Tomás Lagos, Junyeong Choi, Brittany Segundo, Jianbang Gan, Lewis Ntaimo, Oleg A Prokopyev, Bilevel optimization approach for fuel treatment planning, European Journal of Operational Research, Vol. 320 (1), 2025. (Journal Article)
Various fuel treatment practices involve removing all or some of the vegetation (fuel) from a landscape to reduce the potential for fires and their severity. Fuel treatments form the first line of defense against large-scale wildfires. In this study, we formulate and solve a bilevel integer programming model, where the fuel treatment planner (modeled as the leader) determines appropriate locations and types of treatments to minimize expected losses from wildfires. The follower (i.e., the lower-level decision-maker) corresponds to nature, which is adversarial to the leader and designs a wildfire attack (i.e., locations and time periods, where and when, respectively, wildfires occur) to disrupt the leader’s objective function, e.g., the total expected area burnt. Both levels in the model involve integrality restrictions for decision variables; hence, we explore the model’s difficulty from the computational complexity perspective. Then, we design specialized solution methods for general and some special cases. We perform experiments with semi-synthetic and real-life instances to illustrate the performance of our approaches. We also explore numerically the fundamental differences in the structural properties of solutions arising from bilevel model and its single-level counterpart. These disparities encompass factors like the types of treatments applied and the choice of treated areas. Additionally, we conduct various types of sensitivity analysis on the performance of the obtained policies and illustrate the value of the bilevel solutions. |
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Xinran Wang, Zisu Wang, Mateusz Dolata, Jay F Nunamaker, How credibility assessment technologies affect decision fairness in evidence-based investigations: A Bayesian perspective, Decision Support Systems, Vol. 187, 2024. (Journal Article)
Recently, a growing number of credibility assessment technologies (CATs) have been developed to assist human decision-making processes in evidence-based investigations, such as criminal investigations, financial fraud detection, and insurance claim verification. Despite the widespread adoption of CATs, it remains unclear how CAT and human biases interact during the evidence-collection procedure and affect the fairness of investigation outcomes. To address this gap, we develop a Bayesian framework to model CAT adoption and the iterative collection and interpretation of evidence in investigations. Based on the Bayesian framework, we further conduct simulations to examine how CATs affect investigation fairness with various configurations of evidence effectiveness, CAT effectiveness, human biases, technological biases, and decision stakes. We find that when investigators are unconscious of their own biases, CAT adoption generally increases the fairness of investigation outcomes if the CAT is more effective than evidence and less biased than the investigators. However, the CATs' positive influence on fairness diminishes as humans become aware of their own biases. Our results show that CATs' impact on decision fairness highly depends on various technological, human, and contextual factors. We further discuss the implications for CAT development, evaluation, and adoption based on our findings. |
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Mamiza Haq, Steven Ongena, Juying Pu, Eric K M Tan, Do banks engage in earnings management? The role of dividends and institutional factors, Journal of Banking and Finance, Vol. 168, 2024. (Journal Article)
We investigate the impact of dividend policy on earnings quality and opportunistic earnings management for individual banks across 45 developed and developing countries between 1996 and 2019. Our estimates show that high dividend payments reduce earnings management, hence mitigate agency problems. This mitigation is especially prevalent among well-capitalised and non-listed banks. Greater investor protection and government regulation appear to strengthen the negative association between dividend policy and earnings management. Our results hold robustly across many different specifications. |
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Kathrin Wardatzky, Evaluating the Pros and Cons of Recommender Systems Explanations, In: RecSys '24: 18th ACM Conference on Recommender Systems, 2024-10-14. (Conference or Workshop Paper published in Proceedings)
Despite the growing interest in explainable AI in the RecSys community, the evaluation of explanations is still an open research topic.
