A Continuous-Time Microsimulation and First Steps Towards a Multi-Level Approach in Demography

Microsimulation is a methodology that closely mimics life-course dynamics. In this thesis, we describe the development of the demographic microsimulation with a continuous time scale that we have realized in the context of the project MicMac - Bridging the micro-macro gap in population forecasting. Furthermore, we detail extensions that we have added to the initial version of the MicMac microsimulation.Mikrosimulation ist eine Prognosetechnik, die sich hervorragend eignet, um Bevölkerungsdynamik realitätsnah abzubilden. In dieser Dissertation beschreiben wir die Entwicklung einer demografischen Mikrosimulation, die wir im Rahmen des Projektes MicMac - Bridging the micro-macro gap in population forecasting erstellt haben. Zudem erläutern wir Erweiterungen, die wir an der ursprünglichen MicMac- Mikrosimulation vorgenommen haben

Bottom-Up Modeling of Building Stock Dynamics - Investigating the Effect of Policy and Decisions on the Distribution of Energy and Climate Impacts in Building Stocks over Time

In Europe, residential and commercial buildings are directly and indirectly responsible for approximately 30–40% of the overall energy demand and emitted greenhouse gas (GHG) emissions. A large share of these buildings was erected before minimum energy-efficiency standards were implemented and are therefore not energy- or carbon-efficient. Consequently, buildings offer significant potential in terms of energy efficiency and the reduction of GHG emissions compared to the status quo. To make use of this potential at scale, targeted policy measures and strategies are needed that should be based on a quantitative assessment of the feasibility and impact of these measures.Building stock models (BSMs) have long been used to assess the current and future energy demand and GHG emissions of building stocks. Most common BSMs characterize the building stock through the use of archetype buildings, which are taken to be representative of large segments of the stock. The increasing availability of disaggregated datasets—such as building registries, 3D city models, and energy performance certificates—has given rise to building-specific BSMs focusing on describing the status quo as an input to energy planning, primarily on the urban scale. Owing to the availability of building-level data, BSMs can be extended beyond policy advice and urban planning, to the assessment of large building portfolios. Thus far, the advances made in building-specific BSMs on the urban scale have not been transferred to the national scale, where such datasets are often not available. Moreover, the focus on an increasingly detailed description of the existing stock has left approaches for modeling stock dynamics without much development. Stock dynamics, therefore, are still primarily modeled through exogenously defined retrofit, demolition, and new construction rates. This limits the applicability and reliability of model results, as the influence of economic, environmental, or policy factors on stock development is not considered.This thesis addresses these shortcomings and advances modeling practices in BSMs. The thesis with appended papers provides a methodology for how the modeling of national building stock can be further developed in terms of building stock characterization through synthetic building stocks as well as stock dynamics through the use of agent-based modeling. Furthermore, the thesis extends BSM applications to inform the strategic planning of large building portfolios through the integration of a maintenance and renovation scheduling method to project the future development of building portfolios

Modelling Individual Evacuation Decisions during Natural Disasters: A Case Study of Volcanic Crisis in Merapi, Indonesia

As the size of human populations increases, so does the severity of the impacts of natural disasters. This is partly because more people are now occupying areas which are susceptible to hazardous natural events, hence, evacuation is needed when such events occur. Evacuation can be the most important action to minimise the impact of any disaster, but in many cases there are always people who are reluctant to leave. This paper describes an agent-based model (ABM) of evacuation decisions, focusing on the emergence of reluctant people in times of crisis and using Merapi, Indonesia as a case study. The individual evacuation decision model is influenced by several factors formulated from a literature review and survey. We categorised the factors influencing evacuation decisions into two opposing forces, namely, the driving factors to leave (evacuate) versus those to stay, to formulate the model. The evacuation decision (to stay/leave) of an agent is based on an evaluation of the strength of these driving factors using threshold-based rules. This ABM was utilised with a synthetic population from census microdata, in which everyone is characterised by the decision rule. Three scenarios with varying parameters are examined to calibrate the model. Validations were conducted using a retrodictive approach by performing spatial and temporal comparisons between the outputs of simulation and the real data. We present the results of the simulations and discuss the outcomes to conclude with the most plausible scenario

