Collective intelligence (CI) emerges from local coordination, collaboration and competition among the individuals within a social group. CI mainly results in a global intelligent behavior. One of the fundamental interactional channels within a CI system is energy flow. Each agent within an artificial or physical ecosystem must absorb energy in order to survive, evolve, breed, and reshape its local environment. In addition because the energy resources are limited in the environment, each agent has to compete with other agents to reach the required level of energy. Understanding the internal energy flow can potentially provide a deep insight into internal activities and external emergent behaviors of a given complex system. This study proposes a stochastic scheme for modeling a multi-species prey-predator artificial ecosystem with two levels of food chain. This will enable us to investigate the influence of energy flow on the ecosystem’s lifetime. The proposed model consists of a stationary hosting environment with dynamic weather condition and fruit trees. The inhabitants of this ecosystem are herbivore and carnivore birds each consisting of species. In our model, the collective behavior emerges in terms of flocking with more added rules consist of breeding, competing, resting, hunting, escaping, seeking and foraging behaviors. Using multi-species scheme, we define the ecosystem as a combination of prey and predator species with inter-competition among species within same level of food chain and intra-competition among those belonging to different levels of food chain. Furthermore, in order to model the energy within the ecosystem, some energy variables as functions of behaviors are incorporated in to the model. Finally, a simulation and visualization structure for implementing the proposed model is developed in this study. The experimental results of 11,000 simulations analyzed by Cox univariate analysis and hazard function suggest that only five out of eight behaviors can statistically significant influence the ecosystem’s lifetime. Furthermore, the results of survival analysis show that out of all possible interactions among energy factors, only two of them, interaction between flocking and seeking energies, and interaction between flocking and hunting energies, have statistically significant impact on the system’s lifetime. In addition, software implementation of the proposed framework validates the stability of simulation and visualization architecture. At last regression results using Nelson-Aalen cumulative hazard function and Cox-Snell variable and scaled Schoenfeld residuals test strongly validate our experimental results. To the best of our knowledge, there are three contributions in this research: First, the high level of complexity in the structure of the proposed model in comparison with the other systems which mostly contains only one species of prey, one species of predator and a kind of resource. While this study introduces two species of prey, capability of competition among species, dynamic weather condition with two element of wind and rain and dynamic resources, various behavioral rules such as escaping, breeding, hunting, resting, etc. Energy flow analysis within an artificial ecosystem is the second contribution. To the best of author’s knowledge there is no similar comprehensive model in the previous literature that investigates the life span of a stochastic multi-species predator-prey artificial ecosystem based on energy flow using Survival Analysis method. Lastly, the simulation results show that the flocking and seeking energy and flocking and hunting energy interactions are the most significant interactions which match with the Thompson iii et al. [ 65] observations in the real life. Their findings indicate that in the real life, birds use flocking behavior for better movement, more efficient food searching and social learning. Flocking motion also decrease predation risk as much as the flock size increases.

