Decision Strategies Research Articles

THEORETICAL EXPLORATION OF DATA-DRIVEN DECISION-MAKING IN DIGITAL MARKETING

In the rapidly evolving landscape of digital marketing, the utilization of data-driven decision-making strategies has become imperative for organizations to stay competitive. This study employs a theoretical exploration methodology to investigate the intricate dynamics of data-driven decision-making in digital marketing. Through a systematic review and analysis of existing theories, concepts, and frameworks, the research aims to construct a comprehensive understanding of the theoretical underpinnings guiding decision-making processes in the digital marketing domain. Key theoretical constructs and models relevant to data analysis techniques, consumer behavior, market trends, and marketing strategy formulation are critically evaluated to elucidate the conceptual foundations of data-driven decision-making. By synthesizing insights from multiple disciplines, including marketing, information technology, and decision sciences, the study develops an integrative theoretical framework that provides a nuanced understanding of the complexities inherent in contemporary digital marketing practices. The findings contribute to the advancement of theoretical knowledge in the field and offer valuable insights for both academia and industry practitioners seeking to leverage data-driven approaches in their marketing strategies.

Vaccine supply chain decision and coordination with manufacturers competition under blockchain technology

With vaccine incidents occur frequently and the wide application of blockchain technology, the construction of blockchain-based vaccine supply chain (VSC) has become a feasible solution to effectively solve vaccine safety problems. However, there is a lack of research on the operation decision of blockchain-based VSC. Based on this, combined with the current situation of vaccine manufacturers competition in the vaccine market, we build a game model of blockchain-based VSC and explore its optimal decision and coordination strategies. The results show that the higher the vaccine substitutability, the higher the profit of each member. When the production cost ratio of the two vaccine manufacturers is low, they will be more willing to join the blockchain system. When the proportion of problematic vaccines is low, it is more beneficial for the VSC to adopt blockchain technology. The cost-sharing contract can neither effectively coordinate the blockchain-based VSC nor realize its Pareto optimality. The blockchain-based VSC coordination can be achieved by two vaccine manufacturers signing revenue-sharing contracts or two-part tariff contracts with the vaccination unit respectively.

Research hotspots and trends of nanomaterials in stomatology: A bibliometric analysis from 2000 to 2023

BackgroundNanomaterials (NMs) have emerged as highly promising candidates for stomatology due to their excellent quality and remarkable progress in recent years. However, with the rapid expansion of the research scale, challenges arise in the technological decision-making and research management processes, and therefore difficulty for researchers to understand the knowledge structure and research hotspots has increased significantly. This study aims to make a comprehensive summary of authors, institutions, journals, research topics, development trends, and research hotspots of NMs in stomatology through bibliometric analysis for the sake of providing references for scientific decision-making, research management, and academic exploration in this filed. MethodsStudies on research and application of NMs in stomatology were retrieved from the Web of Science Core Collection (WoSCC) from January 1, 2000 to April 27, 2023. Bibliometric analysis and visualization were conducted using CiteSpace and VOSviewer. ResultsA total of 620 articles were included in this study, showing a gradual increase in the number of publications focusing on NMs in stomatology. Globally, China ranked first with 130 publications, and the United States (US) enjoyed the highest citation count (n = 5218) and average citation per paper (ACP) (n = 52.18). The top three institutions with the highest publication output were the University of Sao Paulo (n = 22), the Chinese Academy of Sciences (n = 20), and Shanghai Jiaotong University (n = 13). The journals MATERIALS and NANOMATERIALS emerged as the most popular in this field (n = 20), and BIOMATERIALS had the highest co-citations (n = 1597). The most prolific author was Dos Reis and Andrea Candido (n = 7), while Thomas J. Webster enjoyed the highest co-citations (n = 94). Burstness analysis of the references revealed a prominent research focus on nanoparticle drug delivery systems (specifically lipid nanoparticles). Keyword burstness analysis identified "oxide nanoparticle" as the primary frontier keyword in this field. ConclusionThis is the first study of using bibliometric analysis to summarize the research trends and frontiers of NMs in stomatology. With progressive advancements in the research and application of NMs in oral healthcare, their academic impact is steadily increasing. China and the US maintain a leading position in this field. Future directions could primarily focus on the development and application of nanoparticle drug delivery systems (especially lipid nanoparticles) and metal oxide nanoparticles (especially in antibacterial aspects). We hope that this bibliometric analysis could provide researchers with a panoramic view and useful references for future research, thus promoting the development of NMs in stomatology.

