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>> I'm Jessica Flack and I'm the

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co-director of the Center for

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Complexity and Collective

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Computation in the WID, and

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before I introduce tonight's

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topic and speakers, specials

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guests, I would like to first

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thank some of the folks in WID

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who made tonight happen.

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Maria Peot, Marianne English,

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Jenny Eagleton, Sarah Shapiro,

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and Beth Misco, WID's

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outstanding administrative

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outreach team.

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I'd also like to thank Wisconsin

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Public Television, who's in the

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back filming the lectures

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tonight.

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Everybody at University

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Communications who's done a

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great job with help with

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advertising.

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Folks at the Isthmus for

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accommodating changes the last

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minute.

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And, of course, the IT team,

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Alan Ruby in particular here at

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WID for their support tonight.

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And, finally, the Center for

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Complexity and Collective

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Computation is indebted to

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John Wiley and to the Templeton

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Foundation for very much

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appreciated critical support,

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financial support of tonight's

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lecture series.

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Without this support, we could

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not have these kinds of events.

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So, tonight's lecture on the

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roles of energy and information

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in the 21st century biology is

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part of a new public lecture

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series sponsored by C4, that's

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the Center for Complexity and

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Collective Computation, and the

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series is called John

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von Neumann Public Lectures

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in Complexity and Computation.

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And the lectures are going to be

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each month at about 7:00 PM the

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second or third Wednesday

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over the course of the fall

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and spring semesters in 2012

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and 2013.

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We have a great lineup this

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fall.

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The next speaker, the next

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lecture is on October 17th, and

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it's by Graham Spencer who you

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might know as one of the

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original founders of Excite, and

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he's currently a partner at

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Google Ventures.

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And he's going to be talking

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about programming the Internet.

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He's a super smart, broadminded

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computer scientist.

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He gives fun, insightful

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lectures, so I hope to see you

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guys all there.

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Energy and information are two

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of the most fundamental concepts

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in science, effecting phenomena

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over a vase range of scales.

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From quantum mechanics to the

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origin of life to the evolution

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of cooperation in human and

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animals society to, also as

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you'll hear about tonight, the

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size and pace of cities.

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In tonight's lecture,

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Dr. Geoffrey West and Dr. David

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Krakauer, who I will introduce

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momentarily, will explore how

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information and energy have

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shaped nature, and they'll

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discuss the implications and the

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principles of the future of

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science, the future of biology,

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and for society.

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Some of the questions that we

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will tackle over the course of

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the evening tonight include,

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what really are energy and

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information?

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The laws of physics, and

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specifically the first law of

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thermodynamics, tells us that

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energy cannot be created or

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destroyed.

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As such, it's said to be a

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conserved quantity.

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Meaning that the total energy in

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the universe is a constant.

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It is this property of energy

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coupled to the fact that it can

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be hard to transform into

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something useful that makes it

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seem like a finite expendable

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resource.

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Now, is the same true for

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information?

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Or can information be creative

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and destroyed?

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And if so, what does this mean?

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So, as you'll hear tonight,

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existing theories of information

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stress that information

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increases our understanding of

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the world around us.

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In formal terms, we say that

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information reduces the

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uncertainty about the

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environment.

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But there's nothing in this

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definition that captures, I

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think, the fact that most of us,

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for most of us in this audience,

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information is really about

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meaning.

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It's the knowledge itself, not

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about reducing uncertainty about

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the knowledge that we're

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interested in.

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So existing formal theories of

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information have nothing to say

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about this, what you might call,

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semantics.

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Do we need a new theory of

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information that includes

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meaning or semantics?

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That's one of the questions

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we'll ask tonight.

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Can we use existing formal

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theories of information?

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Or the new ones that we might

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build to overcome energetic

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constraints.

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Or does information only help us

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find solutions to harness energy

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more efficiently?

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These are deep and unanswered

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questions in physics and

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biology.

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So, finally, we are going to

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ask, what would a science of

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energy and information mean for

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physics, for computer science,

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for biology, and for society?

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So our first speaker is

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Dr. Geoffrey West.

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Geoffrey is a theoretical

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physicist.

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His primary interests have been

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in fundamental questions in

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physics, especially those

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concerning the elementary

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particles, their interactions in

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cosmological implications.

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Geoffrey received his BA from

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Cambridge in 1961, his doctorate

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from Stanford in 1966 where he

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returned in 1970 as a member of

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the faculty.

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He was the leader and founder of

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the Higher Energy Physics Group

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at Los Alamos National Lab.

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And then he moved to the

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Santa Fe Institute in 2003 where

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he became a distinguished member

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of the faculty and eventually

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became president of SFI for 2005

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to 2009.

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His long-term fascination has

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been in the origin of universal

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scaling laws that pervade

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biology from the molecular

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genomic scale up through

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mitochondrian cells to whole

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organisms and ecosystems.

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He's currently working to extend

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the theory of scaling as it was

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developed in biology to social

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systems, with the goal of

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understanding the structure and

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dynamic of social organizations

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such as cities and corporations.

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Tonight, he will talk on energy

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scaling the future of life on

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Earth for about 45 minutes.

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Our second lecturer is David

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Krakauer.

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David is the director of the

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Wisconsin Institute for

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Discovery, co-director of the

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Center for Complexity and

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Collective Computation,

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professor of genetics at UW

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Madison and external professor

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at the Santa Fe Institute.

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A graduate of University of

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London and Oxford University,

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he's authored more than a

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hundred scientific papers,

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served on several journal

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editorial boards, and works to

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bridge the gap between academia

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and business and also between

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the physical and biological

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sciences on the one hand and the

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social sciences and humanities

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on the other.

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His research focuses on the

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evolutionary history of

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information processing

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mechanisms in adaptive systems.

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But his mind, and I know him

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well, is perhaps best described

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as sitting in an intellectual

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spaces deeply influenced by, I

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guess, I think, a somewhat

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idiosyncratic group of some of

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history's most interesting

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contributors including John

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Louis Borges, John von Neumann,

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Turing, Bill Hamilton,

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and Sir Thomas Browne.

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Tonight, he will talk for

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30 minutes on the power of

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information to transfer in the

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21st century.

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Following the lectures of David

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and Geoffrey, Steve Paulson from

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Wisconsin Public Radio will lead

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a panel discussion on the role

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of energy and information on

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21st century biology.

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Steve is the executive producer

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and interviewer with To the Best

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of Our Knowledge, a well known

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science radio program.

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Paulson has written for Salon,

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Slate, the Huffington Post, the

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Chronicle of Higher Education,

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The Independent, and many other

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publications.

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His radio reports have also been

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broadcast on NPR's

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Morning Edition

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and All Things Considered.

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His recent book, Atoms and Eden:

06:55.000 --> 06:56.000
Conversations on Religion and

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Science, was published from the

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University Press.

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So please join me for a bit of a

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marathon session and welcome

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Steven, David, and Geoffrey.

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Thank you.

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[APPLAUSE]

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>> Okay, thank you.

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Thank you, David, thank you,

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Jessica, and thank you for

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inviting me to give this talk

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and be part of this interesting

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discussion about energy and

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information So, this is the

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title that was given to me,

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mostly because I was delinquent

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in giving one myself, and it

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does sort of cover what I'm

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going to talk about, but this is

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kind of a landscape that I'm

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going to be talking about

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tonight in terms, primarily, of

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energy and the ideas surrounding

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metabolism in biology.

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And it's a very idiosyncratic

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view, and as is clear from the

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introduction, I come to it as a

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theoretical physicists wondering

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into areas of biology and the

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social sciences.

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And I'm going to begin it, I'm

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going to take as a point of

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departure, something that's

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relevant to this, namely this is

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supported by the Center for

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Computation and Complexity, or

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whatever, complexity was in it.

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[LAUGHTER]

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And I wanted to use as a point

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of departure a statement

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by Stephen Hawking,

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who is not someone that works on

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complexity and not someone that

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works on biology, but is a

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highly reductionistic

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physicist.

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And here's something he said

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about 10 years ago: "Some say

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that while the 20th century was

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the century of physics, we are

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now entering the century of

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biology, what do you think of

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this?"

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And many people believe the 21st

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century is the century of

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biology and it certainly will

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be, but more importantly, and

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this I do believe, is what

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Hawking says, "I think the next

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century will be the century of

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complexity" because many of the

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problems we're going to face and

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are facing to do with complex

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adaptive systems, and I'll be

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talking a little bit about them,

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and I want to use this, as I

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say, as a point of departure

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because implicit in this is an

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idea of what complexity is, and

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that's something I'm not going

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to discuss except to use it to

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talk about what is not complex

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first because it brings in the

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whole role of energy.

