Displaying 10 of 68 results for "John Nay" clear search
I am a scientist at the Johns Hopkins Applied Physics Laboratory. Previously, I worked for the Board of Governors of the Federal Reserve System as an internal consultant on statistical computing. I have also been a consultant to numerous government agencies, including the Securities and Exchange Commission, the Executive Office of the President, and the United States Department of Homeland Security. I am a passionate educator, teaching mathematics and statistics at the University of Maryland University College since 2010 and have taught public management at Central Michigan University, Penn State, and the University of Baltimore.
I am fortunate to play in everyone else’s backyard. My most recent published scholarship has modeled the population of Earth-orbiting satellites, analyzed the risks of flood insurance, predicted disruptive events, and sought to understand small business cybersecurity. I have written two books on my work and am currently co-editing two more.
In my spare time, I serve Howard County, Maryland, as a member of the Board of Appeals and the Watershed Stewards Academy Advisory Committee of the University of Maryland Extension. Prior volunteer experience includes providing economic advice to the Columbia Association, establishing an alumni association for the College Park Scholars Program at the University of Maryland, and serving on numerous public and private volunteer advisory boards.
Guido Fioretti, born 1964, graduated in Electronic Engineering in 1991 at La Sapienza University, Rome. In 1995, he received a PhD in Economics from this same university. Guido Fioretti is currently a lecturer of Organization Science at the University of Bologna.
I am interested in combining social with cognitive sciences in order to model decision-making facing uncertainty. I am particularly interested in connectionist models of individual and organizational decision-making.
I may make use of agent-based models, statistical network analysis, neural networks, evidence theory, cognitive maps as well as qualitative research, with no preference for any particular method. I dislike theoretical equilibrium models and empirical research based on testing obvious hypotheses.
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social-ecological modelling; cognitive modelling; agent-based modeling&simulation; data science; smart city modelling; artificial intelligence; large-scale simulation
I have a particular interest in the way in which social network structure influences dynamic processes operating over the netowrk, such as adoption of behaviour or spread of disease. More generally, I am interested in using complex systems methods to understand social phenomena.
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I’m a trained philosopher, but, besides conceptual problems, I care for conclusions based on systematic observations and I also care for the applicability of those conclusions. One might say that I wish I were a behavioral economist, or maybe an ethologist/behavioral ecologist.
I have been working in the software implementation of different kinds of complex networks inspired in real-life populations. My software may be classified on several categories: complex networks, Aedes aegypti development, dengue epidemics, cultural behavior of populations. I am also researching in education of Deaf people in Colombia.
The Ph.D. research project is mainly focused on the study of the influence of emotional intelligence inside decision-making processes and on the social and emotional aspects of organizations.Furthermore, the research has taken into account the generative science paradigm: in this way, the general aim is the development of social simulations able to account organizational processes related with emotions and with the emotional intelligence from the bottom-up.
In this paper, we explore the dynamic of stock prices over time by developing an agent-based market. The developed artificial market comprises of heterogeneous agents occupied with various behaviors and trading strategies. To be specific, the agents in the market may expose to overconfidence, conservatism or loss aversion biases. Additionally, they may employ fundamental, technical, adaptive (neural network) strategies or simply being arbitrary agents (zero intelligence agents). The market has property of direct interaction. The environment takes the form of network structure, namely, it takes the manifestation of scale-free network. The information will flow between the agents through the linkages that connect them. Furthermore, the tax imposed by the regulator is investigated. The model is subjected to goodness of fit to the empirical observations of the S\&P500. The fitting of the model is refined by calibrating the model parameters through heuristic approach, particularly, scatter search. Conclusively, the parameters are validated against normality, absence of correlations, volatility cluster and leverage effect using statistical tests.
Displaying 10 of 68 results for "John Nay" clear search