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BEGIN:VEVENT
DTSTAMP:20260828T022105Z
LAST-MODIFIED:20171027T182312Z
DTSTART:20171103T160000Z
DTEND:20171103T173000Z
UID:event1838@bu.edu
URL:http://physics.bu.edu/internal/events/show/1838
SUMMARY:"When and why is a simpler model better?"
DESCRIPTION:Featuring Ben Machta\, Princeton University\nHosted by: Robert 
	Marsland\n\nPart of the Biophysics Seminars.\n\nScience is filled with toy 
	models: abstractions of complicated systems that ignore microscopic details
	 even when they are known.  For a special class of models in physics\, the 
	renormalization group rigorously justifies the use of effective theories co
	ntaining just a small number of relevant parameters.  This philosophy seems
	 to apply more broadly\, even when the renormalization group cannot be used
	.  But why?  In this talk I will discuss an information theory approach to 
	answering this question\, or at least towards quantifying it.  I will first
	 review that typical models are sloppy\, defined by a hierarchy in paramete
	r importance (1).  I will argue that sloppiness is both necessary and suffi
	cient for a microscopic system to be amenable to description by a simpler e
	ffective theory.  I will then show how renormalizable models become sloppy 
	as their data is coarse-grained (2).  Finally I will discuss our recent eff
	orts to use the structure of these models to choose simpler effective theor
	ies automatically (3). \n\n(1) Transtrum\, MK\, et al. Perspective: Sloppin
	ess and emergent theories in physics\, biology\, and beyond. The Journal of
	 chemical physics 143.1 (2015) link\n\n(2) Machta\, BB\, Chachra\, R\, Tran
	strum\, MK and  Sethna\, JP. Parameter space compression underlies emergent
	 theories and predictive models. Science 342\, no. 6158 (2013). link\n\n(3)
	 Mattingly\, HH\, Transtrum\, MK\, Abbott\, MC\, & Machta\, BB. Rational ig
	norance: simpler models learn more from finite data.  (2017) arxiv.org/abs/
	1705.01166
LOCATION:SCI 352\, 590 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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