Decision Theory Research Articles

Decision-making under epistemic, strategic and institutional uncertainty during COVID-19: findings from a six-country empirical study

BackgroundUncertainty is defined as limited knowledge or lack of predictability about past, present or future events. The COVID-19 pandemic management was significantly impacted by uncertainty, as the gaps between existing information and the necessary knowledge hindered decision-making. Current uncertainty literature primarily focuses on natural disasters, leaving a gap in understanding decision-making under uncertainty in times of public health emergencies. Analysing strategies for making decisions under uncertainty during the pandemic is crucial for future pandemic preparedness.MethodsUsing a comparative research design, we study the strategies governments used to make decisions under uncertainty during the COVID-19 pandemic. We collected data through desk reviews, stakeholder interviews and focus group discussions with stakeholders from government, academia and civil society from six purposefully selected countries: Nigeria, Singapore, South Africa, Bangladesh, Jordan and the UK.ResultsRegardless of political, geographic and economic context, all six countries adopted common strategies to make decisions under three types of uncertainties. Decision-making under epistemic uncertainty involved seeking expert advice and collecting evidence from other countries and international organisations. Decision-making under strategic uncertainty involved coordination, collaboration and communication. Decision-making under institutional uncertainty involved using or adapting pre-existing experiences, structures and relationships and establishing new institutions and processes.ConclusionsWe contribute to the theory and practice of public health crisis decision-making by presenting a unified national-level applied decision-making framework for events involving uncertainty. We provide practical guidance for approaches to enhance decision-making in future health crises that could also be used for other emergencies.

The Volatility of Sugar and Ethanol Prices: A Tale of Two Markets Using Wavelet Analysis

This study ventures into the dynamic and interconnected realms of sugar and ethanol prices, unveiling their volatility patterns using the powerful tool of wavelet analysis. The first part of the study establishes a link between Brazil's sugar price surge and significant economic events, including the National Alcohol Program (Proálcool), the military dictatorship, and the implementation of the Real Plan. The second part zooms out to global factors, such as concerns about the greenhouse effect, flex-fuel engine technology, and recent challenges like droughts and COVID-19. The analysis uncovers distinct patterns in the volatility of sugar and ethanol prices. Factors like global supply and demand, climate-induced crop variations, and government policies influence sugar prices. In contrast, ethanol prices are more directly influenced by energy market dynamics, particularly crude oil prices, due to its role as a biofuel component. Furthermore, the study reveals time-frequency correlations between these two markets, shedding light on moments of synchronization and divergence. In this narrative of the two markets, wavelet analysis emerges as a powerful tool for deciphering past volatility and predicting future trends. It allows both estimating the degree of correlation and verifying the unfolding of these relationships over time. The findings of this study contribute as a bridge between theory and practice for decision-making, such as resource allocation and risk management in a constantly evolving economic scenario, highlighting the importance of analysis in this context.

Optimal scheduling of hydrogen storage in integrated energy system including multi-source and load uncertainties

Demand response (DR) is a crucial element in the optimization of integrated energy systems (IESs) that incorporate distributed generation (DG). However, its inherent uncertainty poses significant challenges to the economic viability of IESs. This research presents a novel economic dispatch model for IESs utilizing information gap decision theory (IGDT). The model integrates various components to improve IES performance and dispatch efficiency. With a focus on hydrogen energy, the model considers users' energy consumption patterns, thereby improving system flexibility. By applying IGDT, the model effectively addresses the uncertainty associated with DR and DG, surpassing the limitations of traditional methods. The findings indicate that in relation to the baseline method, the proposed model has the potential to reduce operating costs by 6.3% and carbon emissions by 4.2%. The integration of a stepwise carbon trading mechanism helps boost both economic and environmental advantages, achieving a 100% wind power consumption rate in the optimized plan. In addition, daily operating costs are reduced to 23,758.99 ¥, and carbon emissions are reduced to 34,192kg. These findings provide quantitative decision support for IES dispatch planners to help them develop effective dispatch strategies that are consistent with low-carbon economic initiatives.

Application of Bayesian Decision Theory in Detecting Test Fraud.

