This thesis shows that consumers' behaviour has a decisive impact on the innovative behaviour of firms and on the development of industry. As a framework, an evolutionary simulation model is chosen, and market interactions are modelled according to a search theoretic approach.

This thesis shows that consumers' behaviour has a decisive impact on the innovative behaviour of firms and on the development of industry. As a framework, an evolutionary simulation model is chosen, and market interactions are modelled according to a search theoretic approach.

Kognitive Diagnosemodelle (DCMs) sind konfirmatorische probabilistische Modelle mit kategorialen latenten Variablen, die Mehrfachladungsstrukturen erlauben. Sie ermöglichen die Abbildung der Kompetenzen in mehrdimensionalen Profilen, die zur Erstellung informativer Rückmeldungen dienen können. Diese Dissertation untersucht in zwei Anwendungsstudien und einer Simulationsstudie wichtige methodische Aspekte bei der Schätzung der DCMs. In der Arbeit wurde ein neuer Mathematiktest entwickelt basierend auf theoriegeleiteten vorab definierten Q-Matrizen. In den Anwendungsstudien (a) illustrierten wir die Anwendung der DCMs für empirische Daten für den neu entwickelten Mathematiktest, (b) verglichen die DCMs mit konfirmatorischen Faktorenanalysemodellen (CFAs), (c) untersuchten die inkrementelle Validität der mehrdimensionalen Profile und (d) schlugen eine Methode zum Vergleich konkurrierender DCMs vor. Ergebnisse der Anwendungsstudien zeigten, dass die geschätzten DCMs meist einen nicht akzeptablen Modellfit aufwiesen. Zudem fanden wir nur eine vernachlässigbare inkrementelle Validität der mehrdimensionalen Profile nach der Kontrolle der Personenparameter bei der Vorhersage der Mathematiknote. Zusammengenommen sprechen diese Ergebnisse dafür, dass DCMs per se keine zusätzliche Information über die mehrdimensionalen CFA-Modelle hinaus bereitstellen. DCMs erlauben jedoch eine andere Aufbereitung der Information. In der Simulationsstudie wurde die Präzision der Parameterschätzungen in log-linearen DCMs sowie die Sensitivität ausgewählter Indizes der Modellpassung auf verschiedene Formen der Fehlspezifikation der Interaktionsterme oder der Q-Matrix untersucht. Die Ergebnisse der Simulationsstudie zeigen, dass die Parameterwerte für große Stichproben korrekt geschätzt werden, während die Akkuratheit der Parameterschätzungen bei kleineren Stichproben z. T. beeinträchtigt ist. Ein großer Teil der Personen wird in Modellen mit fehlspezifizierten Q-Matrizen falsch klassifiziert. / Cognitive diagnostic classification models (DCMs) have been developed to assess the cognitive processes underlying assessment responses. Current dissertation aims to provide theoretical and practical considerations for estimation of DCMs for educational applications by investigating several important underexplored issues. To avoid problems related to retrofitting of DCMs to an already existing data, test construction of the newly mathematics assessment for primary school DMA was based on a-priori defined Q-matrices. In this dissertation we compared DCMs with established psychometric models and investigated the incremental validity of DCMs profiles over traditional IRT scores. Furthermore, we addressed the issue of the verification of the Q-matrix definition. Moreover, we examined the impact of invalid Q-matrix specification on item, respondent parameter recovery, and sensitivity of selected fit measures. In order to address these issues one simulation study and two empirical studies illustrating applications of several DCMs were conducted. In the first study we have applied DCMs in general diagnostic modelling framework and compared those models to factor analysis models. In the second study we implemented a complex simulation study and investigated the implications of Q-matrix misspecification on parameter recovery and classification accuracy for DCMs in log-linear framework. In the third study we applied results of the simulation study to a practical application based on the data for 2032 students for the DMA. Presenting arguments for additional gain of DCMs over traditional psychometric models remains challenging. Furthermore, we found only a negligible incremental validity of multivariate proficiency profiles compared to the one-dimensional IRT ability estimate. Findings from the simulation study revealed that invalid Q-matrix specifications led to decreased classification accuracy. Information-based fit indices were sensitive to strong model misspecifications.

