Purpose Life cycle costing (LCC) is a state-of-the-art method to analyze investment decisions in infrastructure projects. However, uncertainties inherent in long-term planning question the credibility of LCC results. Previous research has not systematically linked sources and methods to address this uncertainty. Part I of this series develops a framework to collect and categorize different sources of uncertainty and addressing methods. This systematization is a prerequisite to further analyze the suitability of methods and levels the playing field for part II. Methods Past reviews have dealt with selected issues of uncertainty in LCC. However, none has systematically collected uncertainties and linked methods to address them. No comprehensive categorization has been published to date. Part I addresses these two research gaps by conducting a systematic literature review. In a rigorous four-step approach, we first scrutinized major databases. Second, we performed a practical and methodological screening to identify in total 115 relevant publications, mostly case studies. Third, we applied content analysis using MAXQDA. Fourth, we illustrated results and concluded upon the research gaps. Results and discussion We identified 33 sources of uncertainty and 24 addressing methods. Sources of uncertainties were categorized according to (i) its origin, i.e., parameter, model, and scenario uncertainty and (ii) the nature of uncertainty, i.e., aleatoric or epistemic uncertainty. The methods to address uncertainties were classified into deterministic, probabilistic, possibilistic, and other methods. With regard to sources of uncertainties, lack of data and data quality was analyzed most often. Most uncertainties having been discussed were located in the use stage. With regard to methods, sensitivity analyses were applied most widely, while more complex methods such as Bayesian models were used less frequently. Data availability and the individual expertise of LCC practitioner foremost influence the selection of methods. Conclusions This article complements existing research by providing a thorough systematization of uncertainties in LCC. However, an unambiguous categorization of uncertainties is difficult and overlapping occurs. Such a systemizing approach is nevertheless necessary for further analyses and levels the playing field for readers not yet familiar with the topic. Part I concludes the following: First, an investigation about which methods are best suited to address a certain type of uncertainty is still outstanding. Second, an analysis of types of uncertainty that have been insufficiently addressed in previous LCC cases is still missing. Part II will focus on these research gaps.

Organisationen wie Feuerwehren, med. Rettungsdienste oder Spezialeinheiten der Polizei zeigen auch in Situationen, die durch Unsicherheit, unvollständige Informationen oder eine sehr hohe Dynamik gekennzeichnet sind, eine effiziente und effektive Leistungsfähigkeit. Sie können deswegen als Hochleistungssysteme (HLS) bezeichnet werden. Im vorliegenden Artikel wird dargestellt wie sich Erkenntnisse der High Reliabilty Theory auf die Untersuchung von Hochleistungssystemen auswirken und welche Gemeinsamkeiten und Parallelen bestehen. Dabei wird insbesondere ein Schwerpunkt auf die Thematik des Lernens gelegt.

Die zuverlässige Einhaltung von Lieferzusagen stellt ein wichtiges Kriterium bei der Auswahl der Teilnehmer eines auftragsbezogenen Produktionsnetzes dar. Für die objektive Bewertung der Lieferzuverlässigkeit der potenziellen Netzwerkteilnehmer bedarf es der Quantifizierung der relevanten Unsicherheiten integriert in einen allgemein gültigen Ansatz der Verfügbarkeitsprüfung. Die Arbeit stellt daraus resultierend Ansätze zur Berechnung der Unsicherheit vor. Durch die Quantifizierung der Unsicherheit innerhalb der Unternehmen ergibt sich zudem die Möglichkeit der flexiblen, situationsabhängigen Nutzung des für langfristige Rahmenverträge reservierten Sicherheitsbestandes zur Befriedigung kurzfristiger Anfragen. Diese Aufgabe unterstützt ein konfigurierbares Modell zur Entscheidungsunterstützung, das auf einem Neuro-Fuzzy-System basiert. Die Kennzahlen der Lieferzuverlässigkeit unterliegen einem dynamischen Verhalten während des Wertschöpfungsprozesses in dem auftragsbasierten Produktionsnetz. Durch die Integration dieser Kennzahlen in das Management dieses Prozesses ergibt sich die Möglichkeit, aus der Zunahme der Unsicherheit mögliche Störungen und deren Auswirkungen bereits vor ihrem Eintreten zu erfassen und im Rahmen eines präventiven Störungsmanagements zu agieren.

