Single Decision Maker Research Articles

Who Makes Decisions in Hospital? A Scoping Review and Meta-Synthesis

Context: The aim of this study was to identify decision-making issues and key decision-makers within the hospital. Methods: This scoping review was conducted by searching the databases of Web of Science, Scopus, and PubMed in January 2024. Additionally, articles related to Newhouse's main article were searched on Google Scholar to identify decision-makers in the hospital and their areas of decision-making. The 37 articles were analyzed using the framework analysis technique with Atlas-ti software. Results: Decision-makers addressed 160 issues, which were identified and categorized into 7 areas: Strategic management, human resource management, financial management, physical resource management, employee compensation payment, service delivery management, and hospital reputation. Additionally, 21 decision-makers were identified and classified into 6 categories: Group of managers, group of physicians, group of nurses, board of directors, government, and political groups. Hospital managers were identified as the primary decision-makers, responsible for making decisions independently in three areas and jointly with the board of directors in one area. Conclusions: It can be concluded that no single decision-maker holds a dominant role in the decision-making process. Several decision-makers exert varying degrees of influence depending on the issue. Among these decision-makers, managers play a more significant role in decision-making than others.

Hybrid use of Borda count and PROMETHEE method for maintenance strategy selection problem

Abstract For long-term success, organizations and manufacturing companies must exploit the potential strengths of collective decision making in maintenance management. The maintenance strategy selection issue has been studied in a single decision-maker framework for a long time. This research is one of the first attempts at dealing with the enhancement of maintenance management through the participation of stakeholders in the decision making process. In this context, the author introduces a participatory multi criteria decision model that combines Borda count and PROMETHEE methodology to select the most appropriate maintenance strategy; in accordance with the decision makers’ preferences on a set of strategies evaluated according to conflicting criteria. Therefore, the PROMETHEE II method is used to manage the individual decisions of each stakeholder, while the Borda count is in charge of collectively selecting the best maintenance strategy, taking as a starting point stakeholder’s preferences being established thanks to PROMETHEE II. In the same context, the proposed model was applied to a real scenario: a textile company, and can be easily replicated in other industries.

Creating a sustainable closed-loop supply chain: An incentive-based contract with third-party E-waste collector

This paper proposes a closed-loop supply chain model to study the impact of product returns and awareness program on e-waste supply chain. The study focuses on the sustainability aspects of e-waste management and the effects such initiatives bring about to the environment at large. Reusing and recycling of collected products in manufacturing will improve the supply of scarce resources and reduce reliance on virgin raw materials. Engaging in CLSC can enhance brand reputation and competitiveness through sustainability while reducing the health risks to informal recycling workers by formalizing practices. The proposed model consists of a manufacturer, retailer, and third-party collector (TPC) under wholesale price and incentive contracts with deterministic demand. From the analysis it is observed that integrating a third-party collector with higher recycling rate boosts the total profit of the decentralized setting with a TPC. The centralized model, with a single decision maker, results in lower retail prices and higher collection rates compared to the decentralized models thereby commanding a higher overall profit.

Multi-Objective Reinforcement Learning Application in the View of Agent AI Safety

Reinforcement learning is frequently used to make single decisions using a single decision-based algorithm, where a single objective function is solved, for example, in an agent playing chess, the movement of any player is observed from previous action by considering whether it has received positive or negative reward based on this it will proceed. Nowadays, the requirement of making sequential decisions is complex for this reason. Learning involves using two or more objective functions to address a challenge in multiobjective reinforcement. As an illustration, consider a client purchasing a car and maximising conveniences like minimising fuel use, keeping costs low, and fitting additional luxury items in the automobile. Multi-objective reinforcement learning is applicable in decision-making sequential problem-solving applications. Still, at the same time, we need to avoid the risk that Artificial intelligence agents will be having when problem-solving using MOO-based algorithms. In this paper, we discuss how we use Multi-Objective Optimization (MOO)- based algorithm to low-impact agents for artificial intelligence (AI) safety.