Typically, explanations are evaluated using offline metrics, with a case study, or through a user study. In my research, I will have a closer look at the evaluation of the effects of explanations on users. I investigate two possible factors that can impact the effects reported in recent publications, namely the explanation design and content as well as the users themselves. I further address the problem of determining promising explanations for an application scenario from a seemingly endless pool of options. Lastly, I propose a user study to close some of the research gaps established in the surveys and investigate how recommender systems explanations impact the understanding of users with different backgrounds. |
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Armin R Veres, Daria Schumm, Thomas Grübl, Katharina O E Müller, Bruno Rodrigues, Burkhard Stiller, Demo: Secure Inventorying and Lifecycle Management of IoT Devices with DLT, In: 2024 IEEE 49th Conference on Local Computer Networks (LCN), Institute of Electrical and Electronics Engineers, 2024-10-08. (Conference or Workshop Paper published in Proceedings)
Security management of Internet-of-Things (IoT) infrastructures encompassing the entire lifecycle of products and their continuous certification are fundamental functions to guarantee a high level of security. This paper demonstrates a DLT-based platform enabling secure inventorying and management of IoT devices and sensors distributed across multiple stakeholders. This work makes use of the Hyperledger framework, Decentralized Identifiers (DID), and Manufacturer Usage Description (MUD) files for IoT device inventorying and the identification of critical software updates. |
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Jan Von der Assen, Alberto Huertas Celdran, Rinor Sefa, Burkhard Stiller, Gérôme Bovet, MTFS: a Moving Target Defense-Enabled File System for Malware Mitigation, In: 2024 IEEE 49th Conference on Local Computer Networks (LCN), Institute of Electrical and Electronics Engineers, 2024-10-08. (Conference or Workshop Paper published in Proceedings)
Ransomware has remained one of the most notorious threats in the cybersecurity field, for which Moving Target Defense (MTD) has been proposed as a novel defense paradigm. Although various approaches leverage MTD, few of them rely on the operating system and, specifically, the file system, thereby making them dependent on other computing devices, rendering defense against certain threats unrealistic. File-based approaches are less studied here while showing limitations in resource usage and defense effectiveness. Furthermore, existing ransomware defenses merely restore data or detect attacks without preventing them. Thus, this paper introduces the MTFS file system and the design and implementation of three novel MTD techniques – one delaying attackers, one trapping recursive directory traversal, and another one hiding file types. The effectiveness of the techniques is shown in three experiments. First, it is demonstrated that the techniques can delay and mitigate ransomware on real IoT devices. Secondly, in a broader scope, the solution was confronted with 13 ransomware samples, highlighting that it can save 97% of the files. Regarding overhead, the defense system consumes only a small amount of resources, highlighting the feasibility of proactive defense. |
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Agha Durrani, Steven Ongena, Aurea Ponte Marques, Decoding market reactions: The certification role of EU-wide stress tests, Economic Modelling, Vol. 139, 2024. (Journal Article)
We study the market’s reaction to the disclosure of EU-wide stress test results across four periods (2014, 2016, 2018, and 2021). Our novel approach contributes to the literature by studying how stress test disclosures influence both the mean and variance (first and second moments) of bank stock performance, extending beyond previous studies focused mainly on the first moment of equity returns. Using one-factor market and structural Engle–Siriwardane type GARCH models, we find that the publication of stress tests provides new information to the markets: Banks with weaker performance in the tests experience, on average, a reduction in stock returns and an increase in volatility, while the reverse holds for banks performing well. Our findings confirm the important role of stress tests in enhancing transparency and market discipline, thereby supporting investors in assessing the resilience of the banking sector more effectively, particularly during periods of higher uncertainty. |
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Alper Kara, Steven Ongena, Yilmaz Yildiz, Does being a responsible bank pay off? Evidence from the COVID-19 pandemic, Journal of Financial Stability, Vol. 74, 2024. (Journal Article)
We investigate whether banks’ initial responses during the first wave of the COVID-19 pandemic in supporting their customers, communities, and governments were perceived as value-enhancing by investors. Using a unique responsible banking measure for a sample of the largest US and European commercial banks, we find a negative relationship between responsible bank behavior and stock market performance, particularly in the first wave of the pandemic. We also find that riskier banks were affected more negatively if they behaved responsibly. Overall, our findings show that banks’ responsible behavior during a crisis reduces, or at best is not relevant to, shareholder value. |
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Damiano Pregaldini, Uschi Backes-Gellner, How middle-skilled workers adjust to immigration: the role of occupational skill specificity, International Journal of Manpower, Vol. 45 (8), 2024. (Journal Article)
Purpose
Our study explores the effects of immigration on the employment of native middle-skilled workers, focusing on how this effect varies with the specificity of their occupational skill bundles.
Design/methodology/approach
Exploiting the 2002 opening of the Swiss labor market to EU workers and using register data on the location and occupation of these workers, our findings provide novel results on the labor market effects of immigration.
Findings
We show that the inflow of EU workers led to an increase in the employment of native middle-skilled workers with highly specific occupational skills. This finding could be attributed to immigrant workers reducing existing skill gaps, enhancing the quality of job-worker matches, and alleviating firms' capacity restrictions. This allowed firms to create new jobs, thereby providing increased employment options for middle-skilled workers with highly specialized skills.
