A probabilistic linguistic thermodynamic method based on the water-filling algorithm and regret theory for emergency decision making

Since thermodynamics can describe the energy of matter and its form of storage or transformation in the system, it is introduced to resolve the uncertain decision-making problems. The paper proposes the thermodynamic decision-making method which considers both the quantity and quality of the probabilistic linguistic decision information. The analogies for thermodynamical indicators: energy, exergy and entropy are developed under the probabilistic linguistic circumstance. The probabilistic linguistic thermodynamic method combines the regret theory which captures decision makers’ regret-aversion and the objective weight of criterion obtained by the water-filling algorithm. The proposed method is applied to select the optimal solution to respond to the floods in Chongqing, China. The self-comparison is conducted to verify the effectiveness of the objective weight obtained by the water-filling algorithm and regret theory in the probabilistic linguistic thermodynamic method. The reliability and feasibility of the proposed method are verified by comparative analysis with other decision-making methods by some simulation experiments and non-parametric tests

Heterogeneous group decision making with thermodynamical parameters

There often exist different types of information due to the subjective and objective criteria in practical decision-making problems, thus it is necessary to develop some efficient frameworks to deal with the decision-making problems with heterogeneous information. The paper proposes a framework for group decisionmaking problems with heterogeneous information with thermodynamical parameters consisting of three parts to achieving this goal. The first part builds the rectifications of criteria weights according to decision makers’ confidence in evaluations. The second part adopts thermodynamical parameters to measure the numerical values and the data distribution of heterogeneous information to characterize the heterogeneous information fully. The last part applies the TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision Making) to aggregate the decision-making results based on the characterized heterogeneous information without transforming it into a unified form. By depicting decision makers’ different sensitive attitudes towards uncertainty by several mathematical expressions, experiments are performed to assess the sensitive attitudes’ impacts on decisionmaking results with the proposed framework. Finally, a case study on the selection of a green supplier under the low-carbon economy is provided to illustrate the flexibility and feasibility of the proposed framework

Green Logistic Provider Selection with a Hesitant Fuzzy Linguistic Thermodynamic Method Integrating Cumulative Prospect Theory and PROMETHEE

In the process of evaluating the green levels of cold-chain logistics providers, experts may hesitate between several linguistic terms rather than give precise values over the alternatives. Due to the potential profit and risk of business decisions, decision-making information is often based on experts’ expectations of programs and is expressed as hesitant fuzzy linguistic terms. The consistency of evaluation information of an alternative can reflect the clarity of the alternative in the mind of experts and its own controversy. This paper proposes a method to use the value transfer function in the cumulative prospect theory to convert the original hesitant fuzzy linguistic terms into evaluation information based on reference points. We also introduce the parameters related to the disorder of the systemin the hesitant fuzzy thermodynamic method to describe the quantity and quality characteristics of the alternatives. In these kinds of multi-criteria decision-making problems, the weights of criteria are of great importance for decision-making results. Considering the conflicting cases among criteria, the weights were obtained by utilizing the PROMETHEE method. An illustrative example concerning green logistics provider selection was operated to show the practicability of the proposed method.The work was supported in part by the National Natural Science Foundation of China (Nos. 71501135, 71771156, and 71771153), the Scientific Research Foundation for Excellent Young Scholars at Sichuan University (No. 2016SCU04A23), and the Scientific Research Foundation for Scholars at Sichuan University (No. YJ201535)

Consistency and Consensus Driven for Hesitant Fuzzy Linguistic Decision Making with Pairwise Comparisons

Hesitant fuzzy linguistic preference relation (HFLPR) is of interest because it provides an efficient way for opinion expression under uncertainty. For enhancing the theory of decision making with HFLPR, the paper introduces an algorithm for group decision making with HFLPRs based on the acceptable consistency and consensus measurements, which involves (1) defining a hesitant fuzzy linguistic geometric consistency index (HFLGCI) and proposing a procedure for consistency checking and inconsistency improving for HFLPR; (2) measuring the group consensus based on the similarity between the original individual HFLPRs and the overall perfect HFLPR, then establishing a procedure for consensus ensuring including the determination of decision-makers weights. The convergence and monotonicity of the proposed two procedures have been proved. Some experiments are furtherly performed to investigate the critical values of the defined HFLGCI, and comparative analyses are conducted to show the effectiveness of the proposed algorithm. A case concerning the performance evaluation of venture capital guiding funds is given to illustrate the availability of the proposed algorithm. As an application of our work, an online decision-making portal is finally provided for decision-makers to utilize the proposed algorithms to solve decision-making problems.Comment: Pulished by Expert Systems with Applications (ISSN: 0957-4174

Strategies for Prioritizing Needs for Accelerated Construction after Hazard Events