Data and Design: Advancing Theory for Complex Adaptive Systems

Complex adaptive systems exhibit certain types of behaviour that are difficult to predict or understand using reductionist approaches, such as linearization or assuming conditions of optimality. This research focuses on the complex adaptive systems associated with public health. These are noted for being driven by many latent forces, shaped centrally by human behaviour. Dynamic simulation techniques, including agent-based models (ABMs) and system dynamics (SD) models, have been used to study the behaviour of complex adaptive systems, including in public health. While much has been learned, such work is still hampered by important limitations. Models of complex systems themselves can be quite complex, increasing the difficulty in explaining unexpected model behaviour, whether that behaviour comes from model code errors or is due to new learning. Model complexity also leads to model designs that are hard to adapt to growing knowledge about the subject area, further reducing model-generated insights. In the current literature of dynamic simulations of human public health behaviour, few focus on capturing explicit psychological theories of human behaviour. Given that human behaviour, especially health and risk behaviour, is so central to understanding of processes in public health, this work explores several methods to improve the utility and flexibility of dynamic models in public health. This work is undertaken in three projects. The first uses a machine learning algorithm, the particle filter, to augment a simple ABM in the presence of continuous disease prevalence data from the modelled system. It is shown that, while using the particle filter improves the accuracy of the ABM, when compared with previous work using SD with a particle filter, the ABM has some limitations, which are discussed. The second presents a model design pattern that focuses on scalability and modularity to improve the development time, testability, and flexibility of a dynamic simulation for tobacco smoking. This method also supports a general pattern of constructing hybrid models --- those that contain elements of multiple methods, such as agent-based or system dynamics. This method is demonstrated with a stylized example of tobacco smoking in a human population. The final line of work implements this modular design pattern, with differing mechanisms of addiction dynamics, within a rich behavioural model of tobacco purchasing and consumption. It integrates the results from a discrete choice experiment, which is a widely used economic method for study human preferences. It compares and contrasts four independent addiction modules under different population assumptions. A number of important insights are discussed: no single module was universally more accurate across all human subpopulations, demonstrating the benefit of exploring a diversity of approaches; increasing the number of parameters does not necessarily improve a module's predictions, since the overall least accurate module had the second highest number of parameters; and slight changes in module structure can lead to drastic improvements, implying the need to be able to iteratively learn from model behaviour

A Spatial Agent-based Model for Volcanic Evacuation of Mt. Merapi

Natural disasters, especially volcanic eruptions, are hazardous events that frequently happen in Indonesia. As a country within the “Ring of Fire”, Indonesia has hundreds of volcanoes and Mount Merapi is the most active. Historical studies of this volcano have revealed that there is potential for a major eruption in the future. Therefore, long-term disaster management is needed. To support the disaster management, physical and socially-based research has been carried out, but there is still a gap in the development of evacuation models. This modelling is necessary to evaluate the possibility of unexpected problems in the evacuation process since the hazard occurrences and the population behaviour are uncertain. The aim of this research was to develop an agent-based model (ABM) of volcanic evacuation to improve the effectiveness of evacuation management in Merapi. Besides the potential use of the results locally in Merapi, the development process of this evacuation model contributes by advancing the knowledge of ABM development for large-scale evacuation simulation in other contexts. Its novelty lies in (1) integrating a hazard model derived from historical records of the spatial impact of eruptions, (2) formulating and validating an individual evacuation decision model in ABM based on various interrelated factors revealed from literature reviews and surveys that enable the modelling of reluctant people, (3) formulating the integration of multi-criteria evaluation (MCE) in ABM to model a spatio-temporal dynamic model of risk (STDMR) that enables representation of the changing of risk as a consequence of changing hazard level, hazard extent and movement of people, and (4) formulating an evacuation staging method based on MCE using geographic and demographic criteria. The volcanic evacuation model represents the relationships between physical and human agents, consisting of the volcano, stakeholders, the population at risk and the environment. The experimentation of several evacuation scenarios in Merapi using the developed ABM of evacuation shows that simultaneous strategy is superior in reducing the risk, but the staged scenario is the most effective in minimising the potential of road traffic problems during evacuation events in Merapi. Staged evacuation can be a good option when there is enough time to evacuate. However, if the evacuation time is limited, the simultaneous strategy is better to be implemented. Appropriate traffic management should be prepared to avoid traffic problems when the second option is chosen