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And this is a system we're

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familiar with, the solar system,

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and this is a system that is

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often thought of as not complex.

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It is simple, and by simple, it

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means that the whole

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dynamic of that system, the

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motion of the planets around the

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sun and, therefore, of the

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satellites that power our

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communication systems, are

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encoded in two very simple

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looking equations.

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This is a highly non-technical

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talk.

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You don't have to understand the

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equations.

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Everybody is familiar with the

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idea F equal MA.

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But there are two laws proposed

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by Newton that kind of covers

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everything to do with the motion

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of the solar system, the planets

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around the sun, but much, much

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else.

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Much of modern physics is based

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on these equations or versions

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of these equations, but critical

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in this is the flow of energy.

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The idea that energy is the kind

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of fundamental thing that's

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underlying this motion.

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And one of the crucial aspects

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of that has already been

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mentioned by Jessica: the

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conservation of energy.

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But if you add to it another set

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of equations which you don't

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have to understand but important

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to understand conceptually, the

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equations of Maxwell for

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unifying electricity and

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magnetism and giving rise to

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understanding radiation,

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electromagnetic radiation.

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That pretty much encompasses

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much of the kind of world that

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we live in, and just to take

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these two equations with these

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equations is how your cell phone

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works and allows everybody to

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communicate and everything else

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that's pretty much around us.

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And the word simple applies too

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because they invoke the idea

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that you can encode everything

11:28.000 --> 11:30.000
in the symbolic fashion, and

11:30.000 --> 11:31.000
from that, everything else

11:31.000 --> 11:33.000
follows, even down to the

11:33.000 --> 11:35.000
quantum level.

11:35.000 --> 11:37.000
And one of the things I want to

11:37.000 --> 11:39.000
emphasize is that, again, these

11:39.000 --> 11:40.000
are to do with the flow of

11:40.000 --> 11:42.000
energy and because they are

11:42.000 --> 11:43.000
simple and they are written in a

11:43.000 --> 11:45.000
mathematically precise form, you

11:45.000 --> 11:47.000
can calculate things somehow in

11:47.000 --> 11:49.000
principled to any degree of

11:49.000 --> 11:50.000
accuracy.

11:50.000 --> 11:52.000
And one of the great triumphs of

11:52.000 --> 11:54.000
20th century science is this

11:54.000 --> 11:56.000
calculation which is calculating

11:56.000 --> 11:58.000
the strength of the magnet

11:58.000 --> 12:00.000
associated with the electron,

12:00.000 --> 12:02.000
and here's the theory to 12

12:02.000 --> 12:04.000
decimal places and here's the

12:04.000 --> 12:06.000
experiment agreeing with it to

12:06.000 --> 12:07.000
12 decimal places.

12:07.000 --> 12:09.000
It's kind of extraordinary, and

12:09.000 --> 12:11.000
this is quite unusual, actually,

12:11.000 --> 12:13.000
in the world that we live in

12:13.000 --> 12:15.000
because the world that we live

12:15.000 --> 12:17.000
in, oh, I forgot about that.

12:17.000 --> 12:19.000
I put this slide in just to show

12:19.000 --> 12:22.000
you some of the main characters

12:22.000 --> 12:23.000
that you're familiar with that I

12:23.000 --> 12:25.000
think played an important role

12:25.000 --> 12:27.000
in developing ideas of energy.

12:27.000 --> 12:30.000
One is Newton.

12:30.000 --> 12:31.000
That's a kind of rock star image

12:31.000 --> 12:33.000
of Newton.

12:33.000 --> 12:34.000
[LAUGHTER]

12:34.000 --> 12:35.000
This is Einstein

12:35.000 --> 12:36.000
looking like Einstein.

12:36.000 --> 12:37.000
And this is Max Planck,

12:37.000 --> 12:39.000
the origin of photons.

12:39.000 --> 12:42.000
And this is someone--

12:42.000 --> 12:44.000
Oops.

12:44.000 --> 12:48.000
This is someone that most people

12:48.000 --> 12:49.000
are not familiar with.

12:49.000 --> 12:52.000
It's another, there was a famous

12:52.000 --> 12:54.000
Bill Hamilton here, biologist,

12:54.000 --> 12:56.000
very famous ecologist/biologist

12:56.000 --> 13:00.000
that Jessica actually mentioned.

13:00.000 --> 13:03.000
This is a really deep

13:03.000 --> 13:04.000
Bill Hamilton.

13:04.000 --> 13:06.000
This is Bill Hamilton.

13:06.000 --> 13:08.000
He was an Irishman who sadly

13:08.000 --> 13:10.000
drank himself to death, but he

13:10.000 --> 13:13.000
encoded something extremely

13:13.000 --> 13:15.000
important about energy, and the

13:15.000 --> 13:17.000
physicists in the audience will

13:17.000 --> 13:18.000
forgive me, but basically he

13:18.000 --> 13:20.000
said that all physical systems,

13:20.000 --> 13:23.000
in some sense, minimize the

13:23.000 --> 13:24.000
amount of energy that needs to

13:24.000 --> 13:26.000
be expended.

13:26.000 --> 13:28.000
So this kind of a least minimum,

13:28.000 --> 13:30.000
least energy principle

13:30.000 --> 13:32.000
Hamilton's principle is actually

13:32.000 --> 13:34.000
called, technically, the

13:34.000 --> 13:35.000
principle of least action, but

13:35.000 --> 13:37.000
roughly speaking, this is the

13:37.000 --> 13:39.000
framework in which all of

13:39.000 --> 13:42.000
physics that encompasses

13:42.000 --> 13:43.000
everything from the macroscopic

13:43.000 --> 13:45.000
down to string theory operates

13:45.000 --> 13:48.000
from is using those principles.

13:48.000 --> 13:49.000
And it's has to do based on

13:49.000 --> 13:51.000
energy.

13:51.000 --> 13:53.000
So, here's a picture of energy.

13:53.000 --> 13:55.000
This is energy.

13:55.000 --> 13:57.000
We're familiar with it.

13:57.000 --> 13:58.000
This is highly complex use of

13:58.000 --> 14:00.000
energy because these are highly

14:00.000 --> 14:01.000
non-linear effects and you're

14:01.000 --> 14:03.000
familiar with them.

14:03.000 --> 14:04.000
You're also familiar with this.

14:04.000 --> 14:06.000
This is the energy that we

14:06.000 --> 14:08.000
create on the planet through

14:08.000 --> 14:10.000
objects like this, and I'm going

14:10.000 --> 14:12.000
to talk a little bit about that

14:12.000 --> 14:14.000
shortly.

14:14.000 --> 14:17.000
And these are driven, of course,

14:17.000 --> 14:21.000
by us, which involve energy.

14:21.000 --> 14:23.000
So they're these multiple levels

14:23.000 --> 14:26.000
of energy, and they go all the

14:26.000 --> 14:28.000
way down to the thing that

14:28.000 --> 14:32.000
drives these, us, that drives us

14:32.000 --> 14:35.000
to create the light and the

14:35.000 --> 14:37.000
energy by which we live, our

14:37.000 --> 14:39.000
brains and ourselves.

14:39.000 --> 14:41.000
And these, of course, also

14:41.000 --> 14:43.000
operate by energy themselves,

14:43.000 --> 14:45.000
but they invoke something new

14:45.000 --> 14:48.000
that has typically not been

14:48.000 --> 14:49.000
considered in physics.

14:49.000 --> 14:51.000
And they invoke information, the

14:51.000 --> 14:53.000
idea that information

14:53.000 --> 14:55.000
necessarily is needed to be put

14:55.000 --> 14:59.000
into the system in order to

14:59.000 --> 15:01.000
understand the way these systems

15:01.000 --> 15:02.000
have evolved and the way they

15:02.000 --> 15:04.000
organize and the way they

15:04.000 --> 15:05.000
behave.

15:05.000 --> 15:07.000
And these systems are typically

15:07.000 --> 15:08.000
outside physics, and they form

15:08.000 --> 15:10.000
much of the work of biology and

15:10.000 --> 15:12.000
the social sciences, and they

15:12.000 --> 15:15.000
are complex but, more

15:15.000 --> 15:18.000
importantly, they evolve and are

15:18.000 --> 15:19.000
therefore adaptive.

15:19.000 --> 15:21.000
So these are very different in

15:21.000 --> 15:23.000
their nature from the kind of

15:23.000 --> 15:25.000
simplistic system that we see in

15:25.000 --> 15:27.000
the motions of the planets or

15:27.000 --> 15:30.000
even, might I say, in thinking

15:30.000 --> 15:32.000
about string theory as the

15:32.000 --> 15:33.000
origins of the universe because

15:33.000 --> 15:35.000
we can write definitive, precise

15:35.000 --> 15:37.000
equations in principle for

15:37.000 --> 15:38.000
those.

