.g., implicit, benevolent, and cognitive) that contribute to MH inequities and decisional uncertainty in acting on identified risk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Communication under Uncertainty and the Role of Founders’ Information Advantage: Evidence from SPAC IPOs

This study develops a novel framework about how a firm’s financing outcome will be shaped by its communication under different types of uncertainty. Whereas prior work has largely focused on uncertainties that arise because of founders having a knowledge advantage over investors, we examine a firm’s communication in situations of more fundamental uncertainty when both founders and investors face knowledge problems. Our framework proposes that in situations where founders have a knowledge advantage over investors (i.e., when there is information asymmetry), firms that reduce uncertainty by sending signals of quality and express less uncertainty in their communications will enjoy better financing outcomes. However, we argue that in situations characterized by high unknowability, and where founders do not have a significant knowledge advantage over investors (i.e., when there is Knightian uncertainty), firms that acknowledge this unknowability by expressing more uncertainty in their communications will have more favorable financing outcomes. Studying the full population of special purpose acquisition companies (SPACs) that sought to complete an initial public offering from the emergence of the sector in 2003–2019, we find support for our predictions. This study expands our understanding of the role of uncertainty in investment decisions, offers deeper insight into how language operates in financial markets, and sheds light on the increasingly popular, but understudied, SPAC vehicle. Funding: Ivana Naumovska received financial support from the INSEAD R&D Committee and the INSEAD Dean’s Annual Fund. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2022.0043 .

Fuzzy Divergence Measure Based on Technique for Order of Preference by Similarity to Ideal Solution Method for Staff Performance Appraisal

Fuzzy set theory has extensively employed various divergence measure methods to quantify distinctions between two elements. The primary objective of this study is to introduce a generalized divergence measure integrated into the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. Given the inherent uncertainty and ambiguity in multi-criteria decision-making (MCDM) scenarios, the concept of the fuzzy α-cut is leveraged. This allows experts to establish a broader spectrum of rankings, accommodating fluctuations in their confidence levels. To produce consistent criteria weights with the existence of outliers, the fuzzy Method based on the Removal Effects of Criteria (MEREC) is employed. To showcase the viability and effectiveness of the proposed approach, a quantitative illustration is provided through a staff performance review. In this context, the findings are compared with other MCDM methodologies, considering correlation coefficients and CPU time. The results demonstrate that the proposed technique aligns with current distance measure approaches, with all correlation coefficient values exceeding 0.9. Notably, the proposed method also boasts the shortest CPU time when compared to alternative divergence measure methodologies. As a result, it becomes evident that the proposed technique yields more sensible and practical results compared to its counterparts in this category.

From inside the head to putting it on the table – supporting reflexive decision-making for unpaid female carers considering higher education

ABSTRACT When unpaid female carers consider whether to pursue higher education (HE), they frequently experience decision-making uncertainties which stem from the structural and cultural challenges associated with their roles as carers. Reflexivity is a critical part of decision-making, as it empowers individuals to mediate between the present and future within the structural and cultural conditions which impact and guide their actions. To investigate the reflexive deliberation processes unpaid female carers engage in as part of their HE decision-making processes, this study analysed data from an in-depth, qualitative Australian study with 15 unpaid female carers who were considering higher education. Dialogic processes including narrative interviews and a card sort activity made visible the array of mental activities involved in reflexive decision-making by carers. Considering the more and less productive types of reflexive inner conversations which had been identified enabled participants to reflexively adjust the efficacy of some of their decision-making processes. This study contributes to the reflective and career counselling literature by theorising the cognitively and emotionally complex personal and structural demands of HE decision-making. By conceptualising reflexive decision-making anew, unpaid female carers and HE institutions can better identify and address the structural and cultural conditions which may be influencing their HE decision-making.

SUSTAINABLE DEVELOPMENT MANAGEMENT OF CULTURAL ORGANIZATIONS IN MODERN CONDITIONS

The article is focused on the issues of sustainable development management of cultural organizations in modern conditions. In the framework of the research, it is identified that the concept of sustainable development of these organizations can be considered from the standpoint of both their functional role (producers, repeaters, carriers of the "culture of sustainable development") and organizational development itself (qualitative changes that form the conditions for the system’s transition to a new level of development while maintaining a balance in the triad of "economic-social-environmental goals"). It is determined that exogenous and endogenous factors in modern conditions form a number of risks and threats, which makes both the entire system of institutions in the field of culture and its individual subsystems and organizations to adapt to new conditions of activity more rapidly. At the same time, ensuring sustainable development through the use of modern management tools is becoming increasingly relevant. It is identified that the vector of environmental factors, the level of uncertainty in decision-making and the available resource potential have a greater impact on the nature of sustainable development management tools. It has been identified that despite the negative environmental conditions, such management is becoming strategic, organizations are gradually switching to the use of crisis and project management, adapting to the use of a market approach to organizing their sustainable development. The author proposes directions for further scientific and applied research that can lay the groundwork for improving the efficiency of sustainable development management and making it systematic in the activities of cultural organizations.

