Orientador: Gustavo Maia Souza / Banca: Carlos Henrique Britto de Assis Prado / Banca: Gustavo Habermann / Resumo: A estabilidade é fundamental para todos os sistemas biológicos, possibilitando que lidem com a variabilidade ambiental. As propriedades que conferem estabilidade a sistemas biológicos ainda são desconhecidas, mas evidências apontam para a complexidade da dinâmica de certas variáveis fisiológicas e para a força de interação entre elementos de suas redes organizacionais subjacentes. Esta relação foi investigada no sistema de trocas gasosas de espécies florestais tropicais, pertencentes a grupos funcionais distintos: pioneiras e não-pioneiras. O modelo de recursos múltiplos atribui maior flexibilidade fisiológica às espécies pioneiras, mas os métodos geralmente empregados não são capazes de avaliar a estabilidade de um sistema adequadamente. Este estudo foi realizado em séries temporais de trocas gasosas, onde foi possível estimar parâmetros relacionados à estabilidade do sistema. A força de interação entre elementos foi avaliada pela conectância da rede (Cg) e a complexidade da dinâmica de assimilação de CO2 (A) e condutância estomática (gs) pelo algoritmo de entropia aproximada (ApEn). Os resultados revelaram que espécies com perfil fisiológico de pioneira, em condições constantes, apresentam maior Cg e ApEn de gs, possivelmente indicando maior estabilidade. Estas espécies tiveram maior aproveitamento de pulsos de luz ("lightflecks"), o que indicou a importância da modulação de rede (mudanças na conectância) na resposta às variações ambientais, e que maior Cg pode conferir maior capacidade de controle. Além da conectância, grau de acoplamento do sistema ao ambiente foi decisivo na resposta a "sunflecks". Os resultados sugerem que dinâmicas mais complexas estão ligadas a redes com maior conectância, que por sua vez conferem maior capacidade de controle ao sistema, sendo fundamental a capacidade de modulação da rede. / Abstract: Stability is a key feature to all biological systems, enabling them to deal with environmental variability. The properties that promote stability in biological systems are still unknown, but evidences point towards the complexity of the dynamics of certain physiological variables and to the strength of interaction among elements pertaining to its underlying organizational networks. This relationship was investigated in the gasexchange system of tropical forest species, belonging to distinct functional groups: pioneer and non-pioneer. The multiple resources model attributes higher physiological flexibility to pioneer species, but the methods usually employed are not capable of properly evaluating a system's stability. This study was carried out in gas-exchange time-series, enabling the calculation of parameters related to the system's stability. The strength of interaction among elements was evaluated by network connectance (Cg), and the complexity of CO2 assimilation (A) and stomatal conductance (gs) dynamics was evaluated by the algorithm of Approximate Entropy (ApEn). The results revealed that, under constant conditions, species with a pioneer-like physiological profile showed higher Cg and ApEn of gs, possibly indicating greater stability. These species showed higher lightfleck use efficiency, indicating the importance of network modulation (connectance changes) in response to environmental variability, and that higher Cg could provide greater control capability. Besides connectance, the strength of coupling between system and environment was decisive in response to sunflecks. The results suggest that more complex dynamics are linked to higher network connectance, which provide greater control capability to the system, network modulation being fundamental. Also, higher coupling of the system with its environment apparently promotes stability in contexts where environmental variability occurs within the system's control capability. / Mestre

This research was conducted to determine if students receiving complex systems instruction in the form of software simulations recognize patterns and underlying elements of complex systems more effectively than students receiving traditional instruction. Complex systems were investigated with an analytic (reductive) approach in a control group and with a synthesis approach in the treatment group. Exploration of this top-down approach to learning complex systems counters traditional bottom-up methodologies, investigating systems and subsystems at the component level. The hypothesis was that students experiencing complex systems scenarios in a computer-based learning environment would outperform their counterparts by constructing a greater number of explanations with emergent-like responses. A mixed method experimental, pretest posttest, control group triangulation design research study was designed for high school students enrolled in an Introduction to Technology and Engineering course. A pretest consisting of one open-ended near transfer problem and one far transfer problem was administered, investigating the generation of reductive (clockwork) and complex (emergent-like) mental models. A stratified sampling procedure was used to assign students to control or treatment groups. Following treatment, an analysis of covariance failed to reveal statistically significant evidence supporting the hypothesis. However, qualitative data in the form of student transcriptions, daily lab reports, and data entry worksheets revealed evidence of emergent-like response and behaviors.

