Network reciprocity describes the emergence of cooperative behaviour where interactions are constrained by incomplete network connectivity. It has been widely studied as an enabling mechanism for the emergence of cooperation and may be of particular interest in explaining cooperative behaviours amongst unrelated individuals or in organisms of lower cognitive abilities. Research in this area has been galvanised by the finding that heterogeneous topology promotes cooperation. Consequently there has been a strong focus on scale-free networks; however, such networks typically presuppose formative mechanisms based on preferential attachment, a process which has no general explanation. This assumption may give rise to models of cooperation that implicitly encode capabilities only generally found in more complex forms of life, thus constraining their relevance with regards to the real world. By considering the connectivity of populations to be dynamic, rather than fixed, cooperation can exist at lower levels of heterogeneity. This thesis demonstrates that a model of network fluctuation, based on random rather than preferential growth, supports cooperative behaviour in simulated social networks of only moderate heterogeneity, thus overcoming difficulties associated with explanations based on scale-free networks. In addition to illustrating the emergence and persistence of cooperation in existing networks, we also demonstrate how cooperation may evolve in networks during their growth. In particular our model supports the emergence of cooperation in populations where it is originally absent. The combined impact of our findings increases the generality of reciprocity as an explanation for cooperation in networks.

Este trabalho possui um duplo objetivo: i) estudar os fatos estilizados do livro de ofertas dos papéis negociados na Bolsa de Valores de São Paulo (BOVESPA), assim como dos retornos engendrados pela dinâmica do livro de ofertas e ii) desenvolver um modelo de livro de ofertas baseado em agentes com o propósito de reproduzir tais fatos estilizados. Trabalhou-se com dados de junho/2006 a janeiro/2009 de uma amostra formada pelos vinte papéis mais negociados da BOVESPA. Os resultados empíricos corroboraram alguns fatos estilizados observados no estudo de papéis de outros países, mas refutaram outros. O modelo baseado em agentes conseguiu emular satisfatoriamente os fatos estilizados relacionados aos retornos, mas em se tratando da reprodução dos fatos estilizados do livro de ofertas o modelo foi menos eficaz. / This study has two aims: i) analyze the stylized facts of the order book of stocks traded in the São Paulo Stock Exchange (BOVESPA), as well as of the returns engendered by the order book dynamics and ii) develop an order book agent-based model able to reproduce such stylized facts. It was used data from June 2006 to January 2009 regarding a sample composed by the twenty most traded stocks in BOVESPA. The empirical results corroborated some stylized facts observed in stocks of other countries, but refuted others. The agent-based model successfully emulated the stylized facts concerning the returns; however, the model was less efficient in reproducing the stylized facts of the order book.

Este trabalho propõe um modelo de um mercado que pode ser simulado em diversas condições. Pode-se pensar neste modelo como um laboratório para simulação e estudo de diferentes configurações de mercado. O modelo foi desenvolvido sob a plataforma de software NetLogo para a execução das simulações. Apresenta-se também, de forma sintética um conceito de mercado, uma visão de economia, de marketing, de estratégia e do comportamento do consumidor. Forma analisadas diversas formas de modelagem e decidiu-se utilizar sistemas multiagentes, pois são flexíveis e permitem a simulação de diversos tipos de fenômenos. A primeira tentativa de desenvolver este modelo apresentou resultados distintos de um mercado real, pois revelou-se muito mecânico e processual. Isso nos levou a busca de modelos adaptativos inteligentes para o comportamento dos agentes, bem como, nos concentrar na dinâmica de interação entre os agentes / This work aims at developing a model to simulate a market under several conditions. This model can be viewed as a laboratory to simulate and study different markets configurations. The model simulation was developed using the NetLogo language. A concept of market, economy, marketing, strategy and consumer behavior are briefly presented. After analyzing several ways of implementing the model it was decided to use agent based simulation because of its flexibility and possibility of simulating a variety of behaviors. A first approach to develop the model resulted in a very mechanic and artificial market simulation. This result lead the simplification of the model and focus on adaptive behavior and the interaction among the agents

At the Rio Conference in 1992, the sustainable development agenda promised a new era for natural resource management, where the wellbeing of human society would be enhanced through a more sustainable use of natural resources. Several decades on, economic growth continues unabated at the expense of natural capital – as evidenced by natural resource depletion, biodiversity loss, climate change and further environmental issues. Why is this happening and what can be done about it? This research examines what socio-economic and governance factors affect sustainability in complex coupled social-ecological systems. Furthermore, it analyses the role of power relations and imbalances between economic and conservation forces with regard to sustainable development. The original contribution to knowledge of this thesis is based on one conceptual and two empirical (Agent-Based) models. These explore, through several case-studies, the potential of different future scenarios in fostering synergies and win-win contexts of ecosystem services and socio-economic indicators. Overall, the research showed the complex and interconnected relationship between the economy and natural systems, and between economic and conservation forces, in coupled social-ecological systems. Addressing complex sustainability issues requires the use of integrative, holistic and interdisciplinary