EURO Journal on Decision Processes最新文献

{"title":"Performance assessment of waste sorting: Component-based approach to incorporate quality into data envelopment analysis","authors":"Harald Dyckhoff ,&nbsp;Rainer Souren ,&nbsp;Marcel Clermont","doi":"10.1016/j.ejdp.2024.100048","DOIUrl":"10.1016/j.ejdp.2024.100048","url":null,"abstract":"<div><p>Waste sorting is an important recovery process in circular product systems. Engineering sciences deal with its performance measurement by analysing various parameter settings based on technical key figures derived from object and component quantities. They are difficult to aggregate, especially since there are no prices for all inputs and outputs. Data Envelopment Analysis (DEA) allows an aggregation without implementing prices or weight restrictions. This paper shows how DEA can be used as performance measurement method for sorting processes, whose purpose it is to separate different material components from a waste mixture as effectively and efficiently as possible. This is achieved when the individual components are sorted out into their respective recycling bins with as little effort as possible and without allowing too many incorrect throws. In this respect, sorting processes represent pure material reallocations – and no material transformations. Thus, valid material balances of sorting technologies do not allow for free disposability. A new multiple criteria DEA approach is suggested, which uses input and output objects that entirely consist of components determining the object's value. Different from common DEA approaches, the technological perspective of modelling the underlying production system is strictly separated from the preference perspective of modelling multiple performance criteria for the relevant input and output types and sorts. In more detail, we develop DEA models for a combined evaluation of operating and quality efficiency where both inputs and outputs are characterised by sorts or grades of varying quality. In contrast to existing approaches that map quality into DEA we reflect upon the underlying technological assumptions and analyse which dominance relations result from that in our case of waste sorting.</p></div>","PeriodicalId":44104,"journal":{"name":"EURO Journal on Decision Processes","volume":"12 ","pages":"Article 100048"},"PeriodicalIF":1.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2193943824000049/pdfft?md5=3d266ef1100d11ad1a53df9cb74987f0&pid=1-s2.0-S2193943824000049-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140279759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Feature issue on fair and explainable decision support systems","authors":"Luis Galárraga ,&nbsp;Miguel Couceiro","doi":"10.1016/j.ejdp.2024.100046","DOIUrl":"10.1016/j.ejdp.2024.100046","url":null,"abstract":"","PeriodicalId":44104,"journal":{"name":"EURO Journal on Decision Processes","volume":"12 ","pages":"Article 100046"},"PeriodicalIF":1.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2193943824000025/pdfft?md5=28f73e98ce2324ebea97aabfe514b3b0&pid=1-s2.0-S2193943824000025-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139885571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating policy alternatives for decision making: A process model, behavioural issues, and an experiment","authors":"Raimo P. Hämäläinen,&nbsp;Tuomas J. Lahtinen,&nbsp;Kai Virtanen","doi":"10.1016/j.ejdp.2024.100050","DOIUrl":"10.1016/j.ejdp.2024.100050","url":null,"abstract":"<div><p>The generation of alternative policies is essential in complex decision tasks with multiple interests and stakeholders. A diverse set of policies is typically desirable to cover the range of options and objectives. Decision modelling literature has often assumed that clearly defined decision alternatives are readily available. This is not a realistic assumption in practice. We present a structured process model for the generation of policy alternatives in settings that include non-quantifiable elements and where portfolio optimisation approaches are not applicable. Behavioural issues and path dependence as well as heuristics and biases which can occur during the process are discussed. The behavioural experiment compares policy alternatives obtained by using two different portfolio generation techniques. The results of the experiment demonstrate that path dependence can occur in policy generation. We report thinking patterns of subjects which relate to biases and heuristics.</p></div>","PeriodicalId":44104,"journal":{"name":"EURO Journal on Decision Processes","volume":"12 ","pages":"Article 100050"},"PeriodicalIF":1.