Engineering complex systems and New Product Development (NPD) are major challenges for contemporary engineering design and must be studied at three levels of: Products, Processes and Organizations (PPO). The science of complexity indicates that complex systems share a common characteristic: they are robust yet fragile. Complex and large scale systems are robust in the face of many uncertainties and variations; however, they can collapse, when facing certain conditions. This is so since complex systems embody many subtle, intricate and nonlinear interactions. If formal modelling exercises with available computational approaches are not able to assist designers to arrive at accurate predictions, then how can we immunize our large scale and complex systems against sudden catastrophic collapse? This thesis is an investigation into complex product design. We tackle the issue first by introducing a template and/or design methodology for complex product design. This template is an integrated product design scheme which embodies and combines elements of both design theory and organization theory; in particular distributed (spatial and temporal) problem solving and adaptive team formation are brought together. This design methodology harnesses emergence and innovation through the incorporation of massive amount of numerical simulations which determines the problem structure as well as the solution space characteristics. Within the context of this design methodology three design methods based on measures of complexity are presented. Complexity measures generally reflect holistic structural characteristics of systems. At the levels of PPO, correspondingly, the Immunity Index (global modal robustness) as an objective function for solutions, the real complexity of decompositions, and the cognitive complexity of a design system are introduced These three measures are helpful in immunizing the complex PPO from chaos and catastrophic failure. In the end, a conceptual decision support system (DSS) for complex NPD based on the presented design template and the complexity measures is introduced. This support system (IMMUNE) is represented by a Multi Agent Blackboard System, and has the dual characteristic of the distributed problem solving environments and yet reflecting the centralized viewpoint to process monitoring. In other words IMMUNE advocates autonomous problem solving (design) agents that is the necessary attribute of innovative design organizations and/or innovation networks; and at the same time it promotes coherence in the design system that is usually seen in centralized systems.

<p>A complex system is a system for which the statement "the whole is greater than the sum of its parts" holds. A network can be viewed as a backbone of a complex system. Combining the knowledge about the entities constituting the complex system with the properties of the interaction patterns we can get a better understanding of why the whole is greater than the sum. One of the purposes of network studies, is to relate the particular structural and dynamical properties of the network to the function it is designed to perform. In the present work I am briefly presenting some of the advances that have been achieved in the field of the complex networks together with the contributions which I have been involved in.</p>