approaches, in addition to considering the particular socio-economic, cultural, political and environmental contexts of the social-ecological system being analysed. The models demonstrated that the current economic system requires an ever-increasing use of natural resources, and that the economy does not protect the natural capital on which it depends. This is based on a disjunction of the economic and conservation elements upon which the sustainable development paradigm is founded. Furthermore, several socio-economic and governance factors appeared to be key for diminishing sustainability in coupled social-ecological systems; namely, the type of economic and production systems, the particular use of monetary debt, technological development, and weak conservation forces (both top-down and bottom-up). However, results also showed alternative scenarios where these same factors could be redirected to enhance social-ecological sustainability. This dual role supports the argument that the current economic system is not inherently (i.e. by definition, per se) unsustainable. Rather, the specific use of economic mechanisms and behaviour of economic entities, as well as their decisions and relationships with the environment, show a tendency to increase unsustainability. Hence, short- and medium-term sustainability can be enhanced by developing mechanisms that start shifting capitalist forces to support environmental conservation; here, the role of Payments for Ecosystem Services will be essential. Enhancing long-term sustainability, however, may require a further paradigm change – where economic and production systems integrate, and fully account for, externalities and the value of natural capital, thus human society is embedded within the wider, and more important, natural environmental system.

Sexually transmitted diseases affect millions of lives every year. In order to most effectively use prevention resources epidemiologists deploy models to understand how the disease spreads through the population and which intervention methods will be most effective at reducing disease perpetuation. Increasingly agent-based models are being used to simulate population heterogeneity and fine-grain sociological effects that are difficult to capture with traditional compartmental and statistical models. A key challenge is using a sufficiently large number of agents to produce robust and reliable results while also running in a reasonable amount of time. In this thesis we show the effectiveness of agent-based modeling in planning coordinated responses to a sexually transmitted disease epidemic and present efficient algorithms for running these models in parallel and in a distributed setting. The model is able to account for population heterogeneity like age preference, concurrent partnership, and coital dilution, and the implementation scales well to large population sizes to produce robust results in a reasonable amount of time. The work helps epidemiologists and public health officials plan a targeted and well-informed response to a variety of epidemic scenarios.

Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section. Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity. Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena. Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool.

Intensive human-environment interactions are taking place in Midwestern agricultural systems. An integrated modeling framework is suitable for predicting dynamics of key variables of the socio-economic, biophysical, hydrological processes as well as exploring the potential transitions of system states in response to changes of the driving factors. The purpose of this dissertation is to address issues concerning the interacting processes and consequent changes in land use, water balance, and water quality using an integrated modeling framework. This dissertation is composed of three studies in the same agricultural watershed, the Clear Creek watershed in East-Central Iowa. In the first study, a parsimonious hydrologic model, the Threshold-Exceedance-Lagrangian Model (TELM), is further developed into RS-TELM (Remote Sensing TELM) to integrate remote sensing vegetation data for estimating evapotranspiration. The goodness of fit of RS-TELM is comparable to a well-calibrated SWAT (Soil and Water Assessment Tool) and even slightly superior in capturing intra-seasonal variability of stream flow. The integration of RS LAI (Leaf Area Index) data improves the model's performance especially over the agriculture dominated landscapes. The input of rainfall datasets with spatially explicit information plays a critical role in increasing the model's goodness of fit. In the second study, an agent-based model is developed to simulate farmers' decisions on crop type and fertilizer application in response to commodity and biofuel crop prices. The comparison between simulated crop land percentage and crop rotations with satellite-based land cover data suggest that farmers may be underestimating the effects that continuous corn production has on yields (yield drag). The simulation results given alternative market scenarios based on a survey of agricultural land owners and operators in the Clear Creek Watershed show that, farmers see cellulosic biofuel feedstock production in the form of perennial grasses or corn stover as a more risky enterprise than their current crop production systems, likely because of market and production risks and lock in effects. As a result farmers do not follow a simple farm-profit maximization rule. In the third study, the consequent water quantity and quality change of the potential land use transitions given alternative biofuel crop market scenarios is explored in a case study in the Clear Creek watershed. A computer program is developed to implement the loose-coupling strategy to couple an agent-based land use model with SWAT. The simulation results show that watershed-scale water quantity (water yield and runoff) and quality variables (sediment and nutrient loads) decrease in values as switchgrass price increases. However, negligence of farmers risk aversions towards biofuel crop adoption would cause overestimation of the impacts of switchgrass price on water quantity and quality.