Individualistic attitudes in Iterated Prisoner's Dilemma undermine evolutionary fitness and may drive cooperative human players to extinction.

Inarguably, humans perform the richest plethora of prosocial behaviours in the animal kingdom, and these are important for understanding how humans navigate their social environment. The success and failure of strategies human players devise also have implications for determining long-term socio-economic/evolutionary fitness. Following the footsteps of Press and Dyson (2012), I implemented their evolutionary game-theoretic modelling from Iterated Prisoner's Dilemma (a behavioural economic probe of interpersonal cooperation) and re-analysed already published data on human proposer behaviour in the Ultimatum Game (a behavioural economic probe of altruistic punishment) involving 50 human participants versus stochastic computerized opponents with prosocial and individualistic social value orientations. Although the results indicate that it is more likely to break cycles of mutual defection in ecosystems in which humans interact with individualistic opponents, analysis of social-economic fitness at the Markov stationary states suggested that this comes at an evolutionary cost. Overall, human players acted in a significantly more cooperative manner than their opponents, but they failed to overcome extortion from individualistic agents, risking 'extinction' in 70% of the cases. These findings demonstrate human players might be short-sighted, and social interactive decision strategies they devise while adjusting to different types of opponents may not be optimal in the long run.

Optimal Decision Strategies for the Generalized Cuckoo Card Game

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cuckoo</i> is a popular card game, which originated in France during the 15th century and then spread throughout Europe, where it is currently well-known under distinct names and with different variants. Cuckoo is an imperfect information game-of-chance, which makes the research regarding its optimal strategies determination interesting. The rules are simple: each player receives a covered card from the dealer; starting from the player at the dealer's left, each player looks at its own card and decides whether to exchange it with the player to their left, or keep it; the dealer plays at last and, if it decides to exchange card, it draws a random one from the remaining deck; the player(s) with the lowest valued card lose(s) the round. We formulate the gameplay mathematically and provide an analysis of the optimal decision policies. Different card decks can be used for this game, e.g., the standard 52-card deck or the Italian 40-card deck. We generalize the decision model for an arbitrary number decks' cards, suites, and players. Lastly, through numerical simulations, we compare the determined optimal decision strategy against different benchmarks, showing that the strategy outperforms the random and naive policies and approaches the performance of the ideal oracle.

Reduced processing efficiency impacts auditory detection of amplitude modulation in children: Evidence from an experimental and modeling study

Auditory detection of the Amplitude Modulation (AM) of sounds, crucial for speech perception, improves until 10 years of age. This protracted development may not only be explained by sensory maturation, but also by improvements in processing efficiency: the ability to make efficient use of available sensory information. This hypothesis was tested behaviorally on 86 6-to-9-year-olds and 15 adults using AM-detection tasks assessing absolute sensitivity, masking, and response consistency in the AM domain. Absolute sensitivity was estimated by the detection thresholds of a sinusoidal AM applied to a pure-tone carrier; AM masking was estimated as the elevation of AM-detection thresholds produced when replacing the pure-tone carrier by a narrowband noise; response consistency was estimated using a double-pass paradigm where the same set of stimuli was presented twice. Results showed that AM sensitivity improved from childhood to adulthood, but did not change between 6 and 9 years. AM masking did not change with age, suggesting that the selectivity of perceptual AM filters was adult-like by 6 years. However, response consistency increased developmentally, supporting the hypothesis of reduced processing efficiency in early childhood. At the group level, double-pass data of children and adults were well simulated by a model of the human auditory system assuming a higher level of internal noise for children. At the individual level, for both children and adults, double-pass data were better simulated when assuming a sub-optimal decision strategy in addition to differences in internal noise. In conclusion, processing efficiency for AM detection is reduced in childhood. Moreover, worse AM detection was linked to both systematic and stochastic inefficiencies, in both children and adults.

