Actual Decision-making Process Research Articles

A Case Study of the Application of Expected Utility Theory to Everyday Decision Making

This paper reviews the current state of research on the application of expected utility theory in daily decision-making and explores the effectiveness and limitations of the theory in different decision-making scenarios by systematically sorting out and deeply analyzing application cases in several fields. As a classical decision theory, the core of expected utility theory is to guide rational decision-making by assessing the expected utility values of different choices. This paper reviews the applied research on expected utility theory by scholars at home and abroad in recent years in various fields, such as consumer choice, financial investment, health management, environmental policy, etc., and reveals its role and shortcomings in explaining and predicting decision-making behavior. This paper argues that expected utility theory provides an important theoretical framework for understanding everyday decision-making, but its applicability in practical applications is constrained by a variety of factors. Future research needs to further explore the modifications and extensions of the theory to better adapt to complex and changing decision-making environments. Decision makers should consider situational factors, personal preferences and theoretical limitations in practical applications, and flexibly use expected utility theory for decision analysis. This paper not only deepens the understanding of expected utility theory, but also provides new directions and ideas for subsequent research, which is of great significance in guiding the actual decision-making process.

Formal governance matters: when, how, and why states act on the IMF Executive Board

International financial institutions (IFIs) are central actors shaping global development. Scholarship on these institutions’ governance has primarily explored unequal voting rights and informal channels of decision-making. Much less is known about the actual decision-making processes that transpire in the IFIs’ formal governance structures, where votes are rarely taken. This article redirects academic scrutiny to these structures to reveal hitherto unobserved state behavior. Empirically, we examine decision-making at the International Monetary Fund (IMF), a key actor in the diffusion of market-oriented reforms. We introduce a novel dataset systematizing all comments of IMF Executive Board members over 3,111 developing-country-specific discussions between 1995 and 2015. First, regression analysis reveals that the interventions by the IMF’s most powerful member-states—the United States, Germany, Japan, France, and the United Kingdom—correlate with their bilateral trade and aid interests. Second, these countries frequently reference each other in debates, demonstrating how coalitions work in practice. Third, we find that the preferences they express for market liberalization vis-à-vis countries in the Global South are associated with an increase in market-liberalizing conditions in subsequent lending programs. Taken together, this article reveals the usefulness of examining formal deliberations in IFIs, contributing to a fuller understanding of decision-making processes in the international political economy.

A hybrid opinion dynamics model with leaders and followers fusing dynamic social networks in large-scale group decision-making

A hybrid opinion dynamics model with leaders and followers fusing dynamic social networks in large-scale group decision-making

The anatomy of clinical decision-making: aligning AI design with ICU routines

PurposeIn the face of numerous explanations for why AI-driven decision support systems (DSS) have failed to deliver on their promise of improving organizational decision-making, this paper problematizes the under-theorized mismatch between the design of DSS and the actual decision-making processes that the technology is supposed to support. We examine this mismatch by studying the implementation of a DSS in the intensive care unit (ICU) of a large academic hospital.Design/methodology/approachBased on 27 months of ethnographic fieldwork, we contend that the studied DSS was designed on the assumption that individual intensivists are responsible for making life-critical discharge decisions at one particular moment in time.FindingsHowever, our study of actual decision-making practices reveals that discharge decision-making is instead a protracted process, involving multiple actors fragmented across time and space. To account for these complexities, we advocate for a “dynamic routines” perspective, which highlights the actual patterns of action pursued throughout a clinical decision-making process.Originality/valueOur application of this perspective contributes to a more granular understanding of discharge decision-making, which can help future DSS designers better grasp the peculiarities and complexities—or “anatomy”—of the decision-making process. We also suggest integrating an “anticipatory ethnographic approach” into the design and pre-implementation phases of future DSS to help bridge the current gap between design assumptions and actual decision-making practices.

