Abstract
Many systems in Biomedical Engineering and across the Biosciences involve collections of semi-autonomous objects which may be naturally occurring or artificial (e.g, nanostructures or nanomachines). Such collections of objects do not typically have any central controller, and yet respond to the same global cues in terms of feedback of information from the environment. They also typically will not be inert or identical. Instead they are heterogenous and may respond differently to a given piece of external information or system history according to the particular state that they are in, meaning that have effectively have a limited set of simple strategies. In this mini-review, we outline an approach to tackling the general challenge of understanding how collections of such heterogeneous, adaptive systems behave dynamically. These issues are important for understanding the inherent risks involved in the collective behavior of next-generation systems of objects (`agents’) across the spectrum of biomedical engineering applications.
Highlights
Whether natural or artificially made, biomedical systems involving large collections of semi-autonomous objects cannot be controlled in a centralized way because of the large overhead of required resources, as well as the risk of losing that central control to an outside attacker
We have outlined a Crowd-Anticrowd theory in order to understand the uctuations in collections of biomedical systems
Since the theory incorporates details concerning the structure of the strategy space, and its possible coupling to history space, we believe that the Crowd-Anticrowd theory will have applicability for more general multi-agent systems
Summary
Physics Department, University of Miami, USA Submission: May 02, 2017; Published: September 07, 2017 *Corresponding author: Neil F Johnson, Physics Department, University of Miami, USA, Email; Abstract. Many systems in Biomedical Engineering and across the Biosciences involve collections of semi-autonomous objects which may be naturally occurring or artificial (e.g, nanostructures or nanomachines) Such collections of objects do not typically have any central controller, and yet respond to the same global cues in terms of feedback of information from the environment. Instead they are heterogenous and may respond differently to a given piece of external information or system history according to the particular state that they are in, meaning that have effectively have a limited set of simple strategies In this mini-review, we outline an approach to tackling the general challenge of understanding how collections of such heterogeneous, adaptive systems behave dynamically. These issues are important for understanding the inherent risks involved in the collective behavior of next-generation systems of objects (`agents’) across the spectrum of biomedical engineering applications
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