Evaluating guidelines as key components of conservation policies: a conceptual framework and a pilot application

SummaryGuidelines for managers of protected areas are an important component of conservation policies, on a par with large-scale frameworks and vehicles for conservation funding. In line with the recent literature proposing evaluations of conservation actions or political strategies to improve them, here we use an innovative, hybrid methodology, based both on an interpretative approach anchored in social sciences and a quantitative literature review, to identify available frameworks for evaluating conservation guidelines. The main result of this analysis is that the relevant literature in conservation is sparse and heterogeneous, but a relevant encompassing framework is provided by the literature in decision sciences based on the policy analytics framework. This evaluation framework consists of three criteria: scientific credibility, operationality and legitimacy. We then implement a pilot application by evaluating guidelines currently used in France to support all of the actors involved in protected areas management. The study concludes that these guidelines are plagued by significant weaknesses that could be overcome by implementing relevant participatory processes.

An evidential linguistic ELECTRE method for selection of emergency shelter sites

In many circumstances, decisions are based on subjective experience. However, some views can be vague, meaning that policymakers do not know exactly how they should express their opinions. Therefore, it is necessary for researchers to provide scientific decision frameworks, among which the multi-criteria decision making (MCDM) method in the linguistic environment is gradually favored by scholars. A large body of literature reports relevant approaches with regard to linguistic term sets, but existing approaches are insufficient to express the subjective thoughts of policymakers in a complex and uncertain environment. In this paper, we address this problem by introducing the concept of evidential linguistic term set (ELTS). ELTS generalizes many other uncertainty representations under linguistic context, such as fuzzy sets, probabilities, or possibility distributions. Measures on ELTS, such as uncertainty measure, dissimilarity measure and expectation function, provide general frameworks to handle uncertain information. Modeling and reasoning of information expressed by ELTSs are realized by the proposed aggregation operators. Subsequently, this paper presents a novel MCDM approach called evidential linguistic ELECTRE method, and applies it to the case of selection of emergency shelter sites. The findings demonstrate the effectiveness of the proposed method for MCDM problems under linguistic context and highlight the significance of the developed ELTS.

Business analytics and decision science: A review of techniques in strategic business decision making

Business analytics and decision science have emerged as pivotal domains in enhancing strategic business decision-making processes. This review delves into various techniques that organizations employ to optimize their operations and achieve competitive advantages. At the forefront of strategic decision-making is data analytics, where vast amounts of data are analyzed to extract valuable insights. Descriptive analytics provides a historical perspective by examining past data trends, enabling businesses to understand their performance over time. This retrospective analysis serves as a foundation for predictive analytics, which utilizes statistical models and machine learning algorithms to forecast future trends and outcomes. By leveraging predictive analytics, organizations can anticipate market shifts, customer preferences, and potential risks, thereby making informed decisions. Prescriptive analytics uses predictive models to guide strategic decision-making, utilizing optimization algorithms and simulation tools to identify optimal actions. Decision science integrates analytical techniques with human judgment, focusing on consumer behavior and psychological factors to tailor marketing strategies and product offerings. Additionally, artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing strategic decision-making by automating complex tasks and providing real-time insights. Natural language processing (NLP) algorithms analyze unstructured data sources, such as customer reviews and social media posts, to extract valuable information and sentiment analysis. This enables businesses to gauge customer satisfaction levels and identify areas for improvement promptly. Decision trees, regression analysis, and clustering techniques are widely used in business analytics to segment customers, identify patterns, forecast sales trends, evaluate alternatives, assess risks, and optimize resource allocation. In conclusion, business analytics and decision science offer a plethora of techniques that empower organizations to make informed, data-driven decisions. By leveraging descriptive, predictive, and prescriptive analytics, along with AI and ML technologies, businesses can navigate complex environments, capitalize on opportunities, and mitigate risks effectively. This review underscores the importance of integrating analytical techniques with human expertise to achieve strategic objectives and sustainable growth.

