Set Of Decisions Research Articles

Assessing cybersecurity risks and prioritizing top strategies In Vietnam's finance and banking system using strategic decision-making models-based neutrosophic sets and Z number

The financial and banking system in Vietnam is currently confronted with a myriad of cybersecurity risks, a matter of paramount concern for the government, businesses, and individuals alike. These risks threaten the financial system's security, reliability, and integrity, potentially resulting in significant economic losses. This research endeavors to identify the prevailing cybersecurity risks in Vietnam's financial and banking system, assess their impact, and elucidate the interrelationships among these risks. Employing the Multi-Criteria Decision Making (MCDM) approach, the study integrates the DELPHI technique, Decision-Making Trial and Evaluation Laboratory (DEMATEL), and Combined Compromise Solution (COCOSO) methods, complemented by Neutrosophic Sets (NS) and Z-number concepts to enhance the accuracy and reliability of the findings. The research findings reveal the existence of 15 cybersecurity risks in Vietnam's financial and banking system, with Malware Infections and Supply Chain Vulnerabilities emerging as the most consequential risks. Moreover, the study identifies investing in advanced threat detection systems as the optimal strategy for mitigating cybersecurity risks in Vietnam. The results underscore the importance of addressing these critical risks to safeguard the financial infrastructure, focusing on deploying robust cybersecurity measures to enhance overall system resilience.

The Group Intertemporal Decision-Making Process.

Intertemporal decision making is the process by which individuals make judgments or choices regarding outcomes that occur at different times. Although intertemporal decision making is widely investigated, most studies explore it in terms of individual decision making, while neglecting group decision making, which holds more practical significance and adaptive value. This study recommends adopting a "two-process" approach that uses self-assessment questionnaires, audiovisual recordings, and visual decision-making tools to quantify interpersonal interaction processes and information processing in group intertemporal decision-making settings. In this way, studies can reveal the psychological and theoretical mechanisms of the group intertemporal decision-making process. At the level of interpersonal interaction processes, such an approach can identify the differential mechanisms between group and individual intertemporal decision making. In terms of information processing, it can reveal the mechanisms of the decision-making process in group intertemporal decision making. The findings of such work can provide a basis for interventions and nudges that encourage more visionary group intertemporal decision making.

Novel Linguistic Intuitionistic Fuzzy Score and Accuracy functions for Linguistic Intuitionistic Fuzzy TOPSIS Method

Abstract: This work proposes various Linguistic Intuitionistic Fuzzy Score and Accuracy functions (LIF-Score and LIFAccuracy), including linguistic degree, membership, non-membership, and hesitancy degree, for Linguistic Intuitionistic Fuzzy Sets (LIFSs) in Multiple Attribute Group Decision Making (MAGDM) problems. The proposed functions are integrated into the Linguistic Intuitionistic Fuzzy-Technique of Order Preference by Similarity to Ideal Solution (LIF-TOPSIS) MAGDM approach to rank the alternatives, and a numerical example demonstrating their use in ranking alternatives is shown in the paper.

On the Value of Information Across Decision Problems

The value of information is an important concept in decision analysis that has been quantified as the buying price (BPI) for the information and the expected utility increase (EUI) obtainable by using the information. These two measures rank information sources identically in a scalar-valued decision problem only when the utility function is linear or exponential. In contrast, this paper focuses on the value of information across scalar-valued decision problems sharing the same utility function such as different divisions within an organization exploring various information sources for their decisions using the same organizational utility function. In this context, it still makes sense to ask which sources are more informative. We show that BPI and EUI rank information sources identically in this context only when the utility function is linear. However, if the certainty equivalent increase is used instead of EUI, then identical ranking with BPI across problems is maintained for the broader class of linear or exponential utility functions. We discuss the importance of these results for distributed decision-making settings, where different departments within an organization may calculate the value of information separately. Our results advise against using EUI to measure information value in this context when risk attitude is important.

