Classical Decision Research Articles

Possible new applications of the interactive programming based on aspiration levels—case of pure and mixed strategies

The interactive programming (IP) using aspiration levels is a well-known method applied to multi-criteria decision making under certainty (M-DMC). However, some essential analogies between M-DMC and scenario-based one-criterion decision making under uncertainty (1-DMU) have been recently revealed in the literature. These observations give the opportunity to adjust the IP to a totaly new issue. The goal of the paper is to create two novel procedures for uncertain problems on the basis of the IP ideas: the first one for pure strategy searching and the second for mixed strategy searching. In many ways, they allow a better consideration of the decision maker's preferences than classical decision rules. One of their significant advantages consists in analyzing particular scenarios sequentially. Another strong point is that the new procedures can be used by any kind of decision makers (optimists, moderate, pessimists). The new approaches may be helpful when solving problems under uncertainty with partially known probabilities. Both methods are illustrated in the paper on the basis of two fictitious decision problems concerning the choice of an optimal location and the optimization of the stock portfolio structure.

Attribute reduction in inconsistent grey decision systems based on variable precision grey multigranulation rough set model

This paper mainly deals with attribute reduction of inconsistent grey decision systems (IGDSs) based on the variable precision grey multigranulation rough set (VP-GMGRS). Firstly, we present two transformation models to transform IGDS into consistent decision confidence system. One is the consistent decision system transformation model, based on which, an IGDS can be transformed into a VP-GMGRS approximate distribution consistent decision system. The other is the decision confidence system transformation model, which can be degenerated to a classical group decision system. Meanwhile, we educe related judgement theorems of approximation distribution consistent set in IGDS. Following that, a theoretical attribute reduction approach is presented by employing discernibility attribute sets and function based on VP-GMGRS approximate distributions. In addition, algorithms and illustrative examples with IGDS are employed and assisted to understand and explain the mechanism of attribute reduction theoretical approaches. Finally, comparison experiments are organized to verify the validity and feasibility of the proposed reduction method.

Energy Security Assessment Based on a New Dynamic Multi-Criteria Decision-Making Framework

Access to energy resources and broadly understood energy security are some of the critical factors influencing the economic development of countries. This article deals with the problem of assessing the energy security of countries, considering this problem in various periods of time, examining the past, present and forecasted future conditions at the same time. For this purpose, the Dynamic Multi-Criteria Decision Making (DMCDM) methodology was developed and applied, based on the classic and fuzzy Multi-Criteria Decision Making (MCDM) methods and the International Energy Security Risk Index (IESRI). In particular, the Simple Additive Weighting (SAW)/Fuzzy SAW and New Easy Approach to Fuzzy PROMETHEE II (NEAT F-PROMETHEE) methods were used. These methods are significantly different from each other in the calculation procedures used. The study showed that methodological differences between these methods cause large differences in the results of the assessment of energy security of countries. However, both methodological approaches indicated the high energy security of New Zealand, Norway, Denmark and the United States, and the very low security of Ukraine, Thailand and South Korea. The results of the assessment of energy security of countries over the 2015–2025 period are the main practical contribution of this article. The scientific contribution of the article consists in developing a framework for dynamic energy security assessment that allows for the aggregation of many periods of time and that defines the aggregation strategies, capturing data from the past, present and future state forecasts while taking into account changes in the weights of criteria and changes in the sets of alternatives and criteria.

Identifying hepatocellular carcinoma patients with survival benefits from surgery combined with chemotherapy: based on machine learning model

BackgroundHepatocellular carcinoma (HCC) is still fatal even after surgical resection. The purpose of this study was to analyze the prognostic factors of 5-year survival rate and to establish a model to identify HCC patients with gain of surgery combined with chemotherapy.MethodsAll patients with HCC after surgery from January 2010 to December 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic analysis were used to analyze the prognostic factors of patients, and the risk prediction model of 5-year survival rate of HCC patients was established by classical decision tree method. Propensity score matching was used to eliminate the confounding factors of whether to receive chemotherapy in high-risk group or low-risk group.ResultsOne-thousand six-hundred twenty-five eligible HCC patients were included in the study. Marital status, α-fetoprotein (AFP), vascular infiltration, tumor size, number of lesions, and grade were independent prognostic factors affecting the 5-year survival rate of HCC patients. The area under the curve of the 5-year survival risk prediction model constructed from the above variables was 0.76, and the classification accuracy, precision, recall, and F1 scores were 0.752, 0.83, 0.842, and 0.836, respectively. High-risk patients classified according to the prediction model had better 5-year survival rate after chemotherapy, while there was no difference in 5-year survival rate between patients receiving chemotherapy and patients not receiving chemotherapy in the low-risk group.ConclusionsThe 5-year survival risk prediction model constructed in this study provides accurate survival prediction information. The high-risk patients determined according to the prediction model may benefit from the 5-year survival rate after combined chemotherapy.