The following thesis deals with methods to increase the reliability of the results in life cycle assessment. The paper is divided into two parts. The first part points out the typologies and sources of uncertainty in LCA and summarises the existing methods dealing with it. The methods are critically discussed and pros and cons are contrasted. Within the second part a case study is carried out. This study calculates the carbon footprint of a cosmetic product of Li-iL GmbH. Thereby the whole life cycle of the powder bath Blaue Traube is analysed. To increase the reliability of the result a procedure, derived from the first part, is applied. Recommendations to enhance the product´s sustainability are then given to the decision-makers of the company. Finally the applied procedure for dealing with uncertainty in LCAs is evaluated. The aims of the thesis are to make a contribution to the understanding of uncertainty in life cycle assessment and to deal with it in a more consistent manner. As well, the carbon footprint of the powder bath shall be based on appropriate assumptions and shall consider occurring uncertainties. Basing on discussed problems, a method is introduced to avoid the problematic merging of variability uncertainty and data uncertainty to generate probability distributions. The introduced uncertainty importance analysis allows a consistent differentiation of these types of uncertainty. Furthermore an assessment of the used data of LCA studies is possible. The method is applied at a PCF study of the bath powder Blaue Traube of Li-iL GmbH. Thereby the analysis is carried out over the whole life cycle (cradle-to-grave) as well as cradle-to-gate. The study gives a practical example to the company determining the carbon footprint of products. In addition, it meets the requirements of ISO guidelines of publishing the study and comparing it with other products. Within the PCF study the introduced method allows a differentiation of variability uncertainty and knowledge uncertainty. The included uncertainty importance analysis supports the assessment of each aggregated unit process within the analysed product system. Finally this analysis can provide a basis to collect additional, more reliable or uncertain data for critical processes.

Die deterministische Modellierung des Niederschlags-Abfluss(N-A)-Prozesses mit flächendifferenzierten, prozessbasierten Modellen ist von zahlreichen Unsicherheiten beeinflusst. Diese Unsicherheiten resultieren hauptsächlich aus den genutzten Daten, die Messfehlern unterliegen sowie für eine flächendifferenzierte Modellierung entsprechend aufbereitet werden müssen, und der Abstraktion der natürlichen Prozesse im Modell selbst. Da N-A-Modelle in der hydrologischen Praxis vielfältig eingesetzt werden, sind Zuverlässigkeitsaussagen im Hinblick auf eine spezielle Anwendung nötig, um das Vertrauen in die Modellergebnisse zu festigen. Die neu entwickelte Strategie zur Analyse und Simulation der Unsicherheiten eines flächendifferenzierten, prozessbasierten N-A-Modells ermöglicht eine umfassende, globale und komponentenbasierte Unsicherheitsbestimmung. Am Beispiel des mesoskaligen Einzugsgebiets der Schwarzen Pockau/Pegel Zöblitz im mittleren Erzgebirge wird der Einfluss maßgebender Unsicherheiten im N-A-Prozess sowie deren Kombination zu einer Gesamt-Unsicherheit auf den Gebietsabfluss aufgezeigt. Zunächst werden die maßgebenden Unsicherheiten separat quantifiziert, wobei die folgenden Methoden eingesetzt werden: (i) Monte-Carlo Simulationen mit flächendifferenzierten stochastischen Bodenparametern zur Analyse des Einflusses unsicherer Bodeninformationen, (ii) Bayes’sche Inferenz und Markov-Ketten-Monte-Carlo Simulationen, die eine Unsicherheitsbestimmung der konzeptionellen Modellparameter der Abflussbildung und -konzentration ermöglichen und (iii) Monte-Carlo Simulationen mit stochastisch generierten Niederschlagsfeldern, die die raum-zeitliche Variabilität interpolierter Niederschlagsdaten beschreiben. Die Kombination der Unsicherheiten zu einer hydrologischen Unsicherheit und einer Gesamt-Unsicherheit erfolgt ebenfalls mit Monte-Carlo Methoden. Dieses Vorgehen ermöglicht die Korrelationen der Zufallsvariablen zu erfassen und die mehrdimensionale Abhängigkeitsstruktur innerhalb der Zufallsvariablen empirisch zu beschreiben. Die Ergebnisse zeigen für das Untersuchungsgebiet eine Dominanz der Unsicherheit aus der raum-zeitlichen Niederschlagsverteilung im Gebietsabfluss gefolgt von den Unsicherheiten aus den Bodeninformationen und den konzeptionellen Modellparametern. Diese Dominanz schlägt sich auch in der Gesamt-Unsicherheit nieder. Die aus Messdaten