An efficient algorithm for solving multilevel multi-objective linear fractional optimization problem with neutrosophic parameters

This work demonstrates the modeling of the multilevel-multiobjective linear fractional optimization (ML-MOLFO) problem in a neutrosophic decision environment. The primary driving force behind this work is to determine how to handle a ML-MOLFO problem while taking into account a real-life decision-making scenario. There is a hierarchy of decision-makers and many decision-making processes, such as agree, not-sure, and disagree, in a real-life decision-making setting. By taking into account such genuine situations, all resource, coefficient of objective, and constraint functions are represented as single-valued trapezoidal neutrosophic numbers. As far as we can tell, there is no established technique for resolving the ML-MOLFO problem with neutrosophic parameters in the literature review. Therefore, by outlining goal programming methods in the intuitionistic fuzzy environment, we create our new intuitionistic fuzzy Chebyshev goal programming (IFCGP) method to address the introduced problem. In the proposed method, first we used a modified ranking function to transform the problem into an analogous crisp ML-MOLFO problem, and then it was converted into a multilevel multiobjective linear optimization problem through linearized techniques. The linearization procedure in the proposed method, unlike the existing one, does not require derivatives, additional variables, or inequality constraints, which makes it simpler. To illustrate the adequacy and performance of the proposed new method, we considered numerical tests and application problems. We also compared the performance of the resulting optimal solution with existing approaches using the distance metric, which indicates our method is non-dominated.

PresAIse, a prescriptive AI solution for enterprise

Prescriptive artificial intelligence (AI) represents a transformative shift in decision-making by offering causal insights and actionable recommendations. Despite its huge potential, enterprise adoption of prescriptive AI faces several challenges. One such challenge is caused by the lack of experimental data for many enterprises, making it hard to attribute differences in outcomes to interventions alone. The second pertains to the explainability of AI recommendations, which is crucial for enterprise decision-making settings. The third challenge is contributed by the silos between technologists and business users, hindering effective collaboration. This paper outlines an initiative from IBM Research, PresAIse, aiming to address some of these challenges by offering a suite of prescriptive AI solutions. Leveraging insights from various research papers, the solutions include scalable causal inference methods, interpretable decision-making approaches, and the integration of large language models (LLMs) to bridge the communication gap via a conversation agent. A proof-of-concept demonstrates the solutions’ potential by enabling non-ML experts to interact with prescriptive AI models via a natural language interface, democratizing advanced analytics for strategic decision-making.

Obedience induces agentic shifts by increasing the perceived time between own action and results

The concept of 'agentic shift,' introduced by Stanley Milgram, suggests that obedience reduces the sense of agency. In a recent study simulating the seminal work of Milgram, Caspar et al., 2016 examined this idea in a financial harm context. They demonstrated that, compared to situations of voluntary decision-making, coercion increases the perceived time between action and outcomes—suggested as a marker of diminished agency. Importantly, in this study, participants were agent and victim (relying on a reciprocal relationship) and first experienced free choices, followed by forced choices. This diverts from Milgram’s original study, where participants were no victims but only agents who were forced to harm. The current study replicates and extends findings from the 2016 study by Caspar et al. in an online meeting setting, where participants served only as agents—similar to the original Milgram studies—while controlling the order of free and forced choice blocks. Substantiating earlier findings, forced choices reduced temporal binding (increased time interval estimations) compared to free choices independent of the order. We briefly discuss the importance of replications of coercion effects on the sense of agency, particularly in online decision-making settings.

Intuitionistic fuzzy sets in group decision making – A novel approach

We use the natural properties of intuitionistic fuzzy sets (IFSs for short) to represent the pros, cons, and lack of knowledge concerning different options/alternatives, aiding in decision making, particularly in group decision making. We present a novel approach. We do not compare options/alternatives in pairs, we do not use distances. The approach is transparent and easily understandable for decision makers. The novel method points out the best option by ranking them.