Les avancées scientifiques et technologiques en matière de stockage d'énergie ont permis le développement d'appareils mobiles énergétiquement autonomes, tels que les smartphones et les véhicules électriques (EVs). Dans ces dispositifs, l'énergie est habituellement stockée sous forme électrochimique, souvent dans des batteries au lithium.Par rapport aux batteries au plomb classiques, les piles au lithium ont une densité d'énergie élevée, un faible taux d'auto-décharge et sont plus respectueuses de l'environnement. Cependant, ces batteries doivent être couplées avec des systèmes électroniques de gestion des batteries (SGBs), chargés d'en assurer la performance etla sécurité. En effet, la performance du « pack » batterie peut être affectée par de multiples mécanismes liés, par exemple, au vieillissement, aux défauts ou aux conditions de fonctionnement et ayant pour impact une réduction importante de l'autonomie du système. Grâce à un contrôle approprié de la structure de la batterie, un SGB est capable de compenser certains de ces mécanismes. De même, d'un point de vue sécurité, un SGB peut aider à prévenir les incendies et d'autres risques en isolant les éléments défectueux du reste du pack.Le sujet de cette thèse porte sur le développement d'un SGB innovant et adaptatif capable de prendre en compte des attentes des utilisateurs en matière de performance et de sécurité. Le SGB proposé s'appuie sur un mécanisme décisionnel distribué basé sur le paradigme des systèmes multi-agents (SMA), dans lequel chaque cellule est considérée comme un agent. Le mécanisme décisionnel proposé repose sur une topologie de câblage dédiée associée à des stratégies de communication et de contrôle adaptées. L'approche proposée améliore l'adaptabilité, la résilience et les performances du système et permet la reconfiguration de la topologie du paquet pour isoler des cellules défectueuses et, le cas échéant, utiliser des cellules de rechange pour recréer une structure de paquets complète ou équilibrer l'énergie entre les cellules. Le SGB permet ainsi une meilleure tolérance aux pannes de l'ensemble, ainsi que l'augmentation de son endurance démontrant ainsi une performance plus élevée que celle obtenu par des SGB classiques.Afin d'évaluer la validité des travaux proposés, une plate-forme de co-simulation est développée afin de valider expérimentalement la solution proposée. Trois catégories des tests ont été réalisées pour valider la fonction d'équilibrage des cellules, la fonction de tolérance aux pannes, et l'intégration de ces deux fonctions dans un système unique. Les tests ont également été exécutés avec un pack batterie de grande taille afin d'évaluer l'évolutivité de l'approche. Les résultats des simulations montrent que la méthode proposée est opérationnelle et fonctionne comme prévu. Bien que les coûts attendus soient plus élevés que pour les méthodes traditionnelles, l'approche proposée pourrait être utilisée pour des applications spécifiques où une fiabilité et une performance élevées sont nécessaires, comme pour les applications militaires par exemple. / Progress in energy storage science and technology enables the development of mobile devices, such as smart-phones and electric vehicles (EVs). In these devices, energy is usually stored in electrochemical form, often in lithium-based batteries.Compared to classical lead-acid batteries, lithium batteries have a high-energy density, a low self-discharge rate and are environmental friendly. However, such batteries must be coupled with electronic Battery Management Systems (BMSs), aimed at ensuring the performance and the safety of the battery pack. The performance of the pack may be affected by multiple mechanisms, for example related to aging, faults, or operation conditions. Through appropriate control of the battery pack structure, a BMS is capable of compensating some of these mechanisms. Similarly, a BMS can help prevent fires and other risk hazards by isolating problematic portions of the pack.This thesis is concerned with the development of a novel and smart BMS, taking into account the concerns of users about performance and safety. The proposed BMS is made of distributed decision-making based on a multi-agent system, in which each cell is considered as an agent. A dedicated pack wiring topology is presented, together with the corresponding communication and control strategies. This approach improves the adaptability, resilience and performance of system, and enables the reconfiguration of the pack topology to either isolate cells and use spare cells to recreate a complete pack structure, or balance energy among cells. The BMS thus enables a better fault-tolerant operation of the pack, as well as increasing its endurance through a higher performance, compared to classical BMSs.In order to evaluate the validity of the proposed work, a co-simulation platform is developed to run multiple tests. Three categories of tests are used to validate the cell balancing function, the fault-tolerant control function, and the integration of both balancing and fault-tolerant functions in a single system. Tests are also run on a larger pack to evaluate the scalability of the approach. Simulation results show that the proposed method is operational and performs as expected. Although the expected costs are higher than those of traditional methods, the results of this work could benefit specific applications where high reliability and performance are required, such as military applications for instance.

