High-quality Decisions Research Articles

Complex Decision-Making in Paediatric Intensive Care: A Discussion Paper and Suggested Model.

Paediatric Intensive Care Units (PICU) are complex interdisciplinary environments where challenging, high stakes decisions arefrequently encountered. We assert that appropriate decisions are more likely to be made if the decision-making process is comprehensive, reasoned, and grounded in thoughtful deliberation. Strategies to overcome barriers to high quality decision-making including, cognitive and implicit bias, group think, inadequate information gathering, and poor quality deliberation should be incorporated. Several general frameworks for decision-making exist, but specific guidance is scarce. In this paper, we provide specific guidance on collaborative complex decision-making for PICUs. The proposed approach is on principles of procedural justice and pragmatic hermeneutics. The process encompasses set-up/planning, information gathering, question formulation, analysis (perspectives, values, and principles), action plan development, decision documentation, and a review and appeal mechanism.The process can be adapted to suit other clinical contexts. Research evaluating the process, exploring how best to develop education for clinicians, and how to build a culture that values high quality deliberation, is worthwhile.

The interplay of humor and creativity: shaping rationality and decision-making quality

AbstractThis study explores the relationship between humor, creativity, rationality, intuitiveness, and decision-making quality, all of which are vital in organizational management and success. Despite the importance attached to decision-making, prior research has not fully addressed how humor and creativity interact with rationality. Drawing from psychology, cognitive science, and management studies, we investigate how humor and creativity jointly influence cognitive reflection and decision quality. Using a sample of 998 participants and employing a mediating-moderation PROCESS model, our findings indicate that rational individuals tend to make higher-quality decisions, with affiliative humor mediating this relationship. Furthermore, we observed that employees’ creativity levels moderated this relationship, especially among rational employees with high creativity, who made higher-quality decisions. These findings highlight the interconnectedness of humor, creativity, and rationality in decision-making processes, offering practical implications for organizational behavior and management practices. Understanding this interplay can provide a nuanced approach to enhancing decision-making processes in management, emphasizing the value of humor in organizational contexts.

The American Cancer Society National Lung Cancer Roundtable strategic plan: Current challenges and future directions for shared decision making for lung cancer screening.

Shared decision making (SDM) between health care professionals and patients is essential to help patients make well informed choices about lung cancer screening (LCS). Patients who participate in SDM have greater LCS knowledge, reduced decisional conflict, and improved adherence to annual screening compared with patients who do not participate in SDM. SDM tools are acceptable to patients and clinicians. The importance of SDM in LCS is emphasized in recommendations from professional organizations and highlighted as a priority in the 2022 President's Cancer Panel Report. The updated 2022 national coverage determination from the Centers for Medicare & Medicaid Services reaffirms the value of SDM in offering LCS to eligible beneficiaries. The Shared Decision-Making Task Group of the American Cancer Society National Lung Cancer Roundtable undertook a group consensus process to identify priorities for research and implementation related to SDM for LCS and then evaluated current knowledge in these areas. Priority areas included: (1) developing feasible, adaptable SDM training programs for health care professionals; (2) understanding the impact of alternative health system LCS models on SDM practice and outcomes; (3) developing and evaluating new patient decision aids for use with diverse populations and in varied settings; (4) offering conceptual clarity about what constitutes a high-quality decision and developing appropriate quality measures; and (5) studying the use of prediction-augmented screening to support SDM in practice. Gaps in current research in all areas were observed. The authors conclude with a research and implementation agenda to advance the quality and implementation of SDM for persons who might benefit from LCS.

Vector Storage Based Long-term Memory Research on LLM

Abstract Current large language model (LLM) intelligences face the challenges of high inference cost and low decision quality when dealing with complex tasks, and are especially deficient in maintaining context coherence during long tasks. This research presents an innovative vector storage long-term memory mechanism model (VIMBank) to enhance the long-term context retention ability and task execution efficiency of LLM intelligences by storing and retrieving historical interaction data through a vector database. VIMBank utilizes a dynamic memory updating strategy and the Ebbinghaus forgetting curve theory to efficiently manage the memory of intelligences and reinforce critical information, forgetting unimportant data, and optimizing storage and reasoning costs. The experimental results show that VIMBank significantly improves the decision quality and efficiency of LLM intelligences in multi-tasking scenarios and reduces the computational cost. Compared with different agents, the success rate of task decision is increased by 10% to 20%, and the reasoning cost is reduced by about 23%, which provides an important theoretical basis and practical support for the future development of intelligences with long term memory and adaptive learning ability.

