Small unmanned aircraft systems (sUAS) will become a disruptive force on the modern battlefield. In recent years, sUAS size and cost have decreased while their capability has increased. They have forced a reconsideration of the air superiority paradigm held since the First World War. Perhaps their most attractive, and worrisome, feature is the huge range of combat roles that they might fulfill. The presence of sUAS on future battlefields is certain, but the role they will play and their impact on those battlefields are not. This work presents a decision support framework for sUAS deployment in small infantry units. The framework is designed to explore and evaluate multiple sUAS-small-unit deployment concepts' impact on small unit effectiveness in a combat scenario of interest. The framework helps decision makers identify high-level sUAS deployment principles for testing and validation in physical experiments before sUAS are implemented on the battlefield. The decision support framework comprises the following: 1) a definition of the sUAS-small-unit deployment concept design space and combat scenario, 2) an agent-based computer model for exploring sUAS deployment concepts, 3) a set of analysis tools for evaluating sUAS deployment impact on combat effectiveness, and 4) suggestions for synthesizing high-level sUAS deployment principles from the analysis. In this work, the decision support framework for sUAS-small-unit deployment is used to explore and evaluate the impact of deploying an infantry platoon with between one and nine unmanned aerial vehicles (UAV) operating in a reconnaissance role while executing one of several sUAS patrol pattern variants. In a scenario in which a defending platoon uses sUAS to intercept and aid in indirect fires targeting against a platoon of attacking infantry, the sUAS were shown to markedly improve the defending platoon's combat effectiveness. The framework is used to synthesize several key principles for sUAS deployment in the scenario. It shows that, when fewer UAVs are deployed, short-range sUAS patrols improve defender combat effectiveness. Conversely, when more UAVs are deployed, long-range sUAS patrols improve the defenders' ability to target attacking units with indirect fires, increasing the firepower concentrated against opponents. The analysis also shows that increasing the number of deployed UAVs improves the likelihood of defending warfighters surviving the engagement and the defenders' ability to detect and engage the attackers with indirect fires. Finally, the framework shows that sUAS can force alterations in attacker behavior, removing them from combat by non-violent, but highly effective, means.

Agent-based asset exchange models serve as an interesting and tractable means by which to study the emergence of an economy's wealth distribution. Although asset exchange models have reproduced certain features of real-world wealth distributions, previous research has largely neglected the effects of economic growth and network connectivity between agents. In this work, we study the effects of globalization on wealth inequality in the Growth, Exchange, and Distribution (GED) model [Liu et al, Klein et al] on a network or lattice that connects potential trading partners. We find that increasing the number of trading partners per agent results in higher levels of wealth inequality as measured by the Gini coefficient and the variance of the agent wealth distribution. However, if globalization is accompanied by a proportionate increase in the economic growth rate, the level of inequality can be held constant. We present a mean-field theory to describe the GED model based on the Fokker-Planck equation and derive the stationary wealth distributions of the network GED model. For large Ginzburg parameter for which mean-field theory is applicable, the wealth distributions for the fully connected model are found to be Gaussian; however, for sparse trade networks, a non-Gaussian "hyperequal" phase is found even for large Ginzburg parameter. It is shown that several networks (Erdos-Renyi, Barabsi-Albert, one-dimensional and two-dimensional lattices) display mean-field critical exponents when the Ginzburg parameter is large and held constant and the system parameters are scaled properly.

Achieving sustainable development to meet the needs of current and future generations is currently on top of the global agenda, both in scientific research as well as global politics. However, achieving sustainable development is still a grand challenge, not least because it is embedded in the context of global change that affects most resource use systems worldwide in multiple ways. Even though many approaches to sustainable management do consider the connection between human activity and environmental dynamics, the role of human behavior as a main driver of system dynamics in coupled human and natural systems is often only poorly addressed. In this thesis, we aim to contribute to an improved understanding under which conditions human resource use decisions lead to sustainable outcomes, with regard to global change. For this, we will take the perspective of human decision-making and its social, ecological and economic consequences in two different resource use contexts, namely a) pastoralism in drylands and b) disaster risk management with respect to floods. We explicitly consider individual human decision-making as driver of social-ecological system dynamics, investigate the feedbacks between system components, as well as the impact of global change on resource use. To analyze such complex system dynamics, simulation models have proven to be helpful analysis tools. Particularly agent-based modeling represents a flexible and powerful analysis tool, as it allows us to model the decisions and interactions of individual agents at the micro level, while at the same time observing the outcome of their behavior on a system level. Within three case studies, we develop agent-based simulation models that capture the dynamics and feedbacks of the social-ecological system under consideration in a spatially explicit way. The first study analyzes the performance of disaster management organizations under change. In the second study, we aim to detect the drivers for polarization in a pastoral system in Morocco. The last study investigates behavioral change of pastoralist households and its impact on social, ecological and economic outcome measures. By analyzing a range of scenarios in each study, we determine both the long-term impact of different decision regimes on the state of the social-ecological system as well as the dimensions of change that have the most profound impact on the system dynamics and the sustainability of resource use. Main results that could be obtained from the modeling experiments include the identification of key resources that have a high influence on the long-term system dynamics. We are also able to show that under the influence of global change, access to certain resources gains in importance, as resources can act as buffer mechanisms to mitigate the adverse effects of global change. Through the operationalization of behavioral theories in model rules and the explicit representation of heterogeneous agent decision making, we could determine under which conditions a more refined representation of human decision making matters, and when a change in behavioral strategies leads to different social-ecological outcomes. Furthermore, all three modeling studies demonstrate the usefulness of stylized agent-based models to gain insights into complex systems. Overall, this thesis contributes to social-ecological systems research by developing appropriate simulation models to address the problem of sustainable resource use under global change.

