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A Theory of Going Public Decision Based on Reputational Capital

Journal of Systems science & Complexity 2025年第3期38卷 1109-1135页

作者： LI Dahai DING Tao LIANG Liang School of Big Data and Statistics Anhui University School of Management Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology School of Economics Hefei University of Technology Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance Ministry of Education

Leveraging the principal-agent theory, this study incorporates reputational capital into a continuous-time framework for analyzing venture capital exit decisions. The authors explore how the principal-agent relationships influence the decision to take capital public by synthesizing optimal incentive alignment and IPO timing within a unified model. The results uncover an intriguing aspect of reputational capital: Despite entrepreneurial efforts to augment this capital, its growth is not linear but tends to reach the highest level before the IPO event. Furthermore, the accumulation of reputational capital expedites the capital exit process and enhances the returns from such exits. Subsequently, as the exit timeline extends, the proportion of exit returns allocated to entrepreneurs escalates to a full share, imposing a constrained decision window for investors on exit timing. The findings suggest that the timing for maximizing investor returns at an IPO is later than the timing that maximizes company revenue. Moreover, excessive IPO costs further delay this exit decision for investors. These findings offer fresh insights into the languishing IPO market observed over the last two decades.

关键词： Exit decision principal-agent theory reputational capital venture capital

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ESTIMATING THE PARAMETERS OF GENERALIZED LOGISTIC DISTRIBUTION VIA GENETIC ALGORITHM BASED ON REDUCED SEARCH SPACE

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Journal of Mathematical sciences (United States) 2025年第1期289卷 28-44页

作者： Yalçınkaya, Abdullah Kılıç, Adil Şenoǧlu, Birdal Department of Statistics Faculty of Science Ankara University Ankara 06100 Turkey Department of Statistics Faculty of Humanities and Social Sciences Kırıkkale University Kırıkkale 71450 Turkey

In this study, maximum likelihood (ML) estimates of the parameters of generalized logistic (GL) distribution are obtained using the genetic algorithm (GA) based on the reduced search space (RSS) proposed by (Yalçınkaya et al. Swarm and Evolutionary Computation 38, 127–138, 2018). RSS is defined in terms of the confidence intervals based on modified maximum likelihood (MML) estimators. MML estimators are the explicit functions of the sample observations and asymptotically equivalent to the ML estimators, (Tiku, Biometrika 54, 155–165, 1967). To see the effectiveness of RSS, we examine the performance of GA based on RSS against GA based on fixed search space (FSS). The efficiencies of these estimators are also compared with the classical ML estimators using Newton–Raphson (NR) algorithm via Monte Carlo simulation study. MML estimators of GL distribution parameters are also included into the study to show the performance of the non-iterative MML methodology against the iterative ML methodology. At the end of the study, a real-life example is given to see the implementation of the proposed methodology. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Efficiency Generalized logistic Genetic algorithm Maximum likelihood Modified maximum likelihood Newton–Raphson

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Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

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data Intelligence 2024年第2期6卷 320-343页

作者： Sergei Romanov Abel Soares Siqueira Jonathan de Bruin Jelle Teijema Laura Hofstee Rens van de Schoot Applied Data Science Department of Information and Computing ScienceFaculty of ScienceUtrecht University Netherlands eScienceCenter AmsterdamNL Department of Research and Data Management Services Information Technology ServicesUtrecht UniversityUtrechtthe Netherlands Department of Methodology and Statistics Faculty of Social and Behavioral SciencesUtrecht University

Active learning can be used for optimizing and speeding up the screening phase of systematic *** simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training *** paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud *** provide a technical explanation of the proposed cloud architecture and its *** addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM *** analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing *** parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.