Genomische Selektion ist ein Routine-Verfahren bei verschiedenen Nutztierarten, aber noch nicht bei der Honigbiene wegen der Besonderheiten dieser Spezies. Für die Zuchtwertschätzung bei der Honigbiene ist eine spezielle genetische Verwandtschaftsmatrix erforderlich, da die Paarungsbiologie dieser Spezies ungesicherte Vaterschaft, diploide Königinnen und haploide Drohnen umfasst. Die Arbeit präsentiert einen neu-entwickelten Algorithmus zur effizienten Berechnung der Inversen der genetischen Verwandtschaftsmatrix und der Inzuchtkoeffizienten auf großen Datensätzen. Die Methode wurde zur Voraussage von genomischen und Stammbaum-basierten Zuchtwerten in einer Simulationsstudie genutzt. Die Genauigkeit und die Verzerrung der geschätzten Zuchtwerte wurden ausgewertet unter Berücksichtigung verschiedener Größen der Referenzpopulation. Außerdem wurde der Zuchtfortschritt im ersten Durchlauf von Zuchtprogrammen ausgewertet, die Zuchtschemata mit genomischer oder Stammbaum-basierter Selektion nutzten. Ein erheblich größerer Zuchtfortschritt als bei Stammbaum-basierter Selektion wurde mit genomischer Vorselektion erzielt, für die junge Königinnen genotypisiert wurden, und nur die Kandidaten mit den höchsten genomischen Zuchtwerten zur Anpaarung oder Leistungsprüfung zugelassen wurden. Für einen realen Datensatz von ungefähr 3000 genotypisierten Königinnen wurden Stammbaum-basierte und genomische Zuchtwerte für sechs wirtschaftlich bedeutende Merkmale vorhergesagt. Drei Merkmale zeigten eine signifikant höhere Vorhersagegenauigkeit bei genomischer Zuchtwertschätzung gegenüber Stammbaum-basierten Verfahren und die Unterschiede zwischen allen sechs Merkmalen konnten im Wesentlichen aus den genetischen Parametern der Merkmale und der begrenzten Größe der Referenzpopulation erklärt werden. Damit zeigt die Arbeit, dass die genomische Selektion bei der Honigbiene genutzt werden kann, den Zuchtfortschritt zu erhöhen. / Genomic selection is a routine practice for several important livestock species but not yet in honey bees, due to the peculiarities of this species. For honey bees, a specialized genetic relationship matrix is required for the prediction of breeding values, since their mating biology involves uncertain paternity, diploid queens, and haploid drones. The thesis presents a novel algorithm for the efficient computation of the inverse of the numerator relationship matrix and the coefficients of inbreeding on large data sets. The method was used to estimate genomic and pedigree-based breeding values in a simulation study. The accuracy and bias of the estimated breeding values were evaluated and various sizes of the reference population were considered. Subsequently, the genetic gain in the initial cycle of breeding programs was evaluated for several breeding schemes employing genomic or pedigree-based selection. A considerably higher genetic gain than with pedigree-based selection was achieved with genomic preselection, for which queens were genotyped early in life, and only the candidates of high genomic breeding value were admitted for mating or phenotyping. On a real data set of about 3000 genotyped queens, pedigree-based and genomic breeding values were predicted for six economically relevant traits. Three traits showed significantly higher prediction accuracy with genomic compared to pedigree-based methods, and the differences between all the six traits could be explained mainly from their genetic parameters and the limited size of the reference population. The results show that genomic selection can be applied in honey bees, and the thesis provides appropriate breeding schemes and mathematical methods for its implementation.