15:38.000 --> 15:39.000
Whereas for these, we have this

15:39.000 --> 15:42.000
extraordinarily challenging

15:42.000 --> 15:44.000
problem of both integrating

15:44.000 --> 15:46.000
information in it and

15:46.000 --> 15:48.000
understanding the role of

15:48.000 --> 15:49.000
energy.

15:49.000 --> 15:51.000
And what I'm going to explore

15:51.000 --> 15:52.000
tonight is how far one can take

15:52.000 --> 15:54.000
the traditional physics thinking

15:54.000 --> 15:56.000
without invoking, explicitly,

15:56.000 --> 15:58.000
information to understand some

15:58.000 --> 16:02.000
of the organization and dynamics

16:02.000 --> 16:04.000
of these systems.

16:04.000 --> 16:06.000
And then David will talk about

16:06.000 --> 16:08.000
information, and maybe in the

16:08.000 --> 16:09.000
discussion we'll talk a little

16:09.000 --> 16:11.000
bit about the integration.

16:11.000 --> 16:13.000
So here's another kind of system

16:13.000 --> 16:14.000
which you're familiar with, a

16:14.000 --> 16:15.000
forest.

16:15.000 --> 16:17.000
And all of these systems obey

16:17.000 --> 16:19.000
the second law of

16:19.000 --> 16:20.000
thermodynamics, the conservation

16:20.000 --> 16:21.000
of energy, and, therefore, the

16:21.000 --> 16:23.000
production of entropy which is

16:23.000 --> 16:26.000
the kind of random stuff, the

16:26.000 --> 16:28.000
dissipative kind of energy that

16:28.000 --> 16:30.000
comes from doing work and

16:30.000 --> 16:32.000
creating order.

16:32.000 --> 16:34.000
It is the resulting disorder

16:34.000 --> 16:35.000
that comes from ordering the

16:35.000 --> 16:37.000
system.

16:37.000 --> 16:38.000
And one of the challenges, and I

16:38.000 --> 16:40.000
would even go as far as to say I

16:40.000 --> 16:42.000
think the major challenge

16:42.000 --> 16:44.000
conceptually in 21st century

16:44.000 --> 16:46.000
science is understanding this

16:46.000 --> 16:48.000
integration between energy and

16:48.000 --> 16:51.000
resources on the one hand,

16:51.000 --> 16:53.000
metabolism and biology with

16:53.000 --> 16:55.000
information genomics, maybe, and

16:55.000 --> 16:57.000
integrate and get a complete

16:57.000 --> 16:59.000
understanding of these.

16:59.000 --> 17:01.000
These are typically considered

17:01.000 --> 17:02.000
separately, roughly speaking, in

17:02.000 --> 17:06.000
the kind of academic way in

17:06.000 --> 17:07.000
which we think about these

17:07.000 --> 17:09.000
things.

17:09.000 --> 17:12.000
So, what I'm going to talk about

17:12.000 --> 17:14.000
in these systems is, as a

17:14.000 --> 17:16.000
physicist, I want a

17:16.000 --> 17:17.000
quantitative, predictive,

17:17.000 --> 17:20.000
mathematizable in principle,

17:20.000 --> 17:22.000
understanding of the dynamics of

17:22.000 --> 17:24.000
organization of these systems.

17:24.000 --> 17:25.000
And it is clear for the kinds of

17:25.000 --> 17:28.000
systems I just referred to, we

17:28.000 --> 17:29.000
cannot do what we did for

17:29.000 --> 17:32.000
understanding the magnetic

17:32.000 --> 17:34.000
moment of the electron.

17:34.000 --> 17:35.000
We're not going to get equations

17:35.000 --> 17:37.000
that produce precise

17:37.000 --> 17:39.000
calculations, but maybe what we

17:39.000 --> 17:41.000
can get is what we call

17:41.000 --> 17:44.000
coarse-grained descriptions.

17:44.000 --> 17:46.000
So, for example, the kinds of

17:46.000 --> 17:49.000
questions that we ought to be

17:49.000 --> 17:50.000
able to answer quantitatively

17:50.000 --> 17:52.000
are questions like this: why is

17:52.000 --> 17:54.000
it that I can look across this

17:54.000 --> 17:55.000
room and say everybody will be

17:55.000 --> 17:57.000
dead in a hundred years?

17:57.000 --> 17:59.000
In fact, if I look more closely,

17:59.000 --> 18:01.000
I would say that...

18:01.000 --> 18:03.000
[LAUGHTER]

18:03.000 --> 18:05.000
Including myself, might I say.

18:05.000 --> 18:07.000
But where does a hundred years

18:07.000 --> 18:09.000
come from for a typical

18:09.000 --> 18:11.000
life span of a human being?

18:11.000 --> 18:12.000
Why isn't it 10 years or a

18:12.000 --> 18:13.000
thousand years?

18:13.000 --> 18:15.000
And how is that connected to

18:15.000 --> 18:18.000
underlying molecular structures

18:18.000 --> 18:20.000
of the genome and the complex?

18:20.000 --> 18:21.000
Where in the hell does that come

18:21.000 --> 18:23.000
from?

18:23.000 --> 18:25.000
And why is it that the same

18:25.000 --> 18:27.000
stuff, if it were a mouse, only

18:27.000 --> 18:29.000
lives two or three years?

18:29.000 --> 18:31.000
So those are the kind of

18:31.000 --> 18:32.000
coarse-grained questions that

18:32.000 --> 18:34.000
one ought to be able to answer

18:34.000 --> 18:36.000
in a predictive framework, and

18:36.000 --> 18:38.000
I'm not going to talk much about

18:38.000 --> 18:40.000
these, but in what role does

18:40.000 --> 18:43.000
energy and metabolism play in

18:43.000 --> 18:45.000
these?

18:45.000 --> 18:46.000
So here's another set of similar

18:46.000 --> 18:47.000
kinds of coarse-grained

18:47.000 --> 18:48.000
questions.

18:48.000 --> 18:50.000
Why do we need eight hours of

18:50.000 --> 18:51.000
sleep?

18:51.000 --> 18:52.000
Why do we need to sleep?

18:52.000 --> 18:53.000
Why is it not two hours or 18

18:53.000 --> 18:55.000
hours like a mouse or four hours

18:55.000 --> 18:56.000
like an elephant?

18:56.000 --> 18:58.000
Where in the hell do these

18:58.000 --> 19:00.000
numbers come from, and why is it

19:00.000 --> 19:01.000
that mice get many more tumors

19:01.000 --> 19:03.000
during their life span per unit

19:03.000 --> 19:07.000
volume than human beings?

19:07.000 --> 19:08.000
And, therefore, why in the hell

19:08.000 --> 19:10.000
are we doing all these

19:10.000 --> 19:12.000
experiments on mice and trying

19:12.000 --> 19:13.000
to draw conclusions about human

19:13.000 --> 19:14.000
beings?

19:14.000 --> 19:16.000
We need to understand those

19:16.000 --> 19:17.000
kinds of questions, and the one

19:17.000 --> 19:19.000
I really love is, why is it very

19:19.000 --> 19:21.000
good to grow babies in your body

19:21.000 --> 19:23.000
but not grow cancer which is

19:23.000 --> 19:25.000
also part of you?

19:25.000 --> 19:26.000
So these are kind of

19:26.000 --> 19:28.000
coarse-grained questions, and

19:28.000 --> 19:29.000
one that has occupied me in the

19:29.000 --> 19:31.000
last few years is, are cities

19:31.000 --> 19:32.000
and companies just large

19:32.000 --> 19:34.000
organisms that are biological or

19:34.000 --> 19:36.000
are cities organisms just, is

19:36.000 --> 19:39.000
New York a great big whale and

19:39.000 --> 19:41.000
Google a great big elephant, and

19:41.000 --> 19:44.000
if that's true, how come

19:44.000 --> 19:49.000
organisms die, companies die,

19:49.000 --> 19:51.000
but cities don't?

19:51.000 --> 19:53.000
And in fact, I would say,

19:53.000 --> 19:55.000
roughly speaking, almost no

19:55.000 --> 19:56.000
cities die, although you can

19:56.000 --> 19:59.000
think of counter-examples.

19:59.000 --> 20:00.000
We have done awful experiments

20:00.000 --> 20:02.000
to test that.

20:02.000 --> 20:03.000
We've dropped two atom bombs on

20:03.000 --> 20:05.000
two cities, and 30 year later,

20:05.000 --> 20:08.000
they're functioning.