As envisaged in the Sustainable Development Goals, eradicating poverty by 2030 is among the most important steps to achieve a better and more sustainable future. A key contribution to reach this target is to ensure that vulnerable households are effectively protected against weather-related extreme events and other economic, social and ecological shocks and disasters. Insurance products specifically designed for the needs of low-income households in developing countries are seen as an effective instrument to encompass also the poor with an affordable risk-coping mechanism and are thus highly promoted and supported by governments in recent years. However, apart from direct positive effects, the introduction of formal insurance may have unintended side effects. In particular, it might affect traditional risk-sharing arrangements where income losses are covered by an exchange of money, labour and in-kind goods between neighbours, relatives or friends. A weakening of informal safety nets may increase social inequality if poor households cannot afford formal insurance. In order to design insurance products in a sustainable way, sound understanding of their interplay with risk-sharing networks is urgently needed. Socio-environmental modelling is a suitable approach to address the complexity of this challenge. In the first part of this thesis, an agent-based model is developed to investigate the effects of formal insurance and informal risk-sharing on the resilience of smallholders. To lay the conceptual foundation for this approach, a literature review is presented which provides an overview of how to couple agent-based modelling with social network analysis. In two subsequent modelling studies, it is analysed (i) how the introduction of insurance influences the overall welfare in a population and (ii) what determines the resilience of the poorest to shocks when income is heterogeneously distributed and not all households can afford formal insurance. The simulation results underline the importance of designing insurance policies in close alignment with established risk-coping arrangements to ensure sustainability while striving to eradicate poverty. It is shown that introducing formal insurance can have negative side effects when insured households have fewer resources to share with their uninsured peers after paying the insurance premium or when they reduce their solidarity. However, especially when many households are simultaneously affected by a shock, e.g. by droughts or floods, formal insurance is a valuable addition to informal risk-sharing. By applying a regression analysis to simulation results for an empirical network from the Philippines, it is furthermore inferred that network characteristics must be considered in addition to individual household properties to identify the most vulnerable households that neither have access to formal insurance nor are adequately protected through informal risk-sharing. In the second part of this thesis, a broader perspective is taken on the use of models in socio-environmental systems. First, it is envisioned how models in combination with empirical studies could improve insurance design under climate change. Second, requirements for making socio-environmental modelling more useful to support policy and management and scientific results more influential on policy-making are synthesised. Overall, this thesis offers new insights into the interplay of formal and informal risk-coping instruments that complement existing empirical research and underlines the potential of socio-environmental modelling to address sustainability and development challenges.

The stock market is a place in which numerous entities interact, operate, andchange state based on the decisions they make. Further, the stock market itselfevolves and changes its dynamics over time as a consequence of the individualactions taking place in it. In this sense, the stock market can be viewed andtreated as a complex adaptive system. In this study, an agent-based model,simulating the trading of a single asset has been constructed with the purposeof investigating how the collective behaviour affects the dynamics of the stockmarket. For this purpose, the agent-based modelling program NetLogo wasused. Lastly, the conclusion of the study revealed that the dynamics of thestock market are clearly dependent on some specific factors of the collectivebehaviour, such as the information source of the investors.

The history of Easter Island, with its cultural and ecological mysteries, has attracted the interests of archaeologists, anthropologists, ecologists, and economists alike. Despite the great scientific efforts, uncertainties in the available archaeological and palynological data leave a number of critical issues unsolved and open to debate. The maximum size reached by the human population before the arrival of Europeans and the temporal dynamics of deforestation are some of the aspects still fraught with controversies. By providing a quantitative workbench for testing hypotheses and scenarios, mathematical models are a valuable complement to the observational-based approaches generally used to reconstruct the history of the island. Previous modelling studies, however, have shown a number of shortcomings in the case of Easter Island, especially when they take no account of the stochastic nature of population growth in a temporally and spatially varying environment. Here, I present a new stochastic, Agent-Based Model characterised by (1) realistic physical geography of the island and other environmental constraints (2) individual agent decision-making processes, (3) non-ergodicity of agent behaviour and environment, and (4) randomised agent-environment interactions. I use the model and the best available data to determine plausible spatial and temporal patterns of deforestation and other socioecological features of Easter Island prior to the European contact. I further identify some non-trivial connections between microscopic decisions or constraints (like local confinement of agents' actions or their adaptation strategy to environmental degradation) and macroscopic behaviour of the system that can not easily be neglected in a discussion about the history of Easter Island before European contact. / Påsköns historia har, med dess kulturella och ekologiska mysterier, väckt intressen hos arkeologer, antropologer, ekologer och ekonomer. Trots de stora vetenskapliga ansträngningarna lämnar osäkerheten i de tillgängliga arkeologiska och palynologiska data ett antal kritiska frågor olösta och öppna för debatt. Den maximala storleken som den mänskliga befolkningen nådde före européernas ankomst, och avskogningens tidsmässiga dynamik, är några av de aspekter som fortfarande är fyllda med kontroverser. Genom att tillhandahålla en kvantitativ arbetsbänk för att testa hypoteser och scenarier är matematiska modeller ett värdefullt komplement till de observationsbaserade metoder som vanligtvis används för att rekonstruera öns historia. Tidigare modelleringsstudier har emellertid visat ett antal brister i fallet med Påskön, särskilt när de inte tar hänsyn till den stokastiska karaktären av befolkningsökningen i en tillfällig och rumsligt varierande miljö. Här presenters en ny stokastisk, agentbaserad modell som kännetecknas av (1) realistisk fysisk geografi av ön och andra miljömässiga begränsningar, (2) individuella beslutsprocesser av agenter, (3) icke-ergodicitet av agentens beteende och miljö och (4) randomiserade agent-miljöinteraktioner. Modellen används tillsammans med de bästa tillgängliga data för att bestämma rimliga rumsliga och temporära mönster av avskogning och andra socioekologiska egenskaper på Påskön före européers ankoms. Vidare identifieras några icke-triviala förbindelser mellan mikroskopiska beslut eller begränsningar (till exempel lokal inneslutning av agentens handlingar eller deras anpassningsstrategi till miljöförstöring) och makroskopiskt beteende hos systemet som inte lätt kan försummas i en diskussion om påsköns historia före europeisk kontakt.