abgeleiteten Unsicherheiten weisen eine Heteroskedastizität auf, die durch den Prozessablauf geprägt ist. Weiterhin sind Indizien für eine Abhängigkeit der Unsicherheit von der Niederschlagsintensität sowie strukturelle Defizite des N-A-Modells sichtbar. Die neu entwickelte Strategie ist prinzipiell auf andere Gebiete und Modelle übertragbar. / Modelling rainfall-runoff (R-R) processes using deterministic, spatial distributed, process-based models is affected by numerous uncertainties. One major source of these uncertainties origins from measurement errors together with the errors occurring in the process of data processing. Inadequate representation of the governing processes in the model with respect to a given application is another source of uncertainty. Considering that R-R models are commonly used in the hydrologic practise a quantification of the uncertainties is essential for a realistic interpretation of the model results. The presented new framework allows for a comprehensive, total as well as component-based estimation of the uncertainties of model results from spatial distributed, process-based R-R modelling. The capabilities of the new framework to estimate the influence of the main sources of uncertainties as well as their combination to a total uncertainty is shown and analysed at the mesoscale catchment of the Schwarze Pockau of the Ore Mountains. The approach employs the following methods to quantify the uncertainties: (i) Monte Carlo simulations using spatial distributed stochastic soil parameters allow for the analysis of the impact of uncertain soil data (ii) Bayesian inference und Markov Chain Monte Carlo simulations, yield an estimate of the uncertainty of the conceptual model parameters governing the runoff formation and - concentration processes. (iii) Monte Carlo simulations using stochastically generated rainfall patterns describing the spatiotemporal variability of interpolated rainfall data. Monte Carlo methods are also employed to combine the single sources of uncertainties to a hydrologic uncertainty and a total uncertainty. This approach accounts for the correlations between the random variables as well as an empirical description of their multidimensional dependence structure. The example application shows a dominance of the uncertainty resulting from the spatio-temporal rainfall distribution followed by the uncertainties from the soil data and the conceptual model parameters with respect to runoff. This dominance is also reflected in the total uncertainty. The uncertainties derived from the data show a heteroscedasticity which is dominated by the process. Furthermore, the degree of uncertainty seems to depend on the rainfall intensity. The analysis of the uncertainties also indicates structural deficits of the R-R model. The developed framework can principally be transferred to other catchments as well as to other R-R models.

Mit deterministischen Sicherheitsanalysen werden Auslegungsstörfälle bei Kernkraftwerken anhand von Rechenmodellen am Computer berechnet, um damit die Funktionalität der installierten Sicherheitssysteme eines jeden Kraftwerkes zu überprüfen. Allerdings sind bei solchen Störfalluntersuchungen stets Unsicherheiten vorhanden, die den zu ermittelnden Störfallablauf stark beeinflussen können. Beispielsweise können aufgrund technisch bedingter Fertigungstoleranzen schwankende Geometrie- und Materialdaten entstehen, die bei der Modellierung des Rechenmodells zu Unsicherheiten führen. Weitere Unsicherheiten können auf die physikalischen Modelle eines Störfall-analyseprogrammes zurückgeführt werden. Insbesondere haben die empirischen Beziehungen Unsicherheiten, da diese aus experimentellen Daten ermittelt wurden. In der vorliegenden Arbeit wurden daher die empirischen Beziehungen des Programmes TRACE analysiert und dessen Unsicherheiten quantifiziert. Mit der entwickelten „Dynamic Best-Estimate Safety Analysis“-Methode (DYBESA-Methode) lässt sich diese programmspezifische Unsicherheit bei der Störfalluntersuchung berücksichtigen. Es wurden 13 verschiedene „Correlation, Identification and Ranking Table“ (CIRT) erstellt, die die relevanten Unsicherheiten bei den unterschiedlichen Auslegungsstörfällen für Druckwasserreaktoren kategorisieren. Damit können Unsicherheitsanalysen basierend auf dem statistischen Verfahren nach S. S. Wilks durchgeführt werden. Schlussendlich werden die sicherheitsrelevanten Rechenergebnisse realistisch und vor allem mit einer hohen Zuverlässigkeit, im Vergleich zu einer herkömmlichen konservativen Berechnungsmethode, ermittelt.:1 Einleitung 2 Sicherheitsanforderungen an Kernkraftwerke 2.1 Einschluss radioaktiver Stoffe