Efficient targets and reference sets in selectively convex technologies

Conventional models of data envelopment analysis (DEA) typically assume that the underlying production technology is a convex set. It is known that such assumption may be clearly unsubstantiated in certain cases. Examples include studies in which some inputs or outputs are stated as proportions or percentages, or are represented by categorical measures. Excluding such “problematic” inputs and outputs from the assumption of convexity while assuming the latter for the remaining measures leads to the notion of selective convexity. Further examples of selectively convex technologies include technologies parameterized by an environmental factor and technologies in which only the input or output sets are convex. In this paper, we consider the identification of efficient targets and reference sets of decision making units in a selectively convex technology, which has not yet been explored in the literature. We show that, for such technologies, the conventional method based on the solution of the additive DEA model may not correctly identify the reference sets and needs an adjustment.

Multi-actor approach to manage the water-energy-food nexus at territory scale

This study presents an innovative water-energy-food nexus (WEFN) generic multi-actor multi-level framework that simulates dynamic nexus systems and generates optimal resource management schemes and land-use allocation scenarios. The framework combines operational integrated multi-agent based simulations, dynamic agro-hydrological and renewable energy generation units (solar and wind) models and multi-criteria decision-making methods. It has been applied to a case study area of 800 km2 located downstream the Aveyron watershed, in Southwestern France. Local and regional strategic multi-criteria decision-making settings are compared. Under the local decision-making setting land-use allocation decisions are taken at agricultural parcel level whereas under the regional decision-making settings a single land allocation decision is taken for the whole territory. Possible land allocations are either crop rotations allocations, renewable energy generation units allocations or a mix of both. The framework is useful to generate scenarios of spatial allocations of renewable energy production units in an agricultural watershed facing water scarcity.

Reflections on the Consideration of Greenhouse Gas Emissions in Environmental Impact Assessment

Abstract Environmental impact assessment (EIA) involves assessing the implications of proposed activities on the environment to inform decisions about whether those actions should proceed and under what conditions. Efforts are being made to incorporate climate change considerations into EIA internationally, but the assessment of greenhouse gas (GHG) emissions poses particular challenges. This article compares the incorporation of GHG emissions into EIA in two jurisdictions: Canada (under the Impact Assessment Act 2019) and Western Australia (under the Environmental Protection Act 1986). Four questions are considered, relating to screening and scoping; information requirements; decision-making and condition setting; and post-approval activities. Key differences between the two jurisdictions were found in relation to screening and scoping (Western Australia applies an emissions threshold while Canada utilizes a project list coupled with tailored guidelines); decision-making (Western Australia generally considers a straight line trajectory to net zero by 2050 as acceptable whereas Canada considers emissions in the context of international commitments and against other sustainability considerations); and post-approval activities (a strength of the Western Australian system is mechanisms enabling review and tightening of GHG conditions over time). It will be important to continue to review the effectiveness of EIA as a tool for climate mitigation as practice evolves.

How framing bias impacts preferences for innovation in bone tissue engineering.

It is currently unknown if surgeons and biomaterial scientists &or tissue engineers (BS&orTE) process and evaluate information in similar or different (un)biased ways. For the gold standard of surgery to move "from bench to bedside", there must naturally be synergies between these key stakeholders' perspectives. Because only a small number of biomaterials & tissue engineering innovations have been translated into the clinic today, we hypothesised this lack of translation is rooted in the psychology of surgeons and BS&orTE. Presently, both clinicians and researchers doubt the compatibility of surgery and research in their daily routines. This has led to the use of a metaphorical expression "squaring of the circle," which implies an unsolvable challenge. As bone tissue engineering belongs to the top 5 research areas in tissue engineering we choose the field of bone defect treatment options for our bias study. Our study uses an online survey instrument for data capture: incorporating a behavioural economics cognitive framing experiment methodology. Our study sample consisted of surgeons (n=208) and BS&orTE (n=59). And we employed a convenience sampling method, with participants (conference attendants) being approached both in person and via email - 22 October 2022-13 March 2023. We find no distinct positive-negative cognitive framing differences by occupation. That is, any framing bias present in this surgical decision-making setting does not appear to differ significantly between surgeon and BS&orTE specialisation. When we explored within group differences by frames, we see statistically significant (p<0.05) results for surgeons in the positive frame ranking autologous bone graft transplantation lower compared to surgeons in the negative frame. Further, surgeons in the positive frame rank Ilizarov bone transport method higher compared to surgeons in the negative frame (p<0.05).