This paper is based on Amouzad Mahdiraji’s research about an agent-based ambulance dispatchment simulator. A simulator which is currently not focused on visual user interfaces and usability. In this study, we implemented design science to structure our research for creating a user-friendly Graphical User Interface (GUI) prototype for an ambulance dispatchment system. We used theories concerning usability and human cognition to support the development of the prototype which was later evaluated through interviews. Cognitive overload or confusion concerning a GUI can cause stress and problems which results in various errors and costs, affecting businesses and users in multiple ways. Our study resulted in a second version of the prototype in which interview analysis and theories, mainly the Gestalt principles and heuristics, were applied. Design decisions for the second version were made to improve the usability and minimize risks of confusion. Usability in a graphical user interface can be achieved in different approaches, this study shows one of them. However, to create a suitable interface for an ambulance dispatchment system, more research is required.

Ecosystem disturbances are a significant and ongoing threat to wildlife, caused by both natural environmental changes and human impacts. These disturbances can have a range of impacts, but one of the most crucial is on the wildlife habitat. In tropical forests, one such disturbance that is occurring at an alarming rate is peat fires. Peatfires impact the forest structure and fragmentation, which in turn directly relate to the wildlife habitat, ultimately threatening the population and even risking extinction for certain species. Of particular concern is the population of orangutans in Indonesia, which is at risk due to the impact of peat fires. This research used an agent-based modelling approach to explore the impact of ecosystem disturbances on wildlife habitat. The focus was on the orangutan population in tropical forests affected by peat fires. A systematic review of agent-based models revealed a shift towards a more mechanistic representation of entities in wildlife response to disturbances. However, fire disturbances and primate species such as orangutans still have a limited number of models. To address this gap, two agent-based models are presented: PeatFire, a model of the ignition and spread of tropical peatfire, validated using data from a fire pattern in South Sumatra; and the BORNEO model, which simulates the movement behaviour of orangutans in a disturbed forest using real tree inventory data and orangutan tracking data from the Sebangau forest in Central Kalimantan. The models were calibrated and validated using state-of-the-art methods and high-performance computing. The study demonstrates the ability of ABM to tackle complex research problems in various fields, including wildlife response to disturbances. The models developed in this study are important examples of the shift towards a more mechanistic representation of agents in ABM, and contribute to advancing the field in this direction. The research offers insights into the impact of ecosystem disturbances on wildlife habitat and highlights the potential of ABM in addressing these issues.

Yes / The subject area of the Europe's Lost Frontiers project, the submerged landscape of Doggerland, is inaccessible and the data by which we can understand it is complex and hard for the non-specialist to understand. In order to be able to present the project at public events, an Augmented Reality sandbox was constructed, which records the shape of sand in a box, interprets it as a landscape inhabited by humans, animals and plants, and projects this simulated land back on to the sand. Different software packages can be used to highlight the effects of climate change or provide examples of the different types of evidence available to archaeologists researching submerged landscapes. The end result is an interactive, accessible display which attracts all ages and can be used as a starting point to conversation regarding the project's archaeological, scientific and technological aspects.