und Abschirmung ionisierender Strahlung 2.2 Störfallkategorien 2.3 Thermohydraulische Nachweiskriterien 3 Analysetechniken für die Durchführung von Störfalluntersuchungen 3.1 Das Störfallanalyseprogramm TRACE 3.2 Identifikation von Unsicherheiten bei Störfalluntersuchungen 3.3 Konservative Methode und Best-Estimate-Methode mit Unsicherheitsanalyse 3.4 Stand von Wissenschaft und Technik der wesentlichen Best-Estimate-Methoden 3.4.1 CSAU-Methode 3.4.2 UMAE-Methode 3.4.3 CIAU-Methode 3.4.4 GRS-Methode 3.4.5 ASTRU-Methode 4 Entwicklung der DYBESA-Methode für DWR-Störfalluntersuchungen 4.1 Thermohydraulische Phänomene beim Störfallablauf 4.2 Regressionsverfahren für die experimentelle Datenanalyse 4.2.1 Vertrauens- und Vorhersagebereich 4.2.2 Statistische Toleranzgrenzen 4.3 Identifikation von empirischen Beziehungen und deren Bewertung 4.4 Erzeugen und Kombinieren von geeigneten Stichproben 4.5 Programm zur Vorbereitung und Auswertung von Unsicherheitsanalysen 4.6 Modifikation des Störfallanalyseprogramms TRACE für die Berücksichtigung der programmspezifischen Unsicherheit 4.7 Verifikation der DYBESA-Methode 5 Ergebnisse 5.1 FEBA 5.1.1 Verifikation Rechenmodell 5.1.2 Verifikation CIRT 5.2 Marviken-Test-Station 5.2.1 Verifikation Rechenmodell 5.2.2 Verifikation CIRT 5.3 Druckwasserreaktor 5.3.1 Mittlerer Bruch einer Hauptkühlmittelleitung 5.3.2 Notstromfall 6 Diskussion und Ausblick 7 Zusammenfassung 8 Quellenverzeichnis 9 Anlagenverzeichnis

The success of a cement production project depends on the raw material supply. Longterm quarry production planning (LTQPP) is essential to maintain the supply to the cement plant. The quarry manager usually attempts to fulfil the complicated calculations, ensuring a consistent supply of raw materials to the cement plant while guaranteeing technical and operational parameters in mining. Modern quarry management relies on block models and mathematical algorithms integrated into the software to optimize the LTQPP. However, this method is potentially sensitive to geological uncertainty in resource estimation, resulting in the deviation of the supply production of raw materials. More importantly, quarry managers lack the means to deal with these requirements of LTQPP. This research develops a stochastic optimization framework based on the combination of geostatistical simulation, clustering, and optimization techniques to optimize the LTQPP. In this framework, geostatistical simulation techniques aim to model the quarry deposit while capturing the geological uncertainty in resource estimation. The clustering techniques are to aggregate blocks into selective mining cuts that reduce the optimization problem size and generate solutions in a practical timeframe. Optimization techniques were deployed to develop a new mathematical model to minimize the cost of producing the raw mix for the cement plant and mitigate the impact of geological uncertainty on the raw material supply. Matlab programming platform was chosen for implementing the clustering and optimization techniques and creating the software application. A case study of a limestone deposit in Southern Vietnam was carried out to verify the proposed framework and optimization models. Geostatistical simulation is applied to capture and transfer geological uncertainty into the optimization process. The optimization model size decreases significantly using the block clustering techniques and allowing generate solutions in a reasonable timeframe on ordinary computers. By considering mining and blending simultaneously, the optimization model minimizes the additive purchases to meet blending requirements and the amount of material sent to the waste dump. The experiments are also compared with the traditional optimization framework currently used for the deposit. The comparisons show a higher chance of ensuring a consistent supply of raw materials to the cement plant with a lower cost in the proposed framework. These results proved that the proposed framework provides a powerful tool for planners to optimize the LTQPP while securing the raw material supply in cement operations under geological uncertainty.:Title page _ i Declaration_ ii Acknowledgements _ i Publications during candidature_ii Abstract _iii Table of contents _v List of figures_viii List of tables _ xi List of abbreviations _xii Chapter 1 . Introduction _1 1.1 Background _1 1.2 Statement of the problem _2 1.3 Research aims and objectives_ 3 1.4 Scope of research _4 1.5 Research methodology _ 4 1.6 Significance of theresearch_5 1.7 Organization of thesis _6 Chapter 2 . Literature review _ 8 2.1 Introduction _ 8 2.2 Cement raw materials _8 2.3 Cement