An Estimated δ-Based Iterative Block Decision Feedback Equalization in SC-FDE System

We provide a novel nonlinear frequency domain equalization algorithm for the frequency domain equalization of an SC-FDE system by improving the classical iterative block decision feedback equalization (IBDFE) algorithm and applying δ estimation to the improved algorithm. The improvement of the IBDFE algorithm is carried out by replacing the ZF equalization in the feedback branch with the MMSE equalization and eliminating the iteration of the correlation factor, thus reducing the noise error and the computational complexity of the original algorithm. δ estimation can estimate residual inter-symbol interference in the signal after MMSE equalization and reject it, thus further improving the equalization accuracy. The simulation results show that the performance of the novel algorithm is better than that of the IBDFE algorithm with similar complexity, or the complexity of the novel algorithm is lower than that of the IBDFE algorithm with similar performance.

Decisions, decisions, decisions in an uncertain environment

AbstractDecision‐makers abhor uncertainty, and it is certainly true that the less there is of it the better. However, recognizing that uncertainty is part of the equation, particularly for deciding on environmental policy, is a prerequisite for making wise decisions. Even making no decision is a decision that has consequences, and using the presence of uncertainty as the reason for failing to act is a poor excuse. Statistical science is the science of uncertainty, and it should play a critical role in the decision‐making process. This opinion piece focuses on the summit of the knowledge pyramid that starts from data and rises in steps from data to information, from information to knowledge, and finally from knowledge to decisions. Enormous advances have been made in the last 100 years ascending the pyramid, with deviations that have followed different routes. There has generally been a healthy supply of uncertainty quantification along the way but, in a rush to the top, where the decisions are made, uncertainty is often left behind. In my opinion, statistical science needs to be much more pro‐active in evolving classical decision theory into a relevant and practical area of decision applications. This article follows several threads, building on the decision‐theoretic foundations of loss functions and Bayesian uncertainty.

An improved decision tree algorithm based on variable precision neighborhood similarity

The decision tree algorithm has been widely used in data mining and machine learning due to its high accuracy, low computational cost and high interpretability. However, when dealing with the continuous data, the classical decision tree algorithm needs to replace continuous attributes with discretized attributes by the strategy of discretization. Discretization may cause a loss of information structure, which will affect the performance of classification. To tackle this problem, many researchers have proposed different decision tree methods based on variable precision neighborhood rough sets. However, these methods do not consider the geometric structure of neighborhood systems, which may lead to a contradiction in the transitivity of the equivalence relation. In this paper, we first define a novel neighborhood geometric similarity in a neighborhood system from the perspective of geometry. Second, by combining the neighborhood geometric similarity and the neighborhood algebraic similarity, we propose four new kinds of neighborhood similarities, which can solve the contradictory transitivity of the equivalence relation. Third, a variable precision neighborhood rough set model is constructed using the new similarities, and a novel decision tree algorithm is proposed based on this model, where the degree of attribute dependence is used as the partition measure. Experimental results on 14 selected datasets from the UCI Machine Learning Repository show that our algorithm is effective. The average accuracy of our algorithm is over 90%, which is 10% higher than the classical decision tree algorithms, and the number of leaf nodes increases slightly.

Theories of Context Effects in Multialternative, Multiattribute Choice

Over the past several decades, researchers in psychology, neuroscience, marketing, and economics have been keen to understand context effects in multialternative, multiattribute decision making. These effects occur when choices among existing alternatives are altered by the addition of a new alternative to the choice set. The effects violate classic decision theories and have led to the development of computational and mathematical models that explain how underlying cognitive and neural mechanisms give rise to the effects. This article reviews dynamic models of these effects, comparing mechanisms across models. Most models of context effects incorporate an attention mechanism, which suggests that attention plays an important role in multialternative, multiattribute decision making. I conclude by discussing recent empirical studies of attention and context effects and hypothesize that changes in attention could be responsible for recently observed reversals in context effects.

