Decision Theory Research Articles

Investigating the dynamics of managerial dialogue in HE merger reform

AbstractThe paper argues that the empirical study of public policy making almost exclusively deals with structural arrangements and power relations, while giving insufficient attention to how policy entrepreneurs in government develop policy initiatives through venue selection, framing, and dialogue. Drawing on insight from theories of public policymaking and organizational decision-making, public management, and on data from a case study of higher education merger reform in Norway, the article provides a conceptual and empirical contribution by suggesting the significance of managerial dialogue — highlighting a specific combination of dialogue, power asymmetries, managerialism, and venue selection as important elements in higher education reform policymaking.

Application of Fuzzy Decision Theory in Multi Objective Logistics Distribution Center Site Selection

Application of Fuzzy Decision Theory in Multi Objective Logistics Distribution Center Site Selection

Can quasi-cyclical preferences be rational?

One important dimension in the work of José Luis Bermúdez concerns broadening our ideal of deliberational rationality beyond the narrow confines of standard rational decision theory. In this vein, he (2022) presents two bold claims: (i) there are ‘quasi-cyclical preferences’. (ii) Quasi-cyclical preferences can be rational. These claims contradict received wisdom in decision theory. I shall critically discuss both claims, expressing doubts as to whether there are quasi-cyclical preferences, and whether they can be rational.

On Science, Faith, Quantum Theory, and Decisions

The author explores the consequences of a new approach towards the foundation of quantum theory. The final aim of this approach has been to try to build a bridge between modern physics and other scientific disciplines, in particular statistical science. One aspect of the approach is first that arguments for a God which has arbitrarily large knowledge can be given. Secondly, that God can be seen as being perfectly rational. One of the consequences is a theory of decisions, a theory that also is valid for decisions made by political leaders. Some implications are sketched.

Improving the forecasting approach for crude oil sourcing at an oil refining firm: a decision theory perspective

Accurate price forecasting is pivotal for making informed sourcing decisions. This study addresses this need by empowering logistics and supply planning managers at a leading European oil refining company through the innovative use of unstructured big data analytics. By applying the CIMO (Context, Intervention, Mechanism, and Outcome) framework, the research refined the oil price forecasting process, which is crucial for optimising sourcing strategies. A novel approach was employed, combining sentiment analysis via deep learning models with traditional autoregressive integrated moving average (ARIMA) models, introducing sentiment as a vital exogenous variable. Through comprehensive stakeholder interviews, the study identified critical gaps and opportunities, leading to the development of a forecasting solution that significantly enhanced predictive accuracy. This advancement eliminated the need for manual interventions by senior decision-makers, streamlining the decision-making process. This research utilises decision theory and also demonstrates the transformative power of unstructured big data analytics in supply chain management. By integrating time series modelling with advanced sentiment analysis, this study provides a blueprint for leveraging technological advancements in rapidly evolving markets. The originality of this work lies in its practical application, showcasing essential capabilities that are increasingly vital in supporting sourcing practices within a volatile global landscape.

The Role of Culture in Shaping Managerial Overconfidence and Its Impact on Financing Decisions

Traditional economic models that assume rational decision-making may not accurately capture the complexities of real-world financial decision-making. Behavioral Decision Theory can explain the gap between classical capital structure theories and inconsistent empirical evidence. Under the assumption of bounded rationality Behavioral Decision Theory linking cultural values, overconfidence and financial decision making. Culture is an important aspect in organization which need close attention. Culture play a vital role in shaping behavioral biases. Overconfidence is one of the well-known cognitive biases in financial decision making that often seen in managers. Cultural differences and managerial overconfidence have impact on financing decisions.

Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation

High noise resistance and high boundary fitting accuracy have always been the goals of image classification. However, the two mutually constrain each other, making it extremely difficult to reach equilibrium. To deal with this problem, the unsupervised image classification algorithm based on fully fuzzy Voronoi tessellation is proposed. It extends Voronoi tessellation from hard to fuzzy, and proposes a hierarchical fuzzy membership model, i.e., pixels fuzzily belong to Voronoi polygons and polygons fuzzily belong to clusters. The objective function is established based on the hierarchical fuzzy membership model by fully considering the transitivity of fuzziness between different levels. Then, the optimal classification result can be obtained by the fuzzy comprehensive decision theory under the best parameter solution. The first level retains the flexibility of pixels while modeling spatial constraints. The second level determines which class the polygon belongs to under the constraint of the first level. It provides an effective way of balancing noise resistance and boundary fitting. In addition, the Voronoi tessellation is explicitly expressed in the objective function in the form of the mathematical model, which allows it to obtain the optimal value through analytical solutions instead of the previous random sampling method. It greatly increases the convergence speed of the algorithm. Experiments have been performed on simulated and several remote sensing images with seven comparing algorithms to demonstrate the effectiveness of the proposed algorithm.