production process _ 8 2.3.1 Raw material recovery _9 2.3.2 Raw material processing_10 2.4 Impact of raw materials on the cement production process _12 2.5 Quarry planning and optimization _13 2.6 Long-term production planning (LTPP) problem _14 2.6.1 Deterministic approaches to solve the LTPP problem_15 2.6.2 Stochastic approaches for solving the LTPP problem_21 2.7 Conclusion_26 Chapter 3 . A stochastic optimization framework for LTQPP problem_28 3.1 Introduction_28 3.2 Deposit simulation_29 3.2.1 Simulating the rock type domains using SIS_30 3.2.2 Simulating the chemical grades within each domain conditionally to rock type domains, using SGS_30 3.3 Block clustering _31 3.4 The mathematical formulation for the LTQPP problem_32 3.4.1 Notation_34 3.4.2 Mathematical formulation_36 3.5 Numerical modelling_39 3.5.1 Clustering _39 3.5.2 SMIP formulation_41 3.6 Conclusion _47 Chapter 4 . Hierarchical simulation of cement raw material deposit_ 49 4.1 Introduction _49 4.2 Research area _ 50 4.2.1 General description_50 4.2.2. Data set_50 4.3. Application of hierarchical simulation _53 4.3.1 Rock-type simulation _ 53 4.3.2 Grade simulation _60 4.4. Discussion_73 4.5. Conclusion_76 Chapter 5 . Application of the stochastic optimization framework_77 5.1 Introduction_77 5.2 Implementation of KHRA _77 5.3 Implementation of the SMIP model _78 5.3.1 Sensitivity of the penalty cost _80 5.3.2 The effectiveness of the SMIP model _82 5.4 Risk mitigation _85 5.5 Conclusion _87 Chapter 6 . Conclusions and future works _ 89 6.1 Conclusions _89 6.2 Future works _91 References_ 93 Appendix I. Software Application _100 A.I.1 Introduction _100 A.I.2 Input preparation _101 A.I.2.1 Format of block model input _101 A.I.2.2 Import block model input _102 A.I.2.2 Cost assignment _104 A.I.2.3 Size reduction _ 107 A.I.3 Optimization _110 A.I.3.1 Destination _110 A.I.3.2 Production capacity _ 111 A.I.3.3 Additive purchase _ 111 A.I.3.4 Pit slopes _ 111 A.I.3.5 Optimization _ 112 A.I.4 Visualization of optimization results _112

Life cycle costing (LCC) is the state-of-the-art method to economically evaluate long-term projects over their life spans. However, uncertainty in long-range planning raises concerns about LCC results. In Part I of this series, we developed a holistic framework of the different types of uncertainty in infrastructure LCCs. We also collected methods to address these uncertainties. The aim of Part II is to evaluate the suitability of methods to cope with uncertainty in LCC. Part I addressed two research gaps. It presented a systematic collection of uncertainties and methods in LCC and, furthermore, provided a holistic categorization of both. However, Part I also raised new issues. First, a combined analysis of sources and methods is still outstanding. Such an investigation would reveal the suitability of different methods to address a certain type of uncertainty. Second, what has not been assessed so far is what types of uncertainty are insufficiently addressed in LCC. This would be a feature to improve accuracy of LCC results within LCC, by suggesting options to better cope with uncertainty. To address these research gaps, we conducted a systematic literature review. Part II analyzed the suitability of methods to address uncertainties. The suitability depends on data availability, type of data (tangible, intangible, random, non-random), screened hotspots, and tested modeling specifications. We identified types of uncertainties and methods that have been insufficiently addressed. The methods include probabilistic modeling such as design of experiment or subset simulation and evolutionary algorithm and Bayesian modeling such as the Bayesian latent Markov decision process. Subsequently, we evaluated learning potential from other life cycle assessment (LCA) and life cycle sustainability assessment (LCSA). This analysis revealed 28 possible applications that have not yet been used in LCC. Lastly, we developed best practices for LCC practitioners. This systematic review complements prior research on uncertainty in LCC for infrastructure, as laid out in Part I. Part II concludes that all relevant methods to address uncertainty are currently applied in LCC. Yet, the level of application is different. Moreover, not all methods are equally suited to address different categories of uncertainty. This review offers guidance on what to do for each source and type of uncertainty. It illustrates how methods can address both based on current practice in LCC, LCA, and LCSA. The findings of Part II encourage a dialog between practitioners of LCC, LCA, and LCSA to advance research and practice in uncertainty analysis.