There is a need for rapid and responsive infrastructure repair and construction after natural disaster events such as hurricanes, wildfires, and tornadoes. These natural disasters often shut down basic infrastructure systems, as experienced recently in several Region 6 states as well as in other states around the country. Accelerated construction practices are often used in these situations to speed up the traditional, and often slow, project delivery process. However, after a natural disaster, several and different types of transportation infrastructure components are in need of inspection, rehabilitation or reconstruction, and transportation agencies are challenged with the task of prioritizing these accelerated projects. This study conducted an extensive literature review of current accelerated methods, infrastructure prioritization practices, and institutional barriers. Interviews with professionals from the transportation industry, including both private and public services, were conducted. Significant input from the railroad industry was used to compare private and public transportation systems responses after disasters. The results of this survey were used to quantify the importance of the accelerate methods and prioritization criteria, and which are the barriers to implement a prioritization model. Lastly, a decision support tool for prioritizing needs for accelerated construction after disaster events, specifically hurricanes and flooding, which commonly affect Region 6, was developed using the data collected

Explainable Crowd Decision Making methodology guided by expert natural language opinions based on Sentiment Analysis with Attention-based Deep Learning and Subgroup Discovery

There exist a high demand to provide explainability to artificial intelligence systems, where decision making models are included. This paper focuses on crowd decision making using natural language evaluations from social media with the aim to provide explainability. We present the Explainable Crowd Decision Making based on Subgroup Discovery and Attention Mechanisms (ECDM-SDAM) methodology as an a posteriori explainable process that captures the wisdom of crowds that is naturally provided in social media opinions. It extracts the opinions from social media texts using a deep learning based sentiment analysis approach called Attention based Sentiment Analysis Method. The methodology includes a backward process that provides explanations to justify its sense-making procedure by applying mainly the attention mechanism on texts and subgroup discovery on opinions. We evaluate the methodology in the real case study of the TripR-2020Large dataset for restaurant choice. The results show that the ECDM-SDAM methodology provides easy understandable explanations that elucidates the key reasons that support the output of the decision processPID2020-119478GBI00,PID2019-103880RB-I00PID2020-116118GA-I00MCIN/AEI/10.13039/501100011033ERDF A way of making EuropePRE2018-083884 funded by MCIN/AEI/10.13039/501100011033ESF Investing in your futureUniversidad de Granada / CBU

Probe: Learning Users' Personalized Projection Bias in Intertemporal Bundle Choices

Intertemporal choices involve making decisions that require weighing the costs in the present against the benefits in the future. One specific type of intertemporal choice is the decision between purchasing an individual item or opting for a bundle that includes that item. Previous research assumes that individuals have accurate expectations of the factors involved in these choices. However, in reality, users' perceptions of these factors are often biased, leading to irrational and suboptimal decision-making. In this work, we specifically focus on two commonly observed biases: projection bias and the reference-point effect. To address these biases, we propose a novel bias-embedded preference model called Probe. The Probe incorporates a weight function to capture users' projection bias and a value function to account for the reference-point effect, and introduce prospect theory from behavioral economics to combine the weight and value functions. This allows us to determine the probability of users selecting the bundle or a single item. We provide a thorough theoretical analysis to demonstrate the impact of projection bias on the design of bundle sales strategies. Through experimental results, we show that the proposed Probe model outperforms existing methods and contributes to a better understanding of users' irrational behaviors in bundle purchases. This investigation can facilitate a deeper comprehension of users' decision-making mechanisms, enable the provision of personalized services, and assist users in making more rational and optimal decisions

Predicting Human Operator’s Decisions Based on Prospect Theory

The aim of this work is to predict human operator's decisions in a specific operational context, such as a cooperative human-robots mission, by approximating her utility function based on Prospect Theory. To this aim, a within-subject experiment was designed in which the human operator has to decide with limited time and incomplete information. This experiment also involved a framing effect paradigm, a typical cognitive bias causing people to react differently depending on the context. Such an experiment allowed to acquire data concerning the human operator's decisions in two different mission scenarios: search and rescue and Mars rock sampling. The framing was manipulated (e.g. positive vs. negative) and the probability of the outcomes causing people to react differently depending on the context. Statistical results observed for this experiment supported the hypothesis that the way the problem was presented (positively or negatively framed) and the emotional commitment affected the human operator's decisions. Thus, based on the collected data, the present work is willed to propose: (i) a formal approximation of the human operator's utility function founded on the Prospect Theory; and (ii) a model used to predict the human operator's decisions based on the economics approach of multi-dimensional consumption bundle and Prospect Theory. The obtained results, in terms of utility function fit and prediction accuracy, are promising and show that similar modeling and prediction method should be taken into account when an intelligent cybernetic system drives human-robots interaction. The advantage of predicting the human operator's decision, in this operational context, is to anticipate her decision, given the way a question is framed to the human operator. Such a predictor lays the foundation for the development of a decision-making system capable of choosing how to present the information to the operator while expecting to align her decision with the given operational guideline

A new measure of volatility using induced heavy moving averages

The volatility is a dispersion technique widely used in statistics and economics. This paper presents a new way to calculate volatility by using different extensions of the ordered weighted average (OWA) operator. This approach is called the induced heavy ordered weighted moving average (IHOWMA) volatility. The main advantage of this operator is that the classical volatility formula only takes into account the standard deviation and the average, while with this formulation it is possible to aggregate information according to the decision maker knowledge, expectations and attitude about the future. Some particular cases are also presented when the aggregation information process is applied only on the standard deviation or on the average. An example in three different exchange rates for 2016 are presented, these are for: USD/MXN, EUR/MXN and EUR/USD

Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management