Journey of a committed paleodemographer

This book is dedicated to Jean-Pierre Bocquet-Appel, anthropologist and biologist, one of the founding fathers of palaeodemography in France, who died in 2018. Known and recognised worldwide, he contributed to the development of new techniques for estimating the age at death of skeletal assemblages and promoted the introduction of estimators in palaeodemography. He also participated in the emergence of spatial demography and multi- agent modelling, particularly of Neolithic farmers. We owe him a considerable advance in the understanding of demographic processes linked to the great transitions that humans have experienced in different parts of the world with the discovery of the signature of the demographic transition implied in the passage of societies from a collection economy to an agricultural economy. This book offers a journey to the heart of his life as a researcher, taking in turn, in a diachronic and multidisciplinary approach, anthropological demography from prehistory to the contemporary period. It also paints a generous portrait of this committed man who has never ceased to work for his discipline, whether through a reflective approach to the history of science and epistemology or the transmission of his knowledge to younger generations. This book invites you to an original and innovative experience on the borders of a rare discipline, paleodemography

Agent-based modeling of human-environment interactions in a smallholder agricultural system in the Atlantic Forest (Ribeira Valley, SP, Brazil)

Shifting cultivation systems (SCSs) have been practiced all over the tropics for centuries as the primary subsistence strategy for smallholders. However, since the mid-20th century, SCSs have been submitted to changes, driven by a combination of geographic, economic, socio-political, and demographic factors. Consequently, land use changes lead to agricultural intensification and the replacement of more profitable and permanent practices. The implementation of forest conservation policies (FCPs) is one of the changing drivers to SCSs. They have been designed to reduce or eliminate it, criminalize traditional practices, restrict resources access, displace locals, and increase inequalities and land conflicts. In Brazil, SCSs have been practiced by smallholders and indigenous groups, including Quilombolas, descendants of African enslaved who rebelled against the Portuguese regime. After the abolition of slavery, they remained spread over the country without any state legitimation. Their recognition and rights to ancestors land were possible only in 1988, with the Brazilian Constitution. The Ribeira Valley (Southeastern Brazil) is home to dozens of Quilombos, one of the most significant Atlantic Forest remnants, and high biodiversity. Its first Quilombos were formed in the 18th century and relied on SCS to survive, relatively isolated, up to the 1950s. However, in the context of SCS changes, Quilombos are under a transitional process in different dimensions, including constraints to their traditions by FCPs, generating conflicts. Inspired by this challenging scenario, the Thesis goals are to evaluate Quilombolas socioeconomic conditions and the perception of FCPs implementation and integrate two modeling tools. The tools will model the impact of agricultural transitions on family wealth, income, landscape structure, and tree community β diversity and model the impact of FCPs over the equal economic and ecological dimensions. Socioeconomic data were gathered in 2017 in 14 communities through interviews of 164 farmers. Quilombolas perception of FCPs and constraints for agricultural practice were investigated. The modeling implementation used MPMAS (Mathematical Programming-based Multi-Agent Systems) to simulate land use change in agriculture and forestry. MPMAS was integrated (through land use maps) with a Generalized Dissimilarity Modeling tool (GDM) to predict beta diversity as a function of environmental variation. The modeling exercise was implemented for Pedro Cubas territory, a Quilombo with 52 households located in Eldorado (SP). A combination of primary and secondary data from different sources was used, including a socioeconomic census of 2014 and a collection of tree data in 2016. Five economic/political scenarios