Der systematische Umgang mit Unsicherheiten, die in Form von Wissens- und Definitionslücken vorliegen, stellt eine zentrale Aktivität der Produktentwicklung dar. Im Zuge der Zielsystementwicklung liegen Unsicherheiten insbesondere in Form von aus Kunden- und Anwendersicht nichtzutreffender und fehlender oder unvollständiger Ziele und Anforderungen vor. Um bei der Konkretisierung des initialen Zielsystems dahingehend zu unterstützen, wurde eine Methode abgeleitet, welche die systematische Integration von Kunden und Anwendern in die Erhebung von Zielsystemelementen adressiert. Dabei formulieren Kunden und Anwender gemeinsam mit Produktentwicklern Ziele für das zu entwickelnde Produkt. Um dies zu unterstützen, werden die Ziele in Form von Satzschablonen formuliert, um die Vollständigkeit der Ziele zu gewährleisten. Weiter kann durch den Aufbau der Satzschablone sichergestellt werden, dass die Begründung in Form des Kunden- oder Anwendernutzens dokumentiert ist. Zusätzlich wurde ein Portfolio abgeleitet, welches die Ziele entsprechend der Zielgruppe und des relevanten Use-Cases strukturiert und damit fehlende Ziele darlegt. Im Rahmen einer Evaluation konnte gezeigt werden, dass durch die Anwendung der Methode in einem Entwicklungsprojekt von Hekatron Brandschutz die Vollständigkeit des Zielsystems gesteigert und die vorliegende Unsicherheit reduziert werden konnte.

Individual decision-making and the generation of medical demand are crucial subjects in healthcare economics. The following scientific discussion can be classified into these threads. The demand for health care services is typically connected to characteristic imperfections reflecting a bias between an objective and a subjective assessment of a specific demand situation or externally caused frictions. For that reason, the realized demand is not necessarily connected to an efficient allocation of resources. Hence, it is a crucial objective to analyze individual decision-making related on the one hand to specific treatment alternatives and on the other hand to the specified decision framework. This framework is characterized by both the attributes of the individual as well as by the external conditions in which the decision takes place. Theirby, the analysis focuses specifically on potential sources of demand inefficiency and their effectiveness. The following discussion broach the issue of two significant objectives within health economics: 1. Trade in medical care and patient migration, 2. Patient autonomy and education. Both fields find their analytical basis in a micro-economic discussion of individual decision behavior. The first field analyzes the decision between medical provision at home or abroad. This subject is specifically related to a potential efficiency gain due to the existence of cross-border price and quality gradients, usually a source of gains in trade. In the focus of the analysis is the impact of the specific characteristics of these gradients as determinants of cross-border medical demand. The second field discusses the investment decision in measures of patient education and prevention in a framework of a common consultation and self-care as imperfect treatment alternatives due to imperfect competences of self-diagnosis and medical self-supply. This subject is related to the commonly acknowledged positive correlation between health and education. Education is able to improve the quality of health production and, therefore, has a specific impact with respect to increasing autonomous behavior of the individual in issues of health production. The specific environment of these decisions significantly influences the mechanism of decision-making and the final outcome; this must be assessed according to the effect on the allocative efficiency of medical demand. The role of price and quality gradients between alternatives, the differentiation of illnesses, as well as subjective factors, are crucial to the results. Moreover, the individual's ability to appraise his or her own health stock and demand decisions is itself risky. Therefore, the form of the insurance coverage is another important element when analyzing individual decisions. The following discussion will clarify the decision-making mechanisms and their impact on efficient resource allocation. Since the focus is on demand behavior, the interaction with, and therefore the behavior of, the supply side is not explicitly formulated.