Doctor of Philosophy (PhD) / The aim of this thesis is to understand what drives the evolution of industrial networks and how such understanding can be used to stimulate sustainable development. A complex adaptive systems perspective has been adopted to analyse the complex interaction between organisational behaviour and industrial network evolution. This analysis has formed the basis for the development of a modelling approach that allows for quantitative exploration of how different organisational perceptions about current and future uncertainty affect their behaviour and therefore the network evolution. This analysis results in a set of potential evolutionary pathways for an industrial network and their associated performance in terms of sustainable development. Subsequently, this modelling approach has been used to explore the consequences of interventions in the network evolution and to identify robust interventions for stimulating sustainable development of industrial networks. The analysis, modelling approach and development of interventions has been developed in the context of a bioenergy network in the region of KwaZulu-Natal in South Africa. Industrial networks are an important aspect of today’s life and provide many goods and services to households and individuals all over the world. They consist of a large number of autonomous organisations, where some organisations contribute by transforming or transacting natural resources, such as oil, agricultural products or water, while other organisations contribute to networks by providing information or setting regulation or subsidies (local or national governments) or by influencing decision making processes of other organisations in networks (advocacy groups). Throughout the process from natural resource to product or service, industrial networks have important economic, environmental and social impacts on the socio-economic and biophysical systems in which they operate. The sum of complex interactions between organisations affects the rate in which natural resources are used, environmental impacts associated with transformation and transaction of resources and social impacts on local communities, regions or countries as a whole. The aim of this thesis is to understand how industrial networks evolve and how they can be stimulated towards sustainable development. The first question that has been addressed in this thesis is how to understand the complex interaction between organisational behaviour and industrial network evolution. Organisational behaviour is affected by many functional and implicit characteristics within the environment in which the organisation operates, while simultaneously the environment is a function of non-linear relationships between individual organisational actions and their consequences for both the function and structure of the network. This thesis has identified four different characteristics of industrial networks that affect organisational behaviour: 1) Functional characteristics 2) Implicit behavioural characteristics 3) Implicit relational characteristics 4) Implicit network characteristics. Functional characteristics are those characteristics that are formally recognised by all organisations within an industrial network and which affect their position within the network. Examples of functional characteristics are the price and quantity of resources available, the location and distance of organisations within a network, infrastructure availability or regulation. Implicit characteristics, on the other hand, are those characteristics that impact the decision making process of organisations, but which are not formally part of the network. From an organisational perspective, implicit characteristics are the rules, heuristics, norms and values that an organisation uses to determine its objectives, position and potential actions. Implicit relational characteristics, most importantly trust and loyalty, affect an organisations choice between potential partners and implicit network characteristics are those social norms and values that emerge through social embeddedness. Collectively, these functional and implicit characteristics and their interactions determine the outcome of organisational decisions and therefore the direction of the industrial network evolution. The complex interaction between these large numbers of characteristics requires quantitative models to explore how different network characteristics and different interactions result in different network evolutions. This thesis has developed an agent-based simulation model to explore industrial network evolutions. To represent the multi-scale complexity of industrial networks, the model consists of four scales. Each scale represents different processes that connect the functional and implicit characteristics of an industrial network to each other. The two basic scales represent the strategic actions of the organisations on the one hand and the industrial network function and structure on the other. The third scale represents the processes that take place within the mental models of organisations describing how they make sense of their environment and inform their strategic decision making process. The fourth scale represents the social embeddedness of organisations and how social processes create and destroy social institutions. The model has been developed such that it allows for exploring how changes in different network characteristics or processes affect the evolution of the network as a whole. The second question that has been addressed in this thesis is how to evaluate sustainable development of different evolutionary pathways of industrial networks. First of all, a systems approach has been adopted to explore the consequences of an industrial network to the larger socio-economic and biophysical system in which the network operates. Subsequently, a set of structural indicators has been proposed to evaluate the dynamic performance of industrial networks. These four structural indicators reflect the efficiency, effectiveness, resilience and adaptiveness of industrial