Addressing Barriers to Autopsy and Genetic Testing in Stillbirth Workup

The cause of death in many stillbirths remains unexplained, in part because of systematic barriers to complete workup. We review the importance of placental pathology, fetal autopsy, and genetic testing in stillbirth workup. Placental pathology is useful in 65% of stillbirths, and fetal autopsy is useful in 42%. Currently, only 21% of stillbirths in the United States undergo autopsy. Barriers to complete stillbirth workup include cost of autopsy and genetic testing, availability of perinatal pathology expertise, health care professional knowledge about workup, and availability of skilled counseling to support bereaved parents in making decisions about autopsy and genetic workup. We propose solutions to overcome these barriers, including policy changes to expand access and a framework for high-quality patient counseling and decision support.

Machine Advisors: Integrating Large Language Models Into Democratic Assemblies

ABSTRACT Could the employment of large language models (LLMs) in place of human advisors improve the problem-solving ability of democratic assemblies? LLMs represent the most significant recent incarnation of artificial intelligence and could change the future of democratic governance. This paper assesses their potential to serve as expert advisors to democratic representatives. While LLMs promise enhanced expertise availability and accessibility, they also present specific challenges. These include hallucinations, misalignment and value imposition. After weighing LLMs’ benefits and drawbacks against human advisors, I argue that time-tested democratic procedures, such as deliberation and aggregation by voting, provide safeguards that are effective against human and machine advisor shortcomings alike. Additional protective measures may include custom training for advisor LLMs or boosting representatives’ competencies in query formulation. Implementation of adversarial proceedings in which LLM advisors would debate each other and provide dissenting opinions is likely to yield further epistemic benefits. Overall, promising interventions that would mitigate the LLM risks appear feasible. Machine advisors could thus empower human decision-makers to make more autonomous, higher-quality decisions. On this basis, I defend the hypothesis that LLMs’ careful integration into policymaking could augment democracy’s ability to address today’s complex social problems.

Power and interests in environmental policy processes: the Svalbard case

ABSTRACT Climate and environmental changes pose tremendous challenges to ecosystems worldwide. While this underscores the need for more significant conservation efforts, these measures often clash with human activities. Participation and transparency may be key to reducing conflict and ensuring high-quality decisions, but facilitating such processes has proven difficult with many interests and stakeholders involved. This often leads to disputes over environmental policies but may also reflect a more profound debate about the definition of wilderness and who it is for. This article, therefore, explores what interests prevail and why in environmental policy processes and how this relates to perceptions of wilderness. Drawing on Lukes’ (2021) three-dimensional view of power and the case of the political process of new environmental regulations in the Arctic Archipelago of Svalbard, Norway, the author examines how the power to frame the public discourse about wilderness influences the issues and actors included in the discussion (see also McConnell and Hart 2019, 647). The insights provided are relevant not only to local stakeholders on Svalbard navigating the policy process but also to practitioners and academics elsewhere working in environmental governance, protected areas, and environmental policy and legislation.

Discordant Care and Decision Quality: Patients' Reasons for Not Receiving Their Initial Test of Choice in Colorectal Cancer Screening.

Concordance between a person's values and the test or treatment they ultimately receive is widely considered to be an essential outcome for good decision quality. There is little research, however, on why patients receive "discordant" care. A large, randomized trial of decision aids for colorectal cancer (CRC) screening provided an opportunity to assess why some patients received a different test than the one they preferred at an earlier time point. Of 688 patients who participated in the trial, 43 received a different CRC screening test than the one they selected after viewing a decision aid 6 mo prior. These patients answered 2 brief, open-ended questions about the reasons for this discordance. The research team analyzed their answers using qualitative description. Patient responses reflected 6 major categories: barriers or risks of initially favored test, benefits of alternative test, costs or health insurance coverage, discussion with family or friends, provider factors or recommendation, and health issues. Some of the patients' explanations fit well with the informed concordance approach, which infers poor decision quality from the existence of discordant care, since in these cases it appears that the patient's values and preferences were not adequately respected. Other statements suggest that the patient had an informed rationale for changing their mind about which test to undergo. These cases may reflect high-quality decision making, despite the existence of discordance as measured in the trial. This analysis highlights a major challenge to a popular approach for assessing decision quality, the difficulty of normatively assessing the quality of decision making when apparent discordant care has been provided, and the need to assess patient values and preference over time. Value-choice concordance is an accepted measure for assessing decision quality in decision aid trials, but greater exploration of apparently discordant care challenges key assumptions of this method; this study provides evidence that discordance as typically measured may not always reflect low-quality patient decision making.Researchers evaluating decision aids and assessing decision quality should consider the use of qualitative methods to supplement measures of decision quality and consider assessing patient preferences at multiple time points.