Adapt/Exchange Decisions Depend on Structural and Surface Features: Effects of Solution Costs and Presentation Format.

Problem solvers often need to choose between adapting a current solution and exchanging it for a new one. However, previous studies have not considered how such decisions might depend on structural and surface features of the task. Therefore, the present study investigated the interplay between the costs of the two solutions (a structural feature) and the format in which this information is presented (a surface feature). In a computer-based modular plant scenario, participants chose between modifying process parameters (Adapt) and reconfiguring the module setup (Exchange). The solution costs were presented either as graphs depicting parameter relations, separate numbers for each parameter, or integrated numbers for each solution. It was hypothesised that graphs induce satisficing (i.e., basing decisions only on Adapt), whereas the numeric formats foster a comparison of the solutions (i.e., basing decisions on the Adapt/Exchange ratio). The hypothesised effects were restricted to situations with medium Adapt costs. A second experiment replicated these findings while adjusting the scale of the numeric formats. In conclusion, Adapt/Exchange decisions are shaped by an interaction of structural and surface features of the task. These findings contribute to a more detailed understanding of the influences on decision strategies in complex scenarios that require a balance between stability and flexibility.

Review on clinician bias and its impact on racial and socioeconomic disparities in pediatric heart transplantation.

This expert review seeks to highlight implicit bias in health care, transplant medicine, and pediatric heart transplantation to focus attention on the role these biases may play in the racial/ethnic and socioeconomic disparities noted in pediatric heart transplantation. This review breaks down the transplant decision making process to highlight points at which implicit bias may affect outcomes and discuss how the science of human decision making may help understand these complex processes.

Government mechanism in waste electrical and electronic equipment recycling among prospect theory and evolutionary game theory

Improper disposal of waste electrical and electronic equipment (WEEE) poses challenges for resource loss and public environmental pollution. Government policies are required to regulate WEEE recycling. To better explore the governance mechanism in the WEEE recycling network, a tripartite evolutionary game model composed of the government, recyclers, and manufacturers under bounded rationality and uncertainty was established using evolutionary game theory and prospect theory by underscoring the WEEE processing fee (WPF) exemption to adjust the behaviour of the WEEE recycling network participants. The research found that nine equilibrium points and eight possible ESSs exist and that the evolutionary stability strategies of tripartite decisions mainly depend on the trade-off between costs and revenues. Through numerical simulations, it was observed that the psychological perception of risk for recyclers and manufacturers can affect the behaviour and strategies of WEEE recycling stakeholders in uncertain environments. As WEEE recycling stakeholders increase their pursuit of safety risks, both recyclers’ and manufacturers’ willingness to engage in green behaviour is increasing. In addition, under the WPF exemption, the government can achieve the goal of encourage recyclers and manufacturers to choose green behaviour at a relatively low cost. We suggest that the government should strive to establish a stable WEEE recycling environment, establish an appropriate WPF exemption mechanism, and fully consider the cross-impact of recyclers and manufacturers to promote the green behaviour and efficiency of WEEE recycling.

Impact of the digital economy on rural and urban incomes

As the digital economy in our country continues to develop and improve, It has entered into a new phase of development. For purpose of facilitating a widespread adoption of the digital economy, it is essential to address the issue of income inequality between urban and rural residents and enhance the government's ability to formulate economic policies based on evidence. Therefore, this study takes 31 provinces and cities in our country from 2011 to 2019 as the research objects and conducts empirical analysis on them. Based on this, the project aims to deeply explore the mechanism of the influence regarding the income gap of digital economy between cities and countryside through empirical analysis, and conduct empirical analysis from two aspects: widespread implementation, reduction of the gap, and scientific decision-making. The research conclusion indicates that over the next few years, as the digital economy develops, people's income levels in different regions and cities will increase in China and it will experience significant improvements.