networks. Efficiency and effectiveness relate to the operational features by which industrial networks provides a particular contribution to society. Resilience and adaptiveness relate to the system’s capacity to maintain or adapt its contribution to society while under stress of temporary shocks or permanent shifts, respectively. Finally, different multi-criteria decision analysis (MCDA) tools have been applied to provide a holistic evaluation of sustainable development of industrial networks. The third important question that is addressed in this thesis is how to systematically explore the potential evolutionary pathways of an industrial network, which has led to the development of agent-based scenario analysis. Agent-based scenario analysis systematically explores how industrial network evolutions might evolve depending on the perceptions of organisations towards the inherent uncertainty associated with strategic decision making in networks. The agent-based scenario analysis consists of two steps. Firstly, analysts develop a set of coherent context scenarios, which represents their view on the context in which an industrial network will operate within the future. For a bioenergy network, for example, this step results in a set of scenarios that each represent a coherent future of the socio-economic system in which the network might evolve. The second step is the development of a set of ‘agent scenarios’. Each agent-based scenario is based on a different ‘mental model’ employed by organisations within the network about how to deal with the inherent ambiguity of the future. The organisational perspective towards uncertainty is of major importance for the evolution of industrial networks, because it determines the innovative behaviour of organisations, the structure of the network and the direction in which the network evolves. One the one hand, organisations can ignore future ambiguity and base their actions on the environment that they can observe in their present state. On the other extreme, organisations can adopt a view that the future is inherently uncertain and in which they view social norms and values more important than functional characteristics to make sense of their environment. The mental models are differentiated according to two dimensions: 1) different mental representation of the world and 2) different cognitive processes that can be employed to inform strategic actions. Along these dimensions, different processes can be employed to make sense of the environment and to inform decision making. The thesis has shown that by systematically exploring the different perceptions possible, an adequate understanding of the different evolutionary pathways can be gained to inform the evaluation and development of interventions to stimulate sustainable development. The final part of this thesis has applied the analysis and methodology developed throughout this thesis to a bioenergy network in the province of Kwazulu-Natal in South Africa. The bioenergy network consists of a set of existing sugar mills with large quantities of bagasse, a biomass waste product, available. Bagasse is currently burned inefficiently to produce steam for the sugar mills, but can potentially be used for the production of green electricity, biodiesel, bioethanol or gelfuel. All of these products have important consequences for the region in terms of associated reductions in CO2 emissions, electrification of and/or energy provision for rural households and local economic development of the region. This thesis has modelled strategic decisions of the sugar mills, the existing electricity generator, potential independent energy producers, local and national governments and how their actions and interactions can lead to different evolutionary pathways of the bioenergy network. The agent-based scenario analysis has been used to explore how different perceptions of organisations can lead to different network evolutions. Finally, the model has been used to explore the consequences of two categories of interventions on stimulating sustainable development. The conclusions are that both categories of interventions, financial interventions by national government and the introduction of multi-criteria decision analysis (MCDA) tools to aid strategic decision making, can have both positive and negative effects on the network evolutions, depending on what ‘mental models’ are employed by organisations. Furthermore, there is no single intervention that outperforms the others in terms of stimulating both functional and structural features of sustainable development. The final conclusion is that instead of focusing on individual or collective targets, emphasis should be placed on the development of interventions that focus on evolutionary aspects of industrial networks rather than functional performance criteria. This thesis has also highlighted interesting research questions for future investigation. The methodology developed in this thesis is applied to a single case study, but there are still many questions concerning how different industrial networks might benefit from different organisational perceptions towards uncertainty. Furthermore, the role between the mental models and sustainable development requires further investigation, especially in the light of globalisation and the interconnectiveness of industrial networks in different countries and continents. Finally, this methodology has provided a platform for investigating how new technologies might be developed that anticipate needs of future generations. This thesis has provided a first and important step in developing a methodology that addresses the complex issues associated with sustainable development, benefiting both academics and practitioners that aim to stimulate sustainable development.

