pwc-archive/papers-with-abstracts huggingface dataset:
Integrating Sustainability in Controlling and Accounting Practices: A Critical Review and Implications for Competences in German Vocational Business Education
Authors: Julia Pargmann, Florian Berding
Date: 2024-06-04 00:00:00

Sustainability in accounting and controlling has traditionally been understood in terms of securing the long-term existence of companies. However, with the introduction of integrated non-financial reporting, sustainability, as per the triple bottom line model, is increasingly being discussed as a component of accounting and controlling. Yet, integration primarily occurs in separate sustainability management and controlling departments. Moreover, the implementation of sustainability efforts requires suitably qualified employees, who drive the transition. The academic discourse surrounding sustainability in businesses in general, and in accounting and controlling specifically, is complex. It remains unclear to what extent sustainability has been integrated into accounting and controlling, and what competencies employees need to manage this transformation. These questions will be critically analyzed in this structured literature review of 79 publications. The results provide insights into a) how companies conceptualize sustainability, b) whether and how they integrate it into their value creation processes, and c) the relevance of accounting and controlling for these developments. To contextualize the role of employees, the competency requirements within companies will be analyzed to enable employees in accounting and controlling to engage effectively in sustainability-oriented activities. Specifically, implications for changes in curricula with a focus on accounting and controlling are derived.
How Inductive Bias in Machine Learning Aligns with Optimality in Economic Dynamics
Authors: Mahdi Ebrahimi Kahou, James Yu, Jesse Perla, Geoff Pleiss
Date: 2024-06-04 00:00:00
Tasks: Inductive Bias

This paper examines the alignment of inductive biases in machine learning (ML) with structural models of economic dynamics. Unlike dynamical systems found in physical and life sciences, economics models are often specified by differential equations with a mixture of easy-to-enforce initial conditions and hard-to-enforce infinite horizon boundary conditions (e.g. transversality and no-ponzi-scheme conditions). Traditional methods for enforcing these constraints are computationally expensive and unstable. We investigate algorithms where those infinite horizon constraints are ignored, simply training unregularized kernel machines and neural networks to obey the differential equations. Despite the inherent underspecification of this approach, our findings reveal that the inductive biases of these ML models innately enforce the infinite-horizon conditions necessary for the well-posedness. We theoretically demonstrate that (approximate or exact) min-norm ML solutions to interpolation problems are sufficient conditions for these infinite-horizon boundary conditions in a wide class of problems. We then provide empirical evidence that deep learning and ridgeless kernel methods are not only theoretically sound with respect to economic assumptions, but may even dominate classic algorithms in low to medium dimensions. More importantly, these results give confidence that, despite solving seemingly ill-posed problems, there are reasons to trust the plethora of black-box ML algorithms used by economists to solve previously intractable, high-dimensional dynamical systems -- paving the way for future work on estimation of inverse problems with embedded optimal control problems.
The Oxford Olympics Study 2024: Are Cost and Cost Overrun at the Games Coming Down?
Authors: Alexander Budzier, Bent Flyvbjerg
Date: 2024-06-03 00:00:00

The present paper is an update of the "Oxford Olympics Study 2016" (Flyvbjerg et al. 2016). We document that the Games remain costly and continue to have large cost overruns, to a degree that threatens their viability. The IOC is aware of the problem and has initiated reform. We assess the reforms and find: (a) Olympic costs are statistically significantly increasing; prior analysis did not show this trend; it is a step in the wrong direction. (b) Cost overruns were decreasing until 2008, but have increased since then; again a step in the wrong direction. (c) At present, the cost of Paris 2024 is USD 8.7 billion (2022 level) and cost overruns is 115% in real terms; this is not the cheap Games that were promised. (d) Cost overruns are the norm for the Games, past, present, and future; they are the only project type that never delivered on budget. We assess a new IOC policy of reducing cost by reusing existing venues instead of building new ones. We find that reuse did not have the desired effect for Tokyo 2020 and also look ineffective for Paris 2024. Finally, we recommend that the Games look to other types of megaprojects for better data, better forecasting, and how to generate the positive learning curves that are necessary for bringing costs and overrun down. Only if this happens are Los Angeles 2028 and Brisbane 2032 likely to live up to the IOC's intentions of a more affordable Games that more cities will want to host.
Utilizing Large Language Models for Automating Technical Customer Support
Authors: Jochen Wulf, Jürg Meierhofer
Date: 2024-06-03 00:00:00
Tasks: Question Answering