Modelling individual plants’ growth: competition of Viola arvensis and wheat

IntroductionCompetition by weeds is a severe threat to agricultural crops. While these days the broadcast of herbicides over the entire field is common praxis, new technologies promise to reduce chemical output by reducing the area sprayed. The maximum precision would be a single plant treatment. This precision will allow a single plant management, which requires single plant management decisions, which is far beyond the possibilities of current praxis. A plant specific management decision can only be made on the basis of a model simulation.Materials and methodsA simulation model was developed to evaluate the effect of spatially explicit weed management covering interaction between single plants. The governing equations consist of coupled nonlinear differential equations for growth and competition of crop and weed plants in a spatial setting i.e. a coordinate is assigned to each plant. The mutual interaction is determined by the parameters strength and range of competition. Furthermore, an experiment was carried out parallel to the development of the model involving wheat and Viola arvensis (Murr.), in which coordinates and growth curves for a large number of plants (~600) were recorded allowing for a reasonable parameterization of the model.Results and discussionThe model is able to evaluate spatially explicit management measures such as weed strip control based on the height growth of single plants. The model is capable of evaluating a variety of control measures such as the frequency and spatial allocation of treatments. In particular, the effect of the width of a treatment zone around the rows of the crop was simulated.ConclusionIn future, the developed model could be extended to a decision support system for single plant weed management. Making decisions plant-by-plant, allows to orchestrate the weed management in a way that takes into account competing goals in plant protection: yield and biodiversity.

The impact of digital household budgets on online purchase decision‐making processes

AbstractRecent literature found positive effects of digital household budget feedback when used over longer timespans. This current study, in turn, investigates whether digital budget feedback also influences consumer behavior in single online purchase decisions. The related research question states: Do transparent technology‐mediated household budgets influence consumers' willingness to pay and payment pain in single, individual online purchase decision processes? The underlying study used a quantitative‐, mixed‐design approach and simulated online purchases for one hedonic and one utilitarian product. Three hundred fourteen study subjects were assigned to either a control group or one of three treatment groups manipulated with specific budget statuses to examine differences in consumer reactions. Critical measures of willingness to pay and payment pain have been taken to analyze potential significant effects. Results found significant differences in consumer willingness to pay and payment pain between and within groups concerning both products. The study could, however, not confirm differences to occur between utilitarian and hedonic purchases. The findings extend existing literature by demonstrating that consumers adjust their online spending based on individual household budget feedback. Consumers that wish to align spending with their current budget status might thus rely on app‐mediated budget updates. At the same time, marketers could offer individualized pricing offers to consumers based on situational budget status information.

Refined tastes, coarse tastes: Solving the stratification-of-goods enigma

The “Stratification-of-Goods” expresses social ranking where the lower status group consumes almost exclusively coarse goods such as Rambo films while the upper status group consumes almost exclusively refined goods such as Shakespearean plays. The Stratification-of-Goods is an enigma for the social welfare function (SWF)—which also applies at the level of the individual utility function. It is an enigma because it makes SWF and individual utility function ill-defined: there is no single metric that allows us to compare the utility functions across groups, as well as the tastes across a single decision maker (DM), insofar as they are segregated by the refinement of taste. This paper proposes a model that promises to solve the Stratification-of-Goods Enigma. The model, consistent with rational choice theory, starts with DMs who have identical tastes but differ with respect to income level. If income inequality is non-trivial, DMs invest differently in what this paper calls “sophistication capital”—the education needed to appreciate refined goods. The difference in investment in sophistication capital sets in motion dynamics that generates hard-to-reverse status stratification. In this fashion, this paper offers a solid endogenous account that solves the Stratification-of-Goods Enigma.

Preference disaggregation analysis for sorting problems in the context of group decision-making with uncertain and inconsistent preferences

Preference disaggregation analysis is effective for inferring a preference model of decision makers from decision examples. Although multiple criteria sorting problems are often handled within the context of group decision-making, it has not been well studied how to estimate a collective sorting model for a group with uncertain and inconsistent preferences through preference disaggregation. To this end, this paper proposes a threshold-based value-driven preference disaggregation method to solve multiple criteria sorting problems involving multiple decision-makers. We allow decision-makers to reduce the cognitive effort of preference expressions by providing possible classes of reference alternatives or pairwise comparisons between them through uncertain expressions. In addition, we develop a two-stage inconsistency identification and elimination procedure that includes dealing with contradictory preference information provided by a single decision maker and managing disagreement among a group of decision makers. A collective preference model consisting of an additive value function and value thresholds is estimated by linear programming. The proposed method is demonstrated by a practical example of financial investment decisions for government-funded projects. The results show that dealing with uncertain and inconsistent preferences can improve the accuracy of preference disaggregation.

From local to global estimations of confidence in perceptual decisions.