Phase Space Reconstruction is a method that allows to reconstruct the phase space of a system using only an one dimensional time series as input. It can be used for calculating Lyapunov-exponents and detecting chaos. It helps to understand complex dynamics and their behavior. And it can reproduce datasets which were not measured. There are many different methods which produce correct reconstructions such as time-delay, Hilbert-transformation, derivation and integration. The most used one is time-delay but all methods have special properties which are useful in different situations. Hence, every reconstruction method has some situations where it is the best choice. Looking at all these different methods the questions are: Why can all these different looking methods be used for the same purpose? Is there any connection between all these functions? The answer is found in the frequency domain : Performing a Fourier transformation all these methods getting a similar shape: Every presented reconstruction method can be described as a multiplication in the frequency domain with a frequency-depending reconstruction function. This structure is also known as a filter. From this point of view every reconstructed dimension can be seen as a filtered version of the measured time series. It contains the original data but applies just a new focus: Some parts are amplified and other parts are reduced. Furthermore I show, that not every function can be used for reconstruction. In the thesis three characteristics are identified, which are mandatory for the reconstruction function. Under consideration of these restrictions one gets a whole bunch of new reconstruction functions. So it is possible to reduce noise within the reconstruction process itself or to use some advantages of already known reconstructions methods while suppressing unwanted characteristics of it. / Attraktorrekonstruktion („Phase Space Reconstruction“) ist eine Technik, die es ermöglicht, aus einer einzelnen Zeitreihe den vollständigen Phasenraum eines Systems zu rekonstruieren und somit Rückschlüsse auf topologische Eigenschaften dieses dynamischen Systems zu ziehen. Sie findet Verwendung in der Bestimmung von Lyapunov-Exponenten und zur Reproduktion von unbeobachteten Systemgrößen. Es gibt viele verschiedene Methoden zur Attraktorrekonstruktion wie z.B. die Time-Delay-Methode or Rekonstruktion durch Ableitung, Integration oder mithilfe einer Hilbert-Transformation. Zumeist wird der Time-Delay-Ansatz verwendet, es gibt jedoch auch diverse Problemstellungen, in welchen die alternativen Methoden bessere Ergebnisse liefern. Die Kernfragen, die beim Vergleich dieser Methoden entsteht, sind: Wie kommt es, dass alle Ansätze, trotz ihrer teilweise sehr unterschiedlichen Struktur, denselben Zweck erfüllen? Gibt es Übereinstimmungen zwischen all diesen Methoden? Die Antwort lässt sich im Frequenzraum finden: Nach einer Fourier-Transformation besitzen alle genannten Methoden plötzlich eine sehr ähnliche Struktur. Jede Methode transformiert sich im Frequenzraum zu einer einfachen Multiplikation des Eingangssignals mit einer frequenzabhängigen Rekonstruktionsfunktion. Diese Struktur ist in der Datenanalyse auch bekannt als Filter. Aus dieser Perspektive lässt sich jede Rekonstruktionsdimension als gefilterte Zeitreihe der ursprünglichen Zeitreihe interpretieren: Sie enthält den Originaldatensatz, allerdings mit einem verschobenen Fokus: Einige Eigenschaften der Originalzeitreihe werden unterdrückt, während andere Teile verstärkt wiedergegeben werden. Des weiteren zeige ich in der Diplomarbeit, dass nicht jede beliebige Funktion im Frequenzraum zur Rekonstruktion verwendet werden kann. Ich stelle drei Eigenschaften vor, welche jede Rekonstruktionsfunktion erfüllen muss. Unter Beachtung dieser Bedingungen ergeben sich nun diverse Möglichkeiten für neue Rekonstruktionsfunktionen. So ist es z.B. möglich gleichzeitig mit der Rekonstruktion das Ursprungssignal auch zu filtern, oder man kann bereits bestehende Rekonstruktionsfunktionen so abwandeln, dass unerwünschte Nebeneffekte der Rekonstruktion abgemildert oder gar ganz unterdrückt werden.