Systemic barriers preventing farmer engagement in the agricultural climate transition: a qualitative study

AbstractFarmers and the agricultural sector are facing an uncertain future, due to rapidly changing agricultural policy and a nexus of commercial, environmental and biodiversity challenges brought on by anthropogenic climate change. Balancing the interests of the farming community and natural environment is generating organisational and personal conflict, rather than effective agricultural adaption and a focus on the net zero transition. In this paper, we consider how farmers and the wider farming community can participate in engagement and decision-making for the transition, on their own terms, whilst ensuring social justice for fair and equitable decision-making. We discuss findings from our research project, that used questionnaires and follow up semi-structured interviews with farmers and farming groups to reveal systemic issues that prevent farmers engaging with the climate transition to net zero during their everyday practice. We highlight the structural relationships that form these barriers and frustrate farmers when designing and implementing farm interventions to agricultural systems during times of transition. Farmers have highlighted barriers to be associated with policy, (uncertain government strategy, landowner bias in government policy and tenancy restrictions, government bureaucracy—particularly environment schemes), commercial (market forces—downward pressure from supermarkets on price points, availability of labour due to seasonal migration restrictions, increasing cost of labour) and interpersonal (succession—uncertain future of the family farm due to limited income). This frustrates the ability of farmers to address climate change through farming practices and undermines the implementation of innovative engagement practices, such as co-produced interventions, in policy and decision-making settings.

Contraceptive Attitudes and Beliefs of Women With Sickle Cell Disease: A Qualitative Study

ObjectivesWe aimed to qualitatively explore factors influencing contraceptive use in women living with sickle cell disease (SCD). Study DesignWe conducted a semi-structured qualitative study using data from interviews with reproductive-aged women with SCD. The Theory of Planned Behavior, which describes an individual's attitudes, subjective norms, and perceived behavioral control as drivers of a health behavior, served as a framework for data collection and thematic analysis. ResultsWe analyzed interviews from 20 participants. Attitudes toward contraception showed skepticism regarding hormonal contraception and concern about adverse effects of contraception related to SCD. The role of subjective norms in contraceptive choice depended on whether participants possessed trusted sources of information, such as health care providers, family, and friends, or whether they relied on themselves alone in their contraceptive decision-making. The influence of health care providers was complicated by inconsistent and sometimes conflicting counseling. Finally, with regard to participants’ perceived control in their contraceptive choices, some were motivated to alleviate menstrual pain or prevent SCD-related pregnancy risks, but many reported feeling disempowered in contraceptive decision-making settings because of ways that healthcare providers exerted pressure or responded to participants’ demographic characteristics. ConclusionsWomen living with SCD are influenced by many factors similar to those documented for other populations when making decisions about contraception. However, SCD amplifies both the importance and risks of contraception and may also complicate individuals' autonomy and contraceptive access. Hematology and reproductive health providers should recognize the risk of coercion, maximize patients’ reproductive agency, and coordinate reproductive health counseling for patients with SCD.

