A Brief Discussion on KPI Development in Public Administration
- URL: http://arxiv.org/abs/2412.09142v1
- Date: Thu, 12 Dec 2024 10:27:55 GMT
- Title: A Brief Discussion on KPI Development in Public Administration
- Authors: Simona Fioretto, Elio Masciari, Enea Vincenzo Napolitano,
- Abstract summary: This paper presents an innovative framework for construction within performance evaluation systems, leveraging Random Forest algorithms and variable importance analysis.<n>The proposed approach identifies key variables that significantly influence PA performance, offering valuable insights into the critical factors driving organizational success.<n>This study aims to enhance PA performance through the application of machine learning techniques, fostering a more agile and results-driven approach to public administration.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Efficient and effective service delivery in Public Administration (PA) relies on the development and utilization of key performance indicators (KPIs) for evaluating and measuring performance. This paper presents an innovative framework for KPI construction within performance evaluation systems, leveraging Random Forest algorithms and variable importance analysis. The proposed approach identifies key variables that significantly influence PA performance, offering valuable insights into the critical factors driving organizational success. By integrating variable importance analysis with expert consultation, relevant KPIs can be systematically developed, ensuring that improvement strategies address performance-critical areas. The framework incorporates continuous monitoring mechanisms and adaptive phases to refine KPIs in response to evolving administrative needs. This study aims to enhance PA performance through the application of machine learning techniques, fostering a more agile and results-driven approach to public administration.
Related papers
- CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey [38.281260447611395]
This survey systematically explores the applications of Contrastive Language-Image Pretraining (CLIP) in domain generalization (DG) and domain adaptation (DA)
CLIP offers powerful zero-shot capabilities that allow models to perform effectively in unseen domains.
Key challenges, including overfitting, domain diversity, and computational efficiency, are addressed.
arXiv Detail & Related papers (2025-04-19T12:27:24Z) - Evaluating DAO Sustainability and Longevity Through On-Chain Governance Metrics [2.114921680609289]
Decentralised Autonomous Organisations (DAOs) automate governance and resource allocation through smart contracts, aiming to shift decision-making to distributed token holders.
This paper addresses these issues by identifying research gaps in financial evaluation and introducing a framework of Key Performance Indicators.
We apply the framework to a custom-built dataset of real-worlds constructed from on-chain data and analysed using non-parametric methods.
The results reveal recurring governance patterns, including low participation rates and high proposer concentration, which may undermine long-term viability.
arXiv Detail & Related papers (2025-04-15T16:13:20Z) - Evaluation and Incident Prevention in an Enterprise AI Assistant [20.635362734048723]
This paper presents a comprehensive framework for monitoring, benchmarking, and continuously improving such complex, multi-component systems under active development by multiple teams.
By adopting this holistic framework, organizations can systematically enhance the reliability and performance of their AI Assistants.
arXiv Detail & Related papers (2025-04-11T20:10:04Z) - Review, Refine, Repeat: Understanding Iterative Decoding of AI Agents with Dynamic Evaluation and Selection [71.92083784393418]
Inference-time methods such as Best-of-N (BON) sampling offer a simple yet effective alternative to improve performance.
We propose Iterative Agent Decoding (IAD) which combines iterative refinement with dynamic candidate evaluation and selection guided by a verifier.
arXiv Detail & Related papers (2025-04-02T17:40:47Z) - Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs [64.9693406713216]
Internal mechanisms that contribute to the effectiveness of RAG systems remain underexplored.
Our experiments reveal that several core groups of experts are primarily responsible for RAG-related behaviors.
We propose several strategies to enhance RAG's efficiency and effectiveness through expert activation.
arXiv Detail & Related papers (2024-10-20T16:08:54Z) - Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives [54.14429346914995]
Chain-of-Thought (CoT) has become a pivotal method for solving complex problems.
Large language models (LLMs) often struggle to accurately decompose domain-specific tasks.
This paper introduces the Re-TASK framework, a novel theoretical model that revisits LLM tasks from the perspectives of capability, skill, and knowledge.
arXiv Detail & Related papers (2024-08-13T13:58:23Z) - Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning [14.217528205889296]
Evolutionary computation (EC) algorithms leverage a group of individuals to cooperatively search for the optimum.
We propose a deep reinforcement learning-based framework that autonomously configures and adapts the exploration tradeoff (EET) throughout the EC search process.
Our proposed framework is characterized by its simplicity, effectiveness, and generalizability, with the potential to enhance numerous existing EC algorithms.
arXiv Detail & Related papers (2024-04-12T04:48:32Z) - LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly.
We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models.
It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
arXiv Detail & Related papers (2024-04-01T16:50:54Z) - A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services [46.1428063182192]
This study investigates the balance between performance optimization and algorithmic fairness in shared micromobility services.
Exploiting Q-Learning, the proposed methodology achieves equitable outcomes in terms of the Gini index across different areas.
A case study with synthetic data validates our insights and highlights the importance of fairness in urban micromobility.
arXiv Detail & Related papers (2024-03-23T09:32:23Z) - Analyzing Operator States and the Impact of AI-Enhanced Decision Support
in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning
Framework for Intervention Strategies [0.9378955659006951]
In complex industrial and chemical process control rooms, effective decision-making is crucial for safety andeffi- ciency.
The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface.
arXiv Detail & Related papers (2024-02-20T18:31:27Z) - A conceptual framework for SPI evaluation [6.973622134568803]
SPI-MEF guides the practitioner in scoping the evaluation, determining measures, and performing the assessment.
SPI-MEF does not assume a specific approach to process improvement and can be integrated in existing measurement programs.
arXiv Detail & Related papers (2023-07-24T19:22:58Z) - PerfDetectiveAI -- Performance Gap Analysis and Recommendation in
Software Applications [0.0]
PerfDetectiveAI, a conceptual framework for performance gap analysis and suggestion in software applications is introduced in this research.
Modern machine learning (ML) and artificial intelligence (AI) techniques are used in PerfDetectiveAI to monitor performance measurements and identify areas of underperformance in software applications.
arXiv Detail & Related papers (2023-06-11T02:53:04Z) - ASR: Attention-alike Structural Re-parameterization [53.019657810468026]
We propose a simple-yet-effective attention-alike structural re- parameterization (ASR) that allows us to achieve SRP for a given network while enjoying the effectiveness of the attention mechanism.
In this paper, we conduct extensive experiments from a statistical perspective and discover an interesting phenomenon Stripe Observation, which reveals that channel attention values quickly approach some constant vectors during training.
arXiv Detail & Related papers (2023-04-13T08:52:34Z) - Structure-Enhanced Deep Reinforcement Learning for Optimal Transmission
Scheduling [47.29474858956844]
We develop a structure-enhanced deep reinforcement learning framework for optimal scheduling of a multi-sensor remote estimation system.
In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure.
Our numerical results show that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25%.
arXiv Detail & Related papers (2022-11-20T00:13:35Z) - Implementation Matters in Deep Policy Gradients: A Case Study on PPO and
TRPO [90.90009491366273]
We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms.
Specifically, we investigate the consequences of "code-level optimizations:"
Our results show that they (a) are responsible for most of PPO's gain in cumulative reward over TRPO, and (b) fundamentally change how RL methods function.
arXiv Detail & Related papers (2020-05-25T16:24:59Z)
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.