关键词： Active learning Systematic review Simulation study Multiprocessing Cloud architecture

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A Functional Connectivity-Based EEG Analysis for the Decision-Makings in Ultimatum Game 17

A Functional Connectivity-Based EEG Analysis for the Decisio...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Gao, Xiang Zhuang, Yue Chen, LiJun Huang, ZhiHua College of Computer and Data Science Fuzhou University Fuzhou China School of Humanities and Social Sciences Fuzhou University Fuzhou China

ISBN: (纸本)9798331507398

In the ultimatum game (UG), individuals usually reject extremely unfair offers and accept fair ones. A mid-value offer is unfair, but not to the point of immediate rejection. When faced with this, most individuals hesitate to either accept or reject. As a result, there is rich decision-making information in the brain during mid-value offers, and recognizing whether individuals will accept or reject in this situation is challenging. In recent years, EEG channel connectivity indices have been widely used to study brain networks. Among them, mutual information (MI), phase-locked value (PLV), and phase-lag index (PLI) are commonly employed to construct functional connectivity matrices (FCMs). Since these functional connectivity indices are undirected, the FCMs constructed from them are symmetric matrices with a large amount of redundant information. To address this, this paper utilizes a variety of connectivity indices and constructs a fused functional connectivity matrix by pairing two of them. The results show that the approach using PSI + MI performs optimally in the classification task of recognizing whether an individual accepts or rejects and reliably extracts a network structure that reveals inter-channel connection strength variations in decision-makings. © 2024 IEEE.

关键词： Matrix algebra

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The challenges and opportunities of women working in weaving cultural clothes at Bahir-Dar City, Bahirdar, Ethiopia

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Discover Global Society 2025年第1期3卷 1-9页

作者： Getachew, Tatek Alemu, Chekol Wudu, Habitamu Department of Gender and Development Studies Faculty of Social Science and Humanities Gambella University Gambella Ethiopia Department of Statistics College of Natural and Computational Sciences Gambella University Gambella Ethiopia

Background: Cultural weaving is a crucial component of the cotton industry, linking rural and urban households through traditional handcrafts and providing income for women, thereby enhancing family livelihoods. The demand for hand-woven garments is on the rise, although a study from 2003 to 2009 revealed a decrease in skill-based wage premiums and a slight wage penalty for women in the garment sector. Traditionally, men and women have collaborated in fabric production, with men handling weaving and women focusing on decoration. Ethiopia is highlighted as a prominent African country for its rich cultural clothing and diverse ethnic heritage. Method: The study employed a qualitative research method with a case study design. Participants were selected through purposive and availability sampling for semi-structured interviews and focus group discussions. After reaching saturation with fourteen weavers and key informants, data were analyzed using thematic analysis. Result: The study highlights that women weavers of cultural clothes face numerous challenges, including socio-cultural issues, psychological problems, and economic difficulties such as material shortages and outdated technology. Despite these obstacles, there are opportunities for improvement through institutional support, training programs, and social networking. Ultimately, these women benefit from income generation, work flexibility, skill development, and enhanced social satisfaction, positively impacting their lives and their families' livelihoods. Conclusion: The study suggests that the Women, Children, and social Affairs Office of Keeble should systematically address the challenges faced by women weavers, particularly regarding access to essential services and materials. Additionally, it calls for a shift in community attitudes to recognize weaving as a valuable profession that can improve lives rather than being viewed negatively. © The Author(s) 2025.

关键词： Bahir-Dar Cities Clothes Cultural Weaving Women

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When Artificial Intelligence Meets Human Intelligence: Topics and Sentiments about ChatGPT in Online Knowledge Sharing Communities 58

When Artificial Intelligence Meets Human Intelligence: Topic...

