<div>Social influence networks are collections of entities dealing with a shared issue on which they have individual opinions. These opinions are dynamic, changing over time due to influence from other entities. Mechanisms within the network can affect how influence leads to opinion change, such as the strength and number of social ties between agents and the decision models used by an individual to process information from its neighbors. In real-world scenarios, these mechanisms are often hidden. Much effort in social network analysis involves proposing models and attempting to replicate target output data with them. Can we instead use the evolution of opinions in a network to infer these mechanisms directly?</div><div><br></div><div>This work explores how opinion change in social influence networks can be used to determine characteristics of those networks. Broadly, this is accomplished by simulating social influence networks using various designs and initial conditions to generate opinion data, and then identifying relationships between response variables and changes to the simulation inputs. Key inputs include the population size, the influence model that controls how agents change their opinions, the network structure, the activation regime that controls the sequencing of opinion updates, and probability distributions for communication errors. Analyzing the opinions of individual agents can provide insights about the individuals (microscopic), but in this work, focus is on insights into the social influence network as a complete system (macroscopic), so opinion data is aggregated according to each response variable.</div><div><br></div><div>Response variables are designed through the lens of complexity theory. Three types of complexity measurements are applied to opinion data: regression, entropy, and a new complexity measure. In each case, relationships between design factors and response variables are diverse. The influence model and the distribution of communication errors---a factor often omitted from the literature---are consistently impactful, with their various settings producing distinct profiles in time series plots of the measurements. Activation regime is impactful to some entropy measures. Network structure has little impact on the new complexity measure, and population size has little impact in general. Overall, distinctive relationships can exist between opinions and design factors. These relationships, as well as the measures and problem-solving approaches used in this work, may be helpful to analysts working to infer the properties of real-world social influence networks from the opinion data those systems generate.</div>

The computational Grid has become an important infrastructure as an execution environment for scientific applications that require large amount of computing resources. Applications which would otherwise be unmanageable or take a prohibitively longer execution time under previous computing paradigms can now be executed efficiently on the Grid within a reasonable time. Multi-agent based simulation (MABS) is a methodology used to study and understand the dynamics of real world phenomena in domains involving interaction and/or cooperative problem solving where the participants are characterized by entities having autonomous and social behaviour. For certain domains the size of the simulation is extremely large, intractable without employing adequate computing resources such as the Grid. Although the Grid has come with immense opportunities to resource demanding applications such as MABS, it has also brought with it a number of challenges related to performance. Performance problems may have their origins either on the side of the computing infrastructure or the application itself, or both. This thesis aims at improving the performance of MABS applications by overcoming problems inherent to the behaviour of MABS applications. It also studies the extent to which the MABS technologies have been exploited in the field of simulation and find ways to adapt existing technologies for the Grid. It investigates performance monitoring and prediction systems in the Grid environment and their implementation for MABS application with the purpose of identifying application related performance problems and their solutions. Our research shows that large-scale MABS applications have not been implemented despite the fact that many problem domains that cannot be studied properly with only partial simulation. We assume that this is due to the lack of appropriate tools such as MABS platforms for the Grid. Another important finding of this work is the improvement of application performance through the use of MABS specific middleware.

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

How can we model global phenomenon based on local interactions? Agent-Based (AB) models are local rule-based discrete method that can be used to simulate complex interactions of many agents. Unfortunately, the relative ease of implementing the computational model is often counter-balanced by the difficulty of performing rigorous analysis to determine emergent behaviors. Calculating existence of fixed points and their stability is not tractable from an analytical perspective and can become computationally expensive, involving potentially millions of simulations. To construct meaningful analysis, we need to create a framework to approximate the emergent, global behavior. Our research has been devoted to developing a framework for approximating AB models that move via random walks and undergo state transitions. First, we developed a general method to estimate the density of agents in each state for AB models whose state transitions are caused by neighborhood interactions between agents. Second, we extended previous random walk models of instantaneous state changes by adding a cumulative memory effect. In this way, our research seeks to answer how memory properties can also be incorporated into continuum models, especially when the memory properties effect state changes on the agents. The state transitions in this type of AB model is primarily from the agents’ interaction with their environment. These modeling frameworks will be generally applicable to many areas and can be easily extended.