Originality/value
Previous literature has predominantly highlighted the disadvantages of specific occupational skills compared to general skills in the context of labor market shocks. However, our findings reveal that workers with specific occupational skills can benefit from an immigration-driven labour market shock. These results suggest that policy conclusions regarding the role of specific occupational skills should be more nuanced. |
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Fynn Bachmann, Cristina Sarasua, Abraham Bernstein, Fast and Adaptive Questionnaires for Voting Advice Applications, In: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, Springer, Cham, 2024-09-09. (Conference or Workshop Paper published in Proceedings)
The effectiveness of Voting Advice Applications (VAA) is often compromised by the length of their questionnaires. To address user fatigue and incomplete responses, some applications (such as the Swiss Smartvote) offer a condensed version of their questionnaire. However, these condensed versions can not ensure the accuracy of recommended parties or candidates, which we show to remain below 40%. To tackle these limitations, this work introduces an adaptive questionnaire approach that selects subsequent questions based on users’ previous answers, aiming to enhance recommendation accuracy while reducing the number of questions posed to the voters. Our method uses an encoder and decoder module to predict missing values at any completion stage, leveraging a two-dimensional latent space reflective of political science’s traditional methods for visualizing political orientations. Additionally, a selector module is proposed to determine the most informative subsequent question based on the voter’s current position in the latent space and the remaining unanswered questions. We validated our approach using the Smartvote dataset from the Swiss Federal elections in 2019, testing various spatial models and selection methods to optimize the system’s predictive accuracy. Our findings indicate that employing the IDEAL model both as encoder and decoder, combined with a PosteriorRMSE method for question selection, significantly improves the accuracy of recommendations, achieving 74% accuracy after asking the same number of questions as in the condensed version. |
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Nicholas Mayone, Pascal Kunz, Beytullah Yigit, Wissem Soussi, Burkhard Stiller, Gürkan Gür, IPv6 Connection Shuffling for Moving Target Defense (MTD) in SDN, In: 2024 IEEE International Conference on Cyber Security and Resilience (CSR), Institut of Electrical and Electronics Engineers, 2024-09-02. (Conference or Workshop Paper published in Proceedings)
Moving Target Defense (MTD) is the defensive strat-egy of obscuring an entity by controlled changes in its attack surface. This approach alone does not secure a system but challenges potential attacks with additional complexity. This work presents an MTD solution called “Randomized Host and Service Shuffling in IPv6 (RHSS6)” against network reconnaissance attacks and evaluates its effects on performance and security. To achieve this goal, a Software Defined Networking (SDN) based prototype was developed in which RHSS6 performs two MTD strategies, IP Shuffling and Port Shuffling. Measurements on TCP and UDP traffic are made to evaluate the performance impact of RHSS6. An MTD network with both IP Shuffling and Port Shuffling had similar performance results compared to a baseline network; however, the video stream bit rate was significantly lower when Port Shuffling was enforced. Regarding the security gain, RHSS6 was effective in increasing the average number of scan attempts needed to identify a targeted host in a network with a fixed address range. We conclude that a defender using RHSS6 can adjust the probability of an attacker locating targeted hosts in a network by selecting a suitable address space and port range. Such parameters and shuffling trigger time can be optimized to maximize the benefits for different scenarios while minimizing the operational overhead. |
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Jan Von der Assen, Jasmin Hochuli, Thomas Grübl, Burkhard Stiller, The Danger Within: Insider Threat Modeling Using Business Process Models, In: 2024 IEEE International Conference on Cyber Security and Resilience (CSR), Institut of Electrical and Electronics Engineers, 2024-09-02. (Conference or Workshop Paper published in Proceedings)
Threat modeling has been successfully applied to model technical threats within information systems. However, a lack of methods focusing on non-technical assets and their representation can be observed in theory and practice. Following the voices of industry practitioners, this paper explored how to model insider threats based on business process models. Hence, this study developed a novel insider threat knowledge base and a threat modeling application that leverages Business Process Modeling and Notation (BPMN). Finally, to understand how well the theoretic knowledge and its prototype translate into practice, the study conducted a real-world case study of an IT provider's business process and an experimental deployment for a real voting process. The results indicate that even without annotation, BPMN diagrams can be leveraged to automatically identify insider threats in an organization. |
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Tim Schluchter, Nimra Ahmed, Elaine Huang, ""It Actually Affected My Relationship"": A Qualitative Analysis of Affordances and Attitudes Towards Mental Health Content on TikTok, In: Proceedings of Mensch Und Computer 2024, Association for Computing Machinery, New York, NY, USA, 2024. (Conference or Workshop Paper published in Proceedings)