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2193943824000062/pdfft?md5=718cb9fb0dadb1f3672071b91fb2e389&pid=1-s2.0-S2193943824000062-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141035900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel pairwise comparison method with linear programming for multi-attribute decision-making","authors":"Mehdi Soltanifar ,&nbsp;Madjid Tavana","doi":"10.1016/j.ejdp.2024.100051","DOIUrl":"https://doi.org/10.1016/j.ejdp.2024.100051","url":null,"abstract":"<div><p>This study introduces a novel approach to effectively and efficiently solve Multi-Attribute Decision Making (MADM) problems with a considerable number of attributes. We demonstrate the need to categorize the attributes and facilitate a more systematic expert comparison. Our proposed method utilizes pairwise comparisons to assess attributes without requiring additional computations to evaluate the level of consistency. The proposed method offers greater flexibility and precision with reduced computational complexity. We present a comparative analysis with a widely used numerical example in the MADM literature to demonstrate the effectiveness and efficacy of the method proposed in this study.</p></div>","PeriodicalId":44104,"journal":{"name":"EURO Journal on Decision Processes","volume":"12 ","pages":"Article 100051"},"PeriodicalIF":1.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2193943824000074/pdfft?md5=d5abcde8b79e19473e311ad744a285f9&pid=1-s2.0-S2193943824000074-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141290813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk attitudes: The central tendency bias","authors":"Karl Akbari ,&nbsp;Markus Eigruber ,&nbsp;Rudolf Vetschera","doi":"10.1016/j.ejdp.2023.100042","DOIUrl":"10.1016/j.ejdp.2023.100042","url":null,"abstract":"<div><p>Unincentivized measurement instruments of risk attitudes suffer from several weaknesses. One is that respondents do not consistently assign themselves to their respective risk preference categories. In particular, they are subject to a central tendency bias and classify themselves as risk-neutral when they are in fact not. We test the robustness of the central tendency bias in lottery-type questions for risk evaluations and offer an explanation of why respondents behave in a way that contradicts plausible utility models. We explore a wide range of alternative influencing factors, including careless responding, stake levels, deviations in expected value, the cognitive abilities of the respondents, self-assessment of risk attitudes, and monetary incentives. We find that careless responding and higher stakes increase the central tendency bias in risk assessment, while cognitive capabilities and extreme risk self-assessments (both positive and negative) decrease the bias. Deviations in expected value and incentives do not affect the bias. Our study further points to the fact that such problems have to be taken care of explicitly when eliciting risk attitudes.</p></div>","PeriodicalId":44104,"journal":{"name":"EURO Journal on Decision Processes","volume":"12 ","pages":"Article 100042"},"PeriodicalIF":1.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2193943823000158/pdfft?md5=d24e9429425baecfe1d2f0519e910dfc&pid=1-s2.0-S2193943823000158-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138539683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk attitudes: The Central Tendency Bias","authors":"Karl Akbari, Markus Eigruber, Rudolf Vetschera","doi":"10.1016/j.ejdp.2023.100042","DOIUrl":"https://doi.org/10.1016/j.ejdp.2023.100042","url":null,"abstract":"<p>Unincentivized measurement instruments of risk attitudes suffer from several weaknesses. One is that respondents do not consistently assign themselves to their respective risk preference categories. In particular, they are subject to a central tendency bias and classify themselves as risk-neutral when they are in fact not. We test the robustness of the central tendency bias in lottery-type questions for risk evaluations and offer an explanation of why respondents behave in a way that contradicts plausible utility models. We explore a wide range of alternative influencing factors, including careless responding, stake levels, deviations in expected value, the cognitive abilities of the respondents, self-assessment of risk attitudes, and monetary incentives. We find that careless responding and higher stakes increase the central tendency bias in risk assessment, while cognitive capabilities and extreme risk self-assessments (both positive and negative) decrease the bias. Deviations in expected value and incentives do not affect the bias. Our study further points to the fact that such problems have to be taken care of explicitly when eliciting risk attitudes.</p>","PeriodicalId":44104,"journal":{"name":"EURO Journal on Decision Processes","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138539728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-objective optimization design to generate surrogate machine learning models in explainable artificial intelligence applications","authors":"Wellington Rodrigo Monteiro ,&nbsp;Gilberto Reynoso-Meza","doi":"10.1016/j.ejdp.2023.100040","DOIUrl":"https://doi.org/10.1016/j.ejdp.2023.100040","url":null,"abstract":"<div><p>Decision-making is crucial to the performance and well-being of any organization. While artificial intelligence algorithms are increasingly used in the industry for decision-making purposes, the adoption of decision-making techniques to develop new artificial intelligence models does not follow the same trend. Complex artificial intelligence algorithm structures such as gradient boosting, ensembles, and neural networks offer higher