In the present work synchronization phenomena in complex dynamical systems exhibiting multiple time scales have been analyzed. Multiple time scales can be active in different manners. Three different systems have been analyzed with different methods from data analysis. The first system studied is a large heterogenous network of bursting neurons, that is a system with two predominant time scales, the fast firing of action potentials (spikes) and the burst of repetitive spikes followed by a quiescent phase. This system has been integrated numerically and analyzed with methods based on recurrence in phase space. An interesting result are the different transitions to synchrony found in the two distinct time scales. Moreover, an anomalous synchronization effect can be observed in the fast time scale, i.e. there is range of the coupling strength where desynchronization occurs. The second system analyzed, numerically as well as experimentally, is a pair of coupled CO₂ lasers in a chaotic bursting regime. This system is interesting due to its similarity with epidemic models. We explain the bursts by different time scales generated from unstable periodic orbits embedded in the chaotic attractor and perform a synchronization analysis of these different orbits utilizing the continuous wavelet transform. We find a diverse route to synchrony of these different observed time scales. The last system studied is a small network motif of limit cycle oscillators. Precisely, we have studied a hub motif, which serves as elementary building block for scale-free networks, a type of network found in many real world applications. These hubs are of special importance for communication and information transfer in complex networks. Here, a detailed study on the mechanism of synchronization in oscillatory networks with a broad frequency distribution has been carried out. In particular, we find a remote synchronization of nodes in the network which are not directly coupled. We also explain the responsible mechanism and its limitations and constraints. Further we derive an analytic expression for it and show that information transmission in pure phase oscillators, such as the Kuramoto type, is limited. In addition to the numerical and analytic analysis an experiment consisting of electrical circuits has been designed. The obtained results confirm the former findings. / In der vorliegenden Arbeit wurden Synchronisationsphänomene in komplexen Systemen mit mehreren Zeitskalen untersucht. Es gibt mehrere Möglichkeiten wie diese verschiedenen Zeitskalen vorkommen können. Drei verschiedene Systeme, jedes mit einer anderen Art von zeitlicher Multiskalität, wurden mit unterschiedlichen Methoden der Datenanalyse untersucht. Das erste untersuchte System ist ein ausgedehntes heterogenes Netzwerk von Neuronen mit zwei dominanten Zeitskalen, zum einen die schnelle Folge von Aktionspotenzialen und zum anderen einer abwechselnden Folge von einer Phase von Aktionspotenzialen und einer Ruhephase. Dieses System wurde numerisch integriert und mit Methoden der Phasenraumrekurrenz untersucht. Ein interessantes Ergebnis ist der unterschiedliche Übergang zur Synchronisation der Neuronen auf den beiden verschiedenen Zeitskalen. Des weiteren kann auf der schnellen Zeitskala eine anomale Synchronisation beobachtet werden, d.h. es gibt einen Bereich der Kopplungsstärke in dem es zu einer Desynchronisation kommt. Als zweites wurde, sowohl numerisch als auch experimentell, ein System von gekoppelten CO₂ Lasern untersucht, welche in einem chaotischen bursting Modus arbeiten. Dieses System ist auch durch seine Äquivalenz zu Epidemiemodellen interessant. Wir erklären die Bursts durch unterschiedliche Zeitskalen, welche durch in den chaotischen Attraktor eingebettete instabile periodische Orbits generiert werden. Wir führen eine Synchronisationsanalyse mit Hilfe der kontinuierlichen Wavelettransformation durch und finden einen unterschiedlichen Übergang zur Synchronisation auf den unterschiedlichen Zeitskalen. Das dritte analysierte System ist ein Netzwerkmotiv von Grenzzyklusoszillatoren. Genauer handelt es sich um ein Nabenmotiv, welches einen elementaren Grundbaustein von skalenfreien Netzwerken darstellt, das sind Netzwerke die eine bedeutende Rolle in vielen realen Anwendungen spielen. Diese Naben sind von besonderer Bedeutung für die Kommunikation und den Informationstransport in komplexen Netzwerken. Hierbei wurde eine detaillierte Untersuchung des Synchronisationsmechanismus in oszillatorischen Netzwerken mit einer breiten Frequenzverteilung durchgeführt. Insbesondere beobachten wir eine Fernsynchronisation von Netzwerkknoten, die nur indirekt über andere Oszillatoren miteinander gekoppelt sind. Wir erklären den zu Grunde liegenden Mechanismus und zeigen dessen Grenzen und Bedingungen auf. Des weiteren leiten wir einen analytischen Ausdruck für den Mechanismus her und zeigen, dass eine Informationsübertragung in reinen Phasenoszillatoren, wie beispielsweise vom Kuramototyp, eingeschränkt ist. Diese Ergebnisse konnten wir durch Experimente mit elektrischen Schaltkreisen bestätigen.

