Contributions in Merlin

Used car market platforms are interested in extending their offering from information provision to the whole customer journey. Providing certified data on the car’s state and history enables this extension by eliminating the need to physically inspect the car before buying it. Hence, communication and negotiations can move entirely to a used car platform to cover the entire value chain. How can such a market communication be designed when certified data come into play? This study designs and develops a certified data chat for the selective and controlled exchange of blockchain-based certified data in used car negotiations. An experimental market game is played with students to evaluate the usefulness of the chat. The study contributes to the augmentation of market communication with valuable and sensitive data exchange and demonstrates what a key component of a future used car market can look like. It offers three design principles and insight into why certified data chats are useful.

Elections generally involve the simple tasks of counting votes and publishing the final tally to voters. Depending on the election’s scope, these processes require sophisticated methods embedded in the electorate’s various technological and societal factors (e.g., the voting culture). An election’s integrity is the pinnacle of the trust placed in the voting process and its final results. Previous research on cryptographic voting schemes continuously refined voting protocols to achieve private and verifiable elections. These cryptographic schemes enable novel ways to allow remote voting systems to (i) provide a remote voting alternative to on-site voting with a ballot box and (ii) offer a technical path to building an end-to-end verifiable Electronic Voting system by applying cryptographic primitives. This chapter explicitly addresses Remote Electronic Voting (REV), which enables vote casting in an uncontrolled environment and vote transmission over communication channels (e.g., over the Internet). Further, this chapter clarifies how the Distributed Ledger (DL) technology can enhance the decentralization of the electoral process, ensuring transparency and the ability to verify results while guaranteeing the confidentiality of voters. Thus, necessary cryptographic fundamentals and examples of voting schemes, their properties, and trade-offs will be investigated. The different approaches toward REV systems are detailed, followed by an overview of related work. Further, a centralized REV voting system architecture, all stakeholders involved, and critical trust assumptions are outlined. This leads to the proposal of a fully decentralized architecture, which is being evaluated in qualified discussions with respect to long-term privacy, verifiability, and voter authentication. Finally, open research aspects are complemented by overall considerations on decentralized REV.

In this study, we use earnings management to examine (1) how investors regard a CEO’s commitment to honesty and (2) the impact of their perceptions, in light of their own moral values, on their investment decisions. In two laboratory experiments using students as investor proxies, we find that investors perceive a CEO as being more committed to honesty when they believe the CEO has engaged less in earnings management. A one standard deviation increase in a CEO’s perceived commitment to honesty, compared to that of another CEO, leads to a 40% reduction in the importance the investors assigned, when making investment decisions, to differences in the two CEOs’ claimed future returns. This effect is particularly pronounced among investors with a proself value orientation. For prosocial investors, their moral values and those they attribute to the CEO directly influence their investment decisions, with returns playing a secondary role. Our findings contrast with the idea, implicit in the literature on ‘sin’ stocks, that morality is a niche concern. By contrast, we find that moral values play a significant role for distinct types of investors and that they influence investment decisions for both moral and pecuniary reasons.

Künstliche Intelligenz hat die Effizienz der Datenanalyse revolutioniert und bietet die Möglichkeit, das Investment Research und Portfoliomanagement zu automatisieren. Ihr Einsatz ist aber kein uneingeschränkter Erfolgsgarant.

Professor Marc Chesney of the University of Zurich says that the foul stench oil, gas, and coal emanated from global climate summit COP28.

Large Language Models have made remarkable progress in question-answering tasks, but challenges like hallucination and outdated information persist. These issues are especially critical in domains like climate change, where timely access to reliable information is vital. One solution is granting these models access to external, scientifically accurate sources to enhance their knowledge and reliability. Here, we enhance GPT-4 by providing access to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain (refer to the ’Data Availability’ section). We present our conversational AI prototype, available at www.chatclimate.ai, and demonstrate its ability to answer challenging questions in three different setups: (1) GPT-4, (2) ChatClimate, which relies exclusively on IPCC AR6 reports, and (3) Hybrid ChatClimate, which utilizes IPCC AR6 reports with in-house GPT-4 knowledge. The evaluation of answers by experts show that the hybrid ChatClimate AI assistant provide more accurate responses, highlighting the effectiveness of our solution.

The concept of the tragedy of the commons, originally rooted in economics, describes the depletion of shared resources due to self-interested actions by individuals. This work proposes a novel solution to address this economic challenge by leveraging tokens to capture its multidimensional nature. By utilising blockchain and DLTs, this decentralised approach aims to achieve a social optimum while promoting self-regulation. The paper presents a mathematical treatment of the tragedy of the commons, incorporating multi-dimensional tokens and exploring the divergence from the classic optimal solution, highlighting the potential of tokenisation in shaping a sustainable and efficient economy.

When introducing unfamiliar Artificial Intelligence (AI)-based systems, such as conversational agents (CAs), one needs to ensure that users interact with them according to their design. While past research has studied single-user environments, many practical settings involve multiple parties. This study addresses this gap and focuses on financial advisory service encounters and how mental models evolve in multi-party contexts. A multimodal interactive CA is developed and tested in financial consultations with 24 clients. The observations of these consultations and subsequent interviews provide insights into the challenges of using CAs in unfamiliar contexts. The clients have difficulties effectively using the system. This is linked to the institutional setting of financial advisory service encounters and a mismatch between the designer’s conceptual model and the client’s mental model, which we call secondary mental model.

Current approaches to Argument Mining (AM) tend to take a holistic or black-box view of the overall pipeline. This paper, in contrast, aims to provide a solution to achieve increased performance based on current components instead of independent all-new solutions. To that end, it presents the Deployment of Recombination and Ensemble methods for Argument Miners (DREAM) framework that allows for the (automated) combination of AM components. Using ensemble methods, DREAM combines sets of AM systems to improve accuracy for the four tasks in the AM pipeline. Furthermore, it leverages recombination by using different argument miners elements throughout the pipeline. Experiments with five systems previously included in a benchmark show that the systems combined with DREAM can outperform the previous best single systems in terms of accuracy measured by an AM benchmark.

In code reviews, developers examine code changes authored by peers and provide feedback through comments. Despite the importance of these comments, no accepted approach currently exists for assessing their quality. Therefore, this study has two main objectives: (1) to devise a conceptual model for an explainable evaluation of review comment quality, and (2) to develop models for the automated evaluation of comments according to the conceptual model. To do so, we conduct mixed-method studies and propose a new approach: EvaCRC (Evaluating Code Review Comments). To achieve the first goal, we collect and synthesize quality attributes of review comments, by triangulating data from both authoritative documentation on code review standards and academic literature. We then validate these attributes using real-world instances. Finally, we establish mappings between quality attributes and grades by inquiring domain experts, thus defining our final explainable conceptual model. To achieve the second goal, EvaCRC leverages multi-label learning. To evaluate and refine EvaCRC, we conduct an industrial case study with a global ICT enterprise. The results indicate that EvaCRC can effectively evaluate review comments while offering reasons for the grades.