Transdisciplinary approaches to local sustainability: aligning local governance and navigating spillovers with global action towards the Sustainable Development Goals

In an evolving world, effectively managing human–natural systems under uncertainty becomes paramount, particularly when targeting the United Nations 2030 Agenda for Sustainable Development Goals (SDGs). The complexity in multi-actor decision-making and multi-sectoral settings, coupled with intricate relationships and potential conflicting management approaches, makes understanding the local implications of progressing towards the global SDGs challenging. We used a transdisciplinary approach for knowledge co-production with local stakeholders to assess the impact of local action to boost sustainability in the Goulburn–Murray region, Victoria, Australia, and its alignment with global action towards the SDGs. Together, we co-developed 11 local actions geared towards achieving four locally important environmental and socioeconomic SDGs, with a particular emphasis on addressing potential ‘spillovers’—unintended effects that influence SDGs across scales. Through system dynamics modelling, we evaluated the interplay between these local actions and global scenarios, emphasising their synergies, trade-offs, and the resulting impact on SDG indicators. Key findings indicate a predominant synergy between global and local actions across most SDG indicators. However, certain areas like dairy production, riverine algal blooms, and agricultural profit displayed trade-offs. Local actions significantly impacted indicators, such as crop production, dairy output, agricultural land use, and agricultural profitability. Findings highlighted the need for complementary actions in areas, such as water availability management, skilled workforce, and salinity control. This study underscored the importance of harmonising local initiatives with global sustainability objectives and can inspire local governance to champion resilience policies that harmoniously integrate local actions with global sustainability goals, adapting to evolving uncertainty scenarios.Graphical abstract

Explainable Reinforcement Learning: A Survey and Comparative Review

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recent years. The goal of XRL is to elucidate the decision-making process of reinforcement learning (RL) agents in sequential decision-making settings. Equipped with this information, practitioners can better understand important questions about RL agents (especially those deployed in the real world), such as what the agents will do and why. Despite increased interest, there exists a gap in the literature for organizing the plethora of papers—especially in a way that centers the sequential decision-making nature of the problem. In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting. We propose three high-level categories: feature importance, learning process and Markov decision process, and policy-level. We overview techniques according to this taxonomy, highlighting challenges and opportunities for future work. We conclude by using these gaps to motivate and outline a roadmap for future work.

Survival outcome and optimal candidates of primary tumor resection for patients with metastatic medullary thyroid cancer.

Medullary thyroid cancer (MTC) often exhibits aggressive growth with distant organ metastasis, leading to poor survival. The question of whether primary tumor resection (PTR) is beneficial for patients with metastatic MTC remains a subject of debate. In this study, we evaluated the prognostic significance of organ-specific metastases and the number of metastatic organs in these patients, and we also conducted an analysis to determine the therapeutic value of PTR in managing this rare malignancy. Patients initially diagnosed with metastatic MTC were identified within the Surveillance, Epidemiology, and End Results (SEER) database. Univariable and multivariable Cox proportional hazards regression models were performed to identify survival predictors. Survival outcomes were calculated using the Kaplan-Meier method and compared using the log-rank tests. A total of 186 patients with metastatic MTC at initial diagnosis from 2010 to 2020 were included. Bone, lung and liver were the most common metastatic organs. Patients with brain metastasis had significantly worse overall survival (OS) (p = 0.007) and cancer-specific survival (CSS) (p = 0.0013). Among all patients, 105 (56.45%) underwent PTR, and this group showed reduced overall mortality (OM) and cancer-specific mortality (CSM) (all p < 0.05). When analyzing different metastatic patterns, PTR significantly lowered the risk of OM and CSM for patients with bone, lung, liver, or distant lymph node (DLN) involvement (all p < 0.05). Additionally, among patients with one or two metastases, those undergoing surgical resection were significantly associated with favorable OS (p = 0.008) and CSS (p = 0.0247). PTR may confer therapeutic benefits for carefully selected individuals with metastatic MTCs. To integrate these insights into clinical decision-making settings, it is imperative to undertake multicenter prospective studies in the future.