Perceptual confidence has been an important topic recently. However, one key limitation in current approaches is that most studies have focused on confidence judgments made for single decisions. In three experiments, we investigate how these local confidence judgments relate and contribute to global confidence judgments, by which observers summarize their performance over a series of perceptual decisions. We report two main results. First, we find that participants exhibit more overconfidence in their local than in their global judgments of performance, an observation mirroring the aggregation effect in knowledge-based decisions. We further show that this effect is specific to confidence judgments and does not reflect a calculation bias. Second, we document a novel effect by which participants' global confidence is larger for sets which are more heterogeneous in terms of difficulty, even when actual performance is controlled for. Surprisingly, we find that this effect of variability also occurs at the level of local confidence judgments, in a manner that fully explains the effect at the global level. Overall, our results indicate that global confidence is based on local confidence, although these two processes can be partially dissociated. We discuss possible theoretical accounts to relate and empirical investigations of how observers develop and use a global sense of perceptual confidence. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Will you stick to a high-quality collecting standard? order fulfillment vs. limited supply in diseconomy-cost-mattered remanufacturing operations

Consider a remanufacturing system consisting of a brand, a new component supplier (Supplier N), and a remanufactured component supplier (Supplier R). The brand priorly purchases remanufactured components and then assembles them into final products, while Supplier R collects the end-of-life products with random residual quality and then produces them to be “as good as new” components at a diseconomy cost based on the residual quality. Supplier R has the option to provide order fulfillment commitment before production (Scenario O), or, do not provide such a commitment, reserving the right to limit the proportion of the orders to save production costs although it may drive the brand to emergently purchase the new components (Scenario L). Specifically, in Scenario L, Supplier R can set a strict residual quality standard which saves the remanufacturing cost and constrains the brand’s order quantity. We reveal that constraining the brand’s order quantity will not be the optimal choice for Supplier R, because the overreliance on the limited-supply threat will distort Supplier R’s wholesale price decision, resulting in significant profit loss. However, the supply chain system can perform better with the quantity constraint, because it concentrates the channel power in the hands of a single decision maker, which enables the decisions made more closely to the first-best level.

Differences in metacognitive functioning between obsessive-compulsive disorder patients and highly compulsive individuals from the general population.

Our confidence, a form of metacognition, guides our behavior. Confidence abnormalities have been found in obsessive-compulsive disorder (OCD). A first notion based on clinical case-control studies suggests lower confidence in OCD patients compared to healthy controls. Contrarily, studies in highly compulsive individuals from general population samples showed that obsessive-compulsive symptoms related positively or not at all to confidence. A second notion suggests that an impairment in confidence estimation and usage is related to compulsive behavior, which is more often supported by studies in general population samples. These opposite findings call into question whether findings from highly compulsive individuals from the general population are generalizable to OCD patient populations. To test this, we investigated confidence at three hierarchical levels: local confidence in single decisions, global confidence in task performance and higher-order self-beliefs in 40 OCD patients (medication-free, no comorbid diagnoses), 40 controls, and 40 matched highly compulsive individuals from the general population (HComp). In line with the first notion we found that OCD patients exhibited relative underconfidence at all three hierarchical levels. In contrast, HComp individuals showed local and global overconfidence and worsened metacognitive sensitivity compared with OCD patients, in line with the second notion. Metacognitive functioning observed in a general highly compulsive population, often used as an analog for OCD, is distinct from that in a clinical OCD population, suggesting that OC symptoms in these two groups relate differently to (meta)cognitive processes. These findings call for caution in generalizing (meta)cognitive findings from general population to clinical samples.

Decision Making for Patients With Severe Dementia Versus Normal Cognition Near the End of Life.

The clinical progression of severe dementia frequently leads to situations where surrogate decision makers must quickly make choices about potentially burdensome treatments that offer limited clinical benefit. We examined whether the number of decision makers and their access to advance directives were related to treatment choice for patients with severe dementia in comparison to those with normal cognition. We retrospectively linked survey responses about end-of-life treatment decisions to Medicare claims for Health and Retirement Study respondents dying between 2002 and 2015 whose next-of-kin reported a need for surrogate decision making. We estimated multivariable logistic regression models to study measures of aggressive care in the last 6 months of life; in-hospital death, burdensome transfers, and burdensome treatments. Compared to patients who were cognitively normal near the end of life (n = 1 198), patients with severe dementia (n = 722) were less likely to experience burdensome treatments (18% [95% confidence interval {CI} 14-21] vs 32% [95% CI 29-35]), burdensome transfers (20% [95% CI 17-24] vs 30% [95% CI 27-33]), and in-hospital death (24% [95% CI 20-28] vs 30% [95% CI 26-33]) when surrogates were involved. Rates of burdensome treatments, transfers, or in-hospital death for decedents with severe dementia did not vary with single versus multiple decision makers or when decision makers were informed by advance directives. However, among decedents with normal cognition, a single decision maker informed by an advance directive was associated with the lowest rates of burdensome treatments and in-hospital death. Surrogate decision makers made similar choices around end-of-life care for patients with severe dementia regardless of the number of decision makers and availability of advance directives. However, both advance directives and single decision makers were associated with less aggressive care for cognitively normal decedents.