The use of large language models (LLMs) such as OpenAI's GPT-4 in technical customer support (TCS) has the potential to revolutionize this area. This study examines automated text correction, summarization of customer inquiries and question answering using LLMs. Through prototypes and data analyses, the potential and challenges of integrating LLMs into the TCS will be demonstrated. Our results show promising approaches for improving the efficiency and quality of customer service through LLMs, but also emphasize the need for quality-assured implementation and organizational adjustments in the data ecosystem.
Stakeholder-driven research in the European Climate and Energy Modelling Forum
Authors: Emir Fejzic, Will Usher
Date: 2024-06-03 00:00:00

A fast-paced policy context is characteristic of energy and climate research, which strives to develop solutions to wicked problems such as climate change. Funding agencies in the European Union recognize the importance of linking research and policy in climate and energy research. This calls for an increased understanding of how stakeholder engagement can effectively be used to co-design research questions that include stakeholders' concerns. This paper reviews the current literature on stakeholder engagement, from which we create a set of criteria. These are used to critically assess recent and relevant papers on stakeholder engagement in climate and energy projects. We obtained the papers from a scoping review of stakeholder engagement through workshops in EU climate and energy research. With insights from the literature and current EU climate and energy projects, we developed a workshop programme for stakeholder engagement. This programme was applied to the European Climate and Energy Modelling Forum project, aiming to co-design the most pressing and urgent research questions according to European stakeholders. The outcomes include 82 co-designed and ranked research questions for nine specific climate and energy research themes. Findings from the scoping review indicate that papers rarely define the term 'stakeholder'. Additionally, the concepts of co-creation, co-design, and co-production are used interchangeably and often without definition. We propose that workshop planners use stakeholder identification and selection methods from the broader stakeholder engagement literature.
Production planning in 3DPrinting factories
Authors: Juan de Anton, Juan J Senovilla, Jose M Gonzalez-Varona, Fernando Acebes
Date: 2024-05-31 00:00:00
Tasks: Scheduling

Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this technology. In this work, we address the problem for the suppliers that have to schedule their daily production. This problem is part of the LONJA3D model, a managed 3D printing market where the parts ordered by the customers are reorganized into new batches so that suppliers can optimize their production capacity. In this paper, we propose a method derived from the design of combinatorial auctions to solve the nesting problem in 3D printing. First, we propose the use of a heuristic to create potential manufacturing batches. Then, we compute the expected return for each batch. The selected batch should generate the highest income. Several experiments have been tested to validate the process. This method is a first approach to the planning problem in 3D printing and further research is proposed to improve the procedure
Applicability of Business Intelligence Maturity Models to SMEs
Authors: Jose M Gonzalez-Varona, Adolfo Lopez-Paredes, Javier Pajares, Fernando Acebes, Felix Villafanez
Date: 2024-05-31 00:00:00
Tasks: valid

Large companies are fully engaged in their digital transformation, specifically in developing strategic Business Intelligence (BI) projects. They have a Digital Strategy and top-level executives managing the change. BI projects are also being carried out in SMEs. In this paper, we present the results of an interpretative study conducted on a sample of SMEs from different sectors carrying out BI projects. We study whether BI maturity models are valid for SMEs and their usefulness for analysing the projects and evaluating the results at the end of the project.
Building and development of an organizational competence for digital transformation in SMEs
Authors: Jose M Gonzalez-Varona, Adolfo Lopez-Paredes, David Poza, Fernando Acebes
Date: 2024-05-31 00:00:00

Purpose: The new competitive environment characterized by innovation and constant change is forcing a new organizational behavior. This requires a digital transformation of SMEs based on collective performance determinants. SMEs have particular characteristics that differentiate them from large companies and a model that allows them to identify, leverage and develop their digital capabilities can help them to advance in digital maturity. Design/methodology/approach: An in-depth review of the existing literature on digital transformation and organizational competence was carried out on Scopus and Web of Science to identify the digital challenges faced by SMEs, and what digital capabilities they have to develop to face these challenges. In order to obtain the necessary information for the refinement of organizational competence for digital transformation model, six experts were interviewed; three of them are academics and the other three are professionals with management responsibilities in SMEs. We used semi-structured interviews, to keep the interviews focused and facilitate cross-data analysis between experts. In addition, it allowed us the possibility of analyzing new relevant aspects that could arise during the interview. Findings: As a result of this study we have developed a refined model of organizational competence for digital transformation that allows SMEs to identify and develop the digital capabilities necessary to advance in the digital transformation, refined with the opinions of six experts consulted. We were able to observe the importance of organizational learning and organizational knowledge to advance the digital transformation of SMEs.
On the project risk baseline: integrating aleatory uncertainty into project scheduling
Authors: Fernando Acebes, David Poza, Jose M Gonzalez-Varona, Javier Pajares, Adolfo Lopez-Paredes
Date: 2024-05-31 00:00:00
Tasks: Benchmarking, Scheduling