Research on the construction path of large-scale industrial agglomeration circle in Foshan based on computer big data fusion

This paper innovatively proposes the goal of research on the decision support system of the large-scale industrial agglomeration circle in Foshan. Based on the computer big data combined with the process of urban planning and management in Foshan, this paper proposes the construction content of the current planning decision support system for the new industrial agglomeration circle in Foshan. This paper proposes the relevant models for the analysis and construction of the development and evolution model and the construction of the auxiliary decision-making management system. The method of large-scale industrial agglomeration area in Foshan proposed in this paper provides services for the government to make rational and scientific decision-making. The computer big data industry circle planning model proposed in this paper has certain practical significance.

Research and Practice of Computer Application Technology in Big Data Analysis

This article aims to discuss the application of computer application technology in the field of BD (big data) analysis and the challenges it faces, and put forward corresponding countermeasures. Firstly, this article introduces the definition, characteristics and theoretical basis of BD analysis. Then, the specific application of computer application technology in BD analysis is elaborated in detail. Through case analysis, the practical application effect and value of computer application technology in BD analysis are demonstrated. The research results show that computer application technology plays a vital role in BD analysis, which can improve the efficiency of data processing, the accuracy of analysis and the scientific decision-making. Moreover, the research also reveals the technical, methodological and practical challenges faced in the process of BD analysis. In view of these challenges, this article puts forward corresponding countermeasures and suggestions, including strengthening the construction of policies and regulations, promoting technological innovation and research and development, and promoting Industry-University-Research's cooperative cooperation. These countermeasures have certain guiding significance and practical value for promoting the development of BD analysis.

Decision Making in the Real World: Unpacking the Black Box of the Decision Process for Youth Violence

Criminologists typically test rational choice theory by examining the relationship between one aspect of decision making—the perceived costs and benefits of crime—and the likelihood of crime, primarily using (experimental) surveys that lack consideration of the neighborhood context. Building on decades of research in the risk and decision sciences, the current study utilizes broader models of decision making to qualitatively examine how youth living in concentrated disadvantage make decisions during incidents of violence. Our thematic analysis of 22 in-depth interviews with youth in a midsize midwestern city indicates that the decision process regarding violence is much more complex than previously theorized.

Localizing and tracking of in-pipe inspection robots based on distributed optical fiber sensing

Ensuring the safe transportation of energy relies heavily on the timely safety inspection and pigging of energy pipelines using in-pipe inspection robots known as Smart/Intelligent PIGs. Addressing the potential risks of PIG block incidents and the limitations of excessive speed in the maintenance, real-time localization and tracking of these inspection robots has become imperative. However, traditional localization and tracking methods present a challenge due to their resource-intensive nature, requiring significant manpower and resources. To overcome this limitation and enhance the intelligence of the robot operation monitoring, this paper proposes an innovative artificial intelligence (AI) integration algorithm framework based on distributed optical fiber sensing (DOFS) for real-time localization and tracking of robots. It adopts noise reduction and reconstruction techniques in signal processing, effectively enhancing the quality of optic fiber vibration signals. Notably, the integration of two distinct features that complement each other enables dual verification of tracking detection, ultimately bolstering the system’s effectiveness and trustworthiness. Besides, a logical reasoning-based localization decision strategy further enhances the system’s capabilities, allowing for controlled tracking intervals and step sizes that can be tailored to meet the task requirements under diverse working conditions. The collaboration of three modules makes it feasible to monitor dynamic changes of the detection robot in the pipeline along the direction of operation. The experimental results convincingly demonstrate that the integrated framework possesses several key advantages of remarkable robustness, real-time performance, and minimal error. It underscores the system’s potential to ensure energy pipeline safety efficiently and effectively.

An identification of optimal waste disposal method for dumpsite remediation using the Fermatean fuzzy multi-criteria decision-making method.