A renewable energy source, biogas, comprises of methane (80%) and carbon dioxide (15%), and is a great alternative to the conventional fossil-based fuels, such as coal, gas and oil. Biogas is created during anaerobic biological digestion of waste materials, such as landfill material, animal manure, wastewater, algal biomass, industrial organic waste etc. A biogas potential from organic waste in the United States is estimated at about 9 million tons per year and technology allows capture of greenhouse gases, such as methane and carbon dioxide, into a form of a fuel. In the light of global climate change and efforts to decrease carbon footprint of fuels in daily life, usage of biogas as an alternative fuel to fossil fuels looks especially promising. The goal of this research was to develop and test an approach for optimization of biogas production by engineering microorganisms digesting organic waste. Specifically, bacteria that can digest algal biomass, collected from the wastewater lagoons or open waterbodies. The research also expands on the previous efforts to analyze microbial interactions in wastewater treatment systems. A computational model is developed to aid with prognosis of microbial consortia ability to form complex aggregates in reactors with upflow mode of feeding substrate. Combining modeling predictions and laboratory experiments in organic matter digestion will lead to more stable engineered systems and higher yields of biogas.

Large scale distributed computing systems have been extensively utilized to host critical applications in the fields of national defense, finance, scientific research, commerce, etc. However, applications in distributed systems face the risk of service outages due to inevitable faults. Without proper fault management methods, faults can lead to significant revenue loss and degradation of Quality of Service (QoS). An ideal fault management solution should guarantee fast and accurate fault diagnosis, scalability in distributed systems, portability for a variety of systems, and the versatility of recovering different types of faults. This dissertation presents a model-based fault management structure which automatically recovers computing systems from faults. This structure can recover a system from common faults while minimizing the impact on the system’s QoS. It covers all stages of fault management including fault detection, identification and recovery. It also has the flexibility to incorporate various fault diagnosis methods. When faults occur, the approach identifies fault types and intensity, and it accordingly computes the optimal recovery plan with minimum performance degradation, based on a cost function that defines performance objectives and a predictive control algorithm. The fault management approach has been verified on a centralized Web application testbed and a distributed big data processing testbed with four types of simulated faults: memory leak, network congestion, CPU hog and disk failure. The feasibility of the fault recovery control algorithm is also verified. Simulation results show that our approach enabled effective automatic recovery from faults. Performance evaluation reveals that CPU and memory overhead of the fault management process is negligible. To let domain engineers conveniently apply the proposed fault management structure on their specific systems, a component-based modeling environment is developed. The meta-model of the fault management structure is developed with Unified Modeling Language as an abstract of a general fault recovery solution for computing systems. It defines the fundamental reusable components that comprise such a system, including the connections among them, attributes of each component and constraints. The meta-model can be interpreted into a userriendly graphic modeling environment for creating application models of practical domain specific systems and generating executable codes on them.

According to game theory, dominant strategy of Prisoner’s Dilemma game is defecting. Since online trading between two strangers falls in the realm of a Prisoner’s Dilemma, failure should be the only logical conclusion of such electronic commerce situation. Traders are less likely to cooperate since they are not required to deal with the same buyer again given the enormous population of online traders. Thus one could argue that markets like eBay should never exist. Then what is the reason behind resounding success of such electronic markets? The answer lies in the reputation system that they established. Google page rankings and reader reviews on various sites like Amazon.com and Epinions.com are some other examples of the reputation system pointing towards the enormous influence that they wield in our everyday life. This research thus attempts to analyze importance of reputation system in an online trading using Agent Based Simulation. The effect of reputation system on the sustainability of mutual cooperation between online traders is analyzed by taking into account key factors like level of gullibility of online traders and the weight of influence given to their past behavior. Results indicate that reputation system is certainly capable of sustaining cooperation among online traders. However, weightage given to the past behavior of agents should be decided only after taking into account the level of gullibility of agents. Use of agent based modeling for studying dynamics of online trading is a relatively new concept. Sustaining cooperation among online traders is often perceived to be difficult task. The reputation system proposed in this research is expected to make contribution towards this goal.