This paper investigates the factors and platform-specific affordances that contribute to the popularity of mental health (MH) content on TikTok, a topic of growing importance within Human-Computer Interaction (HCI) due to its impact on public MH literacy and individual well-being. Diverging from studies that focus predominantly on the ""For You Page"" algorithm, this research employs qualitative methods to explore a broader array of features that facilitate the platform’s success in MH discussions. Through semi-structured interviews with fourteen TikTok users, including viewers and creators, this study examines how TikTok’s community, algorithmic recommendations, interactive features, and engaging environment support the sharing and consumption of MH content. Additionally, it explores user perceptions and attitudes towards TikTok as an MH platform, revealing various opinions, impacts, and experiences. Our findings highlight how TikTok fosters a unique space for MH discussions and provide design considerations for optimizing such platforms for MH communication. |
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Cecilia Hyunjung Mo, Jon M Jachimowicz, Jochen Menges, Adam D Galinsky, The impact of incidental environmental factors on vote choice: Wind speed is related to more prevention-focused voting, Political Behavior, Vol. 46 (3), 2024. (Journal Article)
How might irrelevant events infiltrate voting decisions? The current research introduces a new mechanism - regulatory focus—by which incidental environmental factors can affect vote choice. Regulatory focus theory proposes that there are two fundamental psychological orientations in how people navigate their worlds: A prevention focus tunes cognition towards security, safety, protection, and risk aversion, whereas a promotion focus orients attention toward achieving growth and positive outcomes. We present a model for how wind speed on Election Day affects voting by shifting the regulatory focus of voters. We propose that increased wind speed shifts voters toward selecting prevention-focused options (e.g., restricting immigration, rejecting Brexit, rejecting Scottish Independence) over promotion-focused options (e.g., promoting immigration, favoring Brexit, favoring Scottish Independence). We further argue that wind speed only affects voting when an election clearly offers a choice between prevention and promotion-focused options. Using a mixed-method approach—archival analyses of the “Brexit” vote, the Scotland independence referendum, and 10 years of Swiss referendums, as well as one field study and one experiment - we find that individuals exposed to higher wind speeds become more prevention-focused and more likely to support prevention-focused electoral options. The findings highlight the political importance of incidental environmental factors. Practically, they speak to the benefit of absentee voting and expanding voting periods beyond traditional election days. |
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David P Newton, Steven Ongena, Ru Xie, Binru Zhao, Firm ESG reputation risk and debt choice, European financial management, Vol. 30 (4), 2024. (Journal Article)
Using a novel sample covering 3783 US public firms from 2007 to 2020, we examine how negative media coverage of firm‐level environmental, social, and governance (ESG) practices affects a firm's debt choice. We find that firms with higher ESG reputation risk rely more on public bond than bank loan. The social and governance components, in particular, matter. Moreover, firms that receive more negative news coverage display a higher propensity to issue new bonds as opposed to securing new bank debt. Overall, our study presents empirical evidence on the relation between firm ESG reputation risk and debt financing. |
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Liudmila Zavolokina, Ingrid Bauer-Hänsel, Janine Hacker, Gerhard Schwabe, Organizing for value creation in blockchain information systems, Information and Organization, Vol. 34 (3), 2024. (Journal Article)
Many blockchain consortia have been established to build blockchain information systems. While the developed blockchain information systems were promising, few have reached market entry. Indeed, blockchain consortia often lost development focus due to high system complexity and a lack of understanding of how to create a system that will serve the needs and bring value to all stakeholders. Thus, stakeholders struggled to leverage blockchain information systems' full value. Prior studies demonstrated that blockchain systems pose not only technical but also organizational challenges. Analysing six blockchain consortia, we identify their value mechanisms, organizational problems, and organizational solutions that successful blockchain consortia experience while organizing themselves for value. As a result, we propose a new organizational form, i.e., a layered organization, for blockchain consortia to achieve better value creation. |
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Linda Weigl, Tamara Roth, Alexandre Amard, Liudmila Zavolokina, When public values and user-centricity in e-government collide – A systematic review, Government Information Quarterly, Vol. 41 (3), 2024. (Journal Article)