20:08.000 --> 20:09.000
Companies, all companies, die.

20:09.000 --> 20:12.000
And so one of the questions is,

20:12.000 --> 20:13.000
in a coarse-grained way, can we

20:13.000 --> 20:15.000
predict when Google or Microsoft

20:15.000 --> 20:17.000
are going to go bust?

20:17.000 --> 20:18.000
Because they surely will

20:18.000 --> 20:19.000
eventually.

20:19.000 --> 20:21.000
Okay, so those are the kind of

20:21.000 --> 20:22.000
questions, and I want to lead

20:22.000 --> 20:24.000
into all of this, this is

20:24.000 --> 20:25.000
biology but I want to lead into

20:25.000 --> 20:28.000
it via cities because we are

20:28.000 --> 20:30.000
facing an extraordinary

20:30.000 --> 20:32.000
challenge, and this is

20:32.000 --> 20:34.000
addressing the question of how

20:34.000 --> 20:36.000
biological our cities and can we

20:36.000 --> 20:38.000
understand them that way.

20:38.000 --> 20:40.000
We are facing this extraordinary

20:40.000 --> 20:41.000
challenge that we live in this

20:41.000 --> 20:44.000
exponentially expanding universe

20:44.000 --> 20:46.000
of socioeconomic quantities And

20:46.000 --> 20:47.000
just to give you an example,

20:47.000 --> 20:49.000
we've gone from a few percent

20:49.000 --> 20:52.000
being urban to over 80% being

20:52.000 --> 20:53.000
urban in just 200 years.

20:53.000 --> 20:55.000
The world's crossed the halfway

20:55.000 --> 20:56.000
mark.

20:56.000 --> 20:58.000
We're going to go to somewhere

20:58.000 --> 21:00.000
close to 80% by 2050.

21:00.000 --> 21:02.000
China's building several hundred

21:02.000 --> 21:04.000
new cities in the next 20 to 30

21:04.000 --> 21:06.000
years, and indeed, this

21:06.000 --> 21:08.000
statistic ought to freak

21:08.000 --> 21:09.000
everybody out that every week

21:09.000 --> 21:11.000
from now to 2050, on the

21:11.000 --> 21:12.000
average, one and a half million

21:12.000 --> 21:14.000
people are being urbanized.

21:14.000 --> 21:16.000
Therefore, every two months,

21:16.000 --> 21:18.000
there's a New York metropolitan

21:18.000 --> 21:19.000
area coming onto this planet.

21:19.000 --> 21:21.000
So by the beginning of December,

21:21.000 --> 21:22.000
there's another New York

21:22.000 --> 21:24.000
metropolitan area.

21:24.000 --> 21:25.000
By the beginning of February,

21:25.000 --> 21:26.000
there's another one.

21:26.000 --> 21:27.000
By the beginning of April,

21:27.000 --> 21:28.000
there's another one and so on,

21:28.000 --> 21:31.000
inextricably for the next 30-40

21:31.000 --> 21:32.000
years.

21:32.000 --> 21:34.000
Now, can I use expletives in

21:34.000 --> 21:35.000
this talk?

21:35.000 --> 21:37.000
>> Sure.

21:37.000 --> 21:38.000
>> Okay, how the [###] are we

21:38.000 --> 21:39.000
going to deal with that?

21:39.000 --> 21:40.000
[LAUGHTER]

21:40.000 --> 21:41.000
This is an extraordinary

21:41.000 --> 21:42.000
challenge.

21:42.000 --> 21:43.000
So I want to get into that, and

21:43.000 --> 21:46.000
it is related to energy.

21:46.000 --> 21:49.000
Okay, so, the other aspect to

21:49.000 --> 21:52.000
recognize is that all of the

21:52.000 --> 21:53.000
tsunami and problems we're

21:53.000 --> 21:55.000
facing from global warming to

21:55.000 --> 21:57.000
the environment to health

21:57.000 --> 22:00.000
problems to crime to resource

22:00.000 --> 22:03.000
problems, water, energy, and so

22:03.000 --> 22:05.000
on, all are driven by

22:05.000 --> 22:07.000
urbanization, by people in

22:07.000 --> 22:08.000
cities.

22:08.000 --> 22:10.000
So that's the problem, but

22:10.000 --> 22:11.000
cities are also the solution

22:11.000 --> 22:13.000
because they suck all of you

22:13.000 --> 22:15.000
into Madison.

22:15.000 --> 22:17.000
Cities suck in people, and they

22:17.000 --> 22:19.000
are the sources of ideas,

22:19.000 --> 22:21.000
innovation, and wealth creation.

22:21.000 --> 22:24.000
So, this is what cities are

22:24.000 --> 22:26.000
represented by.

22:26.000 --> 22:27.000
The good of cities, this is

22:27.000 --> 22:29.000
what, in fact, attracts people

22:29.000 --> 22:30.000
to cities.

22:30.000 --> 22:32.000
All these good things, culture,

22:32.000 --> 22:34.000
music, goods and so on, driven

22:34.000 --> 22:36.000
and participating in an ever

22:36.000 --> 22:39.000
expanding, exponential expanding

22:39.000 --> 22:41.000
economy, but if this is the

22:41.000 --> 22:44.000
thermodynamic system, which it

22:44.000 --> 22:47.000
is, it produces entropy.

22:47.000 --> 22:49.000
So it produces socioeconomic

22:49.000 --> 22:50.000
entropy.

22:50.000 --> 22:53.000
Incidentally, side comment, if

22:53.000 --> 22:54.000
you look in any of the standard

22:54.000 --> 22:58.000
economics books, you never see

22:58.000 --> 23:02.000
the word entropy, but what is

23:02.000 --> 23:04.000
more amazing is we've looked in

23:04.000 --> 23:07.000
five of the standard texts, only

23:07.000 --> 23:09.000
in one did the word energy

23:09.000 --> 23:10.000
appear.

23:10.000 --> 23:13.000
That's a side comment.

23:13.000 --> 23:14.000
Provocative comment.

23:14.000 --> 23:16.000
So here's socioeconomic entropy.

23:16.000 --> 23:18.000
Okay?

23:18.000 --> 23:20.000
You're familiar with all this.

23:20.000 --> 23:22.000
And the question is, is that

23:22.000 --> 23:23.000
what New York and San Francisco

23:23.000 --> 23:26.000
looked like 2050?

23:26.000 --> 23:27.000
Or that?

23:27.000 --> 23:29.000
Or like this?

23:29.000 --> 23:31.000
Or this?

23:31.000 --> 23:32.000
This is what we want.

23:32.000 --> 23:33.000
Or even like that?

23:33.000 --> 23:34.000
Beautiful.

23:34.000 --> 23:36.000
Or like that in a hundred years.

23:36.000 --> 23:38.000
Or that.

23:38.000 --> 23:39.000
And most importantly, to

23:39.000 --> 23:41.000
maintain that which is a social

23:41.000 --> 23:42.000
buzz of a city.

23:42.000 --> 23:45.000
The interaction of people.

23:45.000 --> 23:47.000
So, here's the question, given

23:47.000 --> 23:48.000
this extraordinary role of

23:48.000 --> 23:49.000
cities, we need a science of

23:49.000 --> 23:51.000
cities.

23:51.000 --> 23:54.000
And I'm going to use energy as

23:54.000 --> 23:57.000
the major piece of that.

23:57.000 --> 23:58.000
So the question is, can we have

23:58.000 --> 24:00.000
a science of that, and is that

24:00.000 --> 24:03.000
another version of this?

24:03.000 --> 24:04.000
Well, if it is, that would be

24:04.000 --> 24:06.000
good, actually.

24:06.000 --> 24:08.000
If this were another version of

24:08.000 --> 24:10.000
that because you ask any

24:10.000 --> 24:13.000
question that has a quantitative

24:13.000 --> 24:15.000
metric associated with it, like

24:15.000 --> 24:16.000
how many trees are there of a

24:16.000 --> 24:18.000
given size, how many leaves are

24:18.000 --> 24:20.000
there on a given branch of a

24:20.000 --> 24:21.000
given size, how much energy is

24:21.000 --> 24:23.000
flowing through each one, how

24:23.000 --> 24:25.000
big is the canopy, what is the

24:25.000 --> 24:27.000
mortality rate, what is the

24:27.000 --> 24:29.000
growth rate, how far apart the

24:29.000 --> 24:31.000
trees of given size, etc, all

24:31.000 --> 24:33.000
those can be answered in a

24:33.000 --> 24:36.000
quantitative conceptual

24:36.000 --> 24:37.000
framework with a bunch of

24:37.000 --> 24:39.000
mathematical formulas which,

24:39.000 --> 24:40.000
in a coarse-grained way,

24:40.000 --> 24:42.000
agrees with data from forests

24:42.000 --> 24:44.000
all across the globe.