Climate policy has been mainly studied with economic models that assume representative, rational agents. However, it aims at changing behavior associated with carbon-intensive goods that are often subject to bounded rationality and social preferences, such as status and imitation. Here we use a macroeconomic multi-agent model with such features to test the effect of various policies on both environmental and economic performance. The model is particularly suitable to address distributional impacts of climate policies, not only because populations of many agents are included, but also as these are composed of different classes of households driven by specific motivations. We simulate various policy scenarios, combining in different ways a carbon tax, a reduction of labor taxes, subsidies for green innovation, a price subsidy to consumers for less carbon-intensive products, and green government procurement. The results show pronounced differences with those obtained by rational-agent model studies. It turns out that demand-oriented subsidies lead to lower unemployment and higher output, but perform less well in terms of carbon emissions. The supply-oriented subsidy for green innovation results in a significant reduction of carbon emissions with a slight reduction of unemployment. / Series: WWWforEurope

Von Schmetterlingen und Bienen bis Kolibris und Fledermäusen hat sich eine große Vielfalt von Tieren auf Blumennektar als Nahrung spezialisiert. Die Nektareigenschaften der vielen Pflanzenarten scheinen den Bedarf des Hauptbestäubers widerzuspiegeln, z.B. produzieren die von größeren Tieren bestäubten Pflanzen in der Regel auch größere Mengen an Nektar. Diese Übereinstimmung deutet darauf hin, dass Nektarmerkmale in Erwiderung auf die Auswahlkriterien der Bestäuber evolviert sind. Die evolutionäre und ökologische Interaktion zwischen Pflanze und ihrem Bestäuber hängt in entscheidender Weise von dessen Fähigkeit ab Unterschiede bei den Pflanzenmerkmalen wahrzunehmen, und von den Mechanismen der Entscheidungsfindung. In der vorliegenden Arbeit steht die Ökologie kognitiver Funktionen im Vordergrund, um die Rolle der Informationsverarbeitung bei Bestäubern für die Evolution von Blütennektarmerkmalen zu untersuchen. In den ersten drei Kapiteln konzentriere ich mich auf die Fähigkeiten verschiedener Bestäuber zwischen Zuckerlösungen mit unterschiedlichen Konzentrationen zu diskriminieren. Im vierten Kapitel werden individuelle Unterschiede auch auf der Ebene des Nahrungssuchverhaltens genauer analysiert und mit der Effizienz des Nahrungssuchverhaltens in Zusammenhang gebracht. Das fünfte und letzte Kapitel baut auf den gewonnenen Erkenntnissen zur Psychometrie der Nektarqualitätswahrnehmung auf und befasst sich mit der evolutionären Entstehung von Nektareigenschaften. Diese Studien zeigen, wie die Untersuchung kognitiver Mechanismen von Bestäubern die evolutionäre und ökologische Forschung an zoophilen Pflanzen voranbringen kann. Zusätzlich wird somit Folgendes aufgewiesen: Der Methodenansatz der virtuellen Bestäubungsökologie kann aussagekräftige Erklärungen liefern für die evolutionäre Entstehung sowie Aufrechterhaltung von Pflanzenmerkmalen, die einer durch Kognition vermittelten und von Bestäubern ausgeübten Selektion unterliegen. / A diverse array of animals has specialized in consuming floral nectar – from butterflies and bees to hummingbirds and bats. The nectar characteristics of plant species often appear to reflect the needs of their dominant pollinator, for example plants pollinated by larger animals tend to produce larger amounts of nectar. This correspondence suggests that nectar traits have evolved in response to the choice behavior of pollinators. The evolutionary and ecological interaction between plants and their pollinators crucially depends on the pollinators’ ability to perceive differences in floral nectar traits and on their decision-making mechanisms. In the presented studies I adopt a cognitive ecology approach in order to investigate the role of information-processing in pollinators on the evolution of floral nectar traits. In the first three chapters I focus on the abilities of different pollinators to discriminate among sugar solutions with different concentrations. In Chapter 4 I present a detailed analysis of individual differences in the foraging context and discuss how they might relate to foraging efficiency. In the fifth and final chapter I use the findings on the psychophysics of nectar quality evaluation to address the question of the evolutionary origins of floral nectar traits. With these studies I show how the investigation of cognitive mechanisms of pollinators can inform evolutionary and ecological research on plants pollinated by animals. In addition, I demonstrate how the virtual pollination ecology methodology can explain the evolutionary origin and maintenance of plant traits that are subjected to cognition-mediated selection exerted by pollinators.