We propose a new atmospheric model based on first-principles for the simulation of clouds. Our approach is able to simulate the realistic formation of various cloud types, such as cumulus, stratus, stratocumulus, their temporal evolution, and transitions between cloud types. Moreover, we are able to model strongly rotating thunderstorms known as supercells. Our method allows us to simulate cloud formations of up to about 20 km 20 km at interactive rates. For the intuitive exploration, we identified a light-weight parameter set to interactively explore cloud formations. We demonstrate that our model can be coupled with data from real-time weather services to simulate cloud formations in the now. Moreover, we present a novel approach for the simulation of wildfires. Our model is able to realistically capture the combustion process of trees, heat transfer with the environment and fire propagation between trees. We demonstrate that our approach is capable of realistically simulating the propagation of fire through entire ecosystems with varying vegetation occupancy. We integrated our atmospheric model which allows us to simulated clouds emerging from the evaporation of water from burning trees leading to complex so called flammagenitus patterns which are usually observed over wildfires. Our system runs at interactive rates which enables the exploration of wildfires in different environments.

Grasslands cover 40% of the earth’s land area and provide numerous valuable ecosystem services. However, climate change, global land use change and increasing intensive anthropogenic interventions make grasslands to one of the most endangered ecosystem types in the world. Effective protection in the future requires a fundamental understanding of the dynamics of grasslands and their major drivers. Field experiments have been conducted for impact analyses, for example, with different management intensities, plant community composition and altered climatic conditions. Complementary, ecological models allow to extend the analysis to long-term effects of changes as well as to a deeper understanding of the underlying ecological processes. In this thesis, an individual-based grassland model and network science were applied to understand the community structure and dynamics emerging from individual plant interactions – in relation to plant traits, ecological processes, environmental and anthropogenic impacts, and the small-scale spatial distribution of plants. In the first study, an individual-based process-oriented grassland model was parameterized to simulate field data of a local biodiversity experiment using the concept of plant functional types. The influence of various functional plant traits and ecological processes on grassland productivity and functional composition were analyzed. Different functional plant traits showed partly contrasting effects on plant growth. With regard to the modeled ecological processes, competition for space between plants affected grassland productivity more than shading of plants. In the second study, the parameterized grassland model was used to analyze the impact of functional diversity, mowing frequency and air temperature on ecological processes that lead to changes in grassland productivity. The model reproduced the increase of biomass yields with functional diversity as observed in the field experiment. Modeled plant competition for space showed to be the dominant process and was responsible for an increase in biomass yields in more frequently mown grasslands. In the third study, an approach to generate a regionally transferable parameterization of the grassland model is presented. The impact of management, environment and climate change on productivity and functional composition of grasslands was analyzed within a German-wide scenario analysis. Management intensity had more influence on grassland productivity than environmental factors and correlations of productivity with environmental factors become stronger in less managed grasslands. Climate change showed to have only a minor influence on simulated vegetation attributes. In the fourth study, network science was applied to forest megaplots to quantify the spatial neighborhood structure of species-rich ecosystems. Networks at the individual-tree and tree-species levels revealed similar structures at three investigated forest sites. Tropical tree species coexisted in small-scale networks and only up to 51% of all possible connections between species pairs were realized. A null community analysis showed that details on the tree position and tree size have no major influence on the network structures identified. In summary, this thesis presents the development of advanced methods and analysis tools as well as their application to vegetation ecosystems with high diversity. Thereby, complex structures and dynamics of ecological systems could be systematically explored by combining ecological models with extensive field measurements.

Drawing upon the theories of complexity and complex adaptive systems and the Singerian Inquiring System from C. West Churchman's seminal work The Design of Inquiring Systems the dissertation herein develops a systems design theory for continuous auditing systems. The dissertation consists of discussion of the two foundational theories, development of the Theory of Complex Adaptive Inquiring Organizations (CAIO) and associated design principles for a continuous auditing system supporting a CAIO, and instantiation of the CAIO theory. The instantiation consists of an agent-based model depicting the marketplace for Frontier Airlines that generates an anticipated market share used as an integral component in a mock auditor going concern opinion for the airline. As a whole, the dissertation addresses the lack of an underlying system design theory and comprehensive view needed to build upon and advance the continuous assurance movement and addresses the question of how continuous auditing systems should be designed to produce knowledge--knowledge that benefits auditors, clients, and society as a whole.

京都大学 / 新制・課程博士 / 博士(地球環境学) / 甲第24954号 / 地環博第245号 / 新制||地環||49(附属図書館) / 京都大学大学院地球環境学舎環境マネジメント専攻 / (主査)教授 星野 敏, 准教授 鬼塚 健一郎, 教授 伊藤 孝行 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM