SSRN Electronic Journal最新文献_第4页

Integrating AI Language Models in Qualitative Research: Replicating Interview Data Analysis with ChatGPT 在定性研究中整合人工智能语言模型：使用 ChatGPT 复制访谈数据分析

SSRN Electronic Journal Pub Date : 2024-05-21 DOI: 10.2139/ssrn.4714998

Mohammad S. Jalali, Ali Akhavan

引用次数: 1

Integrating Behavioral Experimental Findings into Dynamical Models to Inform Social Change Interventions 将行为实验结果纳入动态模型，为社会变革干预措施提供依据

SSRN Electronic Journal Pub Date : 2024-05-21 DOI: 10.2139/ssrn.4837583

Radu Tanase, Ren'e Algesheimer, Manuel S. Mariani

{"title":"Integrating Behavioral Experimental Findings into Dynamical Models to Inform Social Change Interventions","authors":"Radu Tanase, Ren'e Algesheimer, Manuel S. Mariani","doi":"10.2139/ssrn.4837583","DOIUrl":"https://doi.org/10.2139/ssrn.4837583","url":null,"abstract":"Addressing global challenges -- from public health to climate change -- often involves stimulating the large-scale adoption of new products or behaviors. Research traditions that focus on individual decision making suggest that achieving this objective requires better identifying the drivers of individual adoption choices. On the other hand, computational approaches rooted in complexity science focus on maximizing the propagation of a given product or behavior throughout social networks of interconnected adopters. The integration of these two perspectives -- although advocated by several research communities -- has remained elusive so far. Here we show how achieving this integration could inform seeding policies to facilitate the large-scale adoption of a given behavior or product. Drawing on complex contagion and discrete choice theories, we propose a method to estimate individual-level thresholds to adoption, and validate its predictive power in two choice experiments. By integrating the estimated thresholds into computational simulations, we show that state-of-the-art seeding methods for social influence maximization might be suboptimal if they neglect individual-level behavioral drivers, which can be corrected through the proposed experimental method.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141117421","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}

引用次数: 0

Deep LPPLS: Forecasting of temporal critical points in natural, engineering and financial systems 深度 LPPLS：预测自然、工程和金融系统中的时间临界点

SSRN Electronic Journal Pub Date : 2024-05-21 DOI: 10.2139/ssrn.4839066

Joshua Nielsen, Didier Sornette, M. Raissi

{"title":"Deep LPPLS: Forecasting of temporal critical points in natural, engineering and financial systems","authors":"Joshua Nielsen, Didier Sornette, M. Raissi","doi":"10.2139/ssrn.4839066","DOIUrl":"https://doi.org/10.2139/ssrn.4839066","url":null,"abstract":"The Log-Periodic Power Law Singularity (LPPLS) model offers a general framework for capturing dynamics and predicting transition points in diverse natural and social systems. In this work, we present two calibration techniques for the LPPLS model using deep learning. First, we introduce the Mono-LPPLS-NN (M-LNN) model; for any given empirical time series, a unique M-LNN model is trained and shown to outperform state-of-the-art techniques in estimating the nonlinear parameters $(t_c, m, omega)$ of the LPPLS model as evidenced by the comprehensive distribution of parameter errors. Second, we extend the M-LNN model to a more general model architecture, the Poly-LPPLS-NN (P-LNN), which is able to quickly estimate the nonlinear parameters of the LPPLS model for any given time-series of a fixed length, including previously unseen time-series during training. The Poly class of models train on many synthetic LPPLS time-series augmented with various noise structures in a supervised manner. Given enough training examples, the P-LNN models also outperform state-of-the-art techniques for estimating the parameters of the LPPLS model as evidenced by the comprehensive distribution of parameter errors. Additionally, this class of models is shown to substantially reduce the time to obtain parameter estimates. Finally, we present applications to the diagnostic and prediction of two financial bubble peaks (followed by their crash) and of a famous rockslide. These contributions provide a bridge between deep learning and the study of the prediction of transition times in complex time series.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141115993","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}