Governance of responsible research and innovation: A social welfare, psychologically grounded multicriteria decision analysis approach

Our article deals with the governance of responsible research and innovation (RRI) and aims to set out a first psychologically grounded decision-theoretic method for the governance of RRI. We approach the governance of RRI as a multicriteria group decision analysis problem of delivering social welfare in an innovation ecosystem. Following such a methodological approach, we develop a psychologically grounded multicriteria group decision analysis method that integrates in its value function the main psychological effects captured in the value function of prospect theory as the main theory of individual decision-making under risk. The method first applies a psychologically motivated multicriteria decision analysis function that measures the welfare delivered to all stakeholders involved in a research and innovation consortium. The method then applies a social welfare function on the welfare measurements of stakeholders to propose a social welfare solution that emerges as an RRI-compliant solution for the consortium. The results are a first psychologically grounded multicriteria group decision analysis method and its first application to the governance of RRI. The implications of our results are theoretical but also practical, as our method contributes not only to the established field of multicriteria decision analysis by setting out new method but also to the field of RRI by delivering a psychologically grounded decision-theoretic general method for the governance of RRI.

An IGDT-WDRCC based optimal bidding strategy of VPP aggregators in new energy market considering multiple uncertainties

This study addresses the volatility and uncertainty challenges in managing renewable energy within electricity markets, particularly focusing on the role of Virtual Power Plant (VPP) aggregators. Recognizing the risks these uncertainties pose to the revenue and stability of power systems, the paper presents a novel information gap decision theory (IGDT)-Wasserstein metric based distributionally robust chance constraint (WDRCC) approach to devise an optimal bidding strategy for VPP operators. It involves a data-driven distributionally robust optimization framework, leveraging the worst-case scenario from the distributed resource uncertainties, guided by an ambiguity set rooted in the Wasserstein metric. Furthermore, the distributionally robust chance constraint modeling is introduced ensuring that uncertainty constraints of distributed resources meet a predefined risk level. Although this method shows promising out-of-sample performance, it relies on forecasted energy prices, a notable limitation given the price volatility and information inadequacy in the newly-opened market. To address this, the risk-averse bidding strategy, grounded in IGDT, is proposed simulataneously to safeguard the operator’s expected returns against price uncertainties, implementing an advanced piecewise linear approximation technique, ”nf4l,” for linearizing the bi-linear term from IGDT. The effectiveness of this approach is empirically validated through a comprehensive case study and sensitivity analysis.

A distance-based three-way conflict analysis for fuzzy multiset

Fuzzy multisets(FMs), as an extension of fuzzy sets, serve as an effective tool in real-life scenarios for describing uncertain information, effectively mitigating the loss of information. In combination with conflict situations, FMs can adequately represent agents’ attitudes in decision-making. However, current research rarely explores conflict analysis(CA) within the framework of fuzzy multiset situation tables(FMSTs). In this paper, we introduce a three-way conflict analysis model(TWCAM) by combining three-way decision theory(TWDT) and CA based on fuzzy multiset information systems(FMISs). First of all, the proposed TWCAM incorporates the concepts of conflict, neutral, and alliance sets for both single and multiple issues, utilizing conflict distance and conflict function to compute these sets. Moreover, a new method is proposed to objectively determine issue weights by considering both absolute and relative weights. According to Bayesian minimum risk theory, this study presents a new way of determining thresholds to trisect the agents. Additionally, we propose the notion of maximal coalitions(MCs) in FMSTs as a generalization of Pawlak’s model. Finally, we utilize a case study to prove the practicability of our model and some comparative analyses to illustrate its effectiveness and superiority.

