A shuman beings, we are continuously struggling to comprehend the mechanism of dierent natural systems. Many times, we face a complex system where the emergent properties of the system at a global level can not be explained by a simple aggregation of the system's components at the micro-level. To better understand the macroscopic system eects, we try to model microscopic events and their interactions. In order to do so, we rely on specialized tools to connect local mechanisms with global phenomena. One such tool is network theory. Networks provide a powerful way of modeling and analyzing complex systems based on interacting elements. The interaction pattern links the elements of the system together and provides a structure that controls how information permeates throughout the system. For example, the passing of information about job opportunities in a society depends on how social ties are organized. The interaction pattern, therefore, often is essential for reconstructing and understanding the global-scale properties of the system. In this thesis, I describe tools and models of network theory that we use and develop to analyze the organization of social or transportation systems. More specifically, we explore complex networks by asking two general questions: First, which mechanistic theoretical models can better explain network formation or spreading processes on networks? And second, what are the signi cant functional units of real networks? For modeling, for example, we introduce a simple agent-based model that considers interacting agents in dynamic networks that in the quest for information generate groups. With the model, we found that the network and the agents' perception are interchangeable; the global network structure and the local information pathways are so entangled that one can be recovered from the other one. For investigating signi cant functional units of a system, we detect, model, and analyze signi cant communities of the network. Previously introduced methods of significance analysis suer from oversimpli ed sampling schemes. We have remedied their shortcomings by proposing two dierent approaches: rst by introducing link prediction and second by using more data when they are available. With link prediction, we can detect statistically signi cant communities in large sparse networks. We test this method on real networks, the sparse network of the European Court of Justice case law, for example, to detect signi cant and insigni cant areas of law. In the presence of large data, on the other hand, we can investigate how underlying assumptions of each method aect the results of the signi cance analysis. We used this approach to investigate dierent methods for detecting signi cant communities of time-evolving networks. We found that, when we highlight and summarize important structural changes in a network, the methods that maintain more dependencies in signi cance analysis can predict structural changes earlier. In summary, we have tried to model the systems with as simple rules as possible to better understand the global properties of the system. We always found that maintaing information about the network structure is essential for explaining important phenomena on the global scale. We conclude that the interaction pattern between interconnected units, the network, is crucial for understanding the global behavior of complex systems because it keeps the system integrated. And remember, everything is connected, albeit not always directly.

Today, except for mathematical operations, our brain functions much faster and more efficient than any supercomputer. It is precisely this form of information processing in neural networks that inspires researchers to create systems that mimic the brain’s information processing capabilities. In this thesis we propose a novel approach to implement these alternative computer architectures, based on delayed feedback. We show that one single nonlinear node with delayed feedback can replace a large network of nonlinear nodes. First we numerically investigate the architecture and performance of delayed feedback systems as information processing units. Then we elaborate on electronic and opto-electronic implementations of the concept. Next to evaluating their performance for standard benchmarks, we also study task independent properties of the system, extracting information on how to further improve the initial scheme. Finally, some simple modifications are suggested, yielding improvements in terms of speed or performance.