A generalized TODIM evaluation approach based on the novel score function and trust network under interval-valued hesitant fuzzy environment

In multi-attribute group decision-making (MAGDM), the social relationships among group members, professional backgrounds and psychological behaviors of decision makers are important factors for achieving high-quality group decisions. However, realizing the consideration of these factors in fuzzy and complex group decision-making scenarios is an important and urgent issue. In MAGDM problems, interval-valued hesitant fuzzy sets (IVHFSs) can better show the hesitant and fuzzy uncertainty information because they are an extension of fuzzy sets. This paper focuses on the score function of IVHFSs and develops a generalized TODIM evaluation method utilizing the novel score functions of IVHFEs and trust networks. The developed method effectively realizes the application of these factors in fuzzy and complex group decision making scenarios. In this contribution, a new deviation function for IVHFSs is created in this paper. It takes into account two types of deviation that are common in IVHFSs: the ambiguity of interval width and the hesitation and uncertainty of multiple intervals. In the sequel, a novel score function for IVHFSs is developed with help of the novel deviation function and the traditional score function. Concerning the perspective of group consistency, a model for determining expert weights is constructed using quality parameters, which combine the novel score function with the trust network. A subjective and objective comprehensive weighting model is made to get attribute weights based on how certain decision information is and the experts’ subjective experience. This is done because the more uncertain something is, the higher the risk. Eventually, we develop a generalized TODIM technique for MAGDM based on the novel score function and trust network in IVHFS environment. The developed approach is demonstrated with a preventive engineering decision-making problem. Sensitivity and comparative analyses are implemented to demonstrate the superiority, stability, and validity of the proposed method in this paper. According to the findings of this study, we conclude that the proposed methodology offers a comprehensive and systematic approach to evaluate MAGDM in the context of IVHFS with trust networks.

Методы и информационная система автоматизированного подбора лица, принимающего решения

The effectiveness of management activities is largely determined by the degree of reasonableness and efficiency of selecting a decision-maker and delegating the necessary powers to him. To solve this problem, it is advisable to use mathematical methods and information technologies that ensure the specified selection in an automated mode. This approach involves the creation of a DSS using unpopulated decision-making technologies. This allows you to identify employees or groups of employees who are most suitable for the implementation of managerial functions in terms of certain quality indicators. The use of unpopulated decision-making technologies in the interests of economic entities makes it possible to reduce the time and cost of their adoption by automating the transit management level. The purpose of the study considered in this article is to solve the problem of organizing the effective selection of a decision maker (DPR), or a group of people, as well as transferring the task to be performed automatically. To achieve this goal, the mathematical modeling of the LPR selection algorithm (LPR group) was carried out by performing operations on sets containing significant indicators and solving a mathematical programming problem. The proposed mathematical apparatus makes it possible to create a subsystem of the DSS responsible for the selection of a DSS (group of DSS) capable of making a high-quality management decision under given constraints, and transferring tasks to it for execution. The specified restrictions contain the type of task to be solved, the possibility of its implementation with the help of unpopulated decision-making technologies, competence limitations of LPR (groups of LPR), time constraints. The DSS subsystem, created on the basis of the proposed mathematical apparatus containing the task of optimizing the selection of LPR (LPR group) according to the cost criterion, makes it possible to minimize the cost of decision-making.

Effect of Private Deliberation: Deception of Large Language Models in Game Play.