A complex system is a system for which the statement "the whole is greater than the sum of its parts" holds. A network can be viewed as a backbone of a complex system. Combining the knowledge about the entities constituting the complex system with the properties of the interaction patterns we can get a better understanding of why the whole is greater than the sum. One of the purposes of network studies, is to relate the particular structural and dynamical properties of the network to the function it is designed to perform. In the present work I am briefly presenting some of the advances that have been achieved in the field of the complex networks together with the contributions which I have been involved in.

Situationsmedvetenhet (Eng. Situational Awareness), (SA), handlar om att ha koll på läget och vara medveten om vad som händer. Redan då ett komplext system utvecklas får vi en möjlighet att påverka vilken SA en framtida användare av systemet kan komma att få. Det gäller att ta tillvara på detta tillfälle! Ibland uppträder skillnader i SA, beroende på en rad olika orsaker. Denna avhandling handlar om SA och hur man kan använda de skillnaderna vid utveckling av komplexa system. Detta är relevant vid utveckling av en rad olika typer av komplexa system, även om de flesta exempel i denna avhandling kommer från flygdomänen. Avhandlingen innehåller beskrivningar hämtade från litteratur inom området och förslag på utveckling av SA-teori utifrån fokus på just skillnader. Skillnaden mellan vad du behöver vara medveten om och vad du verkligen är medveten om föreslås ge en indikation om individens SA. Vidare föreslås skillnaden mellan vad du är medveten om och vad du tror dig vara medveten om också ge en indikation om individens SA. SA kan skattas för en grupp av människor som arbetar tillsammans, genom variationerna i hur samstämmiga deras uppfattningar är. Termen situationshantering (Eng. Situation Management), (SM), föreslås med en vidare mening än SA, inkluderande SA, men också varje del av perceptionscykeln, hantering av mentala resurser och hantering av situationen genom extern påverkan. SM är en väl lämpad term vid utveckling av komplexa system då fokus här är på situationen och hur den kan hanteras, snarare än fokus på vad en individ eller en grupp uppfattar. Att skatta skillnader i SA och att kunna särskilja olika typer av skillnader är viktiga förutsättningar för att kunna hantera skillnader i SA vid utveckling av komplexa system på ett bra sätt. I avhandlingen gås flera sätt att skatta sådana skillnader igenom och speciellt tas för- och nackdelar med ögonrörelsemätning upp. Med referens till litteraturen och till de bilagda artiklarna beskrivs skillnader i SA beroende på a) designalternativ, b) roller i processen från utveckling till användning c) kontext och d) analysnivå. Skillnaderna i SA föreslås ses som både kvantitativa (dvs. hög eller låg SA) och kvalitativa (tex. olika aspekter av en situation). Ansatser såsom SM, realtidsvärdering, mätning och analys av SA på flera nivåer samtidigt samt simulatorbaserad design föreslås för att hantera skillnader i SA vid utveckling av komplexa system. / What’s up, Doc? Situational awareness (SA) is about being aware of what is going on. Already when a complex system is developed there is an opportunity to help a future user of the system to form a better SA. Let us make the best out of this opportunity! When assessing SA, differences in SA will sometimes appear. This dissertation is about SA, and how to manage differences in SA in development of complex systems. This topic is highly valid for development of a variety of complex systems, although most examples in this dissertation are from the aviation domain. Framed by state of the art literature, suggestions are made on theoretical improvements of SA theory, with a focus on differences. The difference between what you are required to be aware of and what you are aware of is suggested as a SA-indicator. Also, the difference between what you are aware of and what you think you are aware of is suggested as another SA-indicator. Further, differences within a team such as variations in degree of agreement could be used for team SA assessment. Also, the term situation management (SM) is suggested, with a proposed wider meaning than SA, including SA and every part of the perception action cycle, the management of mental resources, and external means of managing the situation. SM is a suitable term when developing complex systems due to the focus on the situation and how that could be managed, instead of only focusing on what is perceived by an individual or team. Assessing differences in SA and to differentiate between various types of differences are recognised as important prerequisites to effectively manage differences in SA in development of complex systems. Several assessment techniques are reviewed and especially advantages and disadvantages of the use of eye movements for SA assessment are described. With reference to the literature as well as to the appended papers differences in SA due to a) design alternatives, b) roles in the design-use process, c) context, and d) level of analysis, are described. Differences in SA are suggested to be regarded as both quantitative (i.e. high or low SA) and qualitative (e.g. various aspects of a situation are regarded). Approaches such as, SM, on-line evaluation of SA, simulator based design, as well as measuring and analysing SA on multiple levels simultaneously, are suggested as means to manage differences in SA in the development of complex systems.