A multi-attribute decision making approach considering economic psychology

The magnitude of the utility of a good depends on the subjective psychological evaluation of the good by the consumer, and the difference in the obtained utility will affect consumer behavior, taking into account that consumers are not completely rational in the actual decision-making process, and that under uncertain conditions, consumers’ perception of loss is much greater than gain. This paper investigates an improved scoring function based on consumer utility and loss aversion, which takes into account the effects of different decision makers’ preferences and loss aversion on the decision outcome. First, the existing score function of intuitionistic fuzzy sets is analyzed in depth, an improved score function is defined, and its properties and special cases are studied in detail. Then, a Multi-Attribute Decision-Making (MADM) method is proposed based on the improved scoring function combined with the intuitionistic fuzzy hybrid average (IFHA) operator. Finally, a real case of new energy-used car transaction decision-making is given, and the proposed method is validated by Spearman’s correlation coefficient, WS ranking similarity coefficient, and RW coefficient to prove its practicality and effectiveness.

Similarity Measures of Probabilistic Interval Preference Ordering Sets and Their Applications in Decision-Making

The concept of probabilistic interval preference ordering sets (PIPOSs) provides a scientific and intuitive framework for solving real-life multi-criteria group decision-making problems. In some areas such as investment decision-making and supplier selection, PIPOSs have a wider application space, and the development of similarity and distance measures based on PIPOSs holds great significance. Similarity measure is a basic and prominent tool for dealing with imperfect and ambiguous information in fuzzy sets, but it can also be used to deal with uncertain information in preference ordering. These metrics play an important role in the actual decision-making process, as they effectively quantify the degree of similarity between two PIPOSs, and further allow for the prioritization of different scenarios. In this article, we sort out the definitions and arithmetic rules of PIPOSs, and creatively propose several new similarity measures based on PIPOSs. Then, we propose a group decision-making method based on similarity measures and conduct a comparative study with three existing similarity measures to illustrate its advantages over existing metrics. Finally, we confirm its validity through numerical illustrations in the case study, and also conduct a comparative assessment to verify the scientific validity and effectiveness of the newly introduced measure against the existing metrics.

Modelling the Performance Consequences of Coopetition in Business Relationships – a Quantitative Approach

The objective of the paper is to develop an analytical tool that is capable of modelling decision-making in coopetitive business relationships. Managers in the same industry differ in respect of their willingness to adopt coopetition. To better understand coopetitive decision-making, we need a model whereby such decisions can be experimented with and analysed. An important prerequisite of such a model would be its capacity to measure the performance consequences of coopetitive interactions at both firm and relationship levels. We show that existing operationalisation has limited capacity to do that. Based on existing game theoretical constructs, we propose a new operationalisation of a coopetitive decision-making episode in horizontal business relationships using a two-step sequential game. We suggest developing what we term a “coopetitive composite solution matrix” by summing up the payoff functions of the two steps of the game. The suggested operationalisation has the capacity to measure all the potential performance consequences of a complex piece of coopetitive decision-making in an episode. In this way, the decision problem’s cognitive representation becomes straightforward and analysis of the impact of the behavioural attributes of managers on the actual decision-making process is unambiguous.

Fermatean Fuzzy TOPSIS Method Based on Prospect Theory and Its Application in Credit Assessment

Fermatean Fuzzy TOPSIS Method Based on Prospect Theory and Its Application in Credit Assessment

Improving the Prescriptive Power of Analytic Hierarchy Process

This article addresses three important questions. First, whether the criterion weights computed using pairwise comparison in the analytic hierarchy process (AHP) are range-sensitive. Second, if the decision maker (DM) is able to define the criterion weight without using any method, then whether such criterion weight resembles the criterion weight computed using the AHP for the same DM. Third, whether the AHP's prescriptive power can be improved by adjusting and making the criterion weight range-sensitive. Two separate studies were conducted to investigate the prescriptive power of the AHP. The first study served as a pilot. The second study was conducted relatively on a larger scale. The attention levels of the attribute dynamic attitude model are used to adjust the relative importance of criterion weight in the AHP in order to make it range-sensitive. Both studies found that the criterion weights computed using the AHP are not range-sensitive. The criteria weight computed using the AHP and the weight collected directly from individual DMs resembled considerably. The AHP's prescriptive power can be improved by making the criteria weight range-sensitive, thus resembling the actual decision-making process of the DM.