accuracy at the expense of transparency. In organizations, however, managers and other stakeholders need to understand how an algorithm came to a given decision to properly criticize, learn from, audit, and improve said algorithms. Among the most recent techniques to address this, explainable artificial intelligence (XAI) algorithms offer a previously unforeseen level of interpretability, explainability, and informativeness to different human roles in the industry. XAI algorithms seek to balance the trade-off between interpretability and accuracy by introducing techniques that, for instance, explain the feature relevance in complex algorithms, generate counterfactual examples in “what-if?” analyses, and train surrogate models that are intrinsically explainable. However, while the trade-off between these two objectives is commonly referred to in the literature, only some proposals use multi-objective optimization in XAI applications. Therefore, this document proposes a new multi-objective optimization application to help decision-makers (for instance, data scientists) to generate new surrogate machine learning models based on black-box models. These surrogates are generated by a multi-objective problem that maximizes, at the same time, interpretability and accuracy. The proposed application also has a multi-criteria decision-making step to rank the best surrogates considering these two objectives. Results from five classification and regression datasets tested on four black-box models show that the proposed method can create simple surrogates maintaining high levels of accuracy.</p></div>","PeriodicalId":44104,"journal":{"name":"EURO Journal on Decision Processes","volume":"11 ","pages":"Article 100040"},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2193943823000134/pdfft?md5=c0cfb4113c9d5700533e1ba3c3d4dfd1&pid=1-s2.0-S2193943823000134-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91987226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal preventive policies for parallel systems using Markov decision process: application to an offshore power plant","authors":"Mario Marcondes Machado ,&nbsp;Thiago Lima Silva ,&nbsp;Eduardo Camponogara ,&nbsp;Edilson Fernandes de Arruda ,&nbsp;Virgílio José Martins Ferreira Filho","doi":"10.1016/j.ejdp.2023.100034","DOIUrl":"https://doi.org/10.1016/j.ejdp.2023.100034","url":null,"abstract":"","PeriodicalId":44104,"journal":{"name":"EURO Journal on Decision Processes","volume":"11 ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50203155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fairkit, fairkit, on the wall, who’s the fairest of them all? Supporting fairness-related decision-making","authors":"Brittany Johnson ,&nbsp;Jesse Bartola ,&nbsp;Rico Angell ,&nbsp;Sam Witty ,&nbsp;Stephen Giguere ,&nbsp;Yuriy Brun","doi":"10.1016/j.ejdp.2023.100031","DOIUrl":"10.1016/j.ejdp.2023.100031","url":null,"abstract":"<div><p>Modern software relies heavily on data and machine learning, and affects decisions that shape our world. Unfortunately, recent studies have shown that because of biases in data, software systems frequently inject bias into their decisions, from producing more errors when transcribing women’s than men’s voices to overcharging people of color for financial loans. To address bias in software, data scientists and software engineers need tools that help them understand the trade-offs between model quality and fairness in their specific data domains. Toward that end, we present fairkit-learn, an interactive toolkit for helping engineers reason about and understand fairness. Fairkit-learn supports over 70 definition of fairness and works with state-of-the-art machine learning tools, using the same interfaces to ease adoption. It can evaluate thousands of models produced by multiple machine learning algorithms, hyperparameters, and data permutations, and compute and visualize a small Pareto-optimal set of models that describe the optimal trade-offs between fairness and quality. Engineers can then iterate, improving their models and evaluating them using fairkit-learn. We evaluate fairkit-learn via a user study with 54 students, showing that students using fairkit-learn produce models that provide a better balance between fairness and quality than students using scikit-learn and IBM AI Fairness 360 toolkits. With fairkit-learn, users can select models that are up to 67% more fair and 10% more accurate than the models they are likely to train with scikit-learn.</p></div>","PeriodicalId":44104,"journal":{"name":"EURO Journal on Decision Processes","volume":"11 ","pages":"Article 100031"},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42622011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Special Issue on Decision Processes in Policy Design","authors":"Dr. Irene Pluchinotta ,&nbsp;Dr. Ine Steenmans","doi":"10.1016/j.ejdp.2023.100038","DOIUrl":"10.1016/j.ejdp.2023.100038","url":null,"abstract":"","PeriodicalId":44104,"journal":{"name":"EURO Journal on Decision Processes","volume":"11 ","pages":"Article 100038"},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41445004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}