Obtaining a viable schedule baseline that meets all project constraints is one of the main issues for project managers. The literature on this topic focuses mainly on methods to obtain schedules that meet resource restrictions and, more recently, financial limitations. The methods provide different viable schedules for the same project, and the solutions with the shortest duration are considered the best-known schedule for that project. However, no tools currently select which schedule best performs in project risk terms. To bridge this gap, this paper aims to propose a method for selecting the project schedule with the highest probability of meeting the deadline of several alternative schedules with the same duration. To do so, we propose integrating aleatory uncertainty into project scheduling by quantifying the risk of several execution alternatives for the same project. The proposed method, tested with a well-known repository for schedule benchmarking, can be applied to any project type to help managers to select the project schedules from several alternatives with the same duration, but the lowest risk.
Stochastic Earned Duration Analysis for Project Schedule Management
Authors: Fernando Acebes, David Poza, Jose Manuel Gonzalez-Varona, Adolfo Lopez-Paredes
Date: 2024-05-31 00:00:00
Tasks: Management

Earned duration management (EDM) is a methodology for project schedule management (PSM) that can be considered an alternative to earned value management (EVM). EDM provides an estimation of deviations in schedule and a final project duration estimation. There is a key difference between EDM and EVM: In EDM, the value of activities is expressed as work periods; whereas in EVM, value is expressed in terms of cost. In this paper, we present how EDM can be applied to monitor and control stochastic projects. To explain the methodology, we use a real case study with a project that presents a high level of uncertainty and activities with random durations. We analyze the usability of this approach according to the activities network topology and compare the EVM and earned schedule methodology (ESM) for PSM.
How public funding affects complexity in R&D projects. An analysis of team project perceptions
Authors: Jose M. Gonzalez-Varona, Natalia Martin-Cruz, Fernando Acebes, Javier Pajares
Date: 2024-05-31 00:00:00

In this paper, we apply a case study approach to advance current understanding of what effects public co-funding of R&D projects have on project team members' perceived complexity. We chose an R&D project carried out by an industrial SME in northern Spain. The chosen research strategy was a qualitative approach, and sixteen employees participated in the project. We held in-depth semi-structured interviews at the beginning and end of the co-funded part of the project. NVivo data analysis software was used for qualitative data analysis. Results showed a substantial increase in perceived complexity. We observed that this was due to unresolved tension between the requirements of the project's co-financing entity and normal SME working procedures. New working procedures needed to be developed in order to comply with the co-financing entity's requirements. However, overall perceived complexity significantly decreased once the co-financed part of the project was completed.
Efficiency in Pure-Exchange Economies with Risk-Averse Monetary Utilities
Authors: Mario Ghossoub, Michael Boyuan Zhu
Date: 2024-06-04 00:00:00

We study Pareto efficiency in a pure-exchange economy where agents' preferences are represented by risk-averse monetary utilities. These coincide with law-invariant monetary utilities, and they can be shown to correspond to the class of monotone, (quasi-)concave, Schur concave, and translation-invariant utility functionals. This covers a large class of utility functionals, including a variety of law-invariant robust utilities. We show that Pareto optima exist and are comonotone, and we provide a crisp characterization thereof in the case of law-invariant positively homogeneous monetary utilities. This characterization provides an easily implementable algorithm that fully determines the shape of Pareto-optimal allocations. In the special case of law-invariant comonotone-additive monetary utility functionals (concave Yaari-Dual utilities), we provide a closed-form characterization of Pareto optima. As an application, we examine risk-sharing markets where all agents evaluate risk through law-invariant coherent risk measures, a widely popular class of risk measures. In a numerical illustration, we characterize Pareto-optimal risk-sharing for some special types of coherent risk measures.
Optimal Wage Band for Job Matching with Signaling
Authors: Seungjin Han, Alex Sam, Youngki Shin
Date: 2024-06-04 00:00:00

We study an optimal wage band problem in which a policymaker sets a wage band prior to a competitive matching labor market, where education signals worker ability. We first prove the uniqueness of the competitive signaling equilibrium under a general class of utility and profit functions. We also show that the optimal wage band problem is isomorphic to an optimal ability threshold problem, which is more tractable than the original formulation. Building on this theoretical foundation, we construct a parametric model and analyze the mechanisms through which the policymaker's choice of a wage band leads to improved welfare outcomes compared to a no-intervention scenario. The implications and mechanisms we identify, arising from the asymmetric information, contrast sharply with those in the existing literature. Finally, the generality of our framework makes it suitable for a wide range of competitive matching applications, where agents invest in costly signals under asymmetric information.
Local non-bossiness
Authors: Eduardo Duque, Juan S. Pereyra, Juan Pablo Torres-Martínez
Date: 2024-06-03 00:00:00

