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Most individuals form their opinions about the quality of products, social trends and political issues via their interactions in social and economic networks. While the role of social networks as a conduit for information is as old as humanity, recent social and technological developments, such as Facebook, Blogs and Tweeter, have added further to the complexity of network interactions. Despite the ubiquity of social networks and their importance in communication, we know relatively little about how opinions form and information is transmitted in such networks. For example, does a large social network of individuals holding disperse information aggregate it efficiently? Can falsehoods, misinformation and rumors spread over networks? Do social networks, empowered by our modern communication means, support the wisdom of crowds or their ignorance? Systematic analysis of these questions necessitate a combination of tools and insights from game theory, the study of multiagent systems, and control theory. Game theory is central for studying both the selfish decisions and actions of individuals and the information that they reveal or communicate. Control theory is essential for a holistic study of networks and developing the tools for optimization over networks. In this talk, I report recent work on combining game theoretic and control theoretic approaches to the analysis of social learning over networks.
Over the past decade, game theorists have made substantial progress in identifying simple learning heuristics that lead to equilibrium behavior without making unrealistic demands on agents information or computational abilities, as is the case in the perfect rationality approach to game theory. Recent research shows that very complex, interactive systems can equilibrate even when agents have virtually no knowledge of the environment in which they are embedded. This talk will survey different approaches to the problem of learning in games, show the various senses in which learning rules converge to equilibrium, and sketch the theoretical limits to what is achievable.
The analysis of signals into constituent harmonics and the estimation of their power distribution are considered fundamental to systems engineering. Due to its significance in modeling and identification, spectral analysis is in fact a "hidden technology" in a wide range of application areas, and a variety of sensor technologies, ranging from radar to medical imaging, rely critically upon efficient ways to estimate the power distribution from recorded signals. Robustness and accuracy are of at most importance, yet there is no universal agreement on how these are to be quantified. Thus, in this talk, we will motivate the need for ways to compare power spectral distributions.
Metrics, in any field of scientific endeavor, must relate to physically meaningful properties of the objects under consideration. In this spirit, we will discuss certain natural notions of distance between power spectral densities. These will be motivated by problems in prediction theory and related properties of time-series. Analogies will be drawn with an old subject of a similar vein, that of quantifying distances between probability distributions, which has given rise to information geometry. The contrast and similarities between metrics will be highlighted by analyzing mechanical vibrations, speech, and visual tracking.
As humans look to explore the solar system beyond low Earth orbit, the technology advancements required point heavily towards autonomy. The operation of complex human spacecraft has thus far been solved with heavy human involvement- full ground control rooms and nearly constantly inhabited spacecraft. As the goal of space exploration moves to beyond the International Space Station, the physical and budgetary constraints of business as usual become overwhelming. A new paradigm of delivering spacecraft and other assets capable of self-maintenance and self-operation prior to launching crew solves many problems- and at the same time, it opens up an array of interesting control problems. This talk will focus on robotic and autonomous vehicle system
Since 1987 I have highlighted how attempts to deploy autonomous capabilities into complex, risky worlds of practice have been hampered by brittleness — descriptively, a sudden collapse in performance when events challenge system boundaries. This constraint has been downplayed on the grounds that the next advance in AI, algorithms, or control theory will lead to the deployment of systems that escape from brittle limits. However, the world keeps providing examples of brittle collapse such as the 2003 Columbia Space Shuttle accident or this years’ Texas energy collapse. Resilience Engineering, drawing on multiple sources including safety of complex systems, biological systems, & joint human-autonomy systems, discovered that (a) brittleness is a fundamental risk and (b) all adaptive systems develop means to mitigate that risk through sources for resilient performance.
The fundamental discovery, covering biological, cognitive, and human systems, is that all adaptive systems at all scales have to possess the capacity for graceful extensibility. Viability of a system, in the long run, requires the ability to gracefully extend or stretch at the boundaries as challenges occur. To put the constraint simply, viability requires extensibility, because all systems have limits and regularly experience surprise at those boundaries due to finite resources and continuous change (Woods, 2015; 2018; 2019).
The problem is that development of automata consistently ignores this constraint. As a result, we see repeated demonstrations of the empirical finding: systems-as-designed are more brittle than stakeholders realize, but fail less often as people in various roles adapt to fill shortfalls and stretch system performance in the face of smaller & larger surprises. (Some) people in some roles are the ad hoc source of the necessary graceful extensibility.
The promise comes from the science behind Resilience Engineering which highlights paths to build systems with graceful extensibility, especially systems that utilize new autonomous capabilities. Even better, designing systems with graceful extensibility draws on basic concepts in control engineering, though these are reframed substantially when combined with findings on adaptive systems from biology, cognitive work, organized complexity, and sociology.