Optimal planning for high renewable energy integration considering demand response, uncertainties, and operational performance flexibility

The operational performance of the grid needs to be considered and incorporated with other verified system flexibility measures, such as energy storage system and demand response, when planning a hybrid renewable energy system with a high fraction of renewable energy sources. Therefore, this study developed an integrated model that considers the effects of time-of-use demand response and risk-based uncertainty analysis on the operational performance of a grid-connected hybrid energy system for high renewable power penetration. A multi-stage approach for optimal scheduling of the energy systems with time-of-use pricing considering uncertainties’ impacts on grid and energy storage systems operations is modeled. Techno-economic metrics are developed to assess the decision-making effectiveness of the energy system planning and grid operation under four scenarios based on the information gap decision theory to quantify the energy system and grid operation performance flexibility. The considered scenarios are without and with demand response using risk-neutral, risk-averse, and risk-seeking strategies. The analysis of the results indicates that the risk-averse approach achieves the best techno-economic tradeoff, guaranteeing improved technical performances compared to the other risk strategies for uncertainty management in renewable power outputs and grid operational performances within the cost deviation tolerance limit. The risk-averse strategy further justified the substantial inclusion of energy storage and the adoption of the demand response to provide better operational flexibility to ensure improved grid performance, as seen in the reduced grid loss and improved voltage profile with higher power contribution from the RES. Significantly, the considered model can help system planners manage the tradeoff for efficient system performance under substantial renewable energy integration, considering the impacts of uncertainties.

Transforming Talent Acquisition: How AI is shaping modern HRM

The study investigates the profound influence of artificial intelligence (AI) on the process of recruiting in contemporary human resource management (HRM) methodologies. As organizations strive for efficiency and accuracy in talent acquisition, Advanced artificial intelligence technologies, including machine learning algorithms, predictive analytics, and natural language processing, are revolutionising conventional recruitment approaches. This study provides a comprehensive overview of how AI enhances recruitment efficiency, improves decision-making, and addresses challenges related to fairness and bias. Key theoretical frameworks, including decision theory, are employed to analyze AI’s role and acceptance in recruitment processes. Despite its advantages, the paper also highlights significant challenges, such as algorithmic bias, transparency issues, and data privacy concerns. The ramifications for HRM theory and practice are examined, highlighting the need to adopt a well-rounded strategy that incorporates the technological potential of AI while also addressing ethical concerns. Key areas for future study include enhancing the transparency of AI and investigating its enduring impact on the culture within organizations. This review enhances the comprehension of the function of AI in recruiting and guides the ethical implementation of these technologies in HRM.

A Method for Determining the Importance of Critical Emergency Indicators Based on Multi-granularity Uncertain Language

In view of the complexity of emergencies and the subjectivity of decision-makers, a method of determining key emergency indicators based on multi-granularity uncertainty language is proposed. Firstly, decision members use preferred uncertain language phrases to represent the importance of each key indicator and use transformation functions to carry out the consistent transformation of this multi-granularity uncertain language information. Secondly, the group evaluation vector is obtained by using the extended weighted average operator of uncertainty, and then the weight vector of each key index is obtained by using the decision theory of uncertain language. Finally, an example is given to verify the practicability and effectiveness of the proposed method.

Can improving climate change perception lead to more environmentally friendly choices? Evidence from an immersive virtual environment experiment

Rational decision theory assumes that individuals have perfect knowledge of the consequences of their choices and actions. However, this assumption often fails to align with reality, particularly in the context of environmental degradation, where the impacts of actions can be distant in both time and space. Will an enhanced perception of those impacts encourage pro-environmental choices?To explore this question, we designed and conducted an experiment in an immersive virtual reality environment (IVE). After an initial training phase, participants were asked to choose between using a tumble dryer or a clothesline to dry their laundry. Participants in the treatment group received exaggerated feedback during the training phase, experiencing a simulated sudden outbreak of a thunderstorm when they used the dryer. In contrast, participants in the control group did not receive any feedback. The experiment was conducted at two Italian universities, Siena and Pisa, with a total of 270 subjects.The methodological finding is that even less elaborated IVEs can still be effective as experimental tools. The substantive finding is that exposure to exaggerated feedback in an IVE significantly increased the likelihood of choosing a more environmentally friendly action, such as using a clothesline, which involves lower energy consumption.

Climate risk perception and oil financialization in China: Evidence from a time-varying Granger model

Financialization has introduced a new dimension to the oil market by incorporating various financial instruments and market participants. This study thus firstly measures the oil financialization by using daily data on Shanghai crude oil futures and Shanghai composite index. The results identify a lower level of oil financialization in China, implying that the oil market is independent on the stock market. Moreover, the exogenous events volatile the degree of oil financialization. We then estimate the impact of climate risk perception on oil financialization using a time-varying Granger model. Novel evidence finds negative and significant time-varying coefficients of climate risk perception on oil financialization, indicating that the effect of climate risk perception is non-constant over time. The empirical results reinforce the implications of the bounded rationality decision and signal theory.