A group decision making method with preference analysis to re-build the Global Entrepreneurship Index.

This study proposes a group decision making (GDM) method with preference analysis to re-build the Global Entrepreneurship Index (GEI). Specifically, a single decision maker is firstly identified using a specified individual judgement about the importance order of three sub-indices of the GEI. A preliminary group decision matrix is constructed in terms of taking all possible individual judgments into account. Then the analysis of the preferential differences and preferential priorities with respect to the preliminary group decision matrix is conducted to obtain a revised group decision matrix, in which preferential differences calculate the weighted differences as the degrees of differences among different alternatives for each decision maker, preferential priorities describe the favorite ranking of alternatives for each decision maker. Finally, we employ the Stochastic Multicriteria Acceptability Analysis for group decision-making (SMAA-2) to create the holistic acceptability indices for measuring the entrepreneurship performance. In addition, a satisfaction index is developed to indicate the merits of proposed GDM method. A case study using the GEI-2019 of 19 G20 countries is carried out to validate our GDM method.

Geospatial dimensions of the renewable energy transition — The importance of prioritisation

The renewable energy transition is a priority for many researchers, policy makers, and political leaders because it is projected to stop the dependence of economic growth on increasing fossil fuel use and thus curtail climate change. This study examines how expert judgments affect development decisions to enable the renewable energy transition. Geospatial Multi-Criteria Decision Analyses (MCDA) are frequently used to select offshore wind energy (OWE) sites, however, they are often weak and/or often rely on limited judgement. The Analytical Hierarchy Process is used here with 25 diverse experts to assess the variability in priorities for OWE siting criteria. A geospatial MCDA is implemented using experts' individual priorities, aggregated weights and Monte Carlo simulations. Case study results reveal large variations in expert opinions and bias strongly affecting MCDAs weighted by single decision-makers. A group-decision approach is proposed to strengthen consent for OWE, underpinning the renewable energy transition.

Perceived responsibility for mechanical ventilation and weaning decisions in intensive care units in the Kingdom of Saudi Arabia.

Optimizing patient outcomes and reducing complications require constant monitoring and effective collaboration among critical care professionals. The aim of the present study was to describe the perceptions of physician directors, respiratory therapist managers and nurse managers regarding the key roles, responsibilities and clinical decision-making related to mechanical ventilation and weaning in adult Intensive Care Units (ICUs) in the Kingdom of Saudi Arabia (KSA). A multi-centre, cross-sectional self-administered survey was sent to physician directors, respiratory therapist managers and nurse managers of 39 adult ICUs at governmental tertiary referral hospitals in 13 administrative regions of the KSA. The participants were advised to discuss the survey with the frontline bedside staff to gather feedback from the physicians, respiratory therapists and nurses themselves on key mechanical ventilation and weaning decisions in their units. We performed T-test and non-parametric Mann-Whitney U tests to test the physicians, respiratory therapists, and nurses' autonomy and influence scores, collaborative or single decisions among the professionals. Moreover, logistic regressions were performed to examine organizational variables associated with collaborative decision-making. The response rate was 67% (14/21) from physician directors, 84% (22/26) from respiratory therapist managers and 37% (11/30) from nurse managers. Physician directors and respiratory therapist managers agreed to collaborate significantly in most of the key decisions with limited nurses' involvement (P<0.01). We also found that physician directors were perceived to have greater autonomy and influence in ventilation and waning decision-making with a mean of 8.29 (SD±1.49), and 8.50 (SD±1.40), respectively. The key decision-making was implemented mainly by physicians and respiratory therapists in collaboration. Nurses had limited involvement. Physician directors perceived higher autonomy and influence in ventilatory and weaning decision-making than respiratory therapist managers and nurse managers. A critical care unit's capacity to deliver effective and safe patient care may be improved by increasing nurses' participation and acknowledging the role of respiratory therapists in clinical decision-making regarding mechanical ventilation and weaning.

Visuospatial information foraging describes search behavior in learning latent environmental features

In the real world, making sequences of decisions to achieve goals often depends upon the ability to learn aspects of the environment that are not directly perceptible. Learning these so-called latent features requires seeking information about them. Prior efforts to study latent feature learning often used single decisions, used few features, and failed to distinguish between reward-seeking and information-seeking. To overcome this, we designed a task in which humans and monkeys made a series of choices to search for shapes hidden on a grid. On our task, the effects of reward and information outcomes from uncovering parts of shapes could be disentangled. Members of both species adeptly learned the shapes and preferred to select tiles expected to be informative earlier in trials than previously rewarding ones, searching a part of the grid until their outcomes dropped below the average information outcome—a pattern consistent with foraging behavior. In addition, how quickly humans learned the shapes was predicted by how well their choice sequences matched the foraging pattern, revealing an unexpected connection between foraging and learning. This adaptive search for information may underlie the ability in humans and monkeys to learn latent features to support goal-directed behavior in the long run.