This article suggests a new approach based on Bayesian decision theory (e.g., Cronbach & Gleser, 1965; Ferguson, 1967) for detection of test fraud. The approach leads to a simple decision rule that involves the computation of the posterior probability that an examinee committed test fraud given the data. The suggested approach was applied to a real data set that involved actual test fraud.

Optimizing risk in action: Differences between fixated aim points and movement outcomes in throwing

Introduction Movement outcomes are inherently subject to variance. Handling this variance is crucial for successful sensorimotor behaviour – whether in everyday life or in sports –, particularly in high-risk situations. Research using finger-pointing tasks has shown that humans take into account their own motor variance and costs of potential outcomes in movement planning (Trommershäuser et al., 2008). However, the question remains whether this mechanism extends to more complex tasks (Beck et al., 2023). Here, we investigate sensorimotor behaviour under risk in throwing, across three experiments with 20 participants each. Methods Participants’ task was to throw balls on a target circle in a virtual reality (VR) setup, gaining 100 points for each hit. The target was partially overlapped by a penalty circle. We manipulated the consequences of hitting the penalty circle (0 points vs -500 points vs -2000 points) and the distance between both circles (30 cm vs 45 cm vs 60 cm). This task challenged participants to find strategies that optimally trade-off potential penalties and rewards. To capture participants’ strategies, we measured the location of their final gaze fixation before movement – as an indicator of their planned aiming point – and the ball’s impact location. Models of statistical decision theory (Trommershäuser et al., 2008) predict that the optimal aim point horizontally shifts away from the centre of the target circle as soon as the penalty is non-zero. In other words, participants should incorporate a “safety margin”. This horizontal shift should be larger with (1) higher penalties, (2) smaller distances between the target and penalty circle and (3) with higher motor variance. Results In the no-penalty condition, the final fixation and the ball’s impact location were both centred on the target. In the penalty condition, both their final fixations and the ball’s impact location shifted away from the penalty circle, with larger shifts for higher penalties and smaller distances. Intriguingly, the shifts in the ball’s actual impact location were not only significantly larger (“more conservative”) but also closer to the statistically optimal location compared to the initially fixated aim points. Analysis of movement trajectories shows that, in penalty conditions, the shifts away from the penalty zone increased progressively until the final phases of the movement. Conclusion Our findings show that principles of statistical decision theory generalize to more complex tasks. Extending Trommershäuser et al. (2008)., our results suggest that risk evaluation is not completed in a planning phase before movement execution but is optimized during ongoing movements. ReferencesBeck, D., Hossner, E.-J., & Zahno, S. (2023). Mechanisms for handling uncertainty in sensorimotor control in sports: A scoping review. International Review of Sport and Exercise Psychology, 1–35. https://doi.org/10.1080/1750984X.2023.2280899 Trommershäuser, J., Maloney, L. T., & Landy, M. S. (2008). Decision making, movement planning and statistical decision theory. Trends in Cognitive Sciences, 12(8), 291–297. https://doi.org/10.1016/j.tics.2008.04.010

The transition from resistance to acceptance: Managing a marine invasive species in a changing world

Abstract Marine invasive species can transform coastal ecosystems, yet mitigating their effects can be difficult, and even impractical. Often, marine invasive species are managed at poorly matched spatial scales, and at the same time, rates of spread and establishment are increasing under climate change and can outpace resources available for population suppression. These circumstances challenge traditional conservation goals of maintaining a historic environmental state, especially for a species like the European green crab (Carcinus maenas), a formidable invader with few examples of successful long‐term removal programs. A management paradigm where decision alternatives include resisting or accepting a new ecological trajectory may be needed. We apply mathematical concepts from decision theory to develop a quantitative framework for navigating management decisions in this new resist‐accept paradigm. We develop a model of European green crab growth, removal and colonization, and we find optimal levels of removal effort that minimize both ecological change and removal cost. We establish a benchmark of colonization pressure at which green crab density becomes decoupled from a decision maker's actions, such that population control can no longer shape the invasion trajectory. For informing the decision boundary between resistance and acceptance, our results highlight that a decision maker's understanding of how removal cost scales with removal effort is more important than understanding the density‐impact relationship. We show that assuming stationary system dynamics can result in sub‐optimal levels of species removal effort, highlighting the importance of developing anticipatory management strategies by accounting for non‐stationary dynamics. Policy implications. For marine invasive species that can disperse across long distances and recolonize rapidly after removal, the focus of conservation policy should shift away from understanding how to resist change to understanding when to stop resisting change. Navigating this decision problem involves trade‐offs among competing objectives, highlighting the need for structured approaches to elicit objective weights that reflect the values of the decision maker. For natural resource managers facing possible ecosystem transformation, this decision framework can enable proactive and strategic decisions made under uncertainty in a changing world.