The recent emergence of intelligent agent technology and advances in information gathering have been the important steps forward in efficiently managing and using the vast amount of information now available on the Web to make informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by end users. Good decisions cannot be made without sufficient, timely, and correct information. Traditionally it is said that knowledge is power, however, nowadays sufficient, timely, and correct information is power. So gathering relevant information to meet user information needs is the crucial step for making good decisions. The ideal goal of information gathering is to obtain only the information that users need (no more and no less). However, the volume of information available, diversity formats of information, uncertainties of information, and distributed locations of information (e.g. World Wide Web) hinder the process of gathering the right information to meet the user needs. Specifically, two fundamental issues in regard to efficiency of information gathering are mismatch and overload. The mismatch means some information that meets user needs has not been gathered (or missed out), whereas, the overload means some gathered information is not what users need. Traditional information retrieval has been developed well in the past twenty years. The introduction of the Web has changed people's perceptions of information retrieval. Usually, the task of information retrieval is considered to have the function of leading the user to those documents that are relevant to his/her information needs. The similar function in information retrieval is to filter out the irrelevant documents (or called information filtering). Research into traditional information retrieval has provided many retrieval models and techniques to represent documents and queries. Nowadays, information is becoming highly distributed, and increasingly difficult to gather. On the other hand, people have found a lot of uncertainties that are contained in the user information needs. These motivate the need for research in agent-based information gathering. Agent-based information systems arise at this moment. In these kinds of systems, intelligent agents will get commitments from their users and act on the users behalf to gather the required information. They can easily retrieve the relevant information from highly distributed uncertain environments because of their merits of intelligent, autonomy and distribution. The current research for agent-based information gathering systems is divided into single agent gathering systems, and multi-agent gathering systems. In both research areas, there are still open problems to be solved so that agent-based information gathering systems can retrieve the uncertain information more effectively from the highly distributed environments. The aim of this thesis is to research the theoretical framework for intelligent agents to gather information from the Web. This research integrates the areas of information retrieval and intelligent agents. The specific research areas in this thesis are the development of an information filtering model for single agent systems, and the development of a dynamic belief model for information fusion for multi-agent systems. The research results are also supported by the construction of real information gathering agents (e.g., Job Agent) for the Internet to help users to gather useful information stored in Web sites. In such a framework, information gathering agents have abilities to describe (or learn) the user information needs, and act like users to retrieve, filter, and/or fuse the information. A rough set based information filtering model is developed to address the problem of overload. The new approach allows users to describe their information needs on user concept spaces rather than on document spaces, and it views a user information need as a rough set over the document space. The rough set decision theory is used to classify new documents into three regions: positive region, boundary region, and negative region. Two experiments are presented to verify this model, and it shows that the rough set based model provides an efficient approach to the overload problem. In this research, a dynamic belief model for information fusion in multi-agent environments is also developed. This model has a polynomial time complexity, and it has been proven that the fusion results are belief (mass) functions. By using this model, a collection fusion algorithm for information gathering agents is presented. The difficult problem for this research is the case where collections may be used by more than one agent. This algorithm, however, uses the technique of cooperation between agents, and provides a solution for this difficult problem in distributed information retrieval systems. This thesis presents the solutions to the theoretical problems in agent-based information gathering systems, including information filtering models, agent belief modeling, and collection fusions. It also presents solutions to some of the technical problems in agent-based information systems, such as document classification, the architecture for agent-based information gathering systems, and the decision in multiple agent environments. Such kinds of information gathering agents will gather relevant information from highly distributed uncertain environments.