Ambiguous Decision Research Articles

PKIF-AKA: A Public Key Infrastructure Free Authenticated Key Agreement Protocol for Smart Grid Communication

In smart grid communication, tampering and illegally accessing power data can lead to ambiguous decisions that disturb power demand management and may lead to system failure. Hence, an efficient security protocol is required to secure smart grid communications. This paper proposes a secure authenticated key agreement scheme to establish a session key between smart meters and service providers. The session key is further used to securely share crucial information like power consumption reports and smart appliances. The proposed scheme is free from the requirement of Public Key Infrastructure (PKI) to authenticate the public key, which makes it more reliable to implement. The security analysis shows that the protocol is secure against all security attacks possible over a key agreement protocol. As the proposed technique uses a 160-bit ECC key, it offers the same level of security with lighter computation. As per the performance evaluation, the proposed technique can establish the session key between the smart meter and service provider in just 13.744 ms, showing its efficiency over the other existing protocols in the literature.

Adaptation of The Career Decision Ambiguity Tolerance Scale

The COVID-19 pandemic has led to various changes that increased a possibility for university students to experience career ambiguity. Those with a high tolerance for career ambiguity perceive ambiguous situations as advantageous and do not reject the complexity of the discrepancy. This study aimed to yield the Indonesian version of the Career Decision Ambiguity Tolerance Scale and examine the construct validity and concurrent validity of the adapted version. This study involved 1256 first-year students (58.7% female, mean age = 18.23 years, SD age = .66) from a public university in Central Java, Indonesia. Data were collected using measures of ambiguity tolerance in choosing a career, career decision-making self-efficacy, and vocational identity. Confirmatory Factor Analysis (CFA) was used to examine the structure of the factor of the final scale, showing good fit indices (CMIN/df = 2.93, CFI = .97, TLI = .96, RMSEA = .04). Reliability coefficients of each the three subscales were satisfactory. Concurrent validity was shown by expected associations with measures of career decision-making self-efficacy and vocational identity.

Plant growth information measurement based on object detection and image fusion using a smart farm robot

Traditionally, vegetable and fruit production has relied on empirical and ambiguous decisions made by human farmers. To overcome this uncertainty in agriculture, smart farm robots have been widely studied in recent years. However, measuring growth information with robots remains a challenge because of the similarity in the appearance of the target plant and those around it. In this study, we propose a smart farm robot that accurately measures the growth information of a target plant based on object detection, image fusion, and data augmentation with fused images. The proposed smart farm robot uses an end-to-end real-time deep learning-based object detector that shows state-of-the-art performances. To distinguish the target plant from other plants with a higher accuracy and improved robustness than those of existing methods, we exploited image fusion using both RGB and depth images. In particular, the data augmentation, based on the fused RGB, and depth information, contributes to the precise measurement of growth information from smart farms, regardless of the high density of vegetables and fruits in these farms. We propose and evaluate a real-time measurement system to obtain precise target-plant growth information in precision agriculture. The code and models are publicly available on Github: https://github.com/kistvision/Plant_growth_measurement.

Gender differences in "optimistic" information processing in uncertain decisions.

Decision-makers often are faced with uncertain situations in which they have incomplete information. While risky decisions include the probabilities of the possible outcomes, ambiguous decisions involve both unknown probabilities and unknown outcomes. Prior research has suggested that there are differences in how men and women evaluate risk, but evidence related to gender and ambiguity is mixed. The present work approaches this problem from a novel angle, focusing on the use of information that is present rather than the impact of information that is absent. It examines how individuals assign value in uncertain decisions based on the partial information they do have. While a main effect of gender on value is not observed, there is an enhanced "optimism bias" in how both favorable and unfavorable information influences the subjective value of ambiguous financial prospects for male compared to female participants. Unpacking these effects suggests multiple mechanisms, including a significant contribution of risk processing. Specifically, favorable and unfavorable information are over- and underweighted respectively in male participants' estimated likelihood of a winning outcome, and unfavorable information is underweighted in estimating certainty. There also is an interaction of gender and risk preferences, such that value increases more for male participants as the subjectively estimated likelihood of winning increases. A second experiment demonstrates this risk interaction effect is alsoobserved for objective probabilities of winning, suggesting that the relationship between value and risk uses similar mechanisms across layers of uncertainty.

