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This thesis focuses on approximate inference in assumption-based argumentation frameworks. Argumentation provides a significant idea in the computerization of theoretical and practical reasoning in AI. And it has a close connection with AI, engaging in arguments to perform scientific reasoning. The fundamental approach in this field is abstract argumentation frameworks developed by Dung. Assumption-based argumentation can be regarded as an instance of abstract argumentation with structured arguments. When facing a large scale of data, a challenge of reasoning in assumption-based argumentation is how to construct arguments and resolve attacks over a given claim with minimal cost of computation and acceptable accuracy at the same time. This thesis proposes and investigates approximate methods that randomly select and construct samples of frameworks based on graphical dispute derivations to solve this problem. The presented approach aims to improve reasoning performance and get an acceptable trade-off between computational time and accuracy. The evaluation shows that for reasoning in assumption-based argumentation, in general, the running time is reduced with the cost of slightly low accuracy by randomly sampling and constructing inference rules for potential arguments over a query.
Scientific and public interest in epidemiology and mathematical modelling of disease spread has increased significantly due to the current COVID-19 pandemic. Political action is influenced by forecasts and evaluations of such models and the whole society is affected by the corresponding countermeasures for containment. But how are these models structured?
Which methods can be used to apply them to the respective regions, based on real data sets? These questions are certainly not new. Mathematical modelling in epidemiology using differential equations has been researched for quite some time now and can be carried out mainly by means of numerical computer simulations. These models are constantly being refinded and adapted to corresponding diseases. However, it should be noted that the more complex a model is, the more unknown parameters are included. A meaningful data adaptation thus becomes very diffcult. The goal of this thesis is to design applicable models using the examples of COVID-19 and dengue, to adapt them adequately to real data sets and thus to perform numerical simulations. For this purpose, first the mathematical foundations are presented and a theoretical outline of ordinary differential equations and optimization is provided. The parameter estimations shall be performed by means of adjoint functions. This procedure represents a combination of static and dynamical optimization. The objective function corresponds to a least squares method with L2 norm which depends on the searched parameters. This objective function is coupled to constraints in the form of ordinary differential equations and numerically minimized, using Pontryagin's maximum (minimum) principle and optimal control theory. In the case of dengue, due to the transmission path via mosquitoes, a model reduction of an SIRUV model to an SIR model with time-dependent transmission rate is performed by means of time-scale separation. The SIRUV model includes uninfected (U) and infected (V ) mosquito compartments in addition to the susceptible (S), infected (I) and recovered (R) human compartments, known from the SIR model. The unknwon parameters of the reduced SIR model are estimated using data sets from Colombo (Sri Lanka) and Jakarta (Indonesia). Based on this parameter estimation the predictive power of the model is checked and evaluated. In the case of Jakarta, the model is additionally provided with a mobility component between the individual city districts, based on commuter data. The transmission rates of the SIR models are also dependent on meteorological data as correlations between these and dengue outbreaks have been demonstrated in previous data analyses. For the modelling of COVID-19 we use several SEIRD models which in comparison to the SIR model also take into account the latency period and the number of deaths via exposed (E) and deaths (D) compartments. Based on these models a parameter estimation with adjoint functions is performed for the location Germany. This is possible because since the beginning of the pandemic, the cumulative number of infected persons and deaths
are published daily by Johns Hopkins University and the Robert-Koch-Institute. Here, a SEIRD model with a time delay regarding the deaths proves to be particularly suitable. In the next step, this model is used to compare the parameter estimation via adjoint functions with a Metropolis algorithm. Analytical effort, accuracy and calculation speed are taken into account. In all data fittings, one parameter each is determined to assess the estimated number of unreported cases.
Advanced Auditing of Inconsistencies in Declarative Process Models using Clustering Algorithms
(2021)
To have a compliant business process of an organization, it is essential to ensure a onsistent process. The measure of checking if a process is consistent or not depends on the business rules of a process. If the process adheres to these business rules, then the process is compliant and efficient. For huge processes, this is quite a challenge. Having an inconsistency in a process can yield very quickly to a non-functional process, and that’s a severe problem for organizations. This thesis presents a novel auditing approach for handling inconsistencies from a post-execution perspective. The tool identifies the run-time inconsistencies and visualizes them in heatmaps. These plots aim to help modelers observe the most problematic constraints and help them make the right remodeling decisions. The modelers assisted with many variables can be set in the tool to see a different representation of heatmaps that help grasp all the perspectives of the problem. The heatmap sort and shows the run-time inconsistency patterns, so that modeler can decide which constraints are highly problematic and should address a re-model. The tool can be applied to real-life data sets in a reasonable run-time.
Successful export sectors in manufacturing and agribusiness are important drivers of structural transformation in Sub-Sahara African countries. Backed by industrial policies and active state involvement, a small number of successful productive export sectors has emerged in Sub-Saharan Africa. This thesis asks the question: How do politics shape the promotion of export-driven industrialisation and firm-level upgrading in Sub-Saharan Africa? It exemplifies this question with an in-depth, qualitative study of the cashew processing industry in Mozambique in the period from 1991 until 2019. Mozambique used to be one of the world’s largest producers and processors of cashew nuts in the 1960s and 1970s. At the end of the 20th century, the cashew processing industry broke down completely but has re-emerged as one of the country’s few successful agro-processing exports.
