For the Dam tot damloop, a running event with 36,757 participants and 115,000 visitors, both an economic impact analysis (IEA) and a social cost benefit analysis (SCBA) are made to study the (broader) economic effects. Three overlapping geographical regions are studied and two new estimates of non-market goods are used. For the hosting cities the net social gain from the SCBA is at least three times the EIA’s economic impact. The larger the geographical area studied, the larger the differences between EIA and SCBA, because the EIA outcome falls and the SCBA outcome increases. A lower multiplier than 1 lowers the EIA much more than it lowers the SCBA. This study shows that an EIA is not suited for evaluating the welfare effects of public support for a sport event. The difference in outcome between EIA and SCBA is substantial. Valuing non-market effects is done infrequently but is crucial for understanding the welfare effects of policies supporting sport events. Organizing an event for social and city marketing benefits can be a better reason than organizing for the direct economic gains.
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In this article, we describe the emergence of a new Finance course in line with the concept of the Societal Cost-Benefit Analysis (SCBA). By means of an in-depth case study, we reconstruct the process of dissatisfaction and corresponding discussions among lecturers and students of the Master Integrated Care Design with regard to the learning aims and content of the Finance course, which is a study module of this master. During the period 2015-2021, the aims and content of this module were revised and remoulded several times in order to define a Finance course that was able to both sufficiently and creatively connect the domain of Integrated Care with that of Finance. In this process of reiterating revision both lectures and students played a crucial role. The ultimate result – the indicative Societal Cost-Benefit Analysis – was unexpected and unplanned, producing an outcome that surpassed the sum of its separate parts. In short, the process, as we describe in this case study, bears all the hallmarks of emergence. Moreover, the analysis shows how this process of emergence in combination with emergent leadership led to a practicable and encouraging outcome, which satisfied and committed all stakeholders, setting an example that is worth following.
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Consolidation is the key concept for gaining efficiencies logistics chains and the development of new city distribution centres is a potential way of establishing consolidation. In this paper we investigate how Cost Benefit Analysis (CBA) can play a major role to be supportive in the pre-feasibility of these studies. In order to understand the supportive value of this methodology we have analyzed the effects of a new main road for public transport connecting several small cities combined with a new solution for freight problems in these towns. The new solution encompasses the development of a new public warehouse. This paper contains a detailed explanation of the methodology and we present some conclusions about the pre-feasibility of the new distribution centre and the supportive role of the methodology in terms of stakeholder participation. © 2008 Nova Science Publishers, Inc. All rights reserved.
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The project aim is to improve collusion resistance of real-world content delivery systems. The research will address the following topics: • Dynamic tracing. Improve the Laarhoven et al. dynamic tracing constructions [1,2] [A11,A19]. Modify the tally based decoder [A1,A3] to make use of dynamic side information. • Defense against multi-channel attacks. Colluders can easily spread the usage of their content access keys over multiple channels, thus making tracing more difficult. These attack scenarios have hardly been studied. Our aim is to reach the same level of understanding as in the single-channel case, i.e. to know the location of the saddlepoint and to derive good accusation scores. Preferably we want to tackle multi-channel dynamic tracing. • Watermarking layer. The watermarking layer (how to embed secret information into content) and the coding layer (what symbols to embed) are mostly treated independently. By using soft decoding techniques and exploiting the “nuts and bolts” of the embedding technique as an extra engineering degree of freedom, one should be able to improve collusion resistance. • Machine Learning. Finding a score function against unknown attacks is difficult. For non-binary decisions there exists no optimal procedure like Neyman-Pearson scoring. We want to investigate if machine learning can yield a reliable way to classify users as attacker or innocent. • Attacker cost/benefit analysis. For the various use cases (static versus dynamic, single-channel versus multi-channel) we will devise economic models and use these to determine the range of operational parameters where the attackers have a financial benefit. For the first three topics we have a fairly accurate idea how they can be achieved, based on work done in the CREST project, which was headed by the main applicant. Neural Networks (NNs) have enjoyed great success in recognizing patterns, particularly Convolutional NNs in image recognition. Recurrent NNs ("LSTM networks") are successfully applied in translation tasks. We plan to combine these two approaches, inspired by traditional score functions, to study whether they can lead to improved tracing. An often-overlooked reality is that large-scale piracy runs as a for-profit business. Thus countermeasures need not be perfect, as long as they increase the attack cost enough to make piracy unattractive. In the field of collusion resistance, this cost analysis has never been performed yet; even a simple model will be valuable to understand which countermeasures are effective.