Risk Decision Making and Executive Function among Adolescents and Young Adults.

The dual theory establishes that the decision-making process relies on two different systems, the affective system and the executive function (EF), developed during adolescence. This study analyzes the relationship between the decision-making and EF processes in a group of early adolescents (mean age = 12.51 years, SD = 0.61), where more affective impulse processes are developed, and in young adults (mean age = 19.38 years, SD = 1.97), where cognitive control processes have already matured. For this purpose, 140 participants in Spain completed the Iowa Gambling Task (IGT) to measure their risky decisions and the Wisconsin Card Sorting Task (WCST) to measure their EF. Performance on the IGT improves over blocks; however, adolescents received lower mean scores than young adults. However, controlling for age, perseverative errors were negatively associated with the mean net score on the risky blocks of IGT; thus, those who committed more perseverative errors in the WCST were more likely to take cards from the disadvantageous decks on the last blocks of the IGT. The current study shows that adolescents and adults solve ambiguous decisions by trial and error; however, adolescents are more likely to make risky decisions without attending to the long-term consequences. Following the dual theory hypothesis, the maturation of EF with age partly accounts for this difference in risky decision-making between adolescents and adults.

A novel belief-based QFD-AHP model in interval type-2 fuzzy environment for lean after-sales service in automotive industry

PurposeThe purpose of this paper is to propose a novel lean management tool to provide a comprehensive and flexible evaluation model while converting customer voices into technical characteristics in lean implementations.Design/methodology/approachFor this purpose, the proposed model was constructed by belief space-evaluations, quality function deployment (QFD) and analytic hierarchy process (AHP) in interval type-2 fuzzy (IT2F) environment. This model involves three phases: determining the linguistic weights and belief-based relations with their IT2F-sets, processing information about IT2F-based belief-evaluations and ranking the technical characteristics using the defuzzified belief-based relative importance values.FindingsThe proposed model was applied to automotive after-sales service in Turkey to demonstrate its use in lean service-decisions. This model was compared with its classical and type-1 fuzzy versions. The ranking-results of the proposed model differed from those of the other versions. The reason is that the IT2F-environment offers a sensitive and flexible evaluation of the model’s linguistic scales.Research limitations/implicationsCalculations in the proposed model may be quite involved for practitioners. An Excel-dashboard was created to simplify the computational complexity.Practical implicationsResearchers/practitioners can apply this model to any lean manufacturing/service implementation.Social implicationsCompany managers/employees/customers can recognize their perception-mechanisms via belief space-evaluations and experience how uncertainty in the perception-mechanism affects their decisions.Originality/valueThe proposed model provides a new lean tool due to the Bayesian model combined with QFD-AHP in IT2F-environment. This model eliminates the ambiguity in conceptual change-based lean decisions.

LATTE: Lattice ATTentive Encoding for Character-based Word Segmentation

A character sequence comprises at least one or more segmentation alternatives. This can be considered segmentation ambiguity and may weaken segmentation performance in word segmentation. Proper handling of such ambiguity lessens ambiguous decisions on word boundaries. Previous works have achieved remarkable segmentation performance and alleviated the ambiguity problem by incorporating the lattice, owing to its ability to capture segmentation alternatives, along with graph-based and pre-trained models. However, multiple granularity information, including character and word, in a lattice that encodes with such models may not be attentively exploited. To strengthen multi-granularity representations in a lattice, we propose the Lattice ATTentive Encoding (LATTE) method for character-based word segmentation. Our model employs the lattice structure to handle segmentation alternatives and utilizes graph neural networks along with an attention mechanism to attentively extract multi-granularity representation from the lattice for complementing character representations. Our experimental results demonstrated improvements in segmentation performance on the BCCWJ, CTB6, and BEST2010 datasets in three languages, particularly Japanese, Chinese, and Thai.