引用次数: 0

Wav-KAN: Wavelet Kolmogorov-Arnold Networks Wav-KAN：小波 Kolmogorov-Arnold 网络

SSRN Electronic Journal Pub Date : 2024-05-21 DOI: 10.2139/ssrn.4835325

Zavareh Bozorgasl, Hao Chen

{"title":"Wav-KAN: Wavelet Kolmogorov-Arnold Networks","authors":"Zavareh Bozorgasl, Hao Chen","doi":"10.2139/ssrn.4835325","DOIUrl":"https://doi.org/10.2139/ssrn.4835325","url":null,"abstract":"In this paper , we introduce Wav-KAN, an innovative neural network architecture that leverages the Wavelet Kolmogorov-Arnold Networks (Wav-KAN) framework to enhance interpretability and performance. Traditional multilayer perceptrons (MLPs) and even recent advancements like Spl-KAN face challenges related to interpretability, training speed, robustness, computational efficiency, and performance. Wav-KAN addresses these limitations by incorporating wavelet functions into the Kolmogorov-Arnold network structure, enabling the network to capture both high-frequency and low-frequency components of the input data efficiently. Wavelet-based approximations employ orthogonal or semi-orthogonal basis and also maintains a balance between accurately representing the underlying data structure and avoiding overfitting to the noise. Analogous to how water conforms to the shape of its container, Wav-KAN adapts to the data structure, resulting in enhanced accuracy, faster training speeds, and increased robustness compared to Spl-KAN and MLPs. Our results highlight the potential of Wav-KAN as a powerful tool for developing interpretable and high-performance neural networks, with applications spanning various fields. This work sets the stage for further exploration and implementation of Wav-KAN in frameworks such as PyTorch, TensorFlow, and also it makes wavelet in KAN in wide-spread usage like nowadays activation functions like ReLU, sigmoid in universal approximation theory (UAT).","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141115924","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}

引用次数: 0

Customer Acquisition Via Explainable Deep Reinforcement Learning 通过可解释深度强化学习获取客户

SSRN Electronic Journal Pub Date : 2024-05-21 DOI: 10.2139/ssrn.4802411

Yicheng Song, Wenbo Wang, Song Yao

引用次数: 0

The Behavioral, Economic, and Political Impact of the Internet and Social Media: Empirical Challenges and Approaches 互联网和社交媒体的行为、经济和政治影响：实证挑战与方法

SSRN Electronic Journal Pub Date : 2024-05-21 DOI: 10.2139/ssrn.4683373

Fabio Sabatini

引用次数: 1

Evolutionary mechanism for diversity dynamics in technology using a phylogenetic tree approach: directional suggestions for photovoltaic technology 利用系统发育树方法研究技术多样性动态的进化机制：对光伏技术的方向性建议

SSRN Electronic Journal Pub Date : 2024-05-21 DOI: 10.2139/ssrn.4725953

Hayoung Park, Dawoon Jeong, Jeong-Dong Lee

引用次数: 0

Enhancing User Interest based on Stream Clustering and Memory Networks in Large-Scale Recommender Systems 在大规模推荐系统中基于流聚类和记忆网络增强用户兴趣

SSRN Electronic Journal Pub Date : 2024-05-21 DOI: 10.2139/ssrn.4836975

Peng Liu, Nian Wang, Cong Xu, Min Zhao, Bin Wang, Yi Ren

{"title":"Enhancing User Interest based on Stream Clustering and Memory Networks in Large-Scale Recommender Systems","authors":"Peng Liu, Nian Wang, Cong Xu, Min Zhao, Bin Wang, Yi Ren","doi":"10.2139/ssrn.4836975","DOIUrl":"https://doi.org/10.2139/ssrn.4836975","url":null,"abstract":"Recommender Systems (RSs) provide personalized recommendation service based on user interest, which are widely used in various platforms. However, there are lots of users with sparse interest due to lacking consumption behaviors, which leads to poor recommendation results for them. This problem is widespread in large-scale RSs and is particularly difficult to address. To solve this problem, we propose a novel solution named User Interest Enhancement (UIE) which enhances user interest including user profile and user history behavior sequences using the enhancement vectors and personalized enhancement vector generated based on stream clustering and memory networks from different perspectives. UIE not only remarkably improves model performance on the users with sparse interest but also significantly enhance model performance on other users. UIE is an end-to-end solution which is easy to be implemented based on ranking model. Moreover, we expand our solution and apply similar methods to long-tail items, which also achieves excellent improvement. Furthermore, we conduct extensive offline and online experiments in a large-scale industrial RS. The results demonstrate that our model outperforms other models remarkably, especially for the users with sparse interest. Until now, UIE has been fully deployed in multiple large-scale RSs and achieved remarkable improvements.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141116014","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}

