Evoplex: A platform for agent-based modeling on networks

Agent-based modeling and network science have been used extensively to advance our understanding of emergent collective behavior in systems that are composed of a large number of simple interacting individuals or agents. With the increasing availability of high computational power in affordable personal computers, dedicated efforts to develop multi-threaded, scalable and easy-to-use software for agent-based simulations are needed more than ever. Evoplex meets this need by providing a fast, robust and extensible platform for developing agent-based models and multi-agent systems on networks. Each agent is represented as a node and interacts with its neighbors, as defined by the network structure. Evoplex is ideal for modeling complex systems, for example in evolutionary game theory and computational social science. In Evoplex, the models are not coupled to the execution parameters or the visualization tools, and there is a user-friendly graphical interface which makes it easy for all users, ranging from newcomers to experienced, to create, analyze, replicate and reproduce the experiments.Comment: 6 pages, 5 figures; accepted for publication in SoftwareX [software available at https://evoplex.org

TOWARDS BUILDING AN INTELLIGENT INTEGRATED MULTI-MODE TIME DIARY SURVEY FRAMEWORK

Enabling true responses is an important characteristic in surveys; where the responses are free from bias and satisficing. In this thesis, we examine the current state of surveys, briefly touching upon questionnaire surveys, and then on time diary surveys (TDS). TDS are open-ended conversational surveys of a free-form nature with both, the interviewer and the respondent, playing a part in its progress and successful completion. With limited research available on how intelligent and assistive components can affect TDS respondents, we explore ways in which intelligent systems such as Computer Adaptive Testing, Intelligent Tutoring Systems, Recommender Systems, and Decision Support Systems can be leveraged for use in TDS. The motivation for this work is from realizing the opportunity that an enhanced web based instrument can offer the survey domain to unite the various facets of web based surveys to create an intelligent integrated multi-mode TDS framework. We envision the framework to provide all the advantages of web based surveys and interviewer assisted surveys. The two primary challenges are in determining what data is to be used by the system and how to interact with the user – specifically integrating the (1) Interviewer-assisted mode, and (2) Self-administered mode. Our proposed solution – the intelligent integrated multi-mode framework – is essentially the solution to a set of modeling problems and we propose two sets of overreaching mechanisms: (1) Knowledge Engineering Mechanisms (KEM), and (2) Interaction Mechanisms (IxM), where KEM serves the purpose of understanding what data can be created, used and stored while IxM deals with interacting with the user. We build and study a prototype instrument in the interviewer-assisted mode based on the framework. We are able to determine that the instrument improves the interview process as intended and increases the data quality of the response data and is able to assist the interviewer. We also observe that the framework’s mechanisms contribute towards reducing interviewers’ cognitive load, data entry times and interview time by predicting the next activity. Advisor: Leenkiat So

Middle-out domain-specific aspect languages and their application in agent-based modelling runtime inspection

Domain-Specific Aspect Languages (DSALs) are a valuable tool for separating cross-cutting concerns, particularly within fields with endemic cross-cutting practices. Agent-Based Modelling (ABM) runtime inspection, which cuts across the core concern of model development, serves as a prime example. Despite their usefulness, DSALs face multiple adoption issues: the literature regarding their development and use is incohesive, coupling to a weave target hinders re-use, and available tooling is immature compared to Domain-Specific Languages (DSLs). We believe these issues can be aided by furthering DSL middle-out techniques for DSALs.We first define the background of what a DSAL is and how they may be used, moving onto how we can use DSL techniques to further DSALs. We develop a middle-out semantic model approach for developing domain-level DSALs with transparent aspect orientation using adaptions of DSL techniques. We have implemented the approach for model-specific DSALs for the in-house framework Animaux, and as middleware-specific DSAL for agent messages in the JADE framework, which can be specialised to models using extension DSALs. We give illustrative result cases using our implementations to provide a base of the user development costs and performance of this approach.In conclusion, we believe the adoption of these technologies aids ABM applications and encourage future work in similar fields. This thesis has given a base philosophy toward DSLs, a novel approach for the development of middle-out DSALs and illustrative cases of this approach

Agent-Based Modelling of Public Space Activity in Real-Time

Understanding how urban space is used by its inhabitants is vital in improving the overall quality of a city's built environment, as it can highlight needs and requirements of everyday life to be addressed in any urban development. Our investigation of urban activity is often approached through spatial models and simulations on the one hand, and urban data on the other. The work presented here explores potential combinations of the two, by coupling urban models with real-time urban data feeds for continuous short-term forecasting of urban activity. This aim is approached through the development of a model of activity in urban public spaces using the agent-based modelling paradigm, calibrated to real-time data input, and applied to the simulation of current activity in public spaces at a fine spatio-temporal scale. Observations about human spatial behaviour are identified in the literature on public spaces and implemented within a 3D modelling framework, thereby extending existing pedestrian and crowd agent-based modelling approaches. Furthermore, a review and evaluation of real-time data feeds pertaining to activity in public spaces is performed, focussing on open and publicly available datasets, and a forecasting model is developed using social media and other datasets as a proxy for current user activity. The resulting real-time model of public space activity is then evaluated through two case studies focussing on two major urban parks in London, UK. The model performs well in capturing park visitor activity in terms of spatial dispersion. Real-time data feeds examined are found to be capable of capturing park visitor activity to some degree; however they are found to be inadequate in supporting a fully fledged, detailed real-time model of public space activity. Finally, potential future trajectories of the approaches are identified in the increasing availability of online 3D mapping data when combined with advances in computational efficiency and data availability, in extending current data visualisation approaches into expansive, fine-scale simulations of real-time urban activity