were created for comparisons, with a baseline and four different counterfactual situations, varying in market access and FCPs versions. Seven yield curve scenarios and 30 Sobol repetitions were combined, totalizing 1050 simulations. A tradeoff analysis was applied over the political scenarios. MPMAS sensitivity/uncertainty analyses revealed variation on staples consumptions among yield curve scenarios, the sensitivity of income to different parameters, and each income source relevance. The GDM calibration highlighted the importance of climate predictors for tree species, indicating vulnerability to potential climate variability. Results revealed that only 32% of the families were practicing SCS in 2017, but it was still relevant for food security. 83% of the interviewees were unsatisfied with the FCPs, especially the timing of issuing the licenses for SCS. The political scenarios comparison indicates that agricultural intensification caused an improvement in average income. Still, it was accompanied by economic inequality, diminished rotation of plots, lower diversity of habitats, and a less permeable landscape structure (on fallows and because of the emergence of pasture and perennial areas). GDM results showed a significant change in landscape structure/tree community for at least 10% of the territory in the last decades. Regarding FCPs implementation, scenario comparison showed that well-being conditions improved when FCPs were excluded, although more ecological impacts occur. However, such effects refer to only 2.6% of the territory where 90% is covered by mature forest, and GDM indicates that the total ß diversity would not be significantly affected. The tradeoff analysis showed that FCPs are significant for conservation in the present context when perennials and pasture areas occur. In the isolated scenario case, when SCS is the only economic activity, a combination of good well-being and conservation performances was found, suggesting it is causing even lower environmental impacts. I recommend more flexible policies for SCS implementation in the Quilombos in general, for the potential of improving well-being conditions by impacting a small share of the territories. FCPs flexibilization would be even more relevant to the communities that dont have access to alternatives to SCS.Wanderfeldbau wurde jahrhundertelang von Kleinbauern im gesamten Tropengürtel als Hauptstrategie für Subsistenzlandwirtschaft ausgeübt. Seit Mitte des 20. Jahrhunderts werden Wanderfeldbausysteme durch eine Kombination geographischer, wirtschaftlicher, soziopolitischer und demografischer Faktoren stark beeinflusst. Folglich sind Veränderungen in der Landnutzung zu beobachten, welche zu landwirtschaftlicher Intensivierung und Ersatz durch rentablere, nicht-rotierende Verfahren führen. Die Umsetzung von Waldschutzmaßnahmen spielt eine große Rolle bei Veränderungen von Wanderfeldbausystemen, da diese Schutzmaßnahmen konzipiert wurden, um Wanderfeldbau einzuschränken bzw. zu eliminieren, indem sie traditionelle Methoden kriminalisieren, Ressourcenzugriff beschränken und Vertreibung erzwingen, was zu wachsender Ungleichheit sowie zu Landkonflikten führt. In Brasilien wird Wanderfeldbaus von Kleinbauern und indigenen Bevölkerungsgruppen praktiziert, u.a. auch von den Quilombolas, den Nachkommen afrikanischer Sklaven die gegen die portugiesischen Kolonisatoren rebellierten. Nach der Abschaffung der Sklaverei blieben die ehemaligen Sklaven ohne staatliche Legitimation über das ganze Land verteilt. Ihre Anerkennung und ihre Rechte an den Ländern ihrer Vorfahren wurden erst 1988 mit der brasilianischen Verfassung ermöglicht. Das Ribeira-Tal (Südosten Brasiliens) beherbergt Dutzende von Quilombos sowie die größten verbleibenden Reste Atlantischen Regenwaldes mit hoher Biodiversität. Die ersten Quilombos dort wurden im 18. Jahrhundert gegründet und sie überlebten auf Basis von Wanderfeldbau in relativer Abgeschiedenheit bis in die 1950er Jahren Im Kontext der Veränderungen der Wanderfeldbausysteme befinden sich die Quilombos in einem multidimensionalen Übergangsprozess. Hierzu gehört auch die Einschränkung ihrer Traditionen durch die Waldschutzmaßnahmen, die Konflikte erzeugen. Vor diesem anspruchsvollen Hintergrund hat sich die vorliegende Arbeit zum Ziel gesetzt, sowohl die sozioökonomischen