Leveraging Artificial Intelligence and Machine Learning for Decision-Making in Business Management: A Comprehensive Analysis

This paper focuses on how AI and ML have changed decisions in retailing, healthcare, financing, and manufacturing careers. They demonstrate how AI is used in supply chain management to support the decision-making process by making forecasts, processing data, and optimizing operations, leading to higher efficiency, decreased costs, and increased customer satisfaction. Thus, the research incorporates quantitative and qualitative approaches, such as surveys and interviews with key stakeholders, and employs statistical and content analysis methods. Significant outcomes include a 20% enhancement of forecasting precision reduction while the operational cost decreases by 21 percent. Nonetheless, the research also discovers an essential issue that employs complex challenges that embrace high-cost implementation, resistance from the workforce, allowance of data privacy, and bias besides algorithms. Some are ethical concerns, and the importance of their regulation is noted. While adopting the decision theory and systems thinking perspectives, this research paper highlights the necessity of effectively and adequately implementing AI into an organization permanently to achieve more benefits. The following are realizable out-of-the-box solutions that the study suggests, including audiences for employees, data protection for compliance, and conscientization of fairness in AI algorithms. Future directions include situations where these applications are to be broadened to weigh on ethical issues and to encourage optimal technological fairness that will, in turn, ensure sustainable business improvement and innovation.

Greedy Algorithm Based Travel Route Planning under Multi-objective Decision Making

In this paper, greedy algorithm is used to optimize the planning of tourism routes based on multi-objective decision theory. The main goal of the research is to design the best travel route for foreign tourists by considering various factors such as time, cost and number of attractions. Firstly, this paper establishes a dataset including information of cities, attraction ratings, transportation time and cost through data integration and processing. Secondly, this paper used hierarchical analysis to evaluate 352 cities in multiple dimensions and identified the 50 cities that are most desirable to foreign tourists. Finally, combined with the greedy algorithm, this paper designed multiple optimization strategies under different constraints. The greedy algorithm used in the study can quickly provide tourist routes that meet the needs of tourists under multi-objective decision-making, and at the same time provides a new way of thinking for the planning of tourist routes.

IFRS 16 and firms’ risk in emerging markets: the impact of managerial overconfidence

PurposeThe purpose of this study is to (1) investigate how IFRS 16 affects firms’ risk in Egypt and (2) examine the moderating role of managerial overconfidence on this relation.Design/methodology/approachThis study uses data from the annual reports of 38 Egyptian firms from 2014 to 2022. This study employs the generalized method of moments (GMM) and the three-stage least squares (3SLS) as estimation techniques.FindingsThe results show that IFRS 16 positively affects Egyptian firm risk, while managerial overconfidence reduces this positive effect.Research limitations/implicationsThis study has some limitations. First, the sample size was relatively small. Second, our analysis did not incorporate other metrics of managerial overconfidence owing to the unavailability of relevant data in Egypt.Practical implicationsThis study assists stakeholders and regulators in realising the implications of IFRS 16 on a firm’s risk, especially in emerging markets. Also, it enables managers to identify and assess lease-related risks more accurately to assist in developing appropriate risk mitigation strategies and optimizing lease-related decision-making processes. Furthermore, it aids in enhancing comprehension and knowledge of the interplay between managerial behaviour and firm outcomes.Originality/valueGrounded in agency theory, this study reveals novel empirical insights into the impact of IFRS 16 on firm risk, especially in the context of emerging markets. Utilizing behavioural decision theory and upper echelons theory, it examines the previously unexplored influence of managerial overconfidence on this relationship.