Integrating large language model (LLM) agents within game theory demonstrates their ability to replicate human-like behaviors through strategic decision making. In this paper, we introduce an augmented LLM agent, called the private agent, which engages in private deliberation and employs deception in repeated games. Utilizing the partially observable stochastic game (POSG) framework and incorporating in-context learning (ICL) and chain-of-thought (CoT) prompting, we investigated the private agent's proficiency in both competitive and cooperative scenarios. Our empirical analysis demonstrated that the private agent consistently achieved higher long-term payoffs than its baseline counterpart and performed similarly or better in various game settings. However, we also found inherent deficiencies of LLMs in certain algorithmic capabilities crucial for high-quality decision making in games. These findings highlight the potential for enhancing LLM agents' performance in multi-player games using information-theoretic approaches of deception and communication with complex environments.

Construction and validation of clinical prediction model of acupuncture and moxibustion for Bell's palsy

To establish and validate a clinical prediction model of acupuncture and moxibustion for Bell's palsy so as to provide a tool for predicting the effect of acupuncture and moxibustion on Bell's palsy. A total of 269 patients with Bell's palsy were collected from department of acupuncture, moxibustion and tuina, Shengli Oilfield Central Hospital, neurology department, Shenxian County Central Hospital and department of rehabilitation medicine, Dongying Municipal Hospital of TCM from June 2018 to June 2023. All of these cases were treated with acupuncture and moxibustion. Of them, 182 cases, from department of acupuncture, moxibustion and tuina, Shengli Oilfield Central Hospital and neurology department, Shenxian County Central Hospital, were randomized into a training group (128 cases) and an internal validation group (54 cases); 87 cases from department of rehabilitation medicine, Dongying Municipal Hospital of TCM were assigned to an external validation group. The clinical data of all of the cases were extracted from the electronic medical record information platform. Using SPSS25.0 and R4.2.3, through univariate and multivariate Logistic regression analysis, the independent factors influencing the effects of acupuncture and moxibustion on Bell's palsy were identified. By means of internal and external validations, the receiver operating characteristic curve (ROC), the goodness-of-fit curve (GFC) and the decision curve analysis (DCA) were plotted. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the model were calculated; and its comprehensive performance was evaluated. The results of the multivariate Logistic regression analysis showed that the independent factors for the unsatisfactory effect on Bell's palsy were advanced age, severe symptoms before treatment, no use of steroids within 72 h of onset, and lack of acupuncture-moxibustion therapy during the acute phase or single acupuncture-moxibustion protocol (P<0.05, P<0.01). Based on these factors, nomogram model and online columnar plot prediction tool (https://bmuchen.shinyapps.io/dynnomapp/) were established. The area under the ROC curve of the model was 0.921 (95% CI: 0.877, 0.966), 0.876 (95% CI: 0.787, 0.966), and 0.846 (95% CI: 0.766, 0.926) in the training group, the internal validation group, and the external validation group, respectively, indicating good predictive value. The model showed a satisfactory calibration curve alignment. The decision threshold in the range of 0 to 0.8 provided clinical benefits for participants. The model exhibited the sensitivity from 65.9% to 88.0%, the specificity ranging from 77.3% to 90.7%, the accuracy from 77.8% to 85.9%, the positive predictive value from 83.3% to 90.1%, and the negative predictive value from 70.8% to 78.7%. The comprehensive evaluation indicated a satisfactory clinical application value of the model. The clinical prediction model of acupuncture and moxibustion for Bell's palsy is valuable in its practice and promotion to a certain extent. The predicted results are conductive to clinicians' judgement of the effect of acupuncture and moxibustion for this disease and making effective and high-quality clinical decisions, as well as formulating the optimal therapeutic regimen.

Empirical analysis of decision recommendation models for various processes from a pragmatic perspective

Decision recommendation models allow researchers and process designers to identify and implement high-efficiency processes in ambiguous situations. These models perform multipara metric analysis on the given process sets to recommend high-quality decisions that assist in improving process-based efficiency levels. A wide variety of models have been proposed by researchers for the implementation of such recommenders, and each of them varies in terms of their functional nuances, applicative advantages, internal operating characteristics, contextual limitations, and deployment-specific future scopes. Thus, it is difficult for researchers and process designers to identify optimal models for functionality-specific use. Therefore, they tend to validate multiple process models, which increase deployment time, cost and complexity levels. To overcome this ambiguity, a detailed survey of different decision process recommendation models is discussed in this text. Fuzzy logic, analytical hierarchical processing (AHP), the technique for order performance by similarity to ideal solution (TOPSIS), and their variants are highly useful for the recommendation of efficient decisions. Based on this survey, readers will be able to identify recently proposed decision recommendation models and functionality-specific models for their deployments. To further assist in the model selection process, this paper compares the reviewed models in terms of their computational complexity, recommendation efficiency, delay needed for recommendation, scalability and contextual accuracy. Based on this comparison, readers will be able to identify performance-specific models for their deployments. This paper also evaluates a novel decision recommendation rank metric (DRRM), which combines these parameters, to identify models that can optimally perform w.r.t. multiple process metrics. Referring to this parameter comparison, readers will be able to identify optimal recommendation models for enhancing the performance of their decision recommendations under real-time scenarios.