In this master thesis, multiagent simulation was implemented with MATSim to study the change would take place on travel behaviors in Stockholm County when all residents travel in a sustainable way under a predefined emission limit. In this multiagent simulation, individual person was simulated as agent with attributes, daily travel plans and behaviors. The attributes contained home location, workplace locations, and some socioeconomic attributes, which were assigned according to the demographic data and travelling statistics data collected. Two trips, morning commuting from home to workplace and evening commuting from workplace to home, were simulated while the daily travel plans included travelling by car, public transit, bike and working at home. Each day, the person was set to select a travel plan based on socioeconomic attributes, his current greenhouse gas emission and a monthly emission limit. The selected plan was then executed and his emission was updated. In the model, a working population of 771614 people in Stockholm County was used and one month period with 21 working days was simulated. Totally four monthly emission limits were tested: 30kg, 37kg, 50kg, and infinity representing the current scenario. The research shows that multiagent simulation is effective in simulating individual travel behaviors. The results suggest that under current scenario car is the most frequently selected travel mode accounting for 32%, followed by public transit 31%. There are about 12% of people working at home and 25% travelling by bike. Nearly 1 percent fails to select a plan because of the plan selection setting. When emission limit is set, the percentage of people changing travel behaviors is 21.2%, 25.8% and 29.9% under the emission limit 50kg, 37kg and 30kg respectively. Most of them would abort from car and public transit to bike, public transit or even failing to keep their emission under the limit. The percentage of people changing plan to bike is 9.4%, 11.8%, 13.4% under the three limits 50kg, 37kg and 30kg respectively while the percentage of people changing plan to public transit or failure is 10.2%, 12.5% and 15.2%. The result also shows that when 37kg limit is set, the people having problems with keeping their emission under the limit are mainly distributed at three regions: Stockholm City, some cities in southwest and northeast of Stockholm County, where there would also be more demand for public transit service. The people changing plans to bike are mainly  distributed  in  Stockholm  City  area,  where  sustainable  travel  behavior  should  be promoted

The time to treatment plays a major factor in recovery for stroke patients, and simulation techniques can be valuable tools for testing healthcare policies and improving the situation for stroke patients. However, simulation requires individual-level data about stroke patients which cannot be acquired due to patient’s privacy rules. This thesis presents a hybrid simulation model for generating a synthetic population of stroke patients by combining Agent-based and microsimulation modeling. Subsequently, Agent-based simulation is used to estimate the locations where strokes happen. The simulation model is built by conducting the Design Science research method, where the simulation model is built by following a set of steps including data preparation, conceptual model formulation, implementation, and finally running the simulation model. The generated synthetic population size is based on the number of stroke events in a year from a Poisson Point Process and consists of stroke patients along with essential attributes such as age, stroke status, home location, and current location. The simulation output shows that nearly all patients had their stroke while being home, where the traveling factor is insignificant to the stroke locations based on the travel survey data used in this thesis and the assumption that all patients return home at midnight.

Cancer is a complex illness that affects millions of people every year. Amongst the most frequently encountered variants of this illness are solid tumours. The growth of solid tumours depends on a large number of factors such as oxygen concentration, cell reproduction, cell movement, cell death, and vascular environment. The aim of this thesis is to provide further insight in the interconnections between these factors by means of numerical simulations. We present a multiscale model for tumor growth by coupling a microscopic, agent-based model for normal and tumor cells with macroscopic mean-field models for oxygen and extracellular concentrations. We assume the cell movement to be dominated by Brownian motion. The temporal and spatial evolution of the oxygen concentration is governed by a reaction-diffusion equation that mimics a balance law.To complement this macroscopic oxygen evolution with microscopic information, we propose a lattice-free approach that extends the vascular distribution of oxygen. We employ a Markov chain to estimate the sprout probability of new vessels. The extension of the new vessels is modeled by enhancing the agent-based cell model with chemotactic sensitivity. Our results include finite-volume discretizations of the resulting partial differential equations and suitable approaches to approximate the stochastic differential equations governing the agent-based motion. We provide a simulation framework that evaluates the effect of the various parameters on, for instance, the spread of oxygen. We also show results of numerical experiments where we allow new vessels to sprout, i.e. we explore angiogenesis. In the case of a static vasculature, we simulate the full multiscale model using a coupled stochastic/deterministic discretization approach which is able to reduce variance at least for a chosen computable indicator, leading to improved efficiency and a potential increased reliability on models of this type.

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The lack of legitimate datasets on mobile money transactions toperform research on in the domain of fraud detection is a big prob-lem today in the scientic community. Part of the problem is theintrinsic private nature of mobile transactions, not much infor-mation can be exploited. This will leave the researchers with theburden of rst harnessing the dataset before performing the actualresearch on it. The dataset corresponds to the set of data in whichthe research is to be performed on. This thesis discusses a solutionto such a problem, namely the Paysim simulator. Paysim is a -nancial simulator that simulates mobile money transactions basedon an original dataset. We present a solution to ultimately yieldthe possibility to simulate mobile money transactions in such a waythat they become similar to the original dataset. The similarity orthe congruity will be measured by calculating the error-rate betweenthe synthetic data set and the original data set. With technologyframeworks such as "Agent Based" simulation techniques, and theapplication of mathematical statistics, it can be demonstrated thatthe synthetic data is as prudent as the original data set. The aimof this thesis is to demonstrate with statistical models that PaySimcan be used as a tool for the intents of nancial simulations.