24:44.000 --> 24:45.000
And the question is, can we do

24:45.000 --> 24:47.000
the same for this?

24:47.000 --> 24:49.000
Sorry, oops.

24:49.000 --> 24:50.000
I went backwards.

24:50.000 --> 24:52.000
So, well here's some of the

24:52.000 --> 24:55.000
commonalities, metaphorically

24:55.000 --> 24:56.000
at least, between social

24:56.000 --> 25:00.000
organizations and biological

25:00.000 --> 25:01.000
organisms which we're all

25:01.000 --> 25:03.000
familiar with, and I'm going to

25:03.000 --> 25:06.000
show you just very briefly some

25:06.000 --> 25:09.000
data that substantiates the idea

25:09.000 --> 25:11.000
that we understand a little bit

25:11.000 --> 25:14.000
about generically the way

25:14.000 --> 25:15.000
forests work.

25:15.000 --> 25:17.000
So what this is, is just the

25:17.000 --> 25:19.000
plot of the number of trees of a

25:19.000 --> 25:20.000
given size.

25:20.000 --> 25:22.000
The theory, which I'm going to

25:22.000 --> 25:24.000
talk about momentarily, the

25:24.000 --> 25:25.000
theory predicts that it goes as

25:25.000 --> 25:27.000
the inverse square of the

25:27.000 --> 25:28.000
diameter of the tree trunk.

25:28.000 --> 25:29.000
So the number of trees of a

25:29.000 --> 25:31.000
given size goes to the inverse

25:31.000 --> 25:32.000
square of the diameter of the

25:32.000 --> 25:34.000
tree trunks which means if you

25:34.000 --> 25:35.000
look at trees that are twice the

25:35.000 --> 25:36.000
size of any others, there's a

25:36.000 --> 25:38.000
quarter of the number, two

25:38.000 --> 25:39.000
squared, a quarter of the

25:39.000 --> 25:40.000
number.

25:40.000 --> 25:41.000
If it were three times, it'd be

25:41.000 --> 25:43.000
eighth and so on.

25:43.000 --> 25:44.000
So, you get the idea.

25:44.000 --> 25:46.000
And there's data and what is

25:46.000 --> 25:47.000
important about it, not only

25:47.000 --> 25:48.000
does the data fit the theory,

25:48.000 --> 25:51.000
but you can see it's just over a

25:51.000 --> 25:52.000
period of 30-40 years, this

25:52.000 --> 25:55.000
forest, which is a tropical

25:55.000 --> 25:57.000
forest, has changed

25:57.000 --> 25:58.000
dramatically.

25:58.000 --> 26:00.000
Lots of trees have died, new

26:00.000 --> 26:01.000
ones have grown.

26:01.000 --> 26:02.000
Record turnover.

26:02.000 --> 26:05.000
But this law has remained

26:05.000 --> 26:08.000
robust, and there's data into

26:08.000 --> 26:09.000
the '90s and 2000s that continue

26:09.000 --> 26:12.000
with that.

26:12.000 --> 26:14.000
I'm going to miss this.

26:14.000 --> 26:15.000
So here's the kind of framework

26:15.000 --> 26:17.000
that I want to invoke.

26:17.000 --> 26:19.000
Here's a cartoon of said

26:19.000 --> 26:20.000
ecosystem.

26:20.000 --> 26:21.000
I could have done the city.

26:21.000 --> 26:23.000
And this is the lens that I want

26:23.000 --> 26:25.000
to look at it through, and all

26:25.000 --> 26:26.000
of these equations actually are

26:26.000 --> 26:28.000
really to do with energy flow.

26:28.000 --> 26:30.000
Okay.

26:30.000 --> 26:31.000
So, another concept I want to

26:31.000 --> 26:33.000
talk about very briefly is the

26:33.000 --> 26:35.000
concept of scalability that a

26:35.000 --> 26:37.000
system needs to be scalable if

26:37.000 --> 26:39.000
it is a system that is going to

26:39.000 --> 26:42.000
be resilient and evolvable and

26:42.000 --> 26:45.000
adaptive to change.

26:45.000 --> 26:46.000
It needs to be scalable just in

26:46.000 --> 26:49.000
the sense that we are scalable.

26:49.000 --> 26:50.000
This is us.

26:50.000 --> 26:52.000
We have scaled over a range of a

26:52.000 --> 26:54.000
hundred million in size and

26:54.000 --> 26:55.000
mass.

26:55.000 --> 26:58.000
We go down to something that

26:58.000 --> 27:00.000
sits on the palm of my hand all

27:00.000 --> 27:02.000
the way up to something that's

27:02.000 --> 27:04.000
much bigger than this room, and

27:04.000 --> 27:06.000
we're all pretty much the same

27:06.000 --> 27:08.000
thing.

27:08.000 --> 27:10.000
We may look different, but in

27:10.000 --> 27:12.000
terms of, in a coarse-grained

27:12.000 --> 27:14.000
average way, in terms of our

27:14.000 --> 27:16.000
life history and our physiology,

27:16.000 --> 27:17.000
we're all pretty much the same

27:17.000 --> 27:19.000
thing, and I'm going to show you

27:19.000 --> 27:22.000
that in a moment.

27:22.000 --> 27:24.000
But even going further in going

27:24.000 --> 27:26.000
from the molecular levels, I

27:26.000 --> 27:28.000
could have put the genome here,

27:28.000 --> 27:30.000
this is what the cartoon of the

27:30.000 --> 27:32.000
molecules that produce your

27:32.000 --> 27:33.000
energy, all the way through

27:33.000 --> 27:34.000
mitochondria to cells to

27:34.000 --> 27:36.000
multicellular organisms like

27:36.000 --> 27:39.000
this that produce things like

27:39.000 --> 27:40.000
this, houses, and produce things

27:40.000 --> 27:42.000
like this so that, in fact, what

27:42.000 --> 27:45.000
goes on here scales all the way

27:45.000 --> 27:48.000
through this to keep this going.

27:48.000 --> 27:50.000
All of these have to be scalable

27:50.000 --> 27:52.000
because they are highly complex

27:52.000 --> 27:55.000
and continuously evolving and

27:55.000 --> 27:58.000
continuously adapting.

27:58.000 --> 28:00.000
And so there has to be a kind of

28:00.000 --> 28:01.000
scalability, and out of that

28:01.000 --> 28:03.000
comes the idea that you can't

28:03.000 --> 28:06.000
have this being arbitrary.

28:06.000 --> 28:08.000
There have to be emergent laws

28:08.000 --> 28:11.000
that govern this, and I think

28:11.000 --> 28:13.000
the next slide, ah, yes.

28:13.000 --> 28:15.000
So, in a similar way to the way

28:15.000 --> 28:17.000
we scaled in a much shorter time

28:17.000 --> 28:20.000
frame in terms of our growth,

28:20.000 --> 28:22.000
and one of the things that I

28:22.000 --> 28:23.000
will talk about briefly in a

28:23.000 --> 28:25.000
moment is about growth and the

28:25.000 --> 28:28.000
fact that the same theory, the

28:28.000 --> 28:30.000
same theoretical framework,

28:30.000 --> 28:31.000
which I will come to in a

28:31.000 --> 28:34.000
moment, which purports to

28:34.000 --> 28:36.000
explain the organization,

28:36.000 --> 28:39.000
dynamics, growth of forests that

28:39.000 --> 28:41.000
I talked about earlier applied

28:41.000 --> 28:45.000
to us, to mammals, gives rise to

28:45.000 --> 28:47.000
equations like this or pictures

28:47.000 --> 28:49.000
like this for the,

28:49.000 --> 28:51.000
what is called a growth curve.

28:51.000 --> 28:52.000
This is the weight as a function

28:52.000 --> 28:53.000
of age.

28:53.000 --> 28:54.000
This is us.

28:54.000 --> 28:55.000
It happens to be a rat version

28:55.000 --> 28:57.000
of us.

28:57.000 --> 28:58.000
And the line there, the solid

28:58.000 --> 29:00.000
line is a prediction from the

29:00.000 --> 29:03.000
theory and the points are the

29:03.000 --> 29:04.000
data, and you see it's very

29:04.000 --> 29:06.000
good.

29:06.000 --> 29:07.000
And this can be done for any

29:07.000 --> 29:09.000
organism, and if I had time, I

29:09.000 --> 29:10.000
would show you lots of other

29:10.000 --> 29:11.000
wonderful fits.