Three-way group decisions using evidence theory under hesitant fuzzy linguistic environment

In the actual decision-making process, there will be situations where decision-makers with hesitant attitudes have difficulties in evaluating alternatives numerically, and hesitant fuzzy linguistic term sets can provide decision-makers with an effective way to describe hesitancy in linguistic terms. In multi-attribute group decision-making, each decision maker typically holds different preferences. If the variation in decision makers’ assessment weights across evaluations of each attribute for every alternative is not adequately accounted for, it can result in a problem of coarse-grained calculations, leading to information loss. Additionally, the three-way decision model faces significant challenges in information fusion within the context of the hesitant fuzzy linguistic environment. Therefore, we propose a new three-way decision-making model under the hesitant fuzzy linguistic environment. The model obtains the confidence of different decision makers in attribute evaluations through the fusion of D-S evidence theory, and can perform more fine-grained fusion calculations on the evaluation information of different decision makers. In addition, the model considers the cost function of each alternative in different decision-making actions under hesitant fuzzy linguistic environment, calculates the two thresholds of each alternative in the three-way decision model, and derives the decision rules. The effectiveness of the model is verified through a numerical example and two comparative experiments, therefore, the model can be applied in intelligent classification or recommendation systems of hesitant fuzzy linguistic information systems.

A three-way decision model associated with decision-theoretic rough fuzzy sets based on computing with words

The three-way decision model based on linguistic term sets has been extensively investigated since decision makers frequently utilize natural language to evaluate in an actual decision-making process. The existing models require decision makers to select appropriate linguistic terms from a given linguistic term set. However, making such a choice is not always simple, and decision makers occasionally choose words that are related to their own experience. In order to deal with this kind of decision problem, we appeal to the theory of computing with words pioneered by Zadeh and establish a three-way decision model based on computing with words in this paper. The paper focuses on how to deal with more general linguistic information using the theory of computing with words. Initially, using the concept of computing with words, we translate more broad linguistic information into a linguistic distribution assessment on a balanced linguistic term set in order to better analyze linguistic information. The three-way decision based on computing with words is then discussed. Decision-theoretic rough fuzzy sets take into account the ambiguity of the decision target as a generalization of the classical decision-theoretic rough sets. This is what motivated us to develop a three-way decision based on decision-theoretic rough fuzzy sets using computing with words. Additionally, a fabricated example demonstrates that our three-way decision model is more adaptable in processing linguistic information and can handle more general linguistic information provided by decision makers.

Selection of plastic solid waste treatment technology based on cumulative prospect theory and fuzzy DEMATEL.

Under the global implementation of a low-carbon economy, the treatment of municipal plastic solid waste (PSW) has become an important task to be solved urgently. In the actual decision-making process of PSW treatment, the evaluation information is usually fuzzy, and the decision-makers (DMs) are bounded rational. For selecting the most appropriate PSW treatment technology, we propose a multi-criteria decision-making (MCDM) method based on cumulative prospect theory and fuzzy decision-making trail and evaluation laboratory (DEMATEL). Firstly, we construct the criteria system of PSW treatment that consists of 9 sub-criteria from the perspectives of environment, economy, society, and technology. Then, considering the interdependences and interactions between these evaluation criteria and allowing multiple stakeholders to participate in decision-making, we propose a fuzzy DEMATEL method to deal with the fuzziness of evaluation in the decision-making process and determine the weights of the evaluation criteria. Subsequently, taking into account the different opinions of different stakeholders and psychological factors such as risk preference and loss aversion of stakeholders, we aggregate the evaluation information of different stakeholders and develop the PSW treatment alternatives to rank the orders by using the proposed multi-actor cumulative prospect theory (CPT) method. We study seven alternative processes for PSW treatment by the developed model, including landfill, recycling, pyrolysis, incineration, and the combination of landfilling and recycling, landfill and incineration, and recycling and pyrolysis. According to the ranking results, we find the combination of recycling and incineration is the best treatment alternative. We take the seven PSW treatment technologies in Shanghai as the case study to verify the effectiveness and feasibility of the proposed method. Through the sensitivity analysis and comparison analysis with fuzzy similarity to ideal solution (FTOPSIS) method and an acronym in Portuguese of the interactive and multi-criteria decision-making (TODIM) method, we illustrate the effectiveness and superiority of the proposed method. This research provides significant references for the PSW treatment technology selection problems under uncertain environments and extends the methods in the decision-making field.