Decision-making conditions for participatory student voice research at scale: examples from studying vertical school spaces

Student voice research recognises students’ roles as knowledge makers in cultural institutions like schools. It rarely occurs at scale, as researchers need to work closely with small groups of students to enable dialogue, listen, and negotiate. Gathering rich qualitative data from large groups of students in schools using such participatory methods can be challenging, becoming methodologically “messy” for philosophical and practical reasons. This paper links high-level research principles and pragmatics of on-the-ground research strategy and improvisation to provide ways of managing the ‘mess’ of student voice research at scale. Drawing from researcher experiences working with large groups of students, a reflexive decision-making theory is used to discern the deliberations that guided the research team’s decision-making. Six different dimensions are illustrated, alongside methodological questions to offer guidance to other researchers. Student voice research at scale offers opportunities for diverse participatory roles for students and researchers to be innovative, reflexive knowledge makers.

Do gritty salespersons mitigate relationship damage in unethical sales subcultures?

ABSTRACT Previous research has shown that salesperson grit is not a consistent predictor of ethical sales behaviors. Our study relies on the integrated theory of ethical decision-making to evaluate the influence of the sales ethics subculture and the interaction of salesperson grit across two distinct types of unethical behavior. Unethical sales behaviors are motivated by self-interest, but pro-social rule breaking is more selfless in nature. Empirical data from 197 salespeople were analyzed via structural equation modeling. We find that higher levels of salesperson grit result in lower levels of selfish, unethical sales behaviors, but grit does not change the amount of selfless pro-social rule breaking. Moreover, unethical sales behaviors damage the relationship between salespersons and their clients, but contrary to our beliefs, pro-social rule breaking neither damages nor improves these relationships. Our findings support the notion that salespersons operate according to dual processes when facing ethical dilemmas.

A Review the Role of Artificial Intelligence in Media Content Creation for SDGs Development

Introduction: The rapid advancement of Artificial Intelligence (AI) has significantly transformed digital media content creation, particularly with the rise of AI-Generated Content (AIGC). This systematic review investigates AIGC's role in enhancing content production efficiency, improving content quality, and addressing ethical considerations. AIGC applications—spanning micro-video production, digital storytelling, journalism, and new media art—highlight its profound impact on content workflows, distribution, and audience engagement. Objective: The objective of this study is to explore how AIGC influences the efficiency, quality, and ethical standards of digital media content creation, with the aim of fostering responsible and sustainable integration of AIGC in the media industry. Theoretical Framework: This research is grounded in theories of human-machine interaction, ethical decision-making, and technological adoption models, which provide a solid foundation for examining the dynamics of AIGC in the context of Web 3.0 advancements. Method: The methodology employed for this review involved a systematic analysis of scholarly articles, case studies, and industry reports on AIGC. The data were synthesized to evaluate its transformative potential, ethical challenges, and alignment with sustainable development goals (SDGs). Results and Discussion: The findings reveal that AIGC significantly enhances efficiency and scalability in content production while raising ethical concerns such as professional displacement and algorithmic biases. These results are contextualized within the theoretical framework, emphasizing the need for ethical safeguards and inclusive practices. Challenges, including transparency in AI use and equitable access to AIGC technologies, are also discussed. Research Implications: This study offers both theoretical and practical implications by proposing strategies for responsible AIGC adoption, contributing to the sustainable development of the digital media industry. Implications include its potential to democratize content creation, foster inclusivity, and support SDGs such as Quality Education (SDG 4), Decent Work and Economic Growth (SDG 8), and Industry, Innovation, and Infrastructure (SDG 9). Originality/Value: This study provides a novel contribution to the literature by integrating SDG-oriented perspectives into the discussion of AIGC's impact. Its findings underline the critical balance between leveraging technological advancements and mitigating their societal and ethical challenges.