Improperly managed wastes that have been dumped in landfills over the years pose various challenges, but they also offer potential benefits. The feasibility of recycling such waste depends on the type of wastes, the condition of dumpsites, and the technology implemented for disposal. The selection of an alternative waste disposal method from the many available options for dumpsite remediation is a complex decision-making process among experts. The primary aim of this study is to assist in an extended multi-criteria decision-making (MCDM) method to reduce complexity in the proposed dumpsite remediation problem influenced by multiple criteria and to identify the optimal waste disposal method. Data uncertainties are managed with the proposed Fermatean fuzzy preference scale, and the importance of all socio-economic criteria is assessed using the full consistency method (FUCOM). The final ranking results of the weighted aggregated sum product assessment (WASPAS) method identify that the Waste-to-Energy (WtE) process could play a significant role in the disposal of land-filled unprocessed wastes, promoting sustainable waste management. Meanwhile, the methodology explores the idea that financial and logistical constraints may limit the feasibility of large-scale recycling efforts. This combination of environmental science and decision science addresses real-world challenges, helping municipal solid waste management authorities implement sustainable waste management practices.

Universal screening and treatment towards the elimination of chronic hepatitis C in China: an economic evaluation

ObjectivesChina has the largest number of hepatitis C virus (HCV) infection in the world, but current levels of diagnosis and treatment are low. The objective of this study was to assess the cost-effectiveness of various universal HCV screening and treatment strategies in China and inform decisions on health policy. Study designA cost-effectiveness analytical study. MethodsWe developed a Markov model to investigate cost-effectiveness of different HCV screening and treatment strategies in China. We simulated several screening scenarios for Chinese people aged 18–70 years. We estimated incremental cost-effectiveness ratios (ICERs) of different intervention scenarios compared with status quo. ResultsExpanded HCV screening and treatment strategy with prioritisation for high-risk groups (Scenario S5) was the most cost-effective strategy (ICER: USD $11,667.71/quality-adjusted life-year [QALY] gained), which resulted in great reduction in HCV-related diseases and deaths, with a 67.11% reduction in cases of chronic HCV. Universal HCV screening and treatment implementation remains a cost-effective strategy when delayed until 2025 (ICER: USD $17,093.69/QALY), yet the delayed strategy is less effective in reducing HCV-related deaths. ConclusionsExpanded HCV screening and treatment strategy with prioritisation for high-risk groups is the most cost-effective strategy and has lead to a significant reduction in both HCV morbidity and mortality in China, which would essentially eliminate HCV as a public threat.

Achieving Goals of Care Decisions in Chronic Critical Illness: A Multi-Institutional Qualitative Study

BACKGROUNDClinicians, patients, and families alike perceive a need to improve how goals of care (GOC) decisions occur in chronic critical illness (CCI) but little is currently known about this decision making process. RESEARCH QUESTIONHow do intensivists from various health systems facilitate decision-making about GOC for patients with CCI? What are barriers to, and facilitators of, this decision-making process? STUDY DESIGNAND METHODS: We conducted semi-structured interviews with a purposeful sample of intensivists from the United States and Canada using a ‘mental models’ approach adapted from decision science. We analyzed transcripts inductively using qualitative description. RESULTSWe interviewed 29 intensivists from six institutions. Participants across all sites described GOC decision-making in CCI as a complex, longitudinal, and iterative process that involved substantial preparatory work, numerous stakeholders, and multiple family meetings. Intensivists required considerable time to collect information on prior events and conversations, and to arrive at a prognostic consensus with other involved clinicians prior to meeting with families. Many intensivists stressed the importance of scheduling multiple family meetings to build trust and relationships prior to explicitly discussing GOC. Physician-identified barriers to GOC decision-making included one week staffing models, limited time and cognitive bandwidth, difficulty eliciting patient values, and interpersonal challenges with care team members or families. Potential facilitators included scheduled family meetings at regular intervals, greater interprofessional involvement in decisions, and consistent messaging from care team members. INTERPRETATIONIntensivists described a complex time and labor-intensive group process to achieve GOC decision-making in CCI. System-level interventions that improve how information is shared between clinicians and decrease logistical and relational barriers to timely and consistent communication are key to improving goals-of-care decision making in chronic critical illness.