D-A-CH research project “Technical asset value assessment within asset management” (TAniA) - Using asset valuation as a key performance indicator in asset management

D-A-CH research project “Technical asset value assessment within asset management” (TAniA) - Using asset valuation as a key performance indicator in asset management

Regret Theory-Based Three-Way Decision Model in Hesitant Fuzzy Environments and Its Application to Medical Decision

In real world, a typical decision-making problem in the medical field can be seen as an uncertain hesitant fuzzy multiattribute decision-making problem when existing experiences of decision-makers are insufficient. A three-way decision model is an effective tool to deal with uncertain decision-making problems, which can realize the classification of objects. However, different psychological behaviors of decision-makers are likely to affect decision-making results in actual decision-making processes. In order to address this challenge, we first propose a regret-theory-based three-way decision model in hesitant fuzzy environments, which mainly combines regret theory with hesitant fuzzy sets to calculate the perceived utility value of objects. Second, since one of the core ingredients for regret theory is the regret–rejoice function, we put forward a new regret–rejoice function that preserves the original indecisive uncertainty in hesitant fuzzy environments. In addition, according to the inherent relationship of the used data, this article provides a three-way classification method based on the preference ranking organization method for enrichment evaluations I method. Finally, the practicability, rationality, and superiority of the proposed method are shown via the case analysis and comparative analysis in the medical field.

Evaluating the governance and preparedness of the Lebanese health system for the COVID-19 pandemic: a qualitative study

ObjectivesThis study aimed to assess the capacities and governance of Lebanon’s health system throughout the response to the COVID-19 pandemic until August 2020.DesignA qualitative study based on semi-structured interviews.SettingLebanon, February–August...

Three-way conflict analysis based on incomplete situation tables: A tentative study

The existing three-way conflict analysis models focus on complete situation tables and rarely involve incomplete situation tables. In this paper, we give a preliminary approach to three-way conflict analysis models based on incomplete situation tables. Firstly, we construct three-way conflict analysis models based on an incomplete three-valued situation table (ITVST). We use two different methods to establish the models. One is to establish straightly the model based on the original ITVST. The other is to convert the ITVST into a complete three-valued situation table (TVST) by supplementing the missing values with the maximum probability value method, which is called a complete TVST induced by the maximum probability value method. Then we construct the three-way conflict analysis model based on the induced complete TVST. Secondly, to deal with more uncertainty and complexity in the actual decision-making process, we generalize an ITVST to an incomplete many-valued situation table (IMVST) and establish the corresponding three-way conflict analysis models. Finally, we further extend an IMVST to an incomplete interval-valued situation table (IIVST) and construct three-way conflict analysis models based on an IIVST. We use examples to demonstrate the process of constructing conflict, neutrality, and alliance relations of agents on incomplete situation tables. Some algorithms and basic properties are also addressed.