29:11.000 --> 29:12.000
But the point is that we can

29:12.000 --> 29:13.000
understand growth, and I will

29:13.000 --> 29:15.000
come to that in a moment, but

29:15.000 --> 29:18.000
the important thing I want to

29:18.000 --> 29:20.000
stress now, moving to social

29:20.000 --> 29:23.000
systems, is if this were taken

29:23.000 --> 29:25.000
over to socioeconomic systems,

29:25.000 --> 29:28.000
mainly that the organism grows

29:28.000 --> 29:31.000
quickly and then stops like we

29:31.000 --> 29:34.000
did, I mean one of the amazing

29:34.000 --> 29:36.000
things about biology is that we

29:36.000 --> 29:38.000
eat, we grow, and then we go on

29:38.000 --> 29:40.000
eating but we don't grow, and

29:40.000 --> 29:42.000
the theory explains that.

29:42.000 --> 29:43.000
I don't have time to go into it.

29:43.000 --> 29:45.000
Maybe we can discuss it later.

29:45.000 --> 29:47.000
And it's all to do with the

29:47.000 --> 29:48.000
input of energy and the

29:48.000 --> 29:50.000
distribution of energy and the

29:50.000 --> 29:51.000
scaling of the networks that are

29:51.000 --> 29:53.000
inside us.

29:53.000 --> 29:54.000
But the point is that this would

29:54.000 --> 29:56.000
be very bad if it were a

29:56.000 --> 29:58.000
socioeconomic system because

29:58.000 --> 30:00.000
this is our image of

30:00.000 --> 30:01.000
socioeconomic systems, and I

30:01.000 --> 30:03.000
showed you a picture before of

30:03.000 --> 30:05.000
the economy, always open and

30:05.000 --> 30:07.000
expanding.

30:07.000 --> 30:08.000
And I want to come back to this

30:08.000 --> 30:10.000
in a moment.

30:10.000 --> 30:12.000
So, here's a picture of the

30:12.000 --> 30:15.000
extraordinary scaling in

30:15.000 --> 30:17.000
biology.

30:17.000 --> 30:18.000
So, let me spend a minute on it.

30:18.000 --> 30:19.000
What you see that's plotted here

30:19.000 --> 30:21.000
is the most fundamental

30:21.000 --> 30:22.000
quantity, certainly from a

30:22.000 --> 30:24.000
physicist's view, in biology

30:24.000 --> 30:26.000
That is your metabolic rate.

30:26.000 --> 30:28.000
How much energy do you need per

30:28.000 --> 30:30.000
day to stay alive.

30:30.000 --> 30:32.000
The 2,000 food calories a day

30:32.000 --> 30:35.000
that you eat to stay alive, and

30:35.000 --> 30:38.000
here it is plotted on the

30:38.000 --> 30:41.000
vertical axis against the

30:41.000 --> 30:45.000
weight, the mass of the organism

30:45.000 --> 30:46.000
on the horizontal axis, and it's

30:46.000 --> 30:49.000
plotted in a Byzantine way.

30:49.000 --> 30:50.000
So I can put mice and elephants

30:50.000 --> 30:52.000
on the same graph, and the

30:52.000 --> 30:54.000
Byzantine way is that instead of

30:54.000 --> 30:55.000
being linear, it goes up by

30:55.000 --> 30:57.000
factors of 10.

30:57.000 --> 30:59.000
One, 10, 100, 1,000 watts.

30:59.000 --> 31:02.000
One, 10, 100, 1,000 kilograms.

31:02.000 --> 31:03.000
So we can get everyone on the

31:03.000 --> 31:04.000
same graph.

31:04.000 --> 31:06.000
And what you see is something

31:06.000 --> 31:07.000
extraordinary.

31:07.000 --> 31:08.000
That there's an extraordinary

31:08.000 --> 31:11.000
regularity when we're dealing

31:11.000 --> 31:13.000
with maybe one of the most

31:13.000 --> 31:15.000
complex and diverse phenomena in

31:15.000 --> 31:17.000
the universe.

31:17.000 --> 31:19.000
Something unbelievably simple,

31:19.000 --> 31:21.000
ridiculously simple has emerged,

31:21.000 --> 31:23.000
and this, at some level,

31:23.000 --> 31:25.000
astonishing because we believe

31:25.000 --> 31:27.000
that each one of these

31:27.000 --> 31:29.000
organisms, each subsystem of

31:29.000 --> 31:30.000
that organism, each organ, each

31:30.000 --> 31:32.000
cell type, each genome has

31:32.000 --> 31:34.000
evolved with its own unique

31:34.000 --> 31:35.000
history and its own unique

31:35.000 --> 31:38.000
environmental niche.

31:38.000 --> 31:40.000
So, you might have expected from

31:40.000 --> 31:41.000
that natural selection viewpoint

31:41.000 --> 31:43.000
that if I plotted anything

31:43.000 --> 31:46.000
versus the size of an organism,

31:46.000 --> 31:47.000
it'd be all over the map

31:47.000 --> 31:49.000
representing, manifesting the

31:49.000 --> 31:50.000
historical contingency involved

31:50.000 --> 31:52.000
in all of this.

31:52.000 --> 31:54.000
No, that's not what you see.

31:54.000 --> 31:55.000
You see something ridiculously

31:55.000 --> 31:58.000
regular, and that regularity has

31:58.000 --> 32:03.000
some extraordinary features.

32:03.000 --> 32:07.000
For one, the slope of it is

32:07.000 --> 32:10.000
approximately three-quarters,

32:10.000 --> 32:11.000
and that three-quarters is less

32:11.000 --> 32:12.000
than one.

32:12.000 --> 32:13.000
One would be a slope,

32:13.000 --> 32:16.000
a line like this.

32:16.000 --> 32:17.000
And one is what you might

32:17.000 --> 32:19.000
naively expect if you thought

32:19.000 --> 32:20.000
everything were pretty much the

32:20.000 --> 32:22.000
same at the most naive level.

32:22.000 --> 32:23.000
You'd double the size of an

32:23.000 --> 32:24.000
organism, double the number of

32:24.000 --> 32:25.000
cells, therefore doubling the

32:25.000 --> 32:26.000
amount of energy needed to keep

32:26.000 --> 32:27.000
it alive.

32:27.000 --> 32:29.000
That's not what you see.

32:29.000 --> 32:30.000
You see something less than one

32:30.000 --> 32:32.000
which means, in that language,

32:32.000 --> 32:33.000
double the size of an organism,

32:33.000 --> 32:35.000
approximately, instead of

32:35.000 --> 32:36.000
needing twice the amount of

32:36.000 --> 32:37.000
energy, you only need, roughly,

32:37.000 --> 32:39.000
75%.

32:39.000 --> 32:41.000
So there's this extraordinary

32:41.000 --> 32:42.000
economy of scale as you get

32:42.000 --> 32:44.000
bigger, and this is called

32:44.000 --> 32:46.000
sub-linear behavior.

32:46.000 --> 32:49.000
So it turns out that if you look

32:49.000 --> 32:52.000
at any physiological variable

32:52.000 --> 32:55.000
that you can measure or any life

32:55.000 --> 32:56.000
history event that you can think

32:56.000 --> 32:58.000
of, it all has this kind of

32:58.000 --> 32:59.000
character.

32:59.000 --> 33:01.000
And I'm going to show you a

33:01.000 --> 33:02.000
couple of examples.

33:02.000 --> 33:04.000
There's heart rate, a very

33:04.000 --> 33:05.000
mundane kind of quantity, versus

33:05.000 --> 33:07.000
body size.

33:07.000 --> 33:08.000
I'm not going to explain it in

33:08.000 --> 33:10.000
detail, but what you see is,

33:10.000 --> 33:11.000
again, a very simple scaling

33:11.000 --> 33:13.000
law.

33:13.000 --> 33:15.000
This is your thinking.

33:15.000 --> 33:16.000
This is your white matter to

33:16.000 --> 33:18.000
gray matter in your brain.

33:18.000 --> 33:19.000
Again, a very simple law

33:19.000 --> 33:20.000
evolving.

33:20.000 --> 33:22.000
This is your genomes, and what

33:22.000 --> 33:25.000
you see from this is that you

33:25.000 --> 33:27.000
have this regular behavior and

33:27.000 --> 33:29.000
the other remarkable thing is

33:29.000 --> 33:30.000
all the slopes of those, like

33:30.000 --> 33:32.000
that three-quarters, are simple

33:32.000 --> 33:34.000
multiples of one-quarter.

33:34.000 --> 33:36.000
Four plays this amazing role in

33:36.000 --> 33:38.000
these scaling laws of

33:38.000 --> 33:40.000
extraordinarily complex

33:40.000 --> 33:42.000
phenomena.