Implicit Negativity Bias Leads to Greater Loss Aversion and Learning during Decision-Making

It is widely accepted there is the existence of negativity bias, a greater sensitivity to negative emotional stimuli compared with positive ones, but its effect on decision-making would depend on the context. In risky decisions, negativity bias could lead to non-rational choices by increasing loss aversion; yet in ambiguous decisions, it could favor reinforcement-learning and better decisions by increasing sensitivity to punishments. Nevertheless, these hypotheses have not been tested to date. Our aim was to fill this gap. Sixty-nine participants rated ambiguous emotional faces (from the NimStim set) as positive or negative to assess negativity bias. The implicit level of the bias was also obtained by tracking the mouse's trajectories when rating faces. Then, they performed both a risky and an ambiguous decision-making task. Participants displayed negativity bias, but only at the implicit level. In addition, this bias was associated with loss aversion in risky decisions, and with greater performance through the ambiguous decisional task. These results highlight the need to contextualize biases, rather than draw general conclusions about whether they are inherently good or bad.

A Clustering Multi-Criteria Decision-Making Method for Large-Scale Discrete and Continuous Uncertain Evaluation.

In recent years, Dempster-Shafer (D-S) theory has been widely used in multi-criteria decision-making (MCDM) problems due to its excellent performance in dealing with discrete ambiguous decision alternative (DA) evaluations. In the general framework of D-S-theory-based MCDM problems, the preference of the DAs for each criterion is regarded as a mass function over the set of DAs based on subjective evaluations. Moreover, the multi-criteria preference aggregation is based on Dempster's combination rule. Unfortunately, this an idea faces two difficulties in real-world applications: (i) D-S theory can only deal with discrete uncertain evaluations, but is powerless in the face of continuous uncertain evaluations. (ii) The generation of the mass function for each criterion relies on the empirical judgments of experts, making it time-consuming and laborious in terms of the MCDM problem for large-scale DAs. To the best of our knowledge, these two difficulties cannot be addressed with existing D-S-theory-based MCDM methods. To this end, this paper proposes a clustering MCDM method combining D-S theory with the analytic hierarchy process (AHP) and the Silhouette coefficient. By employing the probability distribution and the D-S theory to represent discrete and continuous ambiguous evaluations, respectively, determining the focal element set for the mass function of each criterion through the clustering method, assigning the mass values of each criterion through the AHP method, and aggregating preferences according to Dempster's combination rule, we show that our method can indeed address these two difficulties in MCDM problems. Finally, an example is given and comparative analyses with related methods are conducted to illustrate our method's rationality, effectiveness, and efficiency.

Multicriteria group decision making for COVID-19 testing facility based on picture cubic fuzzy aggregation information.

The information aggregation of cubic fuzzy numbers and picture fuzzy numbers have played an important role in decision making. This paper introduces a novel approach to address the problem of testing facility of COVID-19 under picture cubic fuzzy environment. As the picture cubic fuzzy set is a generalized fuzzy structure to handle more uncertainty and ambiguity in decision making problems We discuss its various properties. Based on geometric aggregation operators and Hamacher operations, we introduce some Hamacher geometric aggregation operators under picture cubic fuzzy information. Namely, picture cubic fuzzy Hamacher weighted geometric aggregation operator, picture cubic fuzzy Hamacher hybrid geometric operator, picture cubic fuzzy Hamacher order weighted geometric aggregation operator. Discuss some properties of the defined operators. To verify the importance of the proposed operators, develop multicriteria group decision making (MCGDM) algorithm under picture cubic fuzzy environment and apply this strategy for the selection of an authentic laboratory for COVID-19 test. Further to validate the supremacy of our proposed operators, we present a comparative analysis with pre-existing aggregation operators. Results show that the proposed technique is more effective and suitable for MCGDM problems.

Introducing causal inference in the energy-efficient building design process

“What-if” questions are intuitively generated and commonly asked during the design process. Engineers and architects need to inherently conduct design decisions, progressing from one phase to another. They either use empirical domain experience, simulations, or data-driven methods to acquire consequential feedback. We take an example from an interdisciplinary domain of energy-efficient building design to argue that the current methods for decision support have limitations or deficiencies in four aspects: parametric independency identification, gaps in integrating knowledge-based and data-driven approaches, less explicit model interpretation, and ambiguous decision support boundaries. In this study, we first clarify the nature of dynamic experience in individuals and constant principal knowledge in design. Subsequently, we introduce causal inference into the domain. A four-step process is proposed to discover and analyze parametric dependencies in a mathematically rigorous and computationally efficient manner by identifying the causal diagram with interventions. The causal diagram provides a nexus for integrating domain knowledge with data-driven methods, providing interpretability and testability against the domain experience within the design space. Extracting causal structures from the data is close to the nature design reasoning process. As an illustration, we applied the properties of the proposed estimators through simulations. The paper concludes with a feasibility study demonstrating the proposed framework’s realization.

