The concept of ergodicity is a scale and resolution dependent concept. Everything can be ergodic if a process or a pattern is analyzed at a scale or resolution that shows ergodic conditions. Yet, there are very few processes or patterns that are truly ergodic and the validity of ergodicity is more related to the question that is posed and how that question is answered. That essentially depends on the fact if people are solving strict mathematical problems / basic science issues or if they are just applying a model which has its own validity on the ergodic assumption.
All systems in nature can be considered from the perspective that they process information. Information is registered in the state of a system and its elements, implicitly and invisibly. As elements interact, information is transferred. Indeed, bits of information about the state of one element will travel – imperfectly – to the state of the other element, forming its new state. This storage and transfer of information, possibly between levels of a multi level system, is imperfect due to randomness or noise. From this viewpoint, a system can be formalized as a collection of bits that is organized according to its rules of dynamics and its topology of interactions. Mapping out exactly how these bits of information percolate through the system reveals fundamental insights in how the parts orchestrate to produce the properties of the system.
A theory of information processing would be capable of defining a set of universal properties of dynamical multi level complex systems, which describe and compare the dynamics of diverse complex systems ranging from social interaction to brain networks, from financial markets to biomedicine. Each possible combination of rules of dynamics and topology of interactions, with disparate semantics could be translated into the language of information processing which in turn will provide a lingua franca for complex systems.
I’m Paolina.I’m not a fashion victim,I’m not a shopaholic,I’m not longing for fame. I’m a beauty seeker.I’m just addicted to fashion, to its cultural,artistic an…
%% ID models for influenza
%% CxSys leading institutions
%% top national threat pathogens
(note Dengue and Hantaviruses; both can be diagnosed as Lepto and viceversa)
SARS, MERS, and influenza are the top 10 known but not the top 10 in terms of leading cause of death
%% nationwide initiatives on biosafety and biodefense
As you can see this is not biological research but research / development of intelligence methods
Countering Biological Threats and promoting the Global Health Security Agenda (GHSA) continue to be high priorities for the President and his Administration. The attached list of Countering Biological Threats priorities was developed by the National Security Council staff in coordination with the Office of Management and Budget (OMB) and Office of Science and Technology Policy (OSTP) , endorsed by the Interagency Policy Committee on Countering Biological Threats, and is consistent with the objectives and focus of the United States National Strategy for Countering Biological Threats and the GHSA.
old national strategy for biosurveillance
%% Dengue paper and HHS data analytics
I would like to move to this global assessment and predictions than few site specific
maximum entropy like model, same concept
%% weather related deaths material
HumNat lab officially invited at the White House for recognition of the work and discussion on forecasting methods and results, lessons learned to improve forecasting and testing, and future directions. This is particularly related to the Dengue Interagency Forecasting Initiative. Participants will include representatives from departments and agencies of the U.S. Federal Government, dengue researchers, and teams participating in the project. Your participation in this meeting will contribute to advancing the science of infectious disease forecasting.
Maybe, if lucky we are going to go to the White House
“NOAA is America’s environmental intelligence agency. We provide timely, reliable, and actionable information — based on sound science — every day to millions of Americans,” said Kathryn Sullivan, Ph.D., NOAA administrator. “NOAA’s local weather and climate data and models, provide a foundation for the public health community to predict the next outbreak of infectious disease, such as dengue, and ensure they are prepared to meet these challenges head- on.”
Complex systems is a new approach to science, engineering, health, management, and any other field that studies how relationships between parts give rise to the collective emergent behaviours of the entire system, and how the system interacts with its environment. The methods are not necessarily new (at least for physicists) but the way to approach problems is totally novel. The holistic approach and the focus of inquiry is totally different and transcends different disciplines’ methods. The focus is mainly the analysis of the underlying ”connectome” for detection of causal hotspots, attribution of patterns to causal factors, and design (where management is part of the design).
What makes a system ‘complex’? A system can be thought of as complex if its dynamics cannot be easily predicted, or explained, as a linear summation of the individual dynamics of its components. In other words, the many constituent microscopic parts bring about macroscopic phenomena that cannot be understood by considering a single part alone (‘the whole is more than the sum of the parts’). There is a growing awareness that complexity is strongly related to criticality: the behaviour of dynamical spatiotemporal systems at an order/disorder phase transition where scale invariance prevails. This is an important point, where criticality actually means optimality and resilience. Many theorems have been proven this result for many complex systems. At the moment, many bio-inspired technologies are designed by mimicking the optimal patterns in nature.
Complex systems can also be viewed as distributed information-processing systems, particularly in the domains of computational neuroscience, health, bioinformatics, systems biology and artificial life. That is why information theory based models are the majority of models used to analyze and design complex systems together with network and decision sciences. Consciousness emerging from neuronal activity and interactions, cell behaviour resultant from gene regulatory networks and swarming behaviour are all examples of global system behaviour emerging as a result of the local interactions of the individuals (neurons, genes, animals). Can these interactions be seen as a generic computational process? This question is a relevant questions linking computation to complexity and criticality with the aim of a general theory and methods.