引用次数: 0

Risk, utility and sensitivity to large losses 风险、效用和对巨额损失的敏感性

SSRN Electronic Journal Pub Date : 2024-05-20 DOI: 10.2139/ssrn.4739077

Martin Herdegen, Nazem Khan, Cosimo Munari

{"title":"Risk, utility and sensitivity to large losses","authors":"Martin Herdegen, Nazem Khan, Cosimo Munari","doi":"10.2139/ssrn.4739077","DOIUrl":"https://doi.org/10.2139/ssrn.4739077","url":null,"abstract":"Risk and utility functionals are fundamental building blocks in economics and finance. In this paper we investigate under which conditions a risk or utility functional is sensitive to the accumulation of losses in the sense that any sufficiently large multiple of a position that exposes an agent to future losses has positive risk or negative utility. We call this property sensitivity to large losses and provide necessary and sufficient conditions thereof that are easy to check for a very large class of risk and utility functionals. In particular, our results do not rely on convexity and can therefore also be applied to most examples discussed in the recent literature, including (non-convex) star-shaped risk measures or S-shaped utility functions encountered in prospect theory. As expected, Value at Risk generally fails to be sensitive to large losses. More surprisingly, this is also true of Expected Shortfall. By contrast, expected utility functionals as well as (optimized) certainty equivalents are proved to be sensitive to large losses for many standard choices of concave and nonconcave utility functions, including $S$-shaped utility functions. We also show that Value at Risk and Expected Shortfall become sensitive to large losses if they are either properly adjusted or if the property is suitably localized.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119911","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}

引用次数: 1

External Representations in Strategic Decision-Making: Understanding Strategy’s Reliance on Visuals 战略决策中的外部表征：理解战略对视觉的依赖

SSRN Electronic Journal Pub Date : 2024-05-20 DOI: 10.2139/ssrn.4806761

Felipe A. Csaszar, Nicole Hinrichs, M. Heshmati

{"title":"External Representations in Strategic Decision-Making: Understanding Strategy’s Reliance on Visuals","authors":"Felipe A. Csaszar, Nicole Hinrichs, M. Heshmati","doi":"10.2139/ssrn.4806761","DOIUrl":"https://doi.org/10.2139/ssrn.4806761","url":null,"abstract":"External representations, particularly visuals, are important in strategic decision‐making. However, their pervasiveness and impact are not well understood in the strategy literature. Based on cognitive science research, we identify four cognitive functions crucial to strategic decision‐making that benefit from using external representations. We also propose a conceptual model and propositions that explain how the quality of strategic decision‐making depends on the interactions among task environment, external representations, and managers. We show that external representations influence in predictable ways the boundedly rational process of searching for new strategies. Key determinants include the manager's representational capability and the usability and malleability of the external representation. We discuss implications for users, designers, and teachers of external representations in strategy, as well as suggest avenues for future research.This research points to the pivotal role of external representations, especially visuals, in strategic decision‐making. Drawing from cognitive science, this study identifies four critical cognitive functions that benefit from these external representations—working memory, long‐term memory, pattern recognition, and knowledge transfer. Further, the study highlights that external representations significantly influence the process of strategic decision‐making in predictable ways. Finally, we show that not all external representations are alike in their ease of use and a managers' ability to operate on an external representation, referred to as representational capability, greatly affects the decision‐making quality. The implications extend to users, designers, and educators of external representations, urging attention to the design and use of external representations for improved decision outcomes.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121337","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}

引用次数: 0