Метод определения предельно допустимой нагрузки на экосистему

The study aims to solve the problem of information support for the efficient managerial decision-making system oriented to the development and maintenance of anthropogenic landscapes ecosystems. The primary research task is to formulate a benchmark adequate in terms of environmental quality assessment and management. The research method. The study proposes the estimation method of the maximum permissible load on the ecosystem based on the synthesis of environmental quality evaluation approaches, patterns of ecosystem functioning, and data on hydrocarbon fuel consumption in the territory. Determining the parameters of the studied object’s idealized model allows discovering the limit characteristics of its actual prototype, i.e., the human-transformed ecosystem. The analysis of the investigated landscape’s model optimal parameters makes it possible to establish the maximum values of assimilation potential and safe emission mass for the features of the territory. One of the elements of the proposed method is the calculation of supply air volume for the ecosystem based on the analysis of the gas exchanges process, taking into account the mechanisms of biogeochemical cycles and data on the carbon dioxide emission in the territory. The results of research. The representative subject for determining the indicators of anthropogenic landscapes sustainability to technogenic influences is the city of Nazran, the Ingush Republic. The available information on the dendrometric parameters of chosen monoculture (Fraxinus Excelsior) allowed determining the patterns in the dynamics of bio-productivity characteristics in correlation with the age of growing stock, considering its exposure to environmental impacts, which made it possible to evaluate the ecosystems’ assimilation potential. The study represents the results of the carbon dioxide emissions inventory for Nazran and the estimation of its masses absorbed by the monoculture during photosynthesis. It also theoretically determines the supply air mass and volume values needed for the toxicants’ atmosphere mass limits assessment. The obtained data laid out the basis for calculating the parameters of the territorial environmental sustainability, along with the relative aggressiveness indices for each of the studied pollutants. Conclusions. The proposed approach makes it possible to take into account the specifics of the ecosystem of the particular territory and the uncertainty of the motor transport emissions dispersion, eliminate the inconsistencies of elements of the management system, increase transparency and simplify the decision-making process, and reduce the restrictions of information support system, the ambiguity of managerial decisions’ consequences and the risks of opportunistic behavior, predict the situation in the territory considering various strategies for ensuring the environmental stability, and improve the system manageability as a whole.

Brain networks under uncertainty: A coordinate-based meta-analysis of brain imaging studies

In recent years, uncertainty has been extensively studied as a core factor in anxiety models. However, it remains unclear whether there is a stable brain circuitry to cope with uncertainty. Addressing this yet open question, we first distinguish uncertainty into three different states: risky, ambiguity, and threat anticipation. Then, we performed three meta-analyses of fMRI studies to identify those regions that are commonly activated by the three domains using activation likelihood estimation (ALE). The overlapping analyses of the three ALE maps revealed major conjunctions of the risk decision making, ambiguity decision making, and the threat anticipation in specifically the right insula. Contrast analysis further confirmed this finding. In addition, different uncertainty states also have different brain networks involved. Specifically, a large number of brain regions in the frontal-parietal cortex were recruited under ambiguity state, while subcortical gray matter regions were recruited under risk decision making, and the bilateral insula were closely associated with threat anticipation. Additionally, we assessed the co-activation pattern of identified regions using meta-analytic connectivity modeling (MACM) to investigate the potential network underlying the relationship of three domains. The MACM analysis further confirmed that different uncertain states have specific brain network basis. We concluded that the right insula serves as a convergent brain region for brain regions recruited for different uncertain states, and its co-activation pattern also corresponds to the brain network of the three uncertain states. This study is a preliminary attempt to further uncover the brain circuitry of anxiety models with uncertainty at their core.