Bedingungen und die Wahrnehmung der Implementierung solcher Waldschutzmaßnahmen aus Sicht der Quilombolas zu evaluieren, als auch die Auswirkung der landwirtschaftlichen Intensivierung und der Waldschutzmaßnahmen auf Familieneinkommen, und vermögen, Landschaftsstruktur und Beta-Diversität der Baumgemeinschaft zu modellieren. Sozioökonomische Daten wurden im Jahr 2017 durch Interviews mit 164 Bauern aus 14 Gemeinschaften erfasst. Damit wurden die Wahrnehmung der Waldschutzmaßnahmen durch Quilombolas und die Einschränkungen der Landwirtschaftsmethoden untersucht. Als Werkzeug für die Simulation der Landnutzungsänderungen in der Land- und Forstwirtschaft wurde MPMAS (Mathematical Programming-based Multi-Agent-Systems) verwendet. Die Ergebnisse von MPMAS sind dann als Landnutzungskarten in das Generalized Dissimilarity Modeling Tool (GDM) integriert worden, um die Beta-Diversität als Funktion der Umweltvariation abzuschätzen. Die Modellierung wurde für das Pedro Cubas Gebiet durchgeführt, ein Quilombo mit 52 Haushalten in Eldorado, im Bundesstaat São Paulo. Dafür wurde eine Kombination primärer und sekundärer Daten aus verschieden Quellen gesammelt, u.a aus dem sozioökonomischen Zensus von 2014 und Daten einer Baumerhebung von 2016. Fünf wirtschaftliche bzw. politische Szenarien wurden dann zum Vergleich erstellt, davon eines als Referenz und vier als kontrafaktische Szenarien angelegt wurden, die vor allem Unterschiede im Marktzugang und den Fassungen der Waldschutzordnungen abbilden. Um mit Unsicherheit umzugehen, wurden sieben Pflanzenertragskurvenszenarien und 30 Sobol-Wiederholungen kombiniert, was insgesamt 1050 Simulationen ergab. Auf die politischen Szenarien wurde eine Trade-off-Analyse angewandt. Unsicherheitsanalysen für MPMAS-Simulationen zeigten Schwankungen sowohl im Konsum von Grundnahrungsmitteln zwischen den verschiedenen Szenarien, als auch in der Empfindlichkeit der Einkommen gegen die unterschiedlichen Parameter sowie in der Relevanz der unterschiedlichen Einkommensquellen. Die GDM-Kalibrierung hob die Bedeutung von Klimavorhersagen für die Baumarten und die Anfälligkeit für potenzielle Klimavariabilität hervor. Die Ergebnisse zeigten, dass 2017 nur 32% der Familien Wanderfeldbausysteme praktizierten, dies jedoch weiterhin für die Ernährungssicherheit relevant war. 83% der Befragten waren mit den Waldschutzmaßnahmen unzufrieden, insbesondere mit dem Zeitpunkt der Erteilung der Lizenzen für den Wanderfeldbau. Dier Vergleich der politischen Szenarien zeigt an, dass die Intensivierung der Landwirtschaft zu einer Verbesserung der durchschnittlichen Einkommen führt, dies ging jedoch einher mit wirtschaftlicher Ungleichheit, verminderter Rotation der Parzellen, geringerer Vielfalt der Lebensräume und einer weniger durchlässigen Landschaftsstruktur (auf Brachflächen und aufgrund der Entstehung von Weiden und Dauergrünland). Die GDM-Ergebnisse zeigten in den letzten Jahrzehnten für mindestens 10% des Territoriums eine große Veränderung der Landschaftsstruktur bzw. Baumgemeinschaft. In Bezug auf die Umsetzung von Waldschutzmaßnahmen zeigte ein Szenarienvergleich, dass sich die Bedingungen für die Wohlfahrt der Quilombolas verbesserten, wenn diese Maßnahmen abgeschafft würden, das würde gleichzeitig aber die ökologischen Auswirkungen verstärken. Diese Auswirkungen betreffen jedoch nur auf 2,6% des Gebiets, in dem 90% von altem Wald bedeckt sind, und die GDM Simulationen weisen darauf hin, dass die gesamte Beta-Diversität nicht sehr betroffen wäre. Die Trade-Off-Analyse ergab, dass Waldschutzmaßnahmen im gegenwärtigen Kontext mit Dauerkulturen und Weideflächen für die Walderhaltung wichtig sind. Für das Szenario einer wirtschaftlichen Isolation wie bis Anfang der 1950er Jahre, in dem Wanderfeldbau die einzige wirtschaftliche Aktivität ist, haben wir eine Kombination aus gutem wirtschaftlichem Auskommen und Waldschutz festgestellt, was darauf hindeutet, dass dies noch geringere Umweltauswirkungen verursacht. Wir empfehlen flexiblere Richtlinien für die Implementierung solcher Systeme in Quilombos im Allgemeinen, um die wirtschaftliche Wohlfahrt zu verbessern, bei Beeinflussung nur eines kleinen Teils der Gebiete. Die Flexibilisierung von Waldschutzmaßnahmen wäre für die Gemeinschaften, die keine Alternativen zum Wanderfeldbau haben, noch relevanter