The thesis draws on theoretical approaches from the fields of political science, notably the political settlements framework, global value chain analysis and the research on technological capabilities to explore why the Mozambican Government supported the cashew processing industry and how Mozambican cashew processors acquired the technological capabilities needed to access the global cashew value chain and to upgrade. It makes an important theoretical contribution by linking the political settlements framework and the literature on upgrading in global value chains to study how politics shaped productive sector promotion and upgrading in the Mozambican cashew processing industry. The findings of the thesis are based on extensive primary data, including 58 expert interviews and 10 firm surveys, that was collected in Mozambique in 2018 as well as a broad base of secondary literature.
The thesis argues that the Mozambican Government supported the cashew processing industry because it became important for the Government’s political survival. Promoting the cashew sector formed part of an electoral strategy for the ruling FRELIMO coalition and a means to keep FRELIMO factions united by offering economic opportunities to key constituencies. In 1999, it adopted a protectionist cashew law that created strong incentives for cashew processing in Mozambique. This not only facilitated the re-emergence of the cashew processing industry after its breakdown. The law and the active involvement of the National Cashew Institute (INCAJU) also affected the governance of the local cashew value chain, the creation of backward linkages, and the upgrading paths of cashew processors. The findings of the thesis suggest that the cashew law reduced the pressure on the cashew processing industry to upgrade. The law further created opportunities for formal and informal rent creation for members of the political elite and lower level FRELIMO officials that prevented a far-reaching reform of the law. The thesis shows that international buyers do not promote upgrading among Sub-Sahara African firms in global value chains with market-based or modular governance. Moreover, firms that operate in countries where industrial policies are not enforced effectively cannot draw on the support of government institutions to enhance their capabilities and to upgrade. Firms therefore mainly depended on costly learning channels at firm level, e.g. learning by doing or hiring skilled labour, and/or on technical assistance from donors to build the technological capabilities needed to access global value chains and to remain competitive.
The findings of the thesis suggest that researchers, governments, development practitioners and consultants need to rethink their understanding of upgrading in GVCs in four ways. First, they need to move away from understanding upgrading in terms of moving towards more complex, higher value-added activities in GVCs (functional upgrading). Instead, it is important to consider the potential of other, more realistic types of upgrading for firms in low-income countries, such reducing risks by diversifying suppliers and buyers or increasing rewards by making production processes more efficient. Second, they need to replace an overly positive view on upgrading that neglects possible side-effects at sector and/or country level. Third, GVC participation on its own does not promote upgrading among local supplier firms in Sub-Saharan Africa. The interests of lead firms and Sub-Sahara African supplier firms may not be aligned or even conflicting. Targeted industrial policies and the creation of institutions that effectively promote capability building among firms therefore become even more important. Finally, upgrading needs to be understood as a process that is not only shaped by interactions between firms, but also by local domestic politics.
The findings of the thesis are highly relevant for scholars from the fields of political science, development studies, and economics. Its practical implications and tools, e.g. a technological capabilities matrix for the cashew industry, are of interest for development practitioners, members of public institutions in Sub-Sahara African countries, local entrepreneurs, and representatives of local business associations that are involved in promoting export sectors and upgrading among local firms.
Water is used in a way as if it were available infinitely. Droughts, increased rainfall or flooding already lead to water shortages and, thus, deprive entire population groups of the basis of their livelihoods. There is a growing fear that conflicts over water will increase, especially in arid climate zones, because life without water - whether for humans, animals or plants - is not possible.
More than 60 % of the African population depend on land and water resources for their livelihoods through pastoralism, fishing and farming. The water levels of rivers and lakes are decreasing. Hence, the rural population which is dependent on land and water move towards water-rich and humid areas. This internal migration increases the pressure on available water resources. Driven by the desire to strengthen the economic development, African governments align their political agendas with the promotion of macro international and national economic projects.
This doctoral thesis examines the complex interrelationships between water shortages, governance, vulnerability, adaptive capacity and violent and non-violent conflicts at Lake Naivasha in Kenya and Lake Wamala in Uganda. In order to satisfy the overall complexity, this doctoral thesis combines various theoretical and empirical aspects in which a variety of methods are applied to different geographical regions, across disciplines, and cultural and political boundaries.
The investigation reveals that Lake Naivasha is more affected by violent conflicts than Lake Wamala. Reasons for this include population growth, historically grown ethnic conflicts, corruption and the preferential treatment of national and international economic actors. The most common conflict response tools are raiding and the blockage of water access. However, deathly encounters, destruction of property and cattle slaughtering are increasingly used to gain access to water and land.
The insufficient implementation of the political system and the governments’ prioritization to foster economic development results, on the one hand, in the commercialization of water resources and increases, on the other hand, non-violent conflict between national and sub-national political actors. While corruption, economic favours and patronage defuse this conflict, resource access becomes more difficult for the local population. Resulting thereof, a final hypothesis is developed which states that the localization of the political conflict aggravates the water situation for the local population and, thereby, favours violent conflicts over water access and water use in water-rich areas.