arXiv subjects

Jinzhi Lu

Publications and source records attributed to Jinzhi Lu.

8 recordsLinked to original sources

arXiv2026

Audit the Auditors: Commitment versus Professional Judgment

This paper provides a theoretical framework to evaluate the trade-off between the self-regulated peer review system and independent government inspection (PCAOB) in the auditing profession. We model the peer review system as a Judgment Regime, where a stakeholder utilizes professional expertise, captured as a private signal, to make ex-post decisions on verifying audit failures. In contrast, PCAOB inspection is modeled as a Commitment Regime, where the stakeholder lacks private information but can commit ex-ante to a predetermined level of verification. We find that the Judgment Regime benefits from a resource-allocation effect and a deterrence effect driven by informed verification, whereas the Commitment Regime deters audit failures through the first-mover advantage of ex-ante commitment. Our analysis demonstrates that the stakeholder prefers the peer review system if and only if the private signal is sufficiently informative. Furthermore, comparative statics reveal that higher verification costs or stronger audit incentives shift the stakeholder's preference toward PCAOB inspection.

econ.TH↗

arXiv2026

A Theory of Covenant Accounting Adjustment

We develop an incomplete-contracting model with accounting-based covenants to study how covenant accounting adjustments are made and what properties they exhibit. Standard accounting rules (e.g., GAAP) can generate false-alarm errors or undue-optimism errors. The manager can exert costly effort to privately identify these errors and propose adjustments. If errors are not corrected, control rights may be inefficiently allocated, leading to costly renegotiation. We show that (1) adjustments always correct false-alarm errors, but correct undue-optimism errors only when their magnitude is small; and (2) the manager may expend socially wasteful effort to identify these errors. The model yields testable empirical predictions and policy implications.

econ.GN↗

arXiv2025

A Theory of Investors' Disclosure

We investigate investors voluntary disclosure decisions under uncertainty about their information endowment (Dye 1985). In our model, an investor may receive initial evidence about a target firm. Conditional on learning the initial evidence, the investor may receive additional evidence that helps interpret the initial evidence. The investor takes a position in the firms stock, then voluntarily discloses some or all of their findings, and finally closes their position after the disclosure. We present two main findings. First, the investor will always disclose the initial evidence, even though the market is uncertain about whether the investor possesses such evidence. Second, the investors disclosure strategy of the additional evidence increases stock price volatility: they disclose extreme news and withhold moderate news. Due to the withholding of the additional evidence, misleading disclosure arises as an equilibrium outcome, where the investors report decreases (increases) price despite their news being good (bad). These results remain robust when considering the target firms endogenous response to the investors report.

econ.GN↗

arXiv2025

KARMA Approach supporting Development Process Reconstruction in Model-based Systems Engineering

Model reconstruction is a method used to drive the development of complex system development processes in model-based systems engineering. Currently, during the iterative design process of a system, there is a lack of an effective method to manage changes in development requirements, such as development cycle requirements and cost requirements, and to realize the reconstruction of the system development process model. To address these issues, this paper proposes a model reconstruction method to support the development process model. Firstly, the KARMA language, based on the GOPPRR-E metamodeling method, is utilized to uniformly formalize the process models constructed based on different modeling languages. Secondly, a model reconstruction framework is introduced. This framework takes a structured development requirements based natural language as input, employs natural language processing techniques to analyze the development requirements text, and extracts structural and optimization constraint information. Then, after structural reorganization and algorithm optimization, a development process model that meets the development requirements is obtained. Finally, as a case study, the development process of the aircraft onboard maintenance system is reconstructed. The results demonstrate that this method can significantly enhance the design efficiency of the development process.

cs.SE↗

arXiv2022

Ontology-based system to support industrial system design for aircraft assembly

The development of an aircraft industrial system is a complex process which faces the challenge of digital discontinuity in multidisciplinary engineering due to various interfaces between different digital tools, leading to extra development time and costs. This paper proposes an ontology-based system, aiming at functionality integration and design process automation, by Models for Manufacturing methodology principles. A tool-agnostic modelling, simulation and validation platform with Discrete Event Simulation and 3D simulation is enabled and demonstrated in a real case study. An ontology layer collecting the domain knowledge enables integration of the proposed system, accelerating the design process and enhancing design quality.

cs.SE↗

arXiv2021

Actionable Cognitive Twins for Decision Making in Manufacturing

Actionable Cognitive Twins are the next generation Digital Twins enhanced with cognitive capabilities through a knowledge graph and artificial intelligence models that provide insights and decision-making options to the users. The knowledge graph describes the domain-specific knowledge regarding entities and interrelationships related to a manufacturing setting. It also contains information on possible decision-making options that can assist decision-makers, such as planners or logisticians. In this paper, we propose a knowledge graph modeling approach to construct actionable cognitive twins for capturing specific knowledge related to demand forecasting and production planning in a manufacturing plant. The knowledge graph provides semantic descriptions and contextualization of the production lines and processes, including data identification and simulation or artificial intelligence algorithms and forecasts used to support them. Such semantics provide ground for inferencing, relating different knowledge types: creative, deductive, definitional, and inductive. To develop the knowledge graph models for describing the use case completely, systems thinking approach is proposed to design and verify the ontology, develop a knowledge graph and build an actionable cognitive twin. Finally, we evaluate our approach in two use cases developed for a European original equipment manufacturer related to the automotive industry as part of the European Horizon 2020 project FACTLOG.

cs.AI↗

arXiv2020

Towards a Decentralized Digital Engineering Assets Marketplace: Empowered by Model-based Systems Engineering and Distributed Ledger Technology

Model-based Systems Engineering (MBSE) has been widely utilized to formalize system artifacts and facilitate their development throughout the entire lifecycle. During complex system development, MBSE models need to be frequently exchanged across stakeholders. Concerns about data security and tampering using traditional data exchange approaches obstruct the construction of a reliable marketplace for digital assets. The emerging Distributed Ledger Technology (DLT), represented by blockchain, provides a novel solution for this purpose owing to its unique advantages such as tamper-resistant and decentralization. In this paper, we integrate MBSE approaches with DLT aiming to create a decentralized marketplace to facilitate the exchange of digital engineering assets (DEAs). We first define DEAs from perspectives of digital engineering objects, development processes and system architectures. Based on this definition, the Graph-Object-Property-Point-Role-Relationship (GOPPRR) approach is used to formalize the DEAs. Then we propose a framework of a decentralized DEAs marketplace and specify the requirements, based on which we select a Directed Acyclic Graph (DAG) structured DLT solution. As a proof-of-concept, a prototype of the proposed DEAs marketplace is developed and a case study is conducted to verify its feasibility. The experiment results demonstrate that the proposed marketplace facilitates free DEAs exchange with a high level of security, efficiency and decentralization.

eess.SY↗

arXiv2020

Cognitive Twins for Supporting Decision-Makings of Internet of Things Systems

Cognitive Twins (CT) are proposed as Digital Twins (DT) with augmented semantic capabilities for identifying the dynamics of virtual model evolution, promoting the understanding of interrelationships between virtual models and enhancing the decision-making based on DT. The CT ensures that assets of Internet of Things (IoT) systems are well-managed and concerns beyond technical stakeholders are addressed during IoT system development. In this paper, a Knowledge Graph (KG) centric framework is proposed to develop CT. Based on the framework, a future tool-chain is proposed to develop the CT for the initiatives of H2020 project FACTLOG. Based on the comparison between DT and CT, we infer the CT is a more comprehensive approach to support IoT-based systems development than DT.

eess.SY↗