Improved multi-layer binary firefly algorithm for optimizing feature selection and classification of microarray data

Gene microarray technology can detect many gene expressions simultaneously, which is essential for disease diagnosis. However, microarray data are usually characterized by small samples and high dimensionality, which requires obtaining the most matching genes to the disease before building a classification model. In this paper, we propose an improved multilayer binary firefly-based method that is divided into two phases. The first stage reduces the dimensionality space by improved Max-Relevance and Min-Redundancy (mRMR). The second stage refines the feature space from coarse-grained level to fine-grained level by improved multilayer binary firefly algorithm (MBFA) at each recursive step. We built a stacking classifier model based on attention mechanism in publicly available gene microarray data and evaluated them compared to advanced hybrid feature selection methods. The experimental results show that compared with the classical chi square test, variance, logistic regression and decision tree methods, the proposed method can achieve higher classification accuracy with fewer features. In addition, the proposed method also shows excellent results in comparison with different published hybrid feature selection methods.

Making Space for Garbage Cans: How emergent groups organize social media spaces to orchestrate widescale helping in a crisis

During the Covid-19 pandemic, citizens self-organized at an unprecedented scale to support vulnerable people in neighbourhoods, towns and cities. Drawing on an in-depth study of an online volunteering group that emerged at the beginning of the pandemic and helped thousands of people in a city in the United Kingdom, we unpack how citizens co-construct social media spaces to orchestrate helping activity during a crisis. Conceptualizing a novel synthesis of classical garbage can theory and virtual space, we reveal how emergent groups use ‘spatial partitioning’ and ‘spatial mapping’ to create a multi-layered spatial architecture that distributes decision-making and invites impromptu choice occasions: spontaneous matchmaking, proximal chance connects and speculative attraction. Our insights extend the study of emergent organizing and decision-making in crises. Furthermore, we advance a new line of theorizing which exploits garbage can theory, beyond its existing application in classical decision sciences, to posit a spatial view of organizing that paves the way for its novel applications in organization studies.

Quantum decision making in automatic driving

The behavior intention estimation and interaction between Autonomous Vehicles (AV) and human traffic participants are the key problems in Automatic Driving System (ADS). When the classical decision theory studies implicitly assume that the behavior of human traffic participants is completely rational. However, according to the booming quantum decision theory in recent years and actual traffic cases, traffic behaviors and other human behaviors are often irrational and violate the assumptions of classical cognitive and decision theory. This paper explores the decision-making problem in the two-car game scene based on quantum decision theory and compares it with the current mainstream method of studying irrational behavior-Cumulative Prospect Theory (CPT) model. The comparative analysis proved that the Quantum Game Theory (QGT) model can explain the separation effect which the classical probability model can’t reveal, and it has more advantages than CPT model in dealing with game scene decision-making. When two cars interact with each other, the QGT model can consider the interests of both sides from the perspective of the other car. Compared with the classical probability model and CPT model, the QGT is more realistic in the behavior decision-making of ADS.

PENGARUH KOMUNIKASI DAN MOTIVASI TERHADAP KINERJA KARYAWAN DI LEMBAGA PERKREDITAN DESA (LPD) KEROBOKAN

The purpose of this research was to determine whether communication and motivation have an influence on employee performance. The respondents in this research were 71 employees. The data analysis used is Validity and reliability tests, multiple linear regression analysis, classical assumption evaluation, t-test analysis, and decision analysis. Based on the results of the study, communication is positive and significant influence partially between employee performance at the Kerobokan Village Credit Institution (LPD). Meanwhile, motivation is positive and significant influence partially between employee performance (LPD). Based on the findings, it is recommended that we continue to sustain and enhance contact in order to build positive relationships with leaders and coworkers, and to be able to take ownership of their work for the company's benefit.