A Risk Analysis Model for Biosecurity in Brazil Using the Analytical Hierarchy Process (AHP)

This study proposes a risk analysis model based on the principles of ISO 31000 and decision theory for biological agents with potential for offensive use in Brazil. Bibliographic research was conducted on the main models already published on the subject. The German risk classification system was adopted as the main reference because it is adjustable and adopts a semiquantitative approach. After translating and adapting this model to the Brazilian context, the analytic hierarchy process (AHP) was applied to generate a hierarchical tree with criteria and subcriteria to be considered in the risk assessment. A questionnaire was administered to a group of experts to evaluate the relevant criteria and subcriteria and the risks related to three biological agents (Bacillus anthracis, Yersinia pestis, and Francisella tularensis), which were selected in an exploratory manner to exemplify the application of the model. According to the results, the criterion of impact of a biological attack was given greater importance when compared with the probability of occurrence of this event. According to the subcriteria, the greatest weight was attributed to human health when compared with agriculture, the environment, or the economy. Finally, in the evaluation of the three pathogens, B. anthracis was identified as having the highest risk for offensive use in Brazil. This research is focused on a practical approach and can be implemented by competent agencies to foster their capacity for biological defense by determining which pathogens represent the greatest risk to society.

Breaking Up Rationally

Abstract The end of a long-term romantic relationship ranks among the most stressful and momentous events in life. Thus, the decision of whether to break up with someone whom one has been with for many years should generally be made very carefully. Unfortunately, decision theory is often thought to be unable to provide rational guidance in such high-stake life choices due to the outcomes’ presumed transformative character. The present paper shows how agents can rationally decide whether to leave their romantic partner even if the decision is transformative. It does so by using a novel five-level account of transformative decision-making, which can also be used for other key life choices, and which is the first to integrate in a systematic way several approaches for making (certain types of) transformative decisions that have been proposed in recent years.

Solving Decision Theory Problems with Probabilistic Answer Set Programming

Solving Decision Theory Problems with Probabilistic Answer Set Programming

Toward a Free-Response Paradigm of Decision-Making in Spiking Neural Networks.

Spiking neural networks (SNNs) have attracted significant interest in the development of brain-inspired computing systems due to their energy efficiency and similarities to biological information processing. In contrast to continuous-valued artificial neural networks, which produce results in a single step, SNNs require multiple steps during inference to achieve a desired accuracy level, resulting in a burden in real-time response and energy efficiency. Inspired by the tradeoff between speed and accuracy in human and animal decision-making processes, which exhibit correlations among reaction times, task complexity, and decision confidence, an inquiry emerges regarding how an SNN model can benefit by implementing these attributes. Here, we introduce a theory of decision making in SNNs by untangling the interplay between signal and noise. Under this theory, we introduce a new learning objective that trains an SNN not only to make the correct decisions but also to shape its confidence. Numerical experiments demonstrate that SNNs trained in this way exhibit improved confidence expression, reduced trial-to-trial variability, and shorter latency to reach the desired accuracy. We then introduce a stopping policy that can stop inference in a way that further enhances the time efficiency of SNNs. The stopping time can serve as an indicator to whether a decision is correct, akin to the reaction time in animal behavior experiments. By integrating stochasticity into decision making, this study opens up new possibilities to explore the capabilities of SNNs and advance SNNs and their applications in complex decision-making scenarios where model performance is limited.

Conceptual framework of coaches' decision-making in conventional sports.

A coach's managerial and pedagogical tasks in the sports training process constitute the substantive core of their work, while decision-making serves as the fundamental method underpinning these tasks. Some decisions made by coaches result from deliberate, analytical thinking, which involves extensive information gathering, analysis, and discussion. Others, however, are made quickly and spontaneously, triggered by unforeseen situations during training or competition that demand immediate action. Consequently, the purpose of this study is to develop a conceptual framework for understanding coaches' decision-making behavior in conventional sports. This framework aims to establish appropriate relationships between the various decisions coaches make during the training process and theoretical concepts related to decision-making, both in general and within the coaching context. To design the research, we used the methodology of a conceptual paper and a "model paper" approach, which seeks to build a theoretical framework that predicts relationships between distinct research concepts and scientific disciplines, aiming to integrate them into a cohesive model of coaches' decision-making behavior. The proposed conceptual framework encompasses a comprehensive range of situations that may arise during the sports training process and potential ways to address them. This framework identifies different types of decisions and characteristics associated with coaches' decision-making behavior. It incorporates various sport-specific and general theories of decision-making and cognitive functioning to offer a deeper understanding of how coaches process and execute decisions in diverse contexts. The developed conceptual framework outlines three primary types of decisions-strategic, tactical, and operational-each playing a distinct role in the broader sports training process. These decisions are based on different cognitive processes, which manifest in varied decision-making behaviors and are reinforced by specific leadership styles. The practical value of this framework lies in its potential application for selecting appropriate experts to address the diverse decision-making scenarios encountered in sports training. This ensures the alignment of decision-making styles with the requirements of specific training situations, thereby enhancing the effectiveness and outcomes of the coaching process.