Performance Optimization Across the Edge-Cloud Continuum: A Multi-agent Rollout Approach for Cloud-Native Application Workload Placement

The advancements in virtualization technologies and distributed computing infrastructures have sparked the development of cloud-native applications. This is grounded in the breakdown of a monolithic application into smaller, loosely connected components, often referred to as microservices, enabling enhancements in the application’s performance, flexibility, and resilience, along with better resource utilization. When optimizing the performance of cloud-native applications, specific demands arise in terms of application latency and communication delays between microservices that are not taken into consideration by generic orchestration algorithms. In this work, we propose mechanisms for automating the allocation of computing resources to optimize the service delivery of cloud-native applications over the edge-cloud continuum. We initially introduce the problem’s Mixed Integer Linear Programming (MILP) formulation. Given the potentially overwhelming execution time for real-sized problems, we propose a greedy algorithm, which allocates resources sequentially in a best-fit manner. To further improve the performance, we introduce a multi-agent rollout mechanism that evaluates the immediate effect of decisions but also leverages the underlying greedy heuristic to simulate the decisions anticipated from other agents, encapsulating this in a Reinforcement Learning framework. This approach allows us to effectively manage the performance–execution time trade-off and enhance performance by controlling the exploration of the Rollout mechanism. This flexibility ensures that the system remains adaptive to varied scenarios, making the most of the available computational resources while still ensuring high-quality decisions.

Criteria, Timing, and Factors Associated With Return to Competitive Sport After Rotator Cuff Surgery.

Rotator cuff pathology in competitive athletes is common and may produce chronic symptoms and joint disability, impairing sports participation and leading to premature retirement. Athletes are a high-functioning patient population with unique characteristics. Decision-making for return to sport is a complex and multifactorial process. Literature is sparse and does not provide precise guidelines to assist physicians to make the right decision. This review aimed to highlight factors affecting outcome, timing, and criteria for return to competitive sport after rotator cuff surgery to help physicians to clearly counsel athletes and make high-quality decisions for return to sport.

Flexible Job Shop Scheduling via Dual Attention Network-Based Reinforcement Learning.

Flexible manufacturing has given rise to complex scheduling problems such as the flexible job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple machines, leading to intricate relationships between operations and machines. Recent works have employed deep reinforcement learning (DRL) to learn priority dispatching rules (PDRs) for solving FJSP. However, the quality of solutions still has room for improvement relative to that by the exact methods such as OR-Tools. To address this issue, this article presents a novel end-to-end learning framework that weds the merits of self-attention models for deep feature extraction and DRL for scalable decision-making. The complex relationships between operations and machines are represented precisely and concisely, for which a dual-attention network (DAN) comprising several interconnected operation message attention blocks and machine message attention blocks is proposed. The DAN exploits the complicated relationships to construct production-adaptive operation and machine features to support high-quality decision-making. Experimental results using synthetic data as well as public benchmarks corroborate that the proposed approach outperforms both traditional PDRs and the state-of-the-art DRL method. Moreover, it achieves results comparable to exact methods in certain cases and demonstrates favorable generalization ability to large-scale and real-world unseen FJSP tasks.

Fair-CMNB: Advancing Fairness-Aware Stream Learning with Naïve Bayes and Multi-Objective Optimization

Fairness-aware mining of data streams is a challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans in critical decision-making processes, e.g., hiring staff, assessing credit risk, etc. This calls for handling massive amounts of incoming information with minimal response delay while ensuring fair and high-quality decisions. Although deep learning has achieved success in various domains, its computational complexity may hinder real-time processing, making traditional algorithms more suitable. In this context, we propose a novel adaptation of Naïve Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance through multi-objective optimization (MOO). Class imbalance is an inherent problem in discrimination-aware learning paradigms. To deal with class imbalance, we propose a dynamic instance weighting module that gives more importance to new instances and less importance to obsolete instances based on their membership in a minority or majority class. We have conducted experiments on a range of streaming and static datasets and concluded that our proposed methodology outperforms existing state-of-the-art (SoTA) fairness-aware methods in terms of both discrimination score and balanced accuracy.

