Displaying 10 of 563 results for "Ian M Hamilton" clear search
Social network analysis has an especially long tradition in the social science. In recent years, a dramatically increased visibility of SNA, however, is owed to statistical physicists. Among many, Barabasi-Albert model (BA model) has attracted particular attention because of its mathematical properties (i.e., obeying power-law distribution) and its appearance in a diverse range of social phenomena. BA model assumes that nodes with more links (i.e., “popular nodes”) are more likely to be connected when new nodes entered a system. However, significant deviations from BA model have been reported in many social networks. Although numerous variants of BA model are developed, they still share the key assumption that nodes with more links were more likely to be connected. I think this line of research is problematic since it assumes all nodes possess the same preference and overlooks the potential impacts of agent heterogeneity on network formation. When joining a real social network, people are not only driven by instrumental calculation of connecting with the popular, but also motivated by intrinsic affection of joining the like. The impact of this mixed preferential attachment is particularly consequential on formation of social networks. I propose an integrative agent-based model of heterogeneous attachment encompassing both instrumental calculation and intrinsic similarity. Particularly, it emphasizes the way in which agent heterogeneity affects social network formation. This integrative approach can strongly advance our understanding about the formation of various networks.
I am a Postdoctoral Associate in the Ecology, Evolution and Behavior department at the University of Minnesota. My research involves using agent-based models combined with lab and field research to test a broad range of hypotheses in biology. I am currently developing an agent-based model of animal cell systems to investigate the epigenetic mechanisms that influence cell behavior. For my PhD work, I created a model, B3GET, which simulates the evolution of virtual primates to better understand the relationships between growth and development, life history and reproductive strategies, mating strategies, foraging strategies, and how ecological factors drive these relationships. I have also conducted fieldwork to inform the modeled behavior of these virtual organisms. Here I am pictured with an adult male gelada in Ethiopia!
I specialize in creating agent-based models of biological systems for research and education in genetics, evolution, demography, ecology, and behavior.
Shibari is a form of interaction between people and besides an exotic spectacle, it is a series of strange but pleasant kinesthetic sensations. Intimate is not equally depraved, but means that during the shibari ropes process, the participants in the session show emotions that are not customary to experience in public: tears, laughter and groans of pleasure.
Mario Ureta holds a BSc in Economics from Birkbeck, University of London, a Graduate Diploma in Data Science from the London School of Economics, and an MSc in Data Science and Analytics from Brunel University London. He is currently a PhD student in Computing Science at Birkbeck, University of London. His research focuses on the economic study of individual preferences and decision-making, and on the use of agent-based models as a bridge between economic theory and computational experimentation. Through economic simulation, his work examines how heterogeneous preferences, social interaction, and firm behaviour jointly shape aggregate market outcomes, including non-linear dynamics and tipping points.
My research interests centre on the study of individual preferences in economics and on understanding how preferences evolve through interaction, learning, and social context. I am particularly interested in how seemingly weak or latent preferences—such as attitudes toward environmental attributes, prices, or social norms—can become amplified through feedback mechanisms and generate non-linear aggregate outcomes. A core methodological focus of my work is the use of agent-based modelling and economic simulation as a bridge between economic theory and experimentation. By treating agent-based models as computational laboratories, I explore how heterogeneous preferences, habit formation, peer influence, and firm behaviour interact dynamically, allowing theoretical mechanisms to be tested, stress-tested, and compared under controlled but flexible conditions that are difficult to achieve using purely analytical or empirical approaches.
Mazaher Kianpour is a PhD candidate at NTNU. He holds a Bachelor’s degree in Computer Engineering (Software) from the Payame Noor University. He obtained his Master’s degree in Architecture of Computer Systems from Shahid Beheshti University, Tehran, Iran. He started his PhD in Information Security at NTNU in May 2018. His PhD research lies at the intersection of economics and information security with a socio-technical perspective. He has several years of work experience at Tehran University of Medical Sciences, and his professional training includes Computer Networks, Cybersecurity and Risk Management.
My main research interest is modelling of information security, business operations and deterrents in complex ICT ecosystem. I will in particular focus on the complex interaction between various stakeholders and actors in the information security business domain. In order to model and better understand the information security ecosystem, I rely on agent-based simulation and quantitative modelling techniques such as stochastic modelling, discrete event simulations and game theory. Of particular interest is to gain increased understanding on how various security threats and measures influence business operations in the digital ecosystem.
I am a geographer interested in exploring tourism system dynamics and assessing tourism’s role in environmental sustainability using agent-based modelling (ABM). My current work focus is on human complex systems interactions with the environment and on the application of tools (such as scenario analysis, network analysis and ABM) to explore topics systems adaptation, vulnerability and resilience to global change. I am also interested in looking into my PhD future research directions which pointed the potential of Big Data, social media and Volunteer Geographical Information to increase destination awareness.
I have extensive experience in GIS, quantitative and qualitative methods of research. My master thesis assessed the potential for automatic feature extraction from QuickBird imagery for municipal management purposes. During my PhD I have published and submitted several scientific papers in ISI indexed journals. I have a good research network in Portugal and I integrate an international research network on the topic “ABM meets tourism”. I am a collaborator in a recently awarded USA NCRCRD grant project “Using Agent Based Modelling to Understand and Enhance Rural Tourism Industry Collaboration” and applied for NSF funding with the project “Understanding and Enhancing the Resilience of Recreation and Tourism Dependent Communities in the Gulf”.
I am interested in using agent based modelling and systematic data collection to understand diachronic human-environment interactions in the Maya region of Guatemala, Mexico, and Belize.
Displaying 10 of 563 results for "Ian M Hamilton" clear search