<p>The physics of complex systems has grown considerably as a field in recent decades, largely due to improved computational technology and increased availability of systems level data. One area in which physics is of growing relevance is molecular biology. A new field, systems biology, investigates features of biological systems as a whole, a strategy of particular importance for understanding emergent properties that result from a complex network of interactions. Due to the complicated nature of the systems under study, the physics of complex systems has a significant role to play in elucidating the collective behavior.</p><p>In this dissertation, we explore three problems in the physics of complex systems, motivated in part by systems biology. The first of these concerns the applicability of Boolean models as an approximation of continuous systems. Studies of gene regulatory networks have employed both continuous and Boolean models to analyze the system dynamics, and the two have been found produce similar results in the cases analyzed. We ask whether or not Boolean models can generically reproduce the qualitative attractor dynamics of networks of continuously valued elements. Using a combination of analytical techniques and numerical simulations, we find that continuous networks exhibit two effects -- an asymmetry between on and off states, and a decaying memory of events in each element's inputs -- that are absent from synchronously updated Boolean models. We show that in simple loops these effects produce exactly the attractors that one would predict with an analysis of the stability of Boolean attractors, but in slightly more complicated topologies, they can destabilize solutions that are stable in the Boolean approximation, and can stabilize new attractors.</p><p>Second, we investigate ensembles of large, random networks. Of particular interest is the transition between ordered and disordered dynamics, which is well characterized in Boolean systems. Networks at the transition point, called critical, exhibit many of the features of regulatory networks, and recent studies suggest that some specific regulatory networks are indeed near-critical. We ask whether certain statistical measures of the ensemble behavior of large continuous networks are reproduced by Boolean models. We find that, in spite of the lack of correspondence between attractors observed in smaller systems, the statistical characterization given by the continuous and Boolean models show close agreement, and the transition between order and disorder known in Boolean systems can occur in continuous systems as well. One effect that is not present in Boolean systems, the failure of information to propagate down chains of elements of arbitrary length, is present in a class of continuous networks. In these systems, a modified Boolean theory that takes into account the collective effect of propagation failure on chains throughout the network gives a good description of the observed behavior. We find that propagation failure pushes the system toward greater order, resulting in a partial or complete suppression of the disordered phase.</p><p>Finally, we explore a dynamical process of direct biological relevance: asymmetric cell division in <italic>A. thaliana</italic>. The long term goal is to develop a model for the process that accurately accounts for both wild type and mutant behavior. To contribute to this endeavor, we use confocal microscopy to image roots in a SHORTROOT inducible mutant. We compute correlation functions between the locations of asymmetrically divided cells, and we construct stochastic models based on a few simple assumptions that accurately predict the non-zero correlations. Our result shows that intracellular processes alone cannot be responsible for the observed divisions, and that an intercell signaling mechanism could account for the measured correlations.</p> / Dissertation

Complex engineering systems involve large numbers of functional elements. Each functional element can exhibit complex behavior itself. Ensuring the ability of such systems to meet the customer's needs and requirements requires modeling the behavior of these systems. Behavioral modeling allows a quantitative assessment of the ability of a system to meet specific requirements. However, modeling the behavior of complex systems is difficult due to the complexity of the elements involved and more importantly the complexity of these elements' interactions. In prior work, formal functional modeling techniques have been applied as a means of performing a qualitative decomposition of systems to ensure that needs and requirements are addressed by the functional elements of the system. Extending this functional decomposition to a quantitative representation of the behavior of a system represents a significant opportunity to improve the design process of complex systems. To this end, a functionality-based behavioral modeling framework is proposed along with a sensitivity analysis method to support the design process of complex systems. These design tools have been implemented in a computational framework and have been used to model the behavior of various engineering systems to demonstrate their maturity, application and effectiveness. The most significant result is a multi-fidelity model of a hybrid internal combustion-electric racecar powertrain that enabled a comprehensive quantitative study of longitudinal vehicle performance during various stages in the design process. This model was developed using the functionality-based framework and allowed a thorough exploration of the design space at various levels of fidelity. The functionality-based sensitivity analysis implemented along with the behavioral modeling approach provides measures similar to a variance-based approach with a computation burden of a local approach. The use of a functional decomposition in both the behavioral modeling and sensitivity analysis significantly contributes to the flexibility of the models and their application in current and future design efforts. This contribution was demonstrated in the application of the model to the 2009 Texas A&M Formula Hybrid powertrain design.

Economic events of this era reflect the fact that the value of information and technology has surpassed the value of physical production. This motivates countries to focus on increasing the education levels of citizens. However, policy making about education system and its returns requires dynamical analyses in order to be sustainable. The study aims to investigate the dynamic characteristics of a country-wide education system, in particular, that of Turkey. System Dynamics modeling, which is one of the most commonly referred tools for understanding the complex social structures, is used. Our model introduces dynamic relationships among different classes of labor forces with varying education levels, university admissions, research quality, and the investments made in education, research and other sectors. Model experimentation provides new insights into the investment and capacity-related aspects of the education system environment.