Development and Initial Validation of the Career Decision-Making Ambiguity Scale

The dual-process theory of career decision-making (DTC) adds to the conversation about understanding and facilitating career decision-making from a more realistic and inclusive perspective and highlights the joint operation of decision ambiguity and ambiguity management strategies in career decision-making. However, a psychometrically sound measure of decision ambiguity is lacking. Thus, the current study used two samples of U.S. college students to develop and initially validate a measure of decision ambiguity (CDMA). Study 1 ( n = 195) developed the CDMA and found initial evidence for a unidimensional structure. Study 2 ( n = 271) cross-validated the unidimensional structure of the CDMA and supported the convergent, discriminant, concurrent, and incremental validities of the CDMA. Specifically, the results supported (1) the differential associations of the CDMA with need for information and with neuroticism/negative affectivity, lack of readiness, and interpersonal conflicts, (2) the positive association between the CDMA and choice/commitment anxiety, and (3) the incremental predictions of the CDMA for career decidedness, career certainty, and major satisfaction over and beyond need for information. Therefore, the current study offers a psychometrically sound measure of decision ambiguity, which has potential to facilitate the scientific inquiry and social justice application of the DTC.

Parental authority as a mediator between career decision-making self-efficacy, career decision ambiguity tolerance, and career choice of nursing students: A path analysis.

Students' decision to pursue a nursing career is influenced by internal (e.g., innate desire, personal experiences) and external variables (e.g., parental influence, social support). However, little is known about how parental authority mediates nursing students' career decision-making self-efficacy, decision-making ambiguity, and nursing as a career choice. This study examined the influence of parental authority on nursing students' decision to pursue nursing and its mediating effect on the association between career choice, career decision ambiguity, and career decision-making self-efficacy. A cross-sectional study that used total enumeration sampling. Four self-report survey instruments were utilized to collect data from nursing students (n=378) of a state-run university in Saudi Arabia. The data were analyzed using Pearson's correlation coefficient, covariance-based structural equation model, and path analysis. The emerging model showed acceptable model fit indices. The path analysis indicated that parental authority mediated the relationship between career decision-making self-efficacy and nursing career choice. Career decision-making self-efficacy positively and indirectly affected nursing career choice through the mediation of parental authority. Career decision ambiguity tolerance did not directly influence parental authority, but it had a positive, indirect effect through the mediation of career decision-making self-efficacy of nursing students. The strong and direct correlation between career decision-making self-efficacy, career decision ambiguity tolerance, and nursing career choice demonstrated in our study indicates that parental authority strongly influences nursing students' career choices. Our study concludes that parental authority over their children is highly predictive of their career choices.

Viewing the Joint Operation of Five Major Decision Difficulties Within the Dual-Process Theory of Career Decision-Making

While research has identified a five-factor model of career decision-making difficulty based on meta-analytical evidence and cross-cultural comparison, the field lacks an elaborated theoretical account of the joint operation of the five decision difficulties. The current study drew on the dual-process theory of career decision-making (DTC) to examine the interplay of the five decision difficulties in predicting career decidedness in the Chinese context. Study 1 developed and validated measures of decision ambiguity, advance in narrowing down, and career decidedness in a sample of Chinese college students ( n = 180). Study 2 examined the structural model using another sample of Chinese college students ( n = 408). Focusing on the relations of the five major decision difficulties to career decidedness, the results did not support the standalone mechanism of confusion management but supported the standalone mechanism of ambiguity management. Additionally, the results supported the crossover mechanism from confusion management to ambiguity management and the crossover mechanism from ambiguity management to confusion management. The results not only shed light on the role of each difficulty but also offer evidence for the interplay of key DTC macro elements. The practical implications based on the joint operation of the five decision-making difficulties are discussed.

Contribution of Heirarchy Ambiguity and Dysfunctional Boundaries in Predicting Emotional Cutoff among Married Women Who Attend Mental Health Centers

This study aims First, to determine the Levels of the emotional Cutoff, the Dysfunctional Boundaries, and the Hierarchy Ambiguity among Married Wives Who Attend Mental Health Services Centers in Jordan, Second, to explore the relationship between the Wives Emotional Cutoff and their dysfunctional Boundaries, and the Relationship between the Wives Emotional Cutoff and their hierarchy Ambiguity. Third, to reveal the Contribution of Hierarchy Ambiguity and Dysfunctional Boundaries in predicting emotional Cutoff among the Sample. In order to achieve the Study Goals, it was guided by the descriptive correlational predictive Method, A convenience Sampling Technique was adopted to collect the Data from the Participants consisted of (163) Wives with specific terms, after examining the Psychometric Properties (Validity and Reliability). Data were gathered applying emotional cutoff, dysfunctional boundaries, and hierarchy ambiguity scales developed by the researcher. The Study Results Indicated that the Levels of the Emotional cutoff and the Enmeshment among wives were High, the Disengagement level was Moderate, and a Low Level for Hierarchy Ambiguity. Moreover, the Result Revealed a Positive significant Relationship between the wives` Emotional Cutoff and their dysfunctional Boundaries and hierarchy Ambiguity, also, the Variables of the Enmeshment, Disengagement, and the Influence Strategies were predictors of emotional cutoff among Participants in Row. However, the dimension of ambiguity decision making was not a predictor of emotional cutoff. The study recommends developing preventive and therapeutic programs to increase the awareness with study variables and to treat the Problematic Cases.