Model for State Cyber Power: Case Study of Russian Behaviour

The emerging cyber environment with new information channels provides a novel avenue for states to project their powers to govern their residents and fulfil their international ambitions. The recent manipulation of elections, coercing companies, blackmailing citizens, and suppressing essential infrastructure services reflects an increased activity and development both by state and non-state entities in the cyber environment. Several models for inter-state power projection are created in studies of international relationships, military strategy, and, recently, hybrid warfare. Do these models recognise the foundational transformation in international power projection? Do they explain the current national cyber strategies? Can they help foresee the possible developments of power projection in international confrontations? The paper seeks a bigger picture from other power strategies in fulfilling the state’s political ambitions. Furthermore, the paper explores the evolution of the cyber environment and its possible emerging features for international power projection. A constructive research method builds a multiple domain power projection model by combining systems thinking with various models from international relationships, military strategies, business strategies to classical decision making. Finally, the feasibility of the model is tested in a case study of Russian cyber strategies and actions between 2007-2020 from a positivistic approach. As a result, the model seems to help explain the past cyber power-wielding and provide insights into current national cyber policies. Further testing is required to evaluate the model’s feasibility in creating a foresight. Nevertheless, the proposed state-level cyber power projection model extends the existing models with a system dynamics viewpoint. Additionally, it adds the dimension of evolution to consider the future changes of international power projections in the information realm. Hence, the model improves the ability of national defence planners to study cyber strategies and estimate the lines of operation and impact of cyber operations.

How Much Reliability Is Enough? A Context-Specific View on Human Interaction With (Artificial) Agents From Different Perspectives

Tasks classically performed by human–human teams in today’s workplaces are increasingly given to human–technology teams instead. The role of technology is not only played by classic decision support systems (DSSs) but more and more by artificial intelligence (AI). Reliability is a key factor influencing trust in technology. Therefore, we investigated the reliability participants require in order to perceive the support agents (human, AI, and DSS) as “highly reliable.” We then examined how trust differed between these highly reliable agents. Whilst there is a range of research identifying trust as an important determinant in human–DSS interaction, the question is whether these findings are also applicable to the interaction between humans and AI. To study these issues, we conducted an experiment ( N = 300) with two different tasks, usually performed by dyadic teams (loan assignment and x-ray screening), from two different perspectives (i.e., working together or being evaluated by the agent). In contrast to our hypotheses, the required reliability if working together was equal regardless of the agent. Nevertheless, participants trusted the human more than an AI or DSS. They also required that AI be more reliable than a human when used to evaluate themselves, thus illustrating the importance of changing perspective.

A decision support system for diagnosis of hepatitis disease using an integrated rough set and fish swarm algorithm

AbstractHealthcare data analysis is a primary concern. It leads to multiple levels of knowledge extraction for decision support systems because of the presence of uncertainties. Therefore, this paper integrates the rough set and artificial fish swarm optimization to develop a decision support system that handles uncertainties present in an information system. In the initial stage, the artificial fish swarm—the rough set procedure is implemented in finding vital features. Further, in the second phase, the rough set uses these vital features to develop a decision support system. The above model is analyzed over hepatitis B disease. The proposed model attains an accuracy of 92.4%. Further, the proposed model is compared with the classical rough set, decision tree, and artificial fish swarm‐decision tree model. The accuracy obtained is 88.9%, 83.3%, and 90.8%, respectively. The proposed model has a greater accuracy of 3.5% than the rough set model and has a greater accuracy of 9.1% than the decision tree model. Simultaneously, the proposed model has 1.6% greater accuracy than the artificial fish swarm‐decision tree model. Therefore, it is believed that the projected decision support system may be used to prevent and detect hepatitis B diseases.

The dispute about sans serif versus serif fonts: An interaction between the variables of serif and stroke contrast

AimIt is a long-lasting dispute whether serif or sans serif fonts are more legible. However, different fonts vary on numerous visual parameters, not just serifs. We investigated whether a difference in word identification can be attributed to the presence or absence of serifs or to the contrast of the letter stroke. MethodParticipants performed a word-recognition two-interval, forced-choice task (Exp. 1) and a classic lexical decision task (Exp. 2). In both experiments the word stimuli were set with four new fonts, which were developed to isolate the stylistic features of serif and letter-stroke contrast. Two measures (i.e., font-size threshold & sensitivity) were analysed. ResultsThe threshold measure of both experiments yielded a single significant main effect of stroke contrast such that low stroke contrast elicited lower than high stroke contrast. The sensitivity measure of Experiment 1 yielded a single significant effect of the interaction between serifs and stroke contrast. Specifically, at the sans-serif level, low stroke contrast revealed better sensitivity, relative to high stroke contrast. At the serif level, the opposite stroke contrast pattern was observed. ConclusionSans serif fonts with low stroke contrast yield better performance and if a serif font is used, high stroke contrast yields better performance than low stroke contrast. Limitations and future directions are discussed.