Analisis Keputusan Rusia dalam Operasi Militer Khusus ke Ukraina 2022 melalui Pendekatan Decision-Making Theory

This study aims to analyze Russia's decision to launch Special Military Operations against Ukraine in 2022 using a Decision-Making Theory approach. The primary background of this research is the geopolitical tension related to Ukraine's potential NATO membership, which was perceived as a direct threat to Russia's national security. The method employed in this study is a qualitative analysis based on a literature review, including official documents, academic articles, and other secondary data sources. The findings reveal that Russia's decision was influenced by a security dilemma stemming from NATO's expansion in Eastern Europe and Ukraine's increasing dependence on the West. From an economic perspective, Ukraine's reliance on Russian energy resources became one of the strategic factors in this decision. The study shows that Russia's decision was driven not only by external factors, such as NATO, but also by internal factors, including domestic political support and strong military capabilities. In conclusion, Russia’s decision to launch military operations was a preventive measure to maintain its influence in Eastern Europe, safeguard its national interests, and prevent Ukraine from joining NATO, which is perceived as a long-term threat to Russia's security.

Multi-Timescale Battery-Charging Optimization for Electric Heavy-Duty Truck Battery-Swapping Stations, Considering Source–Load–Storage Uncertainty

With the widespread adoption of renewable energy sources like wind power and photovoltaic (PV) power, uncertainties in the renewable energy output and the battery-swapping demand for electric heavy-duty trucks make it challenging for battery-swapping stations to optimize battery-charging management centrally. Uncoordinated large-scale charging behavior can increase operation costs for battery-swapping stations and even affect the stability of the power grid. To mitigate this, this paper proposes a multi-timescale battery-charging optimization for electric heavy-duty truck battery-swapping stations, taking into account the source–load–storage uncertainty. First, a model incorporating uncertainties in renewable energy output, time-of-use pricing, and grid load fluctuations is developed for the battery-swapping station. Second, based on day-ahead and intra-day timescales, the optimization problem for battery-charging strategies at battery-swapping stations is decomposed into day-ahead and intra-day optimization problems. We propose a day-ahead charging strategy optimization algorithm based on intra-day optimization feedback information-gap decision theory (IGDT) and an improved grasshopper algorithm for intra-day charging strategy optimization. The key contributions include the following: (1) the development of a battery-charging model for electric heavy-duty truck battery-swapping stations that accounts for the uncertainty in the power output of energy sources, loads, and storage; (2) the proposal of a day-ahead battery-charging optimization algorithm based on intra-day-optimization feedback information-gap decision theory (IGDT), which allows for dynamic adjustment of risk preferences; (3) the proposal of an intra-day battery-charging optimization algorithm based on an improved grasshopper optimization algorithm, which enhances algorithm convergence speed and stability, avoiding local optima. Finally, simulation comparisons confirm the success of the proposed approach. The simulation results demonstrate that the proposed method reduces charging costs by 4.26% and 6.03% compared with the two baseline algorithms, respectively, and improves grid stability, highlighting its effectiveness for managing battery-swapping stations under uncertainty.

Generational diversity and team innovation: the roles of conflict and shared leadership.

The increasing generational diversity in modern teams has sparked an ongoing debate about its impact on team performance. Grounded in decision-making and social identity theories, this study explores the multifaceted relationship between generational diversity and team innovation performance, examining the mediating roles of cognitive and affective conflicts and the moderating role of shared leadership. The findings from a three-wave survey of five multi-generational teams in a Chinese organization reveal that generational diversity predicts both cognitive and affective conflicts, which subsequently exert opposing effects on team innovation. Shared leadership positively moderates the relationship between cognitive conflict and team innovation, amplifying the indirect positive effect of generational diversity. However, shared leadership does not moderate the relationship between affective conflict and team innovation. These results offer a more nuanced understanding of the dual role of generational diversity in team innovation and underscore the importance of shared leadership in harnessing its potential benefits.