Ambiguity attitudes in qualitative contexts: The role of prior beliefs

AbstractMost studies of ambiguity aversion rely on experimental paradigms involving monetary bets. Thus, the extent to which ambiguity aversion occurs outside of such contexts is much less understood, particularly when the situation cannot easily be reduced to numerical terms. The present work seeks to understand whether people prefer to avoid ambiguous decisions in a variety of different qualitative domains (e.g., work, family, love, friendship, exercise, study, and health), and, if so, to determine the role played by prior beliefs in those domains. Across three studies, we presented participants with 24 vignettes and measured the degree to which they preferred risk to ambiguity in each. We also asked them for their prior probability estimates about the likely outcomes in the ambiguous events. Ambiguity aversion was observed in the vast majority of vignettes, but at different magnitudes. It was predicted by whether the vignette involved gain or loss as well as by people's prior beliefs; however, the heterogeneity between people meant that the role of prior beliefs was only evident in an individual‐level analysis (i.e., not at the group level). Our results suggest that the desire to avoid ambiguity occurs in a wide variety of qualitative contexts but to different degrees for different people and may be partially driven by unfavorable prior estimates of the likely outcomes of the ambiguous events.

From ambiguity to action: integrating collective sensemaking and rational decision making in management pedagogy and practice

PurposeManagers are increasingly presented with complex, ambiguous decision problems that affect multiple stakeholder groups. Such problems cannot be tackled solely by classical approaches that prescribe rational methods to weigh evidence and select an optimal course of action. Yet most courses on decision making still focus on these methods. This paper draws attention to the complementary nature of rational decision making and sensemaking techniques in management decision making, and describes a practical pedagogy that demonstrates how the two can be integrated into management curricula.Design/methodology/approachBased on an in-depth review of relevant research, the authors propose a conceptual model that highlights the complementary nature of rational and sensemaking methods for making decisions relating to complex and ambiguous problems. They then describe a course on decision making as an illustration of how the model can inform decision making pedagogy.FindingsDecision makers need to think of their decision problems in terms of two distinct types of uncertainty: those for which uncertainty can be quantified and those for which it cannot. When faced with the latter, decisions are best made by working with relevant stakeholders to collectively frame the problem using practical sensemaking tools prior to applying rational decision making techniques to address it. Decision making under ambiguity is an iterative, social process requiring a combination of rational decision making methods and sensemaking techniques.Practical implicationsThe paper seeks to increase awareness about the complementary nature of sensemaking and rational decision making. It emphasizes the need to integrate the two in management curricula and provides details on how this can be done via an example of a course implemented at an Australian Business School. The techniques described will also be of interest to practitioners.Originality/valueThe paper describes a practical pedagogy that blends rational decision making and collective sensemaking techniques in a way that fosters managers’ decision making skills in contexts characterized by ambiguity.

Influence of Career Decision Ambiguity Tolerance Preference on Career Adaptability: The Role of Individual and Organizational Career Management.

Drawing on the career construction and cognitive appraisal theory, this study constructs a moderated mediation model to explore the mechanism of career decision ambiguity tolerance preference (CDAT-P) on career adaptability via challenge appraisal, as well as the interactive effect of individual and organizational career management. Participants comprised 1023 Chinese university students who completed questionnaires with a three-wave design. The results show that CDAT-P positively predicts career adaptability, and challenge appraisal mediates the direct effect. Moreover, individual career management and organizational career management have an interactive effect on the direct relationship between CDAT-P and challenge appraisal and the first stage of the mediating effect. Specifically, when individual and organizational career management are high, the positive direct effect of CDAT-P on challenge appraisal and the mediating effect are stronger.