The student-optimal stable mechanism (DA), the most popular mechanism in school choice, is the only one that is stable and strategy-proof. However, when DA is implemented, a student can change the schools of others without changing her own. We show that this drawback is limited: a student cannot change her schoolmates while remaining in the same school. We refer to this new property as local non-bossiness and use it to provide a new characterization of DA that does not rely on stability. Furthermore, we show that local non-bossiness plays a crucial role in providing incentives to be truthful when students have preferences over their colleagues. As long as students first consider the school to which they are assigned and then their schoolmates, DA induces the only stable and strategy-proof mechanism. There is limited room to expand this preference domain without compromising the existence of a stable and strategy-proof mechanism.
Absolute and Relative Ambiguity Attitudes
Authors: Francesco Fabbri, Giulio Principi, Lorenzo Stanca
Date: 2024-06-03 00:00:00

We represent preferences that exhibit absolute or relative attitudes towards ambiguity without assuming convexity of preferences. Our analysis is motivated by the recent experimental evidence by Baillon and Placido (2019) indicating that ambiguity becomes more tolerable as individuals are better off overall. Decreasing absolute ambiguity aversion is characterized by constant superadditive certainty equivalents and admits an act-dependent variational representation (Maccheroni et al., 2006). Decreasing relative ambiguity aversion relates to positive superhomogeneity and admits an act-dependent confidence preference representation (Chateauneuf and Faro, 2009). We apply our characterizations to retrieve a classic risk sharing result on the efficiency of trade and subjective beliefs of the individuals (Rigotti et al., 2008).
Generative AI as Economic Agents
Authors: Nicole Immorlica, Brendan Lucier, Aleksandrs Slivkins
Date: 2024-06-01 00:00:00
Tasks: AI Agent, Position

Traditionally, AI has been modeled within economics as a technology that impacts payoffs by reducing costs or refining information for human agents. Our position is that, in light of recent advances in generative AI, it is increasingly useful to model AI itself as an economic agent. In our framework, each user is augmented with an AI agent and can consult the AI prior to taking actions in a game. The AI agent and the user have potentially different information and preferences over the communication, which can result in equilibria that are qualitatively different than in settings without AI.
Games under the Tiered Deferred Acceptance Mechanism
Authors: Jiarui Xie
Date: 2024-06-01 00:00:00

We study the tiered deferred acceptance mechanism used in school admissions, such as in China and Turkey. This mechanism partitions schools into tiers and applies the deferred acceptance algorithm within each tier. Once assigned, students cannot apply to schools in subsequent tiers. We show that this mechanism is not strategy-proof. In the induced preference revelation game, we find that merging tiers preserves all equilibrium outcomes, and within-tier acyclicity is necessary and sufficient for the mechanism to implement stable matchings. We also find that introducing tiers to the deferred acceptance mechanism may not improve student quality at top-tier schools as intended.
CoLLAB: A Collaborative Approach for Multilingual Abuse Detection
Authors: Orchid Chetia Phukan, Yashasvi Chaurasia, Arun Balaji Buduru, Rajesh Sharma
Date: 2024-06-05 00:00:00
Tasks: Abuse Detection

In this study, we investigate representations from paralingual Pre-Trained model (PTM) for Audio Abuse Detection (AAD), which has not been explored for AAD. Our results demonstrate their superiority compared to other PTM representations on the ADIMA benchmark. Furthermore, combining PTM representations enhances AAD performance. Despite these improvements, challenges with cross-lingual generalizability still remain, and certain languages require training in the same language. This demands individual models for different languages, leading to scalability, maintenance, and resource allocation issues and hindering the practical deployment of AAD systems in linguistically diverse real-world environments. To address this, we introduce CoLLAB, a novel framework that doesn't require training and allows seamless merging of models trained in different languages through weight-averaging. This results in a unified model with competitive AAD performance across multiple languages.
Once more Diarization: Improving meeting transcription systems through segment-level speaker reassignment
Authors: Christoph Boeddeker, Tobias Cord-Landwehr, Reinhold Haeb-Umbach
Date: 2024-06-05 00:00:00
Tasks: Attribute, Speech Enhancement

Diarization is a crucial component in meeting transcription systems to ease the challenges of speech enhancement and attribute the transcriptions to the correct speaker. Particularly in the presence of overlapping or noisy speech, these systems have problems reliably assigning the correct speaker labels, leading to a significant amount of speaker confusion errors. We propose to add segment-level speaker reassignment to address this issue. By revisiting, after speech enhancement, the speaker attribution for each segment, speaker confusion errors from the initial diarization stage are significantly reduced. Through experiments across different system configurations and datasets, we further demonstrate the effectiveness and applicability in various domains. Our results show that segment-level speaker reassignment successfully rectifies at least 40% of speaker confusion word errors, highlighting its potential for enhancing diarization accuracy in meeting transcription systems.