The stochastic share-a-ride problem with electric vehicles and customer priorities

We introduce a stochastic share-a-ride problem in which a fleet of electric vehicles (EV) in a ride-hailing system are dynamically dispatched to serve passenger and parcel orders in a shared manner. We assume uncertain demands of both passenger and parcel orders and consider that passenger orders have priority over parcel orders. Passengers must be transported directly from their origins to destinations, while parcels can share a vehicle with other orders. The operator of the ride-hailing platform needs to decide whether to accept a newly arrived service request, how to assign orders to vehicles, and how to route and charge the EVs. To develop dynamic policies for the problem, we formulate the problem as a Markov decision process (MDP) and propose a reinforcement learning (RL) approach to solve the problem. We develop action-space restriction and state-space aggregation schemes to facilitate the implementation of the RL algorithm. We also present two rolling horizon heuristic methods to develop dynamic policies for our problem. We conduct computational experiments based on real-world taxi data from New York City. The computational results show that our RL policies perform better than the three benchmark policies in terms of serving more orders and collecting more rewards. Our RL policies are able to make high-quality decisions more efficiently when compared with the rolling horizon policies.

Knowledge Management: High-Quality Production Decisions in Partial Volume Flexibility to Create Value for Customers

Uncertainty in the demand of individual customers increases demand volatility in the entire market. In this context, partial volume flexibility can help hedge against demand uncertainty and competition pressure, and subsequently help create value by means of high-quality production decisions and market responsiveness, within the assumption of transformation and applied knowledge incorporated into the production stages. The article aims to investigate this phenomenon. To achieve this, as part of the methodology, a unified measure, the flexibility degree, is taken to represent the level of partial volume flexibility. We find that under demand uncertainty, a firm goes through a process with four stages&#x2014;capacity decision, stable production, flexible production, and pricing. In a monopoly model, we find that a slight change in the flexibility degree could significantly increase a firm&#x0027;s adjustable production range to respond to demand changes. Furthermore, we develop a general asymmetric duopoly model. We characterize the equilibrium and find that a firm with a higher flexibility degree is more sensitive to demand changes. Accordingly, in response to specific demand, a higher flexibility firm obtains more profit only when the demand is sufficiently high or low, otherwise, the lower flexibility firm wins. Finally, we observe that a firm&#x0027;s capacity increases in its flexibility degree and decreases in its rival&#x0027;s flexibility degree; this is because the firm balances the market share and product profitability.

Звітність суб’єктів господарювання як джерело аналізу та інструмент управління

The article summarizes the results of the study of the modern specifics of reporting of an economic entity and its types in the context of consideration as a source of analysis and management needs. An analysis of modern types of reporting from the standpoint of their essence, purpose, brief overview of indicators and interconnectedness of data on the activities of an economic entity is carried out. Attention is focused on transformational changes in economic emphasis and the development of sustainability reporting and taking into account strategic prospects. This made it possible to substantiate the conclusion about the growing role of reporting in increasing the level of development and competitiveness of enterprise. It is argued that a comprehensive analysis of reporting provides a wider range of indicators for managerial decision-making, which allows making more reasonable and high-quality managerial decisions on activities and determining directions for improving the processes taking place in the enterprise. The principles of formation and the principles of selection of types of reporting of economic entities necessary for management, in accordance with the objectives and requests of the management system in modern conditions, are determined. A structural-logical scheme of the relationship between types of reporting and the managerial process has been proposed, which reflects the step-by-step analytical component, as well as the corresponding steering algorithms that are adequate to different levels and types of management. It is proved that the use of information data of all types of reporting will allow to effectively analyze and evaluate, as well as determine the directions of development of an economic entity, taking into account the existing modern challenges. The place of types of reporting in the managerial process of enterprise is determined, which shows their importance in determining new vectors of development of economic entities in an unstable and competitive environment. In the perspective, the relevant direction of research is the development and substantiation of new methodological approaches to the analysis of the reporting of economic entities, argumentation of the composition of analytical indicators and solving the problem of identifying tasks and algorithms for their calculations.