Stochastic spreading on complex networks

Complex interacting systems are ubiquitous in nature and society. Computational modeling of these systems is, therefore, of great relevance for science and engineering. Complex networks are common representations of these systems (e.g., friendship networks or road networks). Dynamical processes (e.g., virus spreading, traffic jams) that evolve on these networks are shaped and constrained by the underlying connectivity. This thesis provides numerical methods to study stochastic spreading processes on complex networks. We consider the processes as inherently probabilistic and analyze their behavior through the lens of probability theory. While powerful theoretical frameworks (like the SIS-epidemic model and continuous-time Markov chains) already exist, their analysis is computationally challenging. A key contribution of the thesis is to ease the computational burden of these methods. Particularly, we provide novel methods for the efficient stochastic simulation of these processes. Based on different simulation studies, we investigate techniques for optimal vaccine distribution and critically address the usage of mathematical models during the Covid-19 pandemic. We also provide model-reduction techniques that translate complicated models into simpler ones that can be solved without resorting to simulations. Lastly, we show how to infer the underlying contact data from node-level observations.Komplexe, interagierende Systeme sind in Natur und Gesellschaft allgegenwärtig. Die computergestützte Modellierung dieser Systeme ist daher von immenser Bedeutung für Wissenschaft und Technik. Netzwerke sind eine gängige Art, diese Systeme zu repräsentieren (z. B. Freundschaftsnetzwerke, Straßennetze). Dynamische Prozesse (z. B. Epidemien, Staus), die sich auf diesen Netzwerken ausbreiten, werden durch die spezifische Konnektivität geformt. In dieser Arbeit werden numerische Methoden zur Untersuchung stochastischer Ausbreitungsprozesse in komplexen Netzwerken entwickelt. Wir betrachten die Prozesse als inhärent probabilistisch und analysieren ihr Verhalten nach wahrscheinlichkeitstheoretischen Fragestellungen. Zwar gibt es bereits theoretische Grundlagen und Paradigmen (wie das SIS-Epidemiemodell und zeitkontinuierliche Markov-Ketten), aber ihre Analyse ist rechnerisch aufwändig. Ein wesentlicher Beitrag dieser Arbeit besteht darin, die Rechenlast dieser Methoden zu verringern. Wir erforschen Methoden zur effizienten Simulation dieser Prozesse. Anhand von Simulationsstudien untersuchen wir außerdem Techniken für optimale Impfstoffverteilung und setzen uns kritisch mit der Verwendung mathematischer Modelle bei der Covid-19-Pandemie auseinander. Des Weiteren führen wir Modellreduktionen ein, mit denen komplizierte Modelle in einfachere umgewandelt werden können. Abschließend zeigen wir, wie man von Beobachtungen einzelner Knoten auf die zugrunde liegende Netzwerkstruktur schließt