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58th Hawaii International Conference on System sciences, HICSS 2025

作者： Sun, Yao Wang, Lijing Diggs, Kevin Kulkarni, Avanish Steiger, Kevin Vu, Tai Venkataraman, Vibha Department of Social Sciences and Humanities New Jersey Institute of Technology United States Department of Data Science New Jersey Institute of Technology United States

ISBN: (纸本)9780998133188

OpenAI's release of ChatGPT has sparked worldwide discussion and debates about its potential implications. Knowledge sharing conversations in online Q&A communities, for example, demonstrate how ChatGPT is perceived when human collective intelligence is unleashed. We focus on textual data collected from the Stack Exchange community to investigate the major topics and sentiments generated when community members share their knowledge about ChatGPT. Different topic modeling and sentiment analysis algorithms were applied and compared to understand major perceptions and attitudes toward ChatGPT, and to illustrate the perceived opportunities and challenges surrounding ChatGPT and AI algorithms. Our study presents the differences in terms of ChatGPT-related sentiments and discussion themes across different types of subcommunities and highlights the critical roles of feature extraction and threshold selection in further improving the accuracy of topic models and precisely capturing the patterns of prevalent sentiments. © 2025 IEEE Computer Society. All rights reserved.

关键词： Emotion Recognition

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Energy-Efficient Prediction of Hourly Energy Consumption in Fog/Edge Computing Using Optimized Artificial Neural Networks

Energy-Efficient Prediction of Hourly Energy Consumption in ...

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2024 Global Congress on Emerging Technologies, GCET 2024

作者： Fakhouri, Hussam N. Alkhalaileh, Mohannad Hwaitat, Ahmad Zaitoun, Eman Humanities and Social Sciences Al Ain University College of Education Data Science and Artificial Intelligence Department Al Ain United Arab Emirates

ISBN: (纸本)9798331542603

The rapid increase in global energy demand necessitates innovative strategies for efficient energy management, particularly in decentralized computing infrastructures such as fog and edge computing. These technologies reduce latency and bandwidth usage by bringing computation closer to the data source, but they introduce new energy management challenges. Accurate prediction of hourly energy consumption is crucial for optimizing these systems' operation and sustainability. This study proposes a novel approach for predicting hourly energy consumption using a feed-forward multi-layer perceptron (MLP) artificial neural network (ANN) optimized by state-of-the-art metaheuristic algorithms. Historical energy consumption data, including computational workloads and environmental factors, train the ANN. Five metaheuristic algorithms - Marine Predator Algorithm (MPA), Four Vector Intelligent Metaheuristic (FVIM), Whale Optimization Algorithm (WOA), Equilibrium Optimizer (EO), and Self-Adaptive Differential Evolution (SaDE) - optimize the ANN's weights. Results show that the Equilibrium Optimizer (EO) significantly enhances predictive accuracy, outperforming other algorithms. This optimized ANN model improves resource allocation, load balancing, and energy cost savings, contributing to the sustainability of fog and edge computing infrastructures. The findings highlight the importance of advanced predictive analytics in managing energy consumption within decentralized computing environments. © 2024 IEEE.

关键词： Artificial Neural Networks Energy Consumption Energy-Efficient Fog/Edge Prediction

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A data-Driven Government Response Analysis to COVID-19 in Delta Variant Stage based on FCM-DID Model 6

A Data-Driven Government Response Analysis to COVID-19 in De...

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6th International Conference on Computing and Big data, ICCBD 2023

作者： Wu, Ruibing Wong, Johnston Hong-Chung Peng, Xiaoling BNU-HKBU United International College Department of Statistics and Data Science Zhuhai China United International College Faculty of Humanities and Social Sciences BNU-HKBU Zhuhai China United International College Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application or Data Science BNU-HKBU Zhuhai China

ISBN: (纸本)9798350317008

Aimed to provide insight into the global trend of COVID-19 during the Delta variant stage and offer recommendations for effective epidemic prevention policies. In this study, we propose a novel data-driven causal inference model combining fuzzy c-means (FCM) clustering and difference-in-difference (DID) for multiple time series data. Based on the FCM clustering, a set of panel data with parallel trends can be obtained for further DID analysis. By comparing the change in the outcome variable between the treatment and control groups within each cluster, before and after the intervention, the causal effect of each cluster on the outcome variable is estimated. In government response analysis to COVID-19, the daily updated data of 196 countries all over the world during the transition phase from the outbreak of the SARS-CoV-2 Delta variant to the discovery of Omicron was collected. Application of our method shows that adopting restrictions on internal movement policies had a substantial impact on preventing the spread of the pandemic. The FCM-DID approach we proposed is useful for identifying and comparing the effects of policies on the outcome of different groups, thus can well evaluate the effectiveness of implemented policies and provide valuable guidance for decision-making in future public health crises. © 2023 IEEE.