It appears to be unanimous that alien species in island environments tend to cause considerably more negative than positive impacts. To assess the potential level of threat aliens may pose to the native environment, understanding a species’ population structure and dynamics is of ultimate importance. Assessing both impacts and consequences of management interventions to alien species is likewise only possible through the comprehension of its population structure and dynamics. This can be achieved by estimating the number of individuals in the study site, as well as other population parameters through time, applying population models such as capture-recapture to the collected datasets. Nonetheless, alien species that have low capture rates, such as small mammals, might present a considerable obstacle for conservation, as available capturerecapture models need a relatively large dataset to precisely and accurately estimate population parameters. To improve accuracy and precision of estimates that use sparse datasets, the present study developed an integrated concurrent marking-observation capture-recapture model (C-MOM). The model proposed here, contrary to the commonly available mark-recapture and mark-resight models, allows for two different datasets (i.e. a capture-recapture and a population count) to be integrated, as well as for marking and observation (recapture) data to be collected simultaneously. While few models can integrate different datasets, no model is known to allow for concomitant capture-markobservation activities. To assess the performance of the C-MOM when estimating population parameters for sparse datasets, a virtual ecology study was carried out. The population dynamics of a small rodent, the rock cavy (Kerodon rupestris), as well as capture-recapture and population count datasets, were simulated under different scenarios. The sampled datasets were then analyzed by the C-MOM, and by two other established statistical models: a classical mark recapture (CMR) (based on the Jolly-Seber model), and a zero-truncated Poisson log-normal mixed effects (ZPNE), the only integrated mark-resight model that allows for recapture sampling with replacement. Estimates of population parameters provided by the three models were then compared in terms of bias, precision and accuracy. C-MOM and ZPNE models were afterwards applied to real data collected on a rock cavy colony in the island of Fernando de Noronha. The estimated parameters were used to extrapolate the number of individuals in the rock cavy colony to the whole population in the island. Subsequently, these results were used to develop a risk assessment for the species by modelling historical and management scenarios, simulating both the establishment of the species in the island, and the consequences of different management interventions applied to it. The virtual ecology study showed that, in comparison to the CMR and the ZPNE, the C-MOM presented improved accuracy without overestimating the precision of population parameter’s estimates. The last also presented reduced amplitude of the calculated credible interval at 95% when applied to real data in comparison to the ZPNE. While the extrapolation of C-MOM estimates suggests that the rock cavy population in Fernando de Noronha is 6,652 ± 1,587, ZPNE estimates are of 5,854 ± 3,269 individuals. In the risk assessment, historical simulation models demonstrated that even though different combinations of uncertainty in reproductive parameters of the rock cavy might be possible for the species, these did not interfere significantly in either establishment or spread of the rock cavy population in the island. Moreover, historical yearly mortality has most likely been under 30%. Regarding the species’ management simulations, the most effective management interventions to achieve population extinction were spaying and neutering of both sexes, although harvest effort presented the highest influence on this populations’ extirpation. Nonetheless, the relative influence of female and both sexes’ based interventions did not differ significantly regarding the frequency of extinction of stochastic replicates’. Moreover, none of the management interventions guaranteed the population extinction within the time span and harvest effort proposed for the management program. Neutering of both sexes was most inversely influential on time to extinction of this population, followed by removal of both sexes. Briefly, the C-MOM has proven to be a resourceful and precise model to estimate population parameters when low capture rates result in sparse datasets. Moreover, the rock cavy is well established in the island and likely at carrying capacity. In general, the risk assessment showed that the management interventions in the time span and harvest effort simulated in the present study were ineffective to extinguish the rock cavy population in Fernando de Noronha. Considering this, as well as the importance of investigating other vital factors to decide in favour of or contrary to the management of this species, it is recommended that both an impact assessment of the rock cavy and a cost-effectiveness analysis of the management interventions should be performed to complement the current study.:Acknowledgement III Abstract IV Zusammenfassung VI Resumen IX Table of Contents XII List of Tables and Figures XIV List of Abbreviations XIX 1. Introduction 1 1.1. Invasive alien species and their consequences 1 1.2. Population dynamics analysis 2 Capture-recapture models 3 Observation models 4 Integrated population models 5 Software 7 Model analysis 8 1.3. Fernando de Noronha and the rock cavy 10 1.4. Objectives 12 Overall Objectives 12 Specific Objectives 13 2. Study Framework 15 3. Methods 19 3.1. Study area 19 3.2. Study case species 21 3.3. Research Steps 24 RESEARCH STEP I: Comparing the C-MOM to established models – does this concurrent marking-observation model produces accurate estimates of population parameters for sparse datasets? 