A Decision-Making Method for Air Combat Maneuver Based on Hybrid Deep Learning Network

In this paper, a hybrid deep learning network-based model is proposed and implemented for maneuver decision-making in an air combat environment. The model consists of stacked sparse auto-encoder network for dimensionality reduction of high-dimensional, dynamic time series combat-related data and long short-term memory network for capturing the quantitative relationship between maneuver control variables and the time series combat-related data after dimensionality reduction. This model features: 1) time series data is used as the basis of decision-making, which is more in line with the actual decision-making process. 2) using stacked sparse auto-encoder network to reduce the dimension of time series data to predict the result more accurately. 3) the model takes the maneuver control variables as the output to control the maneuver, making the maneuver process more flexible. The relevant experiments have demonstrated that the proposed model can effectively improve the prediction accuracy and convergence rate in the prediction of maneuver control variables.

“Swallowing the Red Pill”: The Coronavirus Pandemic and the Political Imaginary of Stigmatized Knowledge in the Discourse of the Far-Right

Pandemic disease is not merely a biological reality but also a cognitive and socially constructed phenomenon which intensely mobilizes a multiplicity of political frames. Far-right political entrepreneurs are, despite their remoteness from actual decision-making processes, active stakeholders in the current crisis. Existential threats to societies breed a sense of urgency and heightened cultural warfare that is a hotbed for extremism. Our study seeks to map, compare and contrast the symbolic responses to the Coronavirus crisis articulated by various far-right actors in two established democracies in the transatlantic area: the United States and France. We aim to shed light on how entrenched far-right mythologies and tropes—which appear increasingly transatlantic—are channeled into a new synthesis as part of an “alternative” political epistemology. Infused with the mythos of resistance and insurgency, resolutely anti-systemic, this alternative epistemology can better be described, following Michael Barkun, as a form of “stigmatized knowledge”. Our study will employ a Critical Discourse Analysis framework to bring into focus the ideological semiotics of the current “infodemic” in the Euro-American far-right’s response to the COVID-19 crisis. To some extent, all groups and actors under study exhibited a conspiracist framework. We contend that conspiracist counter-epistemologies are an integral — and increasingly important — part of the semiotics of the new culture wars which have engulfed the COVID-19 response.

A novel illustration of real options analysis to address the problem of probabilities under deep uncertainty and changing climate risk

Changing risk profiles over time that are exacerbated by climate change, challenge assessments of the relative value of investing in different adaptation options. There is a growing discourse around methods that are better suited to addressing changing risk and uncertainties for large infrastructure projects — robust decision making (RDM), real options analysis (ROA) and those based on dynamic adaptive pathways planning (DAPP). While such approaches are receiving greater attention in the academic literature, there is a gap in critiques of them in real-life decision practice. Here we present two novel applications from New Zealand of real options analysis that formed part of actual decision processes — a flood risk management plan and a coastal hazards strategy, both based on DAPP over 100years. In both cases, we examined the value of pursuing adaptation options that are flexible under peak river flows, coastal erosion, imminent inundation, storm surge and long-term sea-level rise. From lessons learned we examined three questions relating to ROA: 1) is ROA ‘fit for purpose’ for investment evaluations in situations of both imminent threat and deep uncertainty and its scalability? 2) can ROA address path dependency and move beyond static applications? 3) can ROA be used alongside DAPP to address managed retreat as an adaptation option. We conclude the affirmative for each question and provide a method for using ROA within a DAPP and RDM framework.

Ranking Consistency Analysis and Evaluation of Multiple Attribute Decision Making Method

In the actual decision-making process, different multiple attribute decision making methods used for the same decision-making problem may get different ranking results. This inconsistent ranking can easily lead to decision-making risks. This paper simulates the ranking consistency of six typical multiple attribute decision making methods, including the ranking consistency indexes, consistency probability, the overall consistency of the decision-making methods, and the influence of attribute number and attribute weight on ranking consistency. The results show that various methods have different ranking consistency indexes; the overall consistency of decision-making methods increases with the decrease of the number of attributes; under the condition of equal attribute weights, the decision-making methods have generally better ranking consistency. The research results have reference value for the selection of multiple attribute decision making methods and attribute optimization.