Decision Support Systems最新文献_第9页

HEX: Human-in-the-loop explainability via deep reinforcement learning HEX：通过深度强化学习实现人在回路中的可解释性

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2024-08-22 DOI: 10.1016/j.dss.2024.114304

Michael T. Lash

{"title":"HEX: Human-in-the-loop explainability via deep reinforcement learning","authors":"Michael T. Lash","doi":"10.1016/j.dss.2024.114304","DOIUrl":"10.1016/j.dss.2024.114304","url":null,"abstract":"<div><div>The use of machine learning (ML) models in decision-making contexts, particularly those used in high-stakes decision-making, are fraught with issue and peril since a person – not a machine – must ultimately be held accountable for the consequences of decisions made using such systems. Machine learning explainability (MLX) promises to provide decision-makers with prediction-specific rationale, assuring them that the model-elicited predictions are made <em>for the right reasons</em> and are thus reliable. Few works explicitly consider this key human-in-the-loop (HITL) component, however. In this work we propose HEX, a human-in-the-loop deep reinforcement learning approach to MLX. HEX incorporates 0-distrust projection to synthesize decider-specific explainers that produce explanations strictly in terms of a decider’s preferred explanatory features using any classification model. Our formulation explicitly considers the decision boundary of the ML model in question using a proposed <em>explanatory point</em> mode of explanation, thus ensuring explanations are specific to the ML model in question. We empirically evaluate HEX against other competing methods, finding that HEX is competitive with the state-of-the-art and outperforms other methods in human-in-the-loop scenarios. We conduct a randomized, controlled laboratory experiment utilizing actual explanations elicited from both HEX and competing methods. We causally establish that our method increases decider’s trust and tendency to rely on trusted features.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"187 ","pages":"Article 114304"},"PeriodicalIF":6.7,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Paradigm changing metaverse: Future research directions in design, technology adoption and use, and impacts 改变范式的元宇宙：设计、技术采用和使用以及影响方面的未来研究方向

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2024-08-21 DOI: 10.1016/j.dss.2024.114307

Viswanath Venkatesh

引用次数: 0

Generalized visible curvature: An indicator for bubble identification and price trend prediction in cryptocurrencies 广义可见曲率：加密货币泡沫识别和价格趋势预测指标

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2024-08-21 DOI: 10.1016/j.dss.2024.114309

Qun Zhang , Canxuan Xie , Zhaoju Weng , Didier Sornette , Ke Wu

引用次数: 0

Enhanced (cyber) situational awareness: Using interpretable principal component analysis (iPCA) to automate vulnerability severity scoring 增强（网络）态势感知：使用可解释主成分分析（iPCA）自动进行漏洞严重性评分

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2024-08-20 DOI: 10.1016/j.dss.2024.114308

Motahareh Pourbehzadi , Giti Javidi , C. Jordan Howell , Eden Kamar , Ehsan Sheybani

{"title":"Enhanced (cyber) situational awareness: Using interpretable principal component analysis (iPCA) to automate vulnerability severity scoring","authors":"Motahareh Pourbehzadi , Giti Javidi , C. Jordan Howell , Eden Kamar , Ehsan Sheybani","doi":"10.1016/j.dss.2024.114308","DOIUrl":"10.1016/j.dss.2024.114308","url":null,"abstract":"<div><p>The Common Vulnerability Scoring System (CVSS) is widely used in the cybersecurity industry to assess the severity of vulnerabilities. However, manual assessments and human error can lead to delays and inconsistencies. This study employs situational awareness theory to develop an automated decision support system, integrating perception, comprehension, and projection components to enhance effectiveness. Specifically, an interpretable principal component analysis (iPCA) combined with machine learning is utilized to forecast CVSS scores using text descriptions from the Common Vulnerabilities and Exposures (CVE) database. Different forecasting approaches, including traditional machine learning models, Long-Short Term Memory Neural Networks, and Transformer architectures (ChatGPT) are compared to determine the best performance. The results show that iPCA combined with support vector regression achieves a high performance (R<sup>2</sup> = 98%) in predicting CVSS scores using CVE text descriptions. The results indicate that the variability, length, and details in the vulnerability description contribute to the performance of the transformer model. These findings are consistent across vulnerability descriptions from six companies between 2017 and 2019. The study's outcomes have the potential to enhance organizations' security posture, improving situational awareness and enabling better managerial decision-making in cybersecurity.</p></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"186 ","pages":"Article 114308"},"PeriodicalIF":6.7,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142150985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing the online word of mouth dynamics: A novel approach 分析网络口碑动态：一种新方法

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2024-08-12 DOI: 10.1016/j.dss.2024.114306