WTD-PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time-Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a type of dementia that affects the elderly population. A machine learning (ML) system has been trained to recognize particular patterns to diagnose AD using an algorithm in an ML system. As a result, developing a feature extraction approach is critical for reducing calculation time. The input image in this article is a Two-Dimensional Discrete Wavelet (2D-DWT). The Time-Dependent Power Spectrum Descriptors (TD-PSD) model is used to represent the subbanded wavelet coefficients. The principal property vector is made up of the characteristics of the TD-PSD model. Based on classification algorithms, the collected characteristics are applied independently to present AD classifications. The categorization is used to determine the kind of tumor. The TD-PSD method was used to extract wavelet subbands features from three sets of test samples: moderate cognitive impairment (MCI), AD, and healthy controls (HC). The outcomes of three modes of classic classification methods, including KNN, SVM, Decision Tree, and LDA approaches, are documented, as well as the final feature employed in each. Finally, we show the CNN architecture for AD patient classification. Output assessment is used to show the results. Other techniques are outperformed by the given CNN and DT.

A new binary genetic programming approach to design public transportation systems according to transit-oriented development criteria

This study introduces a new evolutionary approach called binary genetic programming (BGP) to design and assess public transportation systems from a sustainable development perspective. The BGP combines evolutionary system identification techniques with k-fold cross-validation to obtain an accurate model between the land use and transportation parameters from a sustainable urban development point of view. To assess the new model, two public transportation systems including the new tram line of Antalya (Turkey) and the bus rapid transit line of Bhopal (India) were considered. The model was applied to classify the transportation systems into transit-oriented development (TOD) and non-TOD. The solutions generated by the new model were compared with those of classic decision tree (DT) as well as the state-of-the-art random forest (RF) models evolved as the benchmarks in this study. The results showed that the BGP is highly efficient and may provide less than 5% classification error. It is superior to the DT and RF solutions, which typically require higher datasets to avoid overfitting. Furthermore, the explicit formulation of BGP in combination with the multicriteria evaluation method increases human insight on the factors affecting the design of public transportations from a sustainable urban development point of view.

A Boundedly Rational Decision-Making Model Based on Weakly Consistent Preference Relations

Completeness is one of the basic assumptions about the rational preference relation in classical decision theory. Strongly and weakly consistent preferences are presented by abandoning the completeness of the rational preference relation. Some expansion and contraction conditions are proposed and the relationships between these conditions of rationality are discussed. The relationships between the conditions of rationality and boundedly rational choice behavior based on strongly and weakly consistent preferences are analyzed and discussed. Furthermore, an example about the choices of chocolates with interval ordinal numbers is given to explain some of the main conclusions in this paper. The results can be used as references for the study of boundedly rational decisions.

Analysis of factors that influence the companies’ market value in England, France, and Germany. How can managers maximize such value?

One of the classic issues in business administration is whether managers can maximize profit. Thus, this article aims to identify the causal factors regarding the maximization of companies’ market value to subsidize decision-makers. The sample was extracted from the Capital IQ database and formed of nonfinancial public companies from Germany, France, and England, considering the period from 1999 to 2019, with quarterly frequency. The study finds theoretical support from market-to-book decomposition concepts as in Rhodes-Kropf, Robinson, and Viswanathan (2005), and uses the classical theory decision model with hypotheses proposed by Lindenberg and Ross (1981), Hirschey and Wirchem (1984), Habib and Ljungqvist (2005), Hertzel and Li (2010), and Belenzon (2012). The research adopted a t-1 period lag in the independent variables in relation to the dependent variable to demonstrate the result of actions of independent variables in the dependent variable in subsequent periods. Subsequently, correlation and regression analyses were performed with panel data. The results show that the management of tangible and intangible assets, goodwill, revenue, Ebitda, return on assets, research and development, advertising, Capex, and return on equity contribute to increasing the companies’ market value, and they can be maximized through good management. Therefore, because these variables are manageable, it is possible to infer that managers are in the position to maximize companies’ market value.