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Category: Machine Learning

Graph Theory — Becoming "Organizing Framework"

Graph Theory — Becoming "Organizing Framework"

Something I’ve been noting — both on my own, and in conversations with Jenn Sleeman , who’s in touch with the academic world at UMBC — Graph theory is growing in centrality as a fundamental organizing framework for many current and emerging computational processes. Specifically, anything more complex than a simple “tuple” (RDF or OWL, etc.), needs to be matched against a graph or partial graph. One good “integrative” paper is Understanding Belief Propagation and its Generalizations by J.S. Yedidia,…

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Equilibrium and Utility: Two Different Realms

Equilibrium and Utility: Two Different Realms

Continuing with Beinhocker’s Origin of Wealth, it is important to distinguish carefully between some of the ideas that Beinhocker is expounding. While overall, he is doing a good job of bringing in many related thoughts and ideas, there is a slight tendency towards “mushing.” In that note, I’d like to suggest that we discern carefully between ideas involving utility (Origins, hardcover; pp. 34 & 37), and equilibrium. On pg. 34, Beinhocker begins a discussion of how utility is an underlying…

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