The whole in a complex system is the sum of its parts, plus the interactions between the parts. Understanding social, biological, and economic systems therefore often depends on understanding their patterns of interactions---their networks. In this thesis, the approach is to understand complex systems by making simple network models with nodes and links. It is first of all an attempt to investigate how the communication over the network affects the network structure and, vice versa, how the network structure affects the conditions for communication. To explore the local mechanism behind network organization, we used simplified social systems and modeled the response to communication. Low communication levels resulted in random networks, whereas higher communication levels led to structured networks with most nodes having very few links and a few nodes having very many links. We also explored various models where nodes merge into bigger units, to reduce communication costs, and showed that these merging models give rise to the same kind of structured networks. In addition to this modeling of communication networks, we developed new ways to measure and characterize real-world networks. For example, we found that they in general favor communication on short distance, two-three steps away in the network, within what we call the information horizon. / Helheten i ett komplext system är mer än summan av dess delar, då den även inbegriper interaktionerna mellan dem. Att studera sociala, biologiska och ekonomiska system blir därför ofta en fråga om att förstå deras interaktionsmönster, d.v.s. deras nätverk av noder och länkar. Med utgångspunkt i enkla nätverksmodeller undersöker avhandlingen i huvudsak hur kommunikation i nätverk påverkar nätverksstrukturen och, vice versa, hur nätverksstrukturen påverkar villkoren för kommunikation. Vi utforskade mekanismerna bakom hur nätverk är organiserade genom att modellera effekten av kommunikation i förenklade sociala system. En låg kommunikationsnivå visade sig ge upphov till kaotiska nätverk där ingen nod i princip hade fler länkar än någon annan. En hög kommunikationsnivå resulterade däremot i strukturerade nätverk, med några få centrala noder med många länkar, medan flertalet noder var perifera med enbart några få länkar. Det visade sig också att alla aktörer i nätverket gynnades av kommunikation, även när den var ojämnt fördelad. Kvaliteten på kommunikationen, d.v.s. informationens giltighet, var också avgörande för vilka positioner som gynnades i ett nätverk, vilket vi visade genom att studera aktörer som spred falsk information. Eftersom effektiv kommunikation är en viktig del i många nätverk betraktar vi utvecklingen av dem som en optimeringsprocess. Varje kommunikationshandling mellan noderna tar tid och genom att slå sig samman till större enheter begränsas dessa kostnader och gör nätverket effektivare. Dessa s.k. sammanslagningsmodeller gav upphov till samma typ av strukturerade nätverk som ovan. Genom att utveckla olika sätt att mäta nätverksstrukturer visade vi bland annat att många verkliga system främjar kommunikation över korta avstånd, två-tre steg bort i nätverket, innanför det vi kallar informationshorisonten. Vi uppskattade också den mängd information som krävs för att orientera sig i städer, och fann att det är lättare att hitta i moderna, planerade städer än i äldre städer som utvecklats under lång tid.

