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Does society influence the gender gap in risk attitudes? Evidence from East and West Germany
No full textSimulating Party Competition in Dynamic Voter Distributions
No full textWe study strategic party interaction in a spatial voting model where voters’ ideological positions may change. Building on a rich empirical and theoretical literature, we assume that voters align their ideology with others who are sufficiently close to them (social influence with bounded confidence) as well as with the party that they support (party attraction). We show that these changes have strong implications on the results of the party competition model by Laver (2005). Two strategies stand out in our simulations: Aggregators, who always follow the mean policy of their supporters, and predators, who always chase the strongest party. Aggregators are most likely to win in a large corridor of the parameter space. However, predators can outperform them if party attraction is strong.This is interesting because predators are on average the worst-performing parties in the static voter distribution benchmark. We argue that these results are connected to real-world debates about how mainstream parties should react to the rise of extremist parties, as the two strategies epitomize debates about focusing on own strengths and supporters (aggregators) vs. adapting towards successful extremists (predators). We also demonstrate that the level of polarization and fragmentation of parties and voters is strongly affected by social influence and party attraction. While medium-sized confidence bounds and party attraction increase the polarization of voters and parties, unconstrained social influence decreases it
Measuring News Media Reporting of Administrative Action: A Machine Learning Approach
No full textNews media play a crucial role during crises. They serve as a catalyst for public opinion formation and a mirror reflecting public priorities back to decision-makers. Still, there is a lack of studies examining the portrayal of administrative and governmental actors in traditional news media. Most existing research has focused on social media representation instead. This paper addresses the gap by introducing a novel measurement tool based on deep learning. We manually classify paragraphs from three German-speaking newspapers as containing positive, negative, or neutral reporting of administrative actions. Using this training dataset, we fine-tune a transformer model that performs as good as human coders. The model classifies reporting on administrative and governmental actions with 81 % accuracy. We demonstrate that the fine-tuned large language model (LLM) is a valid tool capable of capturing the complex reporting in newspapers and highlight promising avenues for future research
The when and how of planning: Meta-analysis of the scope and components of implementation intentions in 642 tests
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Kommunale Verwaltungen und freiwilliges Engagement: Chancen und Hindernisse der Kooperation in Zeiten komplexer Herausforderungen
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Making Sense of AI Systems Development
No full textWe identify and describe episodes of sensemaking around challenges in modern Artificial-Intelligence (AI)-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI’s inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems