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<p>Seasonally dry ecosystems cover large areas over the world, have high potential for carbon sequestration, and harbor high levels of biodiversity. They are characterized by high rainfall variability at timescales ranging from the daily to the seasonal to the interannual, and water availability and timing play key roles in primary productivity, biogeochemical cycles, phenology of growth and reproduction, and agricultural production. In addition, a growing demand for food and other natural resources in these regions renders seasonally dry ecosystems increasingly vulnerable to human interventions. Compounded with changes in rainfall regimes due to climate change, there is a need to better understand the role of climate variabilities in these regions to pave the way for better management of existing infrastructure and investment into future adaptations. </p><p>In this dissertation, the ecohydrological responses of seasonally dry ecosystem to climate variabilities are investigated under a comprehensive framework. This is achieved by first developing diagnostic tools to quantify the degree of rainfall seasonality across different types of seasonal climates, including tropical dry, Mediterranean, and monsoon climates. This global measure of seasonality borrows from information theory and captures the essential contributions from both the magnitude and concentration of the rainy season. By decomposing the rainfall signal from seasonality hotspots, increase in the interannual variability of rainfall seasonality is found, accompanied by concurrent changes in the magnitude, timing, and durations of seasonal rainfall, suggesting that increase in the uncertainty of seasonal rainfall may well extend into the next century. Next, changes in the hydrological partitioning, and the temporal responses of vegetation resulting from these climate variabilities, are analyzed using a set of stochastic models that accounts for the unpredictability rainfall as well as its seasonal trajectories. Soil water storage is found to play a pivotal role in regulating seasonal soil water hysteresis, and the balance between seasonal soil water availability and growth duration is found to induce maximum plant growth for a given amount of annual rainfall. Finally, these methods are applied in the context of biodiversity and the interplay of irrigation and soil salinity, which are prevailing management issues in seasonally dry ecosystems.</p> / Dissertation

This Master Thesis hinges on the concept of Innovation and its association with regional development as a phenomenon that has attracted both researchers and policy makers’ attention.  The thesis presents two different innovation perspectives on regional development – Innovation Systems and Complex Systems of Innovation, and applies them into the case-study of the Life Sciences Cluster in Lund. In order to do so, the key aspects of each of the perspectives are highlighted within the part devoted to the Framework of this thesis. Within these, the networks between organizations in the Innovation Systems and the actors and their interrelations in the Complex Systems perspective have been analyzed. The analysis of these aspects brings up similar outcomes in both perspectives applied, i.e. the creation of various organizations within the Cluster. Both of the perspectives account for the importance of Lund University for the creation of these organizations and subsequent development of the Life Sciences cluster.

In this thesis, besides the developing a bilateral automated negotiation model between agents, in incomplete information state, integrating the personality effects of human on the negotiation process and outcomes, we proposed an architecture of such agents ("buyer" or "seller"). To do so, a new offer generation approach of three adaptive families of tactics has been proposed as follows : the time dependent tactics (time supposed as continuous), behavior dependent tactics, and time independent tactics.This thesis takes into consideration also the personality effects (of negotiator agent) on negotiation process and outcome. In fact, with regard to "Big five" personality model and introducing the cognitive orientations, we have developed a negotiator agent's architecture based on personality. This architecture is, mainly, inspired from the game theory. In fact, the artificial agent's cognition in terms of negotiation is considered as a certain negotiator's mental orientation favorising the concession of the negotiator agent towards one of following three equilibria (based on game theory) : Win-Lose, Lose-Win, and Win-Win According to the privileged orientation and the personality of negotiator, such a negotiator agent decides the adequate combination of tactics (models, etc) in order to modulate, consequently, the expected outcomes of negotiation