关键词： Time series

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A Fast Feature Selection for Interpretable Modeling Based on Fuzzy Inference Systems 21

A Fast Feature Selection for Interpretable Modeling Based on...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Tangherloni, Andrea Cazzaniga, Paolo Stranieri, Nicolò Buffa, Francesca M. Nobile, Marco S. Bocconi Institute for Data Science and Analytics Bocconi University Department of Computing Sciences Milan Italy University of Bergamo Department of Human and Social Sciences Bergamo Italy Bocconi University Department of Computing Sciences Milan Italy Ca' Foscari University of Venice Department of Environmental Sciences Informatics and Statistics Venice Italy

ISBN: (纸本)9798350356632

Large datasets are often beneficial for the generation of predictive models using machine learning approaches. However, it is often the case that not all variables in the dataset contain useful information. In fact, some variables might be useless, redundant, misleading, or even harmful to performance, both in terms of accuracy and computational effort. Because of that, Feature Selection (FS) is one of the most delicate and important steps in machine learning. This is even more relevant in the case of interpretable models based on Fuzzy Inference Systems (FIS). The reasons are two-fold: on the one hand, FIS are generally built on top of a data partitioning based on clustering, which can suffer from high dimensionality;on the other hand, the knowledge base of the FIS, to be concretely understandable, should not contain rules involving too many variables. FS can be performed using multiple approaches, most notably filter and wrapper methods. The latter are often based on evolutionary algorithms, where a population of candidate solutions (each representing a possible set of selected variables) evolves towards the optimal selection. Although wrapper methods can be effective, they are, in general, computationally expensive. In this work, we propose a completely different - and more computationally effective - algorithm based on Random Forest (RF) models. Specifically, we exploit RFs to rank variables according to their importance. Then, we use that information to perform a statistical analysis and determine the minimal set of features necessary to build an accurate FIS. We show the effectiveness of our approach by using two (semi)synthetic datasets built on real-world datasets, and we validate our approach by applying the FS method to a medical dataset. © 2024 IEEE.

关键词： Feature Selection

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Visualization and Assessment of Copula Symmetry

arXiv

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arXiv 2023年

作者： Jiménez-Varón, Cristian F. Lee, Hao Genton, Marc G. Sun, Ying Statistics Program King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Statistics & Data Science Dietrich College of Humanities and Social Sciences Carnegie Mellon University United States

Visualization and assessment of copula structures are crucial for accurately understanding and modeling the dependencies in multivariate data analysis. In this paper, we introduce an innovative method that employs functional boxplots and rank-based testing procedures to evaluate copula symmetry. This approach is specifically designed to assess key characteristics such as reflection symmetry, radial symmetry, and joint symmetry. We first construct test functions for each specific property and then investigate the asymptotic properties of their empirical estimators. We demonstrate that the functional boxplot of these sample test functions serves as an informative visualization tool of a given copula structure, effectively measuring the departure from zero of the test function. Furthermore, we introduce a nonparametric testing procedure to assess the significance of deviations from symmetry, ensuring the accuracy and reliability of our visualization method. Through extensive simulation studies involving various copula models, we demonstrate the effectiveness of our testing approach. Finally, we apply our visualization and testing techniques to two real-world datasets: a nutritional habits survey with five variables and wind speed data from three locations in Saudi Arabia. © 2023, CC BY.

关键词： Visualization

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