There have been broad advances in the fields of Artificial Intelligence and Machine Learning (AI/ML) in the past decade, especially in the areas of Deep Learning and Reinforcement Learning (RL) which allow us to more easily learn predictive models and control policies for large, complex systems than ever before. One subset of problems that remains very challenging are domains that contain some form of spatially spreading process (SSP) where some local features change over time across based on proximity in space.
Please join us for a joint seminar sponsored by Waterloo Institute for Complexity & Innovation [WICI] and the Department of Systems Design Engineering.
Professor Yacov Haimes presents: "Understanding, Modeling, and Managing Interdependent Complex Systems of Systems".
Proposals are invited from postdoctoral fellows and faculty at a
Canadian university for workshop funding on topics relating to complex
systems. The workshop will receive $8,000 in funding support from the
Waterloo Institute for Complexity and Innovation (WICI). WICI is an
Understanding covert and criminal behaviour from a social network perspective is gathering increasing currency. While the standard social network paradigm assumes that network data has been collected though eliciting ties from respondents in a predefined set of individuals, covert networks pose obviously challenges in several respects. Firstly, the individuals in the network might not be known a prior. Secondly, what constitutes a relevant set of individuals and ties might be ambiguous.
Join us on May 16 and 17, 2017 at Waterloo Institute for Complexity & Innovation’s (WICI’s) spring conference.
Hosted by the Waterloo Institute for Complexity and Innovation with support from the Field’s Institute for Mathematical Sciences and the Canadian Applied and Industrial Mathematics Society.
Deep learning has given rise to a major revolution in the field of artificial intelligence (AI). A major challenge with the democratization and proliferation of deep learning as commodity AI for all is the sheer complexity of current deep neural networks, making them ill-suited for operational use in a large number of scenarios.
Analysis of complex systems force us into a deeper understanding of heterogeneity. In economics and finance this heterogeneity often appears in the form of individual forecasts. This talk will demonstrate how a simulated agent-based financial market generates, and at times magnifies, the dispersion in individual forecasts.