33:42.000 --> 33:44.000
What is underlying them, and

33:44.000 --> 33:45.000
this is work I did with

33:45.000 --> 33:47.000
colleagues at the Santa Fe

33:47.000 --> 33:49.000
Institute, and I'll tell you who

33:49.000 --> 33:50.000
they are in a moment, the idea

33:50.000 --> 33:52.000
is how in the hell can this be

33:52.000 --> 33:53.000
that all of these phenomena have

33:53.000 --> 33:57.000
simple scaling laws, and why is

33:57.000 --> 33:59.000
it that the scaling laws of the

33:59.000 --> 34:00.000
mammals are the same as for

34:00.000 --> 34:02.000
birds, fish, crustacea, insects,

34:02.000 --> 34:04.000
and so on?

34:04.000 --> 34:05.000
How can that be?

34:05.000 --> 34:07.000
Well, what could be universal

34:07.000 --> 34:08.000
among them?

34:08.000 --> 34:10.000
What could be universal is

34:10.000 --> 34:11.000
somehow the supply of energy and

34:11.000 --> 34:13.000
resources through networks has

34:13.000 --> 34:14.000
to be a commonality among them,

34:14.000 --> 34:17.000
and the idea here was, is that

34:17.000 --> 34:19.000
it is the mathematics, the

34:19.000 --> 34:21.000
universal mathematics and

34:21.000 --> 34:24.000
geometric topological properties

34:24.000 --> 34:27.000
of those networks that transcend

34:27.000 --> 34:29.000
design that are constraining

34:29.000 --> 34:31.000
natural selection.

34:31.000 --> 34:32.000
That's the idea.

34:32.000 --> 34:34.000
And I don't have time to go into

34:34.000 --> 34:36.000
that other than to say that if

34:36.000 --> 34:38.000
you look at these networks,

34:38.000 --> 34:41.000
that's your brain, that's your

34:41.000 --> 34:43.000
white to gray matter of the

34:43.000 --> 34:45.000
brain, that's your lungs, that's

34:45.000 --> 34:46.000
a tree, that's a little thing,

34:46.000 --> 34:48.000
that's inside an elephant, but

34:48.000 --> 34:49.000
they're ubiquitous.

34:49.000 --> 34:51.000
And they have these, the

34:51.000 --> 34:52.000
postulate is they have these

34:52.000 --> 34:54.000
properties, and if you use those

34:54.000 --> 34:56.000
properties, put them into fancy

34:56.000 --> 34:58.000
mathematics and use Bill

34:58.000 --> 35:01.000
Hamilton's principle of energy,

35:01.000 --> 35:03.000
least action kind of ideas, out

35:03.000 --> 35:06.000
pop all of these marvelous

35:06.000 --> 35:08.000
scaling laws, including the

35:08.000 --> 35:09.000
number four, the one-quarter,

35:09.000 --> 35:11.000
which happens to be, for your

35:11.000 --> 35:13.000
information, the dimension that

35:13.000 --> 35:14.000
we live in three plus one.

35:14.000 --> 35:16.000
And just to add to that, the

35:16.000 --> 35:17.000
plus one has to do with the fact

35:17.000 --> 35:19.000
that these networks have a

35:19.000 --> 35:21.000
fractal-like behavior.

35:21.000 --> 35:23.000
That's a kind of tangential

35:23.000 --> 35:25.000
remark.

35:25.000 --> 35:26.000
So, here's others.

35:26.000 --> 35:28.000
This is down at the

35:28.000 --> 35:29.000
intracellular level.

35:29.000 --> 35:30.000
This is the mitochondrial level

35:30.000 --> 35:32.000
and so on.

35:32.000 --> 35:33.000
So here's that growth again.

35:33.000 --> 35:34.000
So, the idea is we have this

35:34.000 --> 35:35.000
theory.

35:35.000 --> 35:36.000
The claim is we have this

35:36.000 --> 35:38.000
wonderful theory based on

35:38.000 --> 35:39.000
networks, the mathematics of

35:39.000 --> 35:40.000
networks, and that from that we

35:40.000 --> 35:42.000
can understand these scaling

35:42.000 --> 35:44.000
laws, but also, if you like, we

35:44.000 --> 35:45.000
understand the network.

35:45.000 --> 35:47.000
We can understand the structure

35:47.000 --> 35:49.000
of the network within each of

35:49.000 --> 35:50.000
the organisms.

35:50.000 --> 35:52.000
And you can use that to go back

35:52.000 --> 35:54.000
to this growth and use this kind

35:54.000 --> 35:55.000
of idea that if you eat, you

35:55.000 --> 35:57.000
metabolize.

35:57.000 --> 35:59.000
That energy goes through the

35:59.000 --> 36:02.000
network, controls the network

36:02.000 --> 36:04.000
which maintains the cells that

36:04.000 --> 36:06.000
are there, replaces ones that

36:06.000 --> 36:08.000
are dead, and grows new ones.

36:08.000 --> 36:09.000
And then, from that, you can

36:09.000 --> 36:11.000
mathematize it, and that gives

36:11.000 --> 36:13.000
rise to this equation which

36:13.000 --> 36:14.000
actually invokes all of the

36:14.000 --> 36:16.000
properties of the network and

36:16.000 --> 36:18.000
gives rise to these kinds of,

36:18.000 --> 36:20.000
what we call, sigmoidal, meaning

36:20.000 --> 36:22.000
they stop growing, curves.

36:22.000 --> 36:25.000
Okay.

36:25.000 --> 36:26.000
So here's kind of a summary of

36:26.000 --> 36:28.000
this part.

36:28.000 --> 36:30.000
We have these amazing non-linear

36:30.000 --> 36:32.000
quarter power scaling laws.

36:32.000 --> 36:33.000
They represent an economy of

36:33.000 --> 36:35.000
scale.

36:35.000 --> 36:37.000
The one-quarter is intimately

36:37.000 --> 36:39.000
related to flows of energy in

36:39.000 --> 36:41.000
networks.

36:41.000 --> 36:43.000
One thing I did not emphasize is

36:43.000 --> 36:45.000
the pace of life systematically

36:45.000 --> 36:47.000
slows with size.

36:47.000 --> 36:49.000
So that, for example, heart

36:49.000 --> 36:51.000
rates decrease systematically

36:51.000 --> 36:53.000
when in to these quarter power

36:53.000 --> 36:54.000
scaling laws.

36:54.000 --> 36:56.000
The rate of diffusion of oxygen,

36:56.000 --> 36:57.000
say, across membranes decreases

36:57.000 --> 36:59.000
with size.

36:59.000 --> 37:01.000
Organisms live longer

37:01.000 --> 37:02.000
systematically.

37:02.000 --> 37:04.000
And so on and so forth.

37:04.000 --> 37:05.000
Everything to do with time slows

37:05.000 --> 37:07.000
down the bigger you are.

37:07.000 --> 37:09.000
Growth is sigmoidal.

37:09.000 --> 37:10.000
It reaches a stable size of

37:10.000 --> 37:11.000
maturity.

37:11.000 --> 37:13.000
And to emphasize again, the idea

37:13.000 --> 37:14.000
is this is all based on

37:14.000 --> 37:16.000
mathematics and properties of

37:16.000 --> 37:18.000
networks.

37:18.000 --> 37:20.000
So, and this is a sustainable

37:20.000 --> 37:22.000
system.

37:22.000 --> 37:24.000
It's sustainable,

37:24.000 --> 37:25.000
phenomenologically, because it's

37:25.000 --> 37:27.000
been around a couple billion

37:27.000 --> 37:29.000
years or more.

37:29.000 --> 37:31.000
Part of that sustainability, I

37:31.000 --> 37:33.000
believe, is actually invoked in

37:33.000 --> 37:36.000
the fact that things stop

37:36.000 --> 37:37.000
growing.

37:37.000 --> 37:39.000
They stabilize and most

37:39.000 --> 37:41.000
organisms, not all, we can

37:41.000 --> 37:43.000
discuss this, most organisms

37:43.000 --> 37:45.000
spend most of their mature life

37:45.000 --> 37:48.000
in a stable size configuration.

37:48.000 --> 37:50.000
Not all and there are

37:50.000 --> 37:51.000
exceptions, and we can discuss

37:51.000 --> 37:52.000
that.

37:52.000 --> 37:54.000
But this leads to a sustainable,

37:54.000 --> 37:55.000
resilient system.

37:55.000 --> 37:57.000
And the question is, I'm sorry.

37:57.000 --> 37:59.000
Yes, so this is the motley crew

37:59.000 --> 38:00.000
that I work with.

