Advancing Employee Behavior Analysis through Synthetic Data: Leveraging ABMs, GANs, and Statistical Models for Enhanced Organizational Efficiency
- URL: http://arxiv.org/abs/2409.14197v1
- Date: Sat, 21 Sep 2024 16:58:23 GMT
- Title: Advancing Employee Behavior Analysis through Synthetic Data: Leveraging ABMs, GANs, and Statistical Models for Enhanced Organizational Efficiency
- Authors: Rakshitha Jayashankar, Mahesh Balan,
- Abstract summary: This study delves into creating synthetic data, a powerful tool that allows us to comprehensively understand employee performance.
Synthetic data provides a detailed and accurate picture of employee activities.
We examine how synthetic data has evolved from a specialized field to an essential resource for researching employee behavior.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Success in todays data-driven corporate climate requires a deep understanding of employee behavior. Companies aim to improve employee satisfaction, boost output, and optimize workflow. This research study delves into creating synthetic data, a powerful tool that allows us to comprehensively understand employee performance, flexibility, cooperation, and team dynamics. Synthetic data provides a detailed and accurate picture of employee activities while protecting individual privacy thanks to cutting-edge methods like agent-based models (ABMs), Generative Adversarial Networks (GANs), and statistical models. Through the creation of multiple situations, this method offers insightful viewpoints regarding increasing teamwork, improving adaptability, and accelerating overall productivity. We examine how synthetic data has evolved from a specialized field to an essential resource for researching employee behavior and enhancing management efficiency. Keywords: Agent-Based Model, Generative Adversarial Network, workflow optimization, organizational success
Related papers
- Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data.
We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
arXiv Detail & Related papers (2024-10-22T06:43:28Z) - Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance [95.03771007780976]
We tackle the challenge of developing proactive agents capable of anticipating and initiating tasks without explicit human instructions.
First, we collect real-world human activities to generate proactive task predictions.
These predictions are labeled by human annotators as either accepted or rejected.
The labeled data is used to train a reward model that simulates human judgment.
arXiv Detail & Related papers (2024-10-16T08:24:09Z) - GenAgent: Build Collaborative AI Systems with Automated Workflow Generation -- Case Studies on ComfyUI [64.57616646552869]
This paper explores collaborative AI systems that use to enhance performance to integrate models, data sources, and pipelines to solve complex and diverse tasks.
We introduce GenAgent, an LLM-based framework that automatically generates complex, offering greater flexibility and scalability compared to monolithic models.
The results demonstrate that GenAgent outperforms baseline approaches in both run-level and task-level evaluations.
arXiv Detail & Related papers (2024-09-02T17:44:10Z) - AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning [93.96463520716759]
Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and hallucinations.
Here, we introduce AvaTaR, a novel and automated framework that optimize an LLM agent to effectively leverage provided tools, improving performance on a given task.
arXiv Detail & Related papers (2024-06-17T04:20:02Z) - EnterpriseEM: Fine-tuned Embeddings for Enterprise Semantic Search [1.2097014193871654]
We propose a methodology for contextualizing pre-trained embedding models to enterprise environments.
By adapting the embeddings to better suit the retrieval tasks prevalent in enterprises, we aim to enhance the performance of AI-driven information retrieval solutions.
arXiv Detail & Related papers (2024-05-18T14:06:53Z) - Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records [4.159498069487535]
We propose an automated approach named AutoDP, which can search for the optimal configuration of task grouping and architectures simultaneously.
It achieves significant performance improvements over both hand-crafted and automated state-of-the-art methods, also maintains a feasible search cost at the same time.
arXiv Detail & Related papers (2024-03-06T22:32:48Z) - Large Language Model-based Human-Agent Collaboration for Complex Task
Solving [94.3914058341565]
We introduce the problem of Large Language Models (LLMs)-based human-agent collaboration for complex task-solving.
We propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC.
This approach includes a policy model designed to determine the most opportune stages for human intervention within the task-solving process.
arXiv Detail & Related papers (2024-02-20T11:03:36Z) - TrainerAgent: Customizable and Efficient Model Training through
LLM-Powered Multi-Agent System [14.019244136838017]
TrainerAgent is a multi-agent framework including Task, Data, Model and Server agents.
These agents analyze user-defined tasks, input data, and requirements (e.g., accuracy, speed), optimizing them from both data and model perspectives to obtain satisfactory models, and finally deploy these models as online service.
This research presents a significant advancement in achieving desired models with increased efficiency and quality as compared to traditional model development.
arXiv Detail & Related papers (2023-11-11T17:39:24Z) - Data-Centric Long-Tailed Image Recognition [49.90107582624604]
Long-tail models exhibit a strong demand for high-quality data.
Data-centric approaches aim to enhance both the quantity and quality of data to improve model performance.
There is currently a lack of research into the underlying mechanisms explaining the effectiveness of information augmentation.
arXiv Detail & Related papers (2023-11-03T06:34:37Z) - An Extensive Analytical Approach on Human Resources using Random Forest
Algorithm [0.0]
Survey indicated that work life imbalances, low pay, uneven shifts and many other factors make employees think about changing their work life.
This paper proposes a model with the help of a random forest algorithm by considering different employee parameters.
It helps the HR department retain the employee by identifying gaps and helping the organisation to run smoothly with a good employee retention ratio.
arXiv Detail & Related papers (2021-05-07T07:35:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.