Complex systems are neither perfectly regular nor completely random. They consist of a multitude of players who, in many cases, playtogether in a way that makes their combined strength greater than the sum of their individual achievements. It is often very effective to represent these systems as networks where the actual connections between the players take on a crucial role.Networks exist all around us and are an important part of our world, from the protein machinery inside our cells to social interactions and man-madecommunication systems. Many of these systems have developed over a long period of time and are constantly undergoing changes driven by complicated microscopic events. These events are often too complicated for us to accurately resolve, making the world seem random and unpredictable. There are however ways of using this unpredictability in our favor by replacing the true events by much simpler stochastic rules giving effectively the same outcome. This allows us to capture the macroscopic behavior of the system, to extract important information about the dynamics of the system and learn about the reason for what we observe. Statistical mechanics gives the tools to deal with such large systems driven by underlying random processes under various external constraints, much like how intracellular networks are driven by random mutations under the constraint of natural selection.This similarity makes it interesting to combine the two and to apply some of the tools provided by statistical mechanics on biological systems.In this thesis, several null models are presented, with this view point in mind, to capture and explain different types of structural properties of real biological networks. The most recent major transition in evolution is the development of language, both spoken and written. This thesis also brings up the subject of quantitative linguistics from the eyes of a physicist, here called linguaphysics. Also in this case the data is analyzed with an assumption of an underlying randomness. It is shown that some statistical properties of books, previously thought to be universal, turn out to exhibit author specific size dependencies. A meta book theory is put forward which explains this dependency by describing the writing of a text as pulling a section out of a huge, individual, abstract mother book. / Komplexa system är varken perfekt ordnade eller helt slumpmässiga. De består av en mängd aktörer, som i många fall agerar tillsammans på ett sådant sätt att deras kombinerade styrka är större än deras individuella prestationer. Det är ofta effektivt att representera dessa system som nätverk där de faktiska kopplingarna mellan aktörerna spelar en avgörande roll. Nätverk finns överallt omkring oss och är en viktig del av vår värld , från proteinmaskineriet inne i våra celler till sociala samspel och människotillverkade kommunikationssystem.Många av dessa system har utvecklats under lång tid och genomgår hela tiden förändringar som drivs på av komplicerade småskaliga händelser.Dessa händelser är ofta för komplicerade för oss att noggrant kunna analysera, vilket får vår värld att verka slumpmässig och oförutsägbar. Det finns dock sätt att använda denna oförutsägbarhet till vår fördel genom att byta ut de verkliga händelserna mot mycket enklare regler baserade på sannolikheter, som ger effektivt sett samma utfall. Detta tillåter oss att fånga systemets övergripande uppförande, att utvinna viktig information om systemets dynamik och att få kunskap om anledningen till vad vi observerar. Statistisk mekanik hanterar stora system pådrivna av sådana underliggande slumpmässiga processer under olika restriktioner, på liknande sätt som nätverk inne i celler drivs av slumpmässiga mutationer under restriktionerna från naturligt urval. Denna likhet gör det intressant att kombinera de två och att applicera de verktyg som ges av statistisk mekanik på biologiska system. I denna avhandling presenteras flera nollmodeller som, baserat på detta synsätt, fångar och förklarar olika typer av strukturella egenskaper hos verkliga biologiska nätverk. Den senaste stora evolutionära övergången är utvecklandet av språk, både talat och skrivet. Denna avhandling tar också upp ämnet om kvantitativ linguistik genom en fysikers ögon, här kallat linguafysik. även i detta fall så analyseras data med ett antagande om en underliggande slumpmässighet. Det demonstreras att vissa statistiska egenskaper av böcker, som man tidigare trott vara universella, egentligen beror på bokens längd och på författaren. En metaboksteori ställs fram vilken förklarar detta beroende genom att beskriva författandet av en text som att rycka ut en sektion ur en stor, individuell, abstrakt moderbok.