After the unconditional surrender of the Third Reich in May 1945, Germany no longer existed as a sovereign, independent nation. It was occupied by the four Allied powers: France, Great Britain, the United States and the Soviet Union. When it came to the postwar European recovery, the biggest obstacle was that the economy in Germany, the dominant continental economic power before the Second World War, was at an almost complete standstill. This not only had severe consequences for Germany itself, but also had strong economic repercussions for surrounding countries, especially the Netherlands. As Germany had been the former’s most important trading partner since the middle of the nineteenth century, it was clear that the Netherlands would be unable to recover economically without a healthy Germany. However, Allied policy, especially that of the British and the Americans, made this impossible for years. This article therefore focuses on the early postwar Dutch-German trade relations and the consequences of Allied policy. While much has been written about the occupation of Germany, far less attention has been paid to the results of this policy on neighbouring countries. Moreover, the main claim of this article is that it was not Marshall Aid which was responsible for the quick and remarkable Dutch economic growth as of 1949, but the opening of the German market for Dutch exports that same year. https://doi.org/10.1515/jbwg-2018-0009 LinkedIn: https://www.linkedin.com/in/martijn-lak-71793013/
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Cooperation is more likely upheld when individuals can choose their interaction partner. However, when individuals differ in their endowment or ability to cooperate, free partner choice can lead to segregation and increase inequality. To understand how decision-makers can decrease such inequality, we conducted an incentivized and preregistered experiment in which participants (n=500) differed in their endowment and cooperation productivity. First, we investigated how these individual differences impacted cooperation and inequality under free partner choice in a public goods game. Next, we calculated if and how decision-makers should restrict partner choice if their goal is to decrease inequality. Finally, we studied whether decision-makers actually did decrease inequality when asked to allocate endowment and productivity factors between individuals, and combine individuals into pairs of interaction partners for a two-player public goods game. Our results show that without interventions, free partner choice, indeed, leads to segregation and increases inequality. To mitigate such inequality, decision-makers should curb free partner choice and force individuals who were assigned different endowments and productivities to form pairs with each other. However, this comes at the cost of lower overall cooperation and earnings, showing that the restriction of partner choice results in an equality-efficiency trade-off. Participants who acted as third-parties were actually more likely to prioritize inequality reduction over efficiency maximization, by forcing individuals with unequal endowment and productivity levels to form pairs with each other. However, decision-makers who had a ‘stake in the game’ self-servingly navigated the equality-efficiency trade-off by preferring partner choice interventions that benefited themselves. These preferences were partly explained by norms on public good cooperation and redistribution, and participants’ social preferences. Results reveal potential conflicts on how to govern free partner choice stemming from diverging preferences ‘among unequals’.
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Collaborative networks for sustainability are emerging rapidly to address urgent societal challenges. By bringing together organizations with different knowledge bases, resources and capabilities, collaborative networks enhance information exchange, knowledge sharing and learning opportunities to address these complex problems that cannot be solved by organizations individually. Nowhere is this more apparent than in the apparel sector, where examples of collaborative networks for sustainability are plenty, for example Sustainable Apparel Coalition, Zero Discharge Hazardous Chemicals, and the Fair Wear Foundation. Companies like C&A and H&M but also smaller players join these networks to take their social responsibility. Collaborative networks are unlike traditional forms of organizations; they are loosely structured collectives of different, often competing organizations, with dynamic membership and usually lack legal status. However, they do not emerge or organize on their own; they need network orchestrators who manage the network in terms of activities and participants. But network orchestrators face many challenges. They have to balance the interests of diverse companies and deal with tensions that often arise between them, like sharing their innovative knowledge. Orchestrators also have to “sell” the value of the network to potential new participants, who make decisions about which networks to join based on the benefits they expect to get from participating. Network orchestrators often do not know the best way to maintain engagement, commitment and enthusiasm or how to ensure knowledge and resource sharing, especially when competitors are involved. Furthermore, collaborative networks receive funding from grants or subsidies, creating financial uncertainty about its continuity. Raising financing from the private sector is difficult and network orchestrators compete more and more for resources. When networks dissolve or dysfunction (due to a lack of value creation and capture for participants, a lack of financing or a non-functioning business model), the collective value that has been created and accrued over time may be lost. This is problematic given that industrial transformations towards sustainability take many years and durable organizational forms are required to ensure ongoing support for this change. Network orchestration is a new profession. There are no guidelines, handbooks or good practices for how to perform this role, nor is there professional education or a professional association that represents network orchestrators. This is urgently needed as network orchestrators struggle with their role in governing networks so that they create and capture value for participants and ultimately ensure better network performance and survival. This project aims to foster the professionalization of the network orchestrator role by: (a) generating knowledge, developing and testing collaborative network governance models, facilitation tools and collaborative business modeling tools to enable network orchestrators to improve the performance of collaborative networks in terms of collective value creation (network level) and private value capture (network participant level) (b) organizing platform activities for network orchestrators to exchange ideas, best practices and learn from each other, thereby facilitating the formation of a professional identity, standards and community of network orchestrators.
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
Organisations are increasingly embedding Artificial Intelligence (AI) techniques and tools in their processes. Typical examples are generative AI for images, videos, text, and classification tasks commonly used, for example, in medical applications and industry. One danger of the proliferation of AI systems is the focus on the performance of AI models, neglecting important aspects such as fairness and sustainability. For example, an organisation might be tempted to use a model with better global performance, even if it works poorly for specific vulnerable groups. The same logic can be applied to high-performance models that require a significant amount of energy for training and usage. At the same time, many organisations recognise the need for responsible AI development that balances performance with fairness and sustainability. This KIEM project proposal aims to develop a tool that can be employed by organizations that develop and implement AI systems and aim to do so more responsibly. Through visual aiding and data visualisation, the tool facilitates making these trade-offs. By showing what these values mean in practice, which choices could be made and highlighting the relationship with performance, we aspire to educate users on how the use of different metrics impacts the decisions made by the model and its wider consequences, such as energy consumption or fairness-related harms. This tool is meant to facilitate conversation between developers, product owners and project leaders to assist them in making their choices more explicit and responsible.