Xian Cao , Timothy B. Folta , Hongfei Li , Ruoqing Zhu

{"title":"Analyzing the online word of mouth dynamics: A novel approach","authors":"Xian Cao , Timothy B. Folta , Hongfei Li , Ruoqing Zhu","doi":"10.1016/j.dss.2024.114306","DOIUrl":"10.1016/j.dss.2024.114306","url":null,"abstract":"<div><p>In today's digital economy, virtually everything from products and services to political debates and cultural phenomena can spark WOM on social media. Analyzing online WOM poses at least three challenges. First, online WOM typically consists of unstructured data that can transform into myriad variables, necessitating effective dimension reduction. Second, online WOM is often continuous and dynamic, with the potential for rapid, time-varying changes. Third, significant events may trigger symmetric or asymmetric responses across various entities, resulting in “bursty” and intense WOM from multiple sources. To address these challenges, we introduce a new computationally efficient method—multi-view sequential canonical covariance analysis. This method is designed to solve the myriad online WOM conversational dimensions, detect online WOM dynamic trends, and examine the shared online WOM across different entities. This approach not only enhances the capability to swiftly interpret and respond to online WOM data but also shows potential to significantly improve decision-making processes across various contexts. We illustrate the method's benefits through two empirical examples, demonstrating its potential to provide profound insights into online WOM dynamics and its extensive applicability in both academic research and practical scenarios.</p></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"185 ","pages":"Article 114306"},"PeriodicalIF":6.7,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uplift modeling and its implications for appointment date prediction in attended home delivery 上浮模型及其对预约上门服务日期预测的影响

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2024-08-03 DOI: 10.1016/j.dss.2024.114303

Dujuan Wang , Qihang Xu , Yi Feng , Joshua Ignatius , Yunqiang Yin , Di Xiao

{"title":"Uplift modeling and its implications for appointment date prediction in attended home delivery","authors":"Dujuan Wang , Qihang Xu , Yi Feng , Joshua Ignatius , Yunqiang Yin , Di Xiao","doi":"10.1016/j.dss.2024.114303","DOIUrl":"10.1016/j.dss.2024.114303","url":null,"abstract":"<div><p>Successful attended home delivery (AHD) is the most important aspect of e-commerce order fulfillment. Prior literature focuses on incentive scheme development for customers' choices of delivery windows and predictive analytics for delivery results, but it is not clear whether the effect of AHD on the appointment date set by customers increases the success rate of AHD. Therefore, we developed an uplift modeling method, PSM-NDML, as a relevant prescriptive analytic tool for AHD on an appointment date, which aims to estimate the causal effect of the by-appointment delivery on the delivery result. PSM-NDML integrates propensity score matching and double machine learning, effectively addressing sample selection bias, low predictive performance, and poor interpretability. Applied to a real-world product delivery dataset of a Chinese logistics company, PSM-NDML achieves superior performance relative to ten other state-of-the-art uplift models in terms of cumulative gain and the Qini coefficient. The predicted responses gained from PSM-NDML are also visually interpreted at the global and local levels, which reveals various managerial insights. In practice, the findings expand managers' understanding of the heterogeneous effects of AHD on appointment dates and provide decision support for logistics companies in the development of home delivery plans.</p></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"185 ","pages":"Article 114303"},"PeriodicalIF":6.7,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incentive hierarchies intensify competition for attention: A study of online reviews 激励等级加剧了注意力竞争：在线评论研究

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2024-07-30 DOI: 10.1016/j.dss.2024.114293

Baojun Zhang , Zili Zhang , Kee-Hung Lai , Ziqiong Zhang

{"title":"Incentive hierarchies intensify competition for attention: A study of online reviews","authors":"Baojun Zhang , Zili Zhang , Kee-Hung Lai , Ziqiong Zhang","doi":"10.1016/j.dss.2024.114293","DOIUrl":"10.1016/j.dss.2024.114293","url":null,"abstract":"<div><p>While many online platforms use incentive hierarchies to stimulate consumers to generate more online reviews, the extent to which these hierarchies influence reviewer behavior is not fully understood. This study, drawing on image motivation theory and consumer attention theory, takes a novel approach to investigate whether reviewers strategically adjust their review behavior after reaching higher ranks in a hierarchy. We use data from rank change timestamps on platforms to accurately identify reviewers' ranks when posting reviews and then employ a quasi-natural experimental design for causal inference. Additionally, we use Fisher's permutation test to explore the different effects at various ranks. The empirical results reveal that reviewers tend to increase their review length and insert more pictures into their reviews after they reach higher ranks. Reviewers at lower ranks tend to submit more extreme ratings upon rank advancement, whereas their higher-ranking counterparts do not demonstrate significant change. Unlike ratings, reviewers tend to consistently increase the sentiment intensity of their expressions in text after reaching higher ranks. Furthermore, our findings indicate that the magnitude of changes in reviewing behavior only shows an increasing trend in the early stages of rank progression. These insights contribute to a better understanding of the efficacy of incentive hierarchies and offer practical implications for decision-making by platform managers.</p></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"185 ","pages":"Article 114293"},"PeriodicalIF":6.7,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guiding attention in flow-based conceptual models through consistent flow and pattern visibility 通过一致的流程和模式可见性，在基于流程的概念模型中引导注意力

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2024-07-28 DOI: 10.1016/j.dss.2024.114292

Kathrin Figl , Pnina Soffer , Barbara Weber

{"title":"Guiding attention in flow-based conceptual models through consistent flow and pattern visibility","authors":"Kathrin Figl , Pnina Soffer , Barbara Weber","doi":"10.1016/j.dss.2024.114292","DOIUrl":"10.1016/j.dss.2024.114292","url":null,"abstract":"<div><p>A critical part of flow-based conceptual modeling, such as process modeling, is visualizing the logical and temporal sequence in which activities in a process should be completed. While there are established standards and recommendations, there is limited empirical research examining the influence of process model layout on model comprehension. To address this research gap, we conducted a controlled eye-tracking experiment with 70 participants comparing different layouts. The experimental results confirm that the visibility of control flow patterns is critical for assisting users with visual processing, particularly attentional allocation, when comprehending process models for both local comprehension tasks and tasks requiring cognitive integration of model components. In models with more directional changes, users’ visual attention is more drawn to irrelevant regions, but comprehension is less affected as long as patterns remain visible. Our findings not only elucidate how cognitive fit between a visual representation and a task can manifest itself and the perceptual benefits it brings, but they can also guide the automated layout of models in tools and complement practical process modeling guidelines.</p></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"185 ","pages":"Article 114292"},"PeriodicalIF":6.7,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167923624001258/pdfft?md5=2baf9307632a49e6559b80fd25878063&pid=1-s2.0-S0167923624001258-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bridging realities into organizations through innovation and productivity: Exploring the intersection of artificial intelligence, internet of things, and big data analytics in the metaverse environment using a multi-method approach 通过创新和生产力将现实与组织连接起来：使用多种方法探索元环境中人工智能、物联网和大数据分析的交叉点

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2024-07-26 DOI: 10.1016/j.dss.2024.114290

Ashutosh Samadhiya , Rohit Agrawal , Anil Kumar , Sunil Luthra

{"title":"Bridging realities into organizations through innovation and productivity: Exploring the intersection of artificial intelligence, internet of things, and big data analytics in the metaverse environment using a multi-method approach","authors":"Ashutosh Samadhiya , Rohit Agrawal , Anil Kumar , Sunil Luthra","doi":"10.1016/j.dss.2024.114290","DOIUrl":"10.1016/j.dss.2024.114290","url":null,"abstract":"<div><p>This study investigates how organizations may increase innovation and productivity through the Metaverse environment efficacy (MVEE), Artificial intelligence usage (AIU), Internet of Things usage (IoTU), and Big Data Analytics usage (BDAU). The study gathers responses from the gaming, information technology, and entertainment industries, using a multi-method involving Partial Least Squares Structural Equation Modeling, Fuzzy-set Qualitative Comparative Analysis, and Artificial Neural Networks to investigate how these technologies might be used to improve the linking of disparate realities in a business context. The use of AI in personalized and decision-support applications, IoT for real-time data collecting, and BDAU for an insights-driven strategy all combine to create a dynamic MVEE ecosystem. The research also delves into theoretical implications concerning the viability of using the MVEE to boost innovation and productivity. This research identifies the applications of using AI, IoT, and BDA to drive organizational performance in terms of innovation and productivity. Also, the research lays out the role of AI, IoT, and BDA in creating a dynamic metaverse ecosystem.</p></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"185 ","pages":"Article 114290"},"PeriodicalIF":6.7,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167923624001234/pdfft?md5=358c5d38e7c9ef28ff47eabad293513e&pid=1-s2.0-S0167923624001234-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141840216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The value of data, machine learning, and deep learning in restaurant demand forecasting: Insights and lessons learned from a large restaurant chain 数据、机器学习和深度学习在餐饮需求预测中的价值：一家大型连锁餐厅的启示和经验教训

IF 6.7 1区计算机科学

Decision Support Systems Pub Date : 2024-07-23 DOI: 10.1016/j.dss.2024.114291

Bongsug (Kevin) Chae , Chwen Sheu , Eunhye Olivia Park

{"title":"The value of data, machine learning, and deep learning in restaurant demand forecasting: Insights and lessons learned from a large restaurant chain","authors":"Bongsug (Kevin) Chae , Chwen Sheu , Eunhye Olivia Park","doi":"10.1016/j.dss.2024.114291","DOIUrl":"10.1016/j.dss.2024.114291","url":null,"abstract":"<div><p>The restaurant industry has been slow to adopt analytics for the supply chain, operations, and demand forecasting, with limited research on this sector. The COVID-19 pandemic's significant impact on the restaurant industry, one of the hardest-hit sectors, has underscored the need for digital technologies and advanced analytics for managing supply chains and making operational decisions. This paper presents a collaborative study with one of the largest restaurant chains in the United States, highlighting the value of advanced data analytics in forecasting restaurant demand. The study offers insights into the benefit of integrating external data, including macroeconomic and pandemic-related factors, into demand forecasting. It explores traditional machine learning algorithms and state-of-the-art deep learning architectures, evaluating their effectiveness in the context of the restaurant industry. The paper further discusses the implications of utilizing advanced forecasting models, providing valuable insights for the restaurant industry in the face of supply chain disruptions and pandemics.</p></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"184 ","pages":"Article 114291"},"PeriodicalIF":6.7,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141844336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0