Extension of the ELECTRE II method to group decision-making

Multi-criteria decision support has long been treated in a single-decision maker framework . It seems that a decision made by a single decision-maker does not reflect reality . There are multi-criteria group decision support methods in the literature . In order to find a collective aggregation method fulfilling good properties , we have in this work , used the median and the quadratic mean to make the Extension of ELECTRE II (EE-II) to the group decision . We have made numerical applications and we have obtained interesting results .

Machine learning-based classification of dual fluorescence signals reveals muscle stem cell fate transitions in response to regenerative niche factors

The proper regulation of muscle stem cell (MuSC) fate by cues from the niche is essential for regeneration of skeletal muscle. How pro-regenerative niche factors control the dynamics of MuSC fate decisions remains unknown due to limitations of population-level endpoint assays. To address this knowledge gap, we developed a dual fluorescence imaging time lapse (Dual-FLIT) microscopy approach that leverages machine learning classification strategies to track single cell fate decisions with high temporal resolution. Using two fluorescent reporters that read out maintenance of stemness and myogenic commitment, we constructed detailed lineage trees for individual MuSCs and their progeny, classifying each division event as symmetric self-renewing, asymmetric, or symmetric committed. Our analysis reveals that treatment with the lipid metabolite, prostaglandin E2 (PGE2), accelerates the rate of MuSC proliferation over time, while biasing division events toward symmetric self-renewal. In contrast, the IL6 family member, Oncostatin M (OSM), decreases the proliferation rate after the first generation, while blocking myogenic commitment. These insights into the dynamics of MuSC regulation by niche cues were uniquely enabled by our Dual-FLIT approach. We anticipate that similar binary live cell readouts derived from Dual-FLIT will markedly expand our understanding of how niche factors control tissue regeneration in real time.

A goal programming strategy for bi-level decentralised multi-objective linear programming problem with neutrosophic numbers

This paper develops a goal programming (GP) algorithm to evaluate bi-level decentralised multi-objective linear programming problem (BLDMOLPP) in neutrosophic number (NN) environment. In a BLDMOLPP, a single decision maker (DM) is present at the upper level and multiple decision makers at the lower level. Here the parameters of the problem are considered to be NNs in the form of [P+QI], where P and Q are real numbers and indeterminacy is represented through the symbol I. I is expressed in the form of a real interval as agreed upon by the DMs. The BLDMOLPP with NNs then gets converted into an interval BLDMOLPP. Using interval programming, the target intervals for the objective functions are identified and subsequently, the goal achievement functions are constructed. The upper level DM provides some possible relaxation on the decision variables under his/her control to cooperate with the lower level DMs to attain a compromise optimal solution. Thereafter, goal programming (GP) models are formulated by minimising the deviational variables and thereby obtaining the most satisfactory solution for all DMs. Finally, a numerical example demonstrates the feasibility and simplicity of the proposed strategy.

Polish FDI in Germany – junior partner with any signs of rising strong?

The economic ties between neighbours are always in the spotlight. Polish-German relations are no exception in this regard, especially given a particularly pronounced asymmetry in the level of mutual direct investment – the flows and stocks of foreign direct investments (FDI). This paper aims threefold: to analyse the recent development trends of Polish investment in Germany, to characterise the profile of investing firms and identify possible patterns of Polish FDI in Germany and finally, based on the diagnosed challenges, to formulate some policy and managerial recommendations. Methodologically it draws on a descriptive analysis of investment figures and a qualitative approach in line with the grounded theory method (GTM) framed in classic FDI theories and concepts. Confrontation of previously (2012-2014) conducted projects with the recent reports or scholarly studies confirmed challenges identified earlier and demonstrated that not much has changed in this respect. Poland when it comes to outward foreign direct investment (OFDI) is still ‘punching below its weight’. Such reflection could be interpreted ambiguously. On the one hand, Polish investments in Germany fluctuate considerably often subject to single one-time investment decisions; on the other, more investments target promising niche sectors such as IT or urban mining. The still unsatisfactory levels of Polish OFDI (development along the investment development path - IDP) can be hence explained ‘pessimistically’ in terms of missing structural competitiveness but also more on a positive note, as a result of the unabating potential and absorptive capacity of the domestic market.