Determination of nitrofuran metabolites in muscle by LC-MS/MS and LC-MS/MS/MS: Alternative validation according to Commission Implementing Regulation (EU) 2021/808

Nitrofurans are banned in food of animal origin in the European Union. A rugged liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) and LC-MS/MS cubed (LC-MS/MS/MS) analytical method allowing the confirmation of five metabolites of nitrofuran antibacterial agents, furaltadone, furazolidone, nifursol, nitrofurantoin and nitrofurazone in animal muscle tissue was developed and validated. The validation of 1-aminohydantoin (AHD), 3-amino-5-methyl-morpholino-2-oxazolidinone (AMOZ), 3-amino-2-oxazolidinone (AOZ), 3,5-dinitrosalicylic acid hydrazide (DNSH) and semicarbazide (SEM) was carried out according to Commission Implementing Regulation (EU) 2021/808, by the adoption of a matrix-comprehensive in-house validation concept and the standard addition calibration. Validation was successfully performed, starting from 0.4 µg kg−1. Decision limit (CCα), recovery, repeatability, within-laboratory reproducibility and uncertainty of measurement were determined together with a factorial effect analysis. The procedure was verified through the analysis of two certified reference materials. The sample preparation consisted in acid hydrolysis and derivatization steps, followed by a liquid-liquid extraction of the analytes with ethylacetate.

Consumer reactions to technology in retail: choice uncertainty and reduced perceived control in decisions assisted by recommendation agents

The emergence of artificial intelligence technologies, such as recommendation agents, presents new challenges and opportunities for marketing. Recommendation agents assist consumers in their online grocery shopping decisions by analyzing data on preferences and behaviors. This research highlights that while recommendation agents can reduce choice overload and make purchase decisions easier for consumers, they are also associated with higher uncertainty in decision-making. Three experimental studies confirmed that purchases aided by recommendation agents are perceived as more uncertain and reduced perceptions of control over the choices explain this outcome. Furthermore, lower choice satisfaction and purchase intentions are confirmed as consequences of perceived uncertainty. Personal characteristics such as risk aversion and maximization tendencies are considered boundary conditions for these effects.

Age and race/ethnicity differences in decisional conflict in women diagnosed with ductal carcinoma in situ

PurposeWomen diagnosed with ductal carcinoma in situ (DCIS) face confusion and uncertainty about treatment options. The objective of this study was to determine whether there are differences in decisional conflict about treatment by age and race/ethnicity.MethodsA cross-sectional survey was conducted of women (age ≥ 18) diagnosed with DCIS enrolled at Kaiser Permanente of Southern California. The Decisional Conflict Scale (DCS) measured personal perceptions of decision uncertainty, values clarity, and effective decision-making. We used a multivariable regression to study whether age, race, and ethnicity were associated with patient-reported DCS.Results45% (N = 1395) of women who received the online survey, participated. The mean age was 56 (± 9.6) years, the majority were white. Compared to women younger than 50, women aged 60–69 reported lower overall DCS scores (-5.4; 95% CI -1.5 to -9.3). Women > 70 had lower values clarity scores (-9.0; 95% CI -2.8 to -15.2) about their treatment compared to women aged 50–59 and 60–69 (-7.1; 95% CI -2.9 to -11.3 and − 7.2; 95% CI -2.9 to -11.5) and likewise, lower effective decision-making scores (-5.4; 95% CI -1.7 to -9.2 and − 5.2; 95% CI -1.4 to -9.0) compared to women < 50. Compared to whites, blacks reported lower decision conflict (-4.4; 95% CI 0.04 to -8.8) and lower informed decision (-5.2; 95% CI -0.18 to -10.3) about DCIS treatment.ConclusionYounger women reported higher decisional conflict about DCIS treatment, compared to older women (> 70). Age based tailored discussions about treatment options, health education, and supportive decision-making interventions/tools may reduce decision conflict in future DCIS patients.Trade registrationThe IRB number is 10678.