24 RESEARCH STEP II: C-MOM application to a real case study 40 RESEARCH STEP III: The rock cavy population in Fernando de Noronha 45 RESEARCH STEP IV: The colonization and eradication of the rock cavy in Fernando de Noronha 47 4. Results 63 4.1. RESEARCH STEP I: Comparing the C-MOM to established models – does this concurrent marking-observation model produces accurate estimates of population parameters for sparse datasets? 63 4.2. RESEARCH STEP II: C-MOM application to a real case study 72 4.3. RESEARCH STEP III: The rock cavy population in Fernando de Noronha 73 4.4. RESEARCH STEP IV: The colonization and eradication of the rock cavy in Fernando de Noronha 74 Sensitivity analysis 74 Simulation experiments 80 5. Discussion 83 5.1. Bias, precision and accuracy of population dynamic models for sparse datasets 85 Simulated data 85 Study case 90 5.2. Advantages and disadvantages of the C-MOM approach 93 5.3. Development and applications of the integrated models and the C-MOM 96 5.4. The reversed use of the PVA software Vortex to simulate AS and IAS populations’ extinction 97 5.5. Status of the rock cavy population in the island of Fernando de Noronha 100 The colonization of the rock cavy in Fernando de Noronha 101 Management of the rock cavy in Fernando de Noronha 104 Study case limitations and future researches 112 6. Conclusion 116 References 118 Appendices 124 APPENDIX I – Assessment of biological invasions 124 APPENDIX II – Population dynamics simulation and dataset sampling 125 APPENDIX III – CMR and C-MOM model codes in R 134 APPENDIX IV – ZPNE model code in R 138 APPENDIX V – C-MOM model used for real datasets 143 APPENDIX VI – Rock cavy colony sizes and number of individuals in Fernando de Noronha 145 APPENDIX VII – Parameter’s ranking of C-MOM, CMR and ZPNE models 148 APPENDIX VIII – Bias, precision and accuracy table 149

Die effektive sowie effiziente Planung von Energieeffizienzmaßnahmen hat große Bedeutung für Produktionsunternehmen. Die um energetische Betrachtungen erweiterte Materialflusssimulation wird in diesem Kontext zunehmend als Werkzeug zur Untersuchung komplexer Wechselwirkungen von Material- und Energieflüssen in Fabriken eingesetzt. Die vorliegende Arbeit hat die Konzeptionierung und Systematisierung einer Methodik zur Ausgestaltung derartiger Simulationsstudien speziell in Umplanungsprojekten zur Energieeffizienzsteigerung zum Inhalt. Unter besonderer Beachtung entscheidungstheoretischer Ansätze soll es diese ermöglichen, unabhängig von der jeweils eingesetzten Simulationslösung projektspezifische Ausprägungen für die relevanten Gestaltungsaspekte einer Simulationsstudie in diesem Problemfeld zu bestimmen (z. B. zur Art und Weise der Modellierung des Energieverbrauchs). Das entwickelte Lösungskonzept besteht aus einem 13 Schritte umfassenden Vorgehensmodell sowie neun Lösungsmodulen zur Unterstützung von Entscheidungen hinsichtlich der Ausprägung ausgewählter Gestaltungsaspekte. Auf diesem Wege wird der gesamte Prozess von der Entwicklung einer Effizienzmaßnahme über die eigentliche Simulationsanwendung (in Anlehnung an die VDI-Richtlinie 3633 Blatt 1:2014) bis hin zur finalen Investitionsentscheidung unterstützt. Exemplarisch wurde die im Ergebnis der Arbeit stehende Methodik an drei Fallstudien erprobt. Dabei konnten alle definierten Anforderungen erfolgreich geprüft werden. / Effective and efficient planning of energy efficiency measures is of great importance to manufacturing companies. Material flow simulation that has been extended to also include energy is increasingly used in this context as a tool for the analysis of complex interactions between material flows and energy flows in factories. This thesis deals with the conception and the systemization of a methodology for designing such simulation studies specifically in re-planning projects that aim for energy efficiency improvements. Taking basic approaches from decision theory into particular consideration, it is intended to provide guidance in deciding on the project-specific manifestation for relevant characteristics of a simulation study in this problem area (e.g. the manner to model energy consumption), regardless of the utilised simulation solution. The developed solution comprises a 13 steps spanning process model as well as nine solution modules to support decisions concerning the choice of manifestation for selected characteristics. In this way, the entire process from the development of an energy efficiency measure through the actual application of simulation (following VDI guideline 3633 Part 1:2014) to the eventual investment decision is assisted. The results of this thesis were exemplarily tested in three case studies. All initially defined requirements could thereby be positively verified.