Supply chain management is a complex process requiring the coordination of numerous decisions in the attempt to balance often-conflicting objectives such as quality, cost, and on-time delivery. To meet these and other objectives, a focal company must develop organized systems for establishing and managing its supplier relationships. A reliable, decision-support tool is needed for selecting the best procurement strategy for each supplier, given knowledge of the existing sourcing environment. Supplier segmentation is a well-established and resource-efficient tool used to identify procurement strategies for groups of suppliers with similar characteristics. However, the existing methods of segmentation generally select strategies that optimize performance during normal operating conditions, and do not explicitly consider the effects of the chosen strategy on the supply chain’s ability to respond to disruption. As a supply chain expands in complexity and scale, its exposure to sources of major disruption like natural disasters, labor strikes, and changing government regulations also increases. With increased exposure to disruption, it becomes necessary for supply chains to build in resilience and robustness in the attempt to guard against these types of events. This work argues that the potential impacts of disruption should be considered during the establishment of day-to-day procurement strategy, and not solely in the development of posterior action plans. In this work, a case study of a laser printer supply chain is used as a context for studying the effects of different supplier segmentation methods. The system is examined using agent-based modeling and simulation with the objective of measuring disruption impact, given a set of initial conditions. Through insights gained in examination of the results, this work seeks to derive a set of improved rules for segmentation procedure whereby the best strategy for resilience and robustness for any supplier can be identified given a set of the observable supplier characteristics.

Ce projet utilise l’analyse de réseaux et la modélisation à base d’agents pour examiner des sujets classiquement traités mais qui peuvent maintenant être abordés, grâce aux riches données rencontrées dans le sud-ouest du Colorado et en France méridionale : comment les Gaules et les marchands méditerranéens établissaient leurs partenariats économiques, comment la violence a pu façonner le développement de niveaux divers de leadership, et comment les premiers agriculteurs interagissaient avec leur environnement. Pour écrire cette thèse composée de trois études de cas différents, deux dans le Sud-Ouest des États-Unis et un en France méridionale, nous utilisons des outils élaborés par les sciences de la complexité pour mieux aborder comment les individus de la préhistoire surmontaient les défis liés à l’acquisition de ressources. La modélisation à base d’agents et l’analyse de réseaux (sociaux et trophiques) nous permettront de décrire les processus décisionnels et d’analyser comment le partage de stratégies au sein du groupe peut entraîner une plus grande aptitude des individus à agir au sein du groupe. / This project utilizes network analysis and agent-based modeling to examine long-standing questions that can only now be asked with the rich data provided in southwestern Coloradoand southern France: how Gauls and colonists established economic partnerships, how violence may have shaped the development of multiple levels of leadership, and how earlyf armers interacted with their environments. Writing a dissertation composed of three distinct case studies, two from the U.S. Southwest and one from the south of France, I use tools developed in complexity science to better address how people in the past dealt with challenges related to resource acquisition. Agent-based modeling and network analysis (both social network analysis and trophic network analysis) will allow me to characterize human decision making processes and discuss how sharing of strategies within a group can lead to greater fitness of those in the in-group.

京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第22717号 / 経博第620号 / 新制||経||294(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 関口 倫紀, 教授 若林 直樹, 教授 椙山 泰生 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM

<div>With the rapid development of clean energy technologies, various bottlenecks on supplies of related critical materials emerged. Since supply chains of critical materials often involved with multiple layers of markets with different characteristics, to better identify bottlenecks and increase critical material availability, it is vital to have better understanding and projection on these markets.</div><div>Agent-based modeling is a bottom-up approach that can imitate heterogenous objects in a changing environment. Therefore, it is an excellent tool to simulate markets with fierce competition and fast revolution. This work demonstrates the application of agent-based modeling by discussing three different topics related to critical material demand and supply induced by clean energy products.</div><div>The first application focused on LED residential lighting market. LED lighting market grew rapidly and introduced potential demand on several critical materials including indium. The work modeled consumers as heterogenous and irrational agents in network purchasing new bulbs based</div>

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<p>Innovation is promoted as a means to address global environmental challenges and achieve resilience in the UN Sustainable Development Goals. Innovation allows for adaptation and transformation in socio-ecological systems as part of the adaptive cycle. Within resilience literature, there are myriad definitions of innovation and disagreement about how to motivate diffusion of innovation, making implementation and the sustainability of innovations difficult. Specifically, matching the correct innovation to a given challenge and motivating the adoption of the innovation remains a roadblock to using innovation to address global environmental change. Here we show that there are explicit conflicts among definitions of innovation, and that innovation in the field does not align with some of these definitions. We found that the diverse definitions of innovation show a more complex view of innovation than normative treatment in policy suggests. We also found that several interacting motivations affect long-term participation in certain innovation activities. We discovered that binary views of innovation as either incremental or radical are generally supported in examples of innovation in the field, although some of the most successful examples of innovation better aligned with a continuum view of innovation associated with the adaptive cycle. Our results add to the warm-glow hypothesis that for altruistic tasks, the degree of participation motivated by a warm-glow feeling which can be enhanced by other motivations. Contrary to crowding out theory, our results suggest that monetary incentives result in higher adoption in Malawi where cost of contributing is high. The findings demonstrate the complexity of innovation, the misalignment between policy and practice, and ways in which adoption might be optimized. This research is a starting point to inform discussion about pragmatic innovation typologies. Such a typology could help operationalize the SDGs by framing the innovation dialogue between policy and practice.</p>

Our model considers a new element in forest fire modeling, namely the dynamics of a forest animal, intimately linked to the trees. We show that animals and trees react differently to different types of fire. A high probability of fire initiation results in several small fires, which do not allow for a large fuel accumulation and thus the destruction of many trees by fire, but is found to be generally devastating to the animal population at the same time. On the other hand, a low fire initiation probability allows for the accumulation of higher quantities of fuel, which in turn results in larger fires, more devastating to the trees than to the animals. Thus, we suggest that optimal fire management should take into account the relation between fire initiation and its different effects on animals and trees. Further, wildfires are often considered as prime examples for power-law-like frequency distributions, yet there is no agreement on the mechanisms responsible for the observed patterns. Our model suggests that instead of a single unified distribution, a superposition of at least two different distributions can be detected and this suggests multiform mechanisms acting on different scales. None of the discovered distributions are compatible with the power-law hypothesis.

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.