User-centricity in e-government is a double-edged sword. While it helps governments design digital services tailored to the needs of citizens, it may also increase the burden on users and deepen the digital divide. From an institutional perspective, these fundamental conflicts are inevitable. To better understand the role and effect of user-centricity in e-government, this paper analyses academic literature on user-centricity and public values. The analysis leads to three main insights: First, there is a conflict in citizen representation that may result from the normative dominance of decision-makers. Second, we identify an accountability conflict that can prevent user-centric innovation from thriving in a highly institutionalized environment. Third, we identify a pluralism conflict that emerges from a clash between the reality of a diverse society and the assumed homogeneity of actors. The need to address these conflicts increases with rapid technological innovation, such as distributed ledger technologies, artificial intelligence, and trust infrastructures. These technologies put the user at the center stage and permeate aspects of social life beyond government. In response to these insights, we outline suggestions for further research and practice. |
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Laurence Kotlikoff, Felix Kübler, Andrey Polbin, Simon Scheidegger, Can today’s and tomorrow’s world uniformly gain from carbon taxation?, European Economic Review, Vol. 168, 2024. (Journal Article)
Climate change will impact current and future generations in different regions very differently. This paper develops the first large-scale, annually calibrated, multi-region, overlapping generations model of climate change and carbon policy. It features region-specific temperature and damage functions with the phased impact of emissions on global and regional temperature calibrated to the latest scientific evidence. Absent policy, climate change may, under high-damage scenarios, dramatically reduce GDP in most regions, with India, Brazil, and the South Asian Pacific suffering long-term catastrophic damages. Carbon taxation, coupled with region- and generation-specific transfers, can both correct the carbon externality and raise the welfare of all current and future agents across all regions by 4.3 percent. The impact on the use and duration of fossil fuels is dramatic as is the reduction in the path of global emissions. However, achieving completely uniform welfare gains leaves future generations in particular regions facing exceptionally high compensatory payments. Fortunately, a carbon tax-cum redistribution policy that limits this burden for any generation in any region to less than 10 percent, measured on a consumption-equivalent basis, can deliver a 4.0 percent or higher welfare gain for all peoplekind — present and future. However, carbon taxes set through time, at carbon’s marginal social cost, do far too little to mitigate climate change unless all major emitters, particularly China, adopt them and do so immediately. |
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Chao Feng, Alberto Huertas Celdran, Jan Von der Assen, Enrique Tomás Martínez Beltrán, Gérôme Bovet, Burkhard Stiller, DART: A Solution for decentralized federated learning model robustness analysis, Array, Vol. 23, 2024. (Journal Article)
Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly those following the Centralized FL (CFL) paradigm, utilize a central server for global aggregation, which exhibits limitations such as bottleneck and single point of failure. To address these issues, the Decentralized FL (DFL) paradigm has been proposed, which removes the client–server boundary and enables all participants to engage in model training and aggregation tasks. Nevertheless, as CFL, DFL remains vulnerable to adversarial attacks, notably poisoning attacks that undermine model performance. While existing research on model robustness has predominantly focused on CFL, there is a noteworthy gap in understanding the model robustness of the DFL paradigm. In this paper, a thorough review of poisoning attacks targeting the model robustness in DFL systems, as well as their corresponding countermeasures, are presented. Additionally, a solution called DART is proposed to evaluate the robustness of DFL models, which is implemented and integrated into a DFL platform. Through extensive experiments, this paper compares the behavior of CFL and DFL under diverse poisoning attacks, pinpointing key factors affecting attack spread and effectiveness within the DFL. It also evaluates the performance of different defense mechanisms and investigates whether defense mechanisms designed for CFL are compatible with DFL. The empirical results provide insights into research challenges and suggest ways to improve the robustness of DFL models for future research. |
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Tsuyoshi Iwata, Marc Weibel, Enhancing Equity Factor Model with Publicly Reported ESG Data, Journal of Impact and ESG Investing, Vol. 5 (1), 2024. (Journal Article)
This study examines the alpha-generating power of the public-report-based ESG score, which is based on ESG incident data collected by RepRisk from various public sources, and its relationship with the self-disclosure-based ESG score obtained from Refinitiv. The authors construct pure ESG factor portfolios to neutralize exposure to common style factors and isolate the pure ESG factor returns. Their results suggest that (i) the source difference is the main cause of the negative correlation between the public report ESG and the self-disclosure ESG score, (ii) the public report ESG score and its subscores produce the mixed results in terms of their adjusted factor returns across regions, and (iii) the combination of the public report ESG and the self-disclosure ESG score significantly improves the risk–return profiles of the combined ESG factor returns in the United States, European Union (EU), and Japan. |
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