La consommation énergétique dans le secteur bâtiment dépend de diverses facteurs parmi lesquels ses caractéristiques physique, ses équipements, l’environnement extérieur, etc… mais il ne faut pas oublier le comportement des habitants qui est déterminant pour la consommation énergétique globale. Or, la plupart des travaux et outils représentent les occupants par des profils d’occupation. Cette thèse s’intéresse à la représentation plus détaillée du comportement des occupants, en particulier les mécanismes cognitifs, réactifs et délibératifs. Le comportement dynamique des occupants est modélisé et co-simulé avec les aspects physiques et des éventuels systèmes de gestion énergétique. L’analyse de la consommation de différents équipements électroménagers met en évidence que le consommation énergétique est très dépendante des comportements des occupants. L’analyse des consommations et des actions des habitants permet d’élaborer un modèle du comportement des occupants impactant la consommation énergétique. Le modèle représente des mécanismes cognitifs, qui représente les causes qui motivent les actions, incluant des échange avec d’autres acteurs humains. Une approche à base d’agents logiciels a été développée. Outre les aspects techniques, une méthodologie de réglage des paramètres des modèles de comportement est proposée. Ces outils sont utilisés pour réaliser une co-simulation représentant la physique du bâtiment, le comportement réactif, c’est-à-dire sensible aux données physiques, et délibératif des habitants mais aussi un système de gestion énergétique qui peut ajuster directement la configuration du logement ou simplement conseiller ces occupants. L’impact de différents types de comportements, avec et sans gestionnaire énergétique est analysé. Ces travaux ouvrent de nouvelles perspectives dans la simulation bâtiment, dans la validation de gestionnaires énergétiques mais aussi dans la représentation des bâtiments dans les réseaux d’énergie dits intelligents, dans lesquels des signaux peuvent être envoyés aux utilisateurs finaux pour les inviter à moduler leur consommation. / Energy consumption in buildings is affected by various factors including its physical characteristics, the appliances inside, and the outdoor environment, etc. However, inhabitants’ behaviour that determines the global energy consumption must not be forgotten. In most of the previous works and simulation tools, human behaviour is modelled as occupancy profiles. In this thesis the focus is more on detailed behaviour representation, particularly the cognitive, reactive, and deliberative mechanisms. The inhabitants’ dynamic behaviour is modelled and co-simulated together with the physical aspects of a building and an energy management system. The analysis of different household appliances has revealed that energy consumption patterns are highly associated with inhabitants’ behaviours. Data analysis of inhabitants’ actions and appliances’ consumptions is used to derive a model of inhabitants’ behaviour that impacts the energy consumption. This model represents the cognitive mechanisms that provide causes that motivate the actions, including the communication with other inhabitants. An approach based on multi-agent systems is developed along with a methodology for parameter tuning in the proposed behaviour model. These tools are used to co-simulate, not only the physical characteristics of the building, the reactive behaviour that is sensitive to physical data, and deliberative behaviour of the inhabitants, but also the building energy management system. The energy management system allows the direct adjustment of the building parameters or simply giving advice to the inhabitants. The impact of different types of inhabitants’ behaviours, with and without the inclusion of an energy management system is analyzed. This work opens new perspectives not only in the building simulation and in the validation of energy management systems but also in the representation of buildings in the smart grid where signals can be sent to end users advising them to modulate their consumption.