Cost-effectiveness analysis of CTZ/TAZ for the treatment of ventilated hospital-acquired bacterial pneumonia and ventilator-associated bacterial pneumonia in Japan

BackgroundResistant bacterial infections, particularly those caused by gram-negative pathogens, are associated with high mortality and economic burdens. Ceftolozane/tazobactam demonstrated efficacy comparable to meropenem in patients with ventilated hospital-acquired bacterial pneumonia in the ASPECT-NP study. One cost-effectiveness analysis in the United States revealed that ceftolozane/tazobactam was cost effective, but no Japanese studies have been conducted. Therefore, the objective of this study was to assess the cost-effectiveness of ceftolozane/tazobactam compared to meropenem for patients with ventilated hospital-acquired bacterial pneumonia/ventilator-associated bacterial pneumonia from a health care payer perspective.MethodsA hybrid decision-tree Markov decision-analytic model with a 5-year time horizon were developed to estimate costs and quality-adjusted life-years and to calculate the incremental cost-effectiveness ratio associated with ceftolozane/tazobactam and meropenem in the treatment of patients with ventilated hospital-acquired bacterial pneumonia/ventilator-associated bacterial pneumonia. Clinical outcomes were based on the ASPECT-NP study, costs were based on the national fee schedule of 2022, and utilities were based on published data. One-way sensitivity analysis and probabilistic sensitivity analysis were also conducted to assess the robustness of our modeled estimates.ResultsAccording to our base-case analysis, compared with meropenem, ceftolozane/tazobactam increased the total costs by 424,731.22 yen (£2,626.96) and increased the quality-adjusted life-years by 0.17, resulting in an incremental cost-effectiveness ratio of 2,548,738 yen (£15,763.94) per quality-adjusted life-year gained for ceftolozane/tazobactam compared with meropenem. One-way sensitivity analysis showed that although the incremental cost-effectiveness ratio remained below 5,000,000 yen (£30,925) for most of the parameters, the incremental net monetary benefit may have been less than 0 depending on the treatment efficacy outcome, especially the cure rate and mortality rate for MEPM and mortality rate for CTZ/TAZ. 53.4% of the PSA simulations demonstrated that CTZ/TAZ was more cost-effective than MEPM was.ConclusionAlthough incremental cost-effectiveness ratio was below ￥5,000,000 in base-case analysis, whether ceftolozane/tazobactam is a cost-effective alternative to meropenem for ventilated hospital-acquired bacterial pneumonia/ventilator-associated bacterial pneumonia in Japan remains uncertain. Future research should examine the unobserved heterogeneity across patient subgroups and decision-making settings, to characterise decision uncertainty and its consequences so as to assess whether additional research is required.

Towards a More Burkean Approach to Computational Social Choice

In the last few years, a lot of the activity of the computational social choice community has focused on novel mechanisms for reaching decisions by large groups of people. While this research makes meaningful scientific contributions, many of these mechanisms are not quite useful in realistic decision-making settings. Moreover, their radicalism ignores the centuries-old experience we have with large-scale human decision-making, and what it teaches us about what works. We believe it is important the community engage with mechanisms which are widely-used in the real world, as they may hold a key to a deeper understanding of how people reach decisions and the way that helps them do that productively. Moreover, letting the community bring its analysis and understanding to these will allow for algorithmic suggestions that have some chance of being implemented (and, thus, can contribute to the public debate on these topics). In particular, we highlight the relatively less-investigated role of parties and grouping of voters and candidates, and the role of executive capacity in analyzing decision-making structures.

Discrimination in Multiphase Systems: Evidence from Child Protection

Abstract We develop empirical tools for studying discrimination in multiphase systems and apply them to the setting of foster care placement by child protective services. Leveraging the quasi-random assignment of two sets of decision-makers—initial hotline call screeners and subsequent investigators—we study how unwarranted racial disparities arise and propagate through this system. Using a sample of over 200,000 maltreatment allegations, we find that calls involving Black children are 55% more likely to result in foster care placement than calls involving white children with the same potential for future maltreatment in the home. Call screeners account for up to 19% of this unwarranted disparity, with the remainder due to investigators. Unwarranted disparity is concentrated in cases with potential for future maltreatment, suggesting that white children may be harmed by “underplacement” in high-risk situations.