Coordinated operation of source–station–road–network system considering traffic flow uncertainty

Promoting large-scale transportation electrification and developing new solar energy harvesting and converting technologies are effective ways to achieve ’dual carbon goals’. In this paper, a new road energy harvesting technology, solar road (SR) power generation technology, is introduced. A SR power generation model considering the variation of solar radiation intensity, traffic flow and dynamic shadows of vehicles is constructed. Secondly, considering the uncertainty of traffic network (TN) caused by users’ stochastic routing behavior, a model of TN under stochastic user equilibrium (SUE) is constructed. Then the influence of traffic flow on the electric load of charging and battery swapping stations (CBSSs) is analyzed, and the local consumption strategy of SR is established. Meanwhile, information gap decision theory (IGDT) is introduced to reduce the impact of uncertainty on the system. Finally, the coupling system of IEEE-33 node of distribution network (DN) and Nguyen–Dupuis of TN is used to verify the effectiveness of the proposed model and optimal management method, which improves the utilization rate of solar energy, promotes carbon emission reduction, reduces the congestion degree of DN, and ensures the safe and stable operation of the system.

Exploring the Role of Symbolic Convergence Theory in Enhancing Group Cohesiveness and Decision Making: A Systematic Literature Review Study

Symbolic Convergence Theory describes group cohesiveness and decision-making influenced by symbolic convergence and rhetorical vision because they affect how group members understand and share common social values. This research aims to provide an overview of the implementation of Symbolic Convergence Theory in group communication, especially in cohesiveness and group decision-making. This research uses the Systematic Literature Review (SLR) method. This research systematically identifies and evaluates group cohesiveness and decision-making in several previous studies. However, along with the development of time and technology, direct and analog communication began to be less researched. Keywords: Symbolic Convergence Theory, group, cohesiveness, decision-making

Paramedic perceptions of decision-making when managing mental health-related presentations: a qualitative study.

Mental health presentations account for a considerable proportion of paramedic workload; however, the decision-making involved in managing these cases is poorly understood. This study aimed to explore how paramedics perceive their clinical decision-making when managing mental health presentations. A qualitative descriptive study design was employed. Overall, 73 paramedics participated in semi structured interviews, and data were analyzed from transcribed interviews in NVivo. Four themes emerged that reflected participants' perceptions: the assessment process, experience, the use of documents and standard procedures, and consultation with other healthcare providers. There were conflicting perceptions about the clinical decision-making process, with perception of role having a potential impact. The dual process theory of clinical decision-making, which includes both analytical and intuitive approaches, was evident in the decision-making process. Incorporating dual process theory into education and training, which highlights the strengths and weaknesses of analytical and intuitive decision-making, may reduce clinical errors made by cognitive bias. To further support clinical decision-making, additional education and training are warranted to promote critical thinking and clarify the scope of practice and roles when attending to mental health-related presentations.

Uncertainty-based optimal planning of service transformers in distribution network considering load profile: risk-averse and risk-taker strategies modelled by information gap decision theory

Uncertainty-based optimal planning of service transformers in distribution network considering load profile: risk-averse and risk-taker strategies modelled by information gap decision theory

Trusted commonsense knowledge enhanced depression detection based on three-way decision

Depression detection on social media aims to identify depressive tendencies within textual posts, providing timely intervention by the early detection of mental health issues. In predominant approaches, the Pre-trained Language Models(PLMs) are trained solely on public datasets, falling short of vertical scenarios due to insufficient domain-specific and commonsense knowledge. In addition, ambiguous commonsense knowledge could be misleading to PLMs and results in false judgments. Therefore, it poses significant challenges to select commonsense knowledge that is trusted. To address this, we propose CoKE, a model that incorporates trusted commonsense knowledge based on three-way decision theory to enhance depression detection. CoKE comprises three key modules: trusted screening, knowledge generation, and knowledge fusion. First, we utilize psychiatric clinical scales and three-way decision theory to screen out the uncertain domain from the massive user posts. Then, an adaptive framework is applied to generate and refine trusted commonsense knowledge that can explain the true semantics of posts in the uncertain domain. Finally, a dynamic integration of posts with highly trusted knowledge is achieved through a gating mechanism, resulting in embeddings enhanced by trusted commonsense knowledge that are more effective in determining depressive tendencies. We evaluate our model on two prominent datasets, eRisk2017 and eRisk2018, demonstrating its superiority over previous state-of-the-art baseline models.