38:00.000 --> 38:01.000
Some biologists, some

38:01.000 --> 38:04.000
physicists, some chemists.

38:04.000 --> 38:06.000
But I then took the work over

38:06.000 --> 38:07.000
into social organizations, and

38:07.000 --> 38:09.000
there's another motley crew.

38:09.000 --> 38:11.000
The top guys did all the work.

38:11.000 --> 38:13.000
All the guys at the bottom are

38:13.000 --> 38:15.000
famous and did none of the work.

38:15.000 --> 38:16.000
[LAUGHTER]

38:16.000 --> 38:18.000
And the guys in the middle

38:18.000 --> 38:20.000
started off with me and then

38:20.000 --> 38:22.000
moved on to other things.

38:22.000 --> 38:24.000
Okay, so here's the question,

38:24.000 --> 38:26.000
the first question about whether

38:26.000 --> 38:28.000
cities and companies are

38:28.000 --> 38:30.000
biological.

38:30.000 --> 38:32.000
The first thing is, do they

38:32.000 --> 38:33.000
scale?

38:33.000 --> 38:36.000
So what we said before was that

38:36.000 --> 38:38.000
whales live in the ocean and

38:38.000 --> 38:40.000
elephants have trunks and

38:40.000 --> 38:41.000
giraffes long necks and we stand

38:41.000 --> 38:43.000
on two feet and mice scurry

38:43.000 --> 38:44.000
around, but in fact, at the kind

38:44.000 --> 38:46.000
of 85% level, they're scale

38:46.000 --> 38:48.000
versions of one another in this

38:48.000 --> 38:50.000
non-linear quarter power

38:50.000 --> 38:51.000
fashion.

38:51.000 --> 38:53.000
In this mathematical fashion,

38:53.000 --> 38:54.000
they're scaled versions of one

38:54.000 --> 38:55.000
another.

38:55.000 --> 38:57.000
Is that true of cities?

38:57.000 --> 38:58.000
And is it the kind of universal

38:58.000 --> 39:00.000
scaling as it is in biology?

39:00.000 --> 39:02.000
So, are cities scaled versions

39:02.000 --> 39:03.000
of one another?

39:03.000 --> 39:04.000
Is New York a scaled up

39:04.000 --> 39:06.000
San Francisco, which is

39:06.000 --> 39:07.000
a scaled up Madison,

39:07.000 --> 39:09.000
which is a scaled up Santa Fe?

39:09.000 --> 39:11.000
Even though they look completely

39:11.000 --> 39:12.000
different.

39:12.000 --> 39:14.000
Well, you can only answer that,

39:14.000 --> 39:15.000
at first light it's hard to

39:15.000 --> 39:17.000
believe, every city feels so

39:17.000 --> 39:19.000
unique.

39:19.000 --> 39:20.000
But of course, you can only

39:20.000 --> 39:22.000
answer that by looking at data.

39:22.000 --> 39:25.000
But one of the reasons you might

39:25.000 --> 39:28.000
think there are scaling

39:28.000 --> 39:30.000
phenomena, there are these kind

39:30.000 --> 39:31.000
of generic universal

39:31.000 --> 39:33.000
similarities, is because cities

39:33.000 --> 39:35.000
are, indeed, networks.

39:35.000 --> 39:37.000
There's the obvious networks of

39:37.000 --> 39:40.000
roads and electrical lines and

39:40.000 --> 39:41.000
all the rest of it, like this,

39:41.000 --> 39:44.000
and transportation systems, but,

39:44.000 --> 39:47.000
more importantly, there's this

39:47.000 --> 39:50.000
network which never occurs,

39:50.000 --> 39:51.000
essentially, in biology.

39:51.000 --> 39:53.000
It is the network of us

39:53.000 --> 39:54.000
talking to one another.

39:54.000 --> 39:56.000
It's us, social networks,

39:56.000 --> 39:57.000
that is unique.

39:57.000 --> 39:59.000
It's only been around on this

39:59.000 --> 40:03.000
planet for maybe 10,000 years.

40:03.000 --> 40:05.000
Maybe in the universe for all we

40:05.000 --> 40:06.000
know for 10,000 years.

40:06.000 --> 40:07.000
Who in the hell knows.

40:07.000 --> 40:08.000
But, certainly, in our solar

40:08.000 --> 40:09.000
system.

40:09.000 --> 40:10.000
This is it.

40:10.000 --> 40:12.000
It's new.

40:12.000 --> 40:13.000
And it's this.

40:13.000 --> 40:15.000
This is just people talking to

40:15.000 --> 40:16.000
one another, how they interact

40:16.000 --> 40:18.000
with one another.

40:18.000 --> 40:20.000
So, oops, I seem to have lost

40:20.000 --> 40:22.000
the slide.

40:22.000 --> 40:23.000
So, I use this slide.

40:23.000 --> 40:24.000
What is the city but the people.

40:24.000 --> 40:26.000
Indeed, the city is not the

40:26.000 --> 40:27.000
buildings and the roads and all

40:27.000 --> 40:29.000
this other stuff.

40:29.000 --> 40:31.000
It's us.

40:31.000 --> 40:33.000
We are the city, and that stuff

40:33.000 --> 40:35.000
is a manifestation of the

40:35.000 --> 40:36.000
interactions between us.

40:36.000 --> 40:38.000
And that's something that is

40:38.000 --> 40:40.000
outside of biology but is

40:40.000 --> 40:41.000
integral to biology because it's

40:41.000 --> 40:43.000
driven by biology, and it's

40:43.000 --> 40:45.000
driven, in large part, by

40:45.000 --> 40:47.000
energy, and I'm going to talk

40:47.000 --> 40:49.000
about that.

40:49.000 --> 40:51.000
So, the other part of the

40:51.000 --> 40:52.000
network is not just we interact

40:52.000 --> 40:54.000
with each other but we prosper.

40:54.000 --> 40:55.000
So, I'm going to not say much

40:55.000 --> 40:57.000
more about that but go straight

40:57.000 --> 40:59.000
to the data and ask the question

40:59.000 --> 41:00.000
about scaling.

41:00.000 --> 41:03.000
So this is a graph plotted in

41:03.000 --> 41:05.000
the same way as those biological

41:05.000 --> 41:07.000
graphs were, but this is a

41:07.000 --> 41:08.000
mundane one.

41:08.000 --> 41:10.000
The number of petrol stations,

41:10.000 --> 41:11.000
I was working with European

41:11.000 --> 41:13.000
colleagues, the number of gas

41:13.000 --> 41:14.000
stations in a city as a function

41:14.000 --> 41:15.000
of its size in various

41:15.000 --> 41:17.000
countries.

41:17.000 --> 41:18.000
And you can see, there's pretty

41:18.000 --> 41:21.000
good evidence of scaling again,

41:21.000 --> 41:23.000
there's a very simple line on

41:23.000 --> 41:25.000
this plot, plotted this way.

41:25.000 --> 41:27.000
Very much like biology, and like

41:27.000 --> 41:29.000
biology, this would be linear so

41:29.000 --> 41:32.000
that you don't, when you double

41:32.000 --> 41:34.000
the size of a city, you don't

41:34.000 --> 41:35.000
need twice as many gas stations.

41:35.000 --> 41:37.000
In fact, what you find from

41:37.000 --> 41:39.000
this, you only need about 85%

41:39.000 --> 41:41.000
more gas stations.

41:41.000 --> 41:42.000
The slope of this line is not

41:42.000 --> 41:44.000
three-quarters as it is in

41:44.000 --> 41:45.000
biology.

41:45.000 --> 41:47.000
It's about .85 it turns out.

41:47.000 --> 41:49.000
Most importantly, though, is

41:49.000 --> 41:50.000
that they're pretty much the

41:50.000 --> 41:52.000
same.

41:52.000 --> 41:53.000
The slopes of these are the

41:53.000 --> 41:54.000
same.

41:54.000 --> 41:56.000
The same economy of scale occurs

41:56.000 --> 41:57.000
in all these European countries

41:57.000 --> 41:59.000
but it occurs in Columbia, it

41:59.000 --> 42:00.000
occurs in Chile, it occurs in

42:00.000 --> 42:02.000
China.

42:02.000 --> 42:03.000
Anywhere you look across the

42:03.000 --> 42:05.000
globe, you get the same scaling,

42:05.000 --> 42:08.000
and if you look at any

42:08.000 --> 42:09.000
infrastructural quantity that

42:09.000 --> 42:11.000
you can measure, you get the

42:11.000 --> 42:12.000
same scaling as this.

42:12.000 --> 42:14.000
There's a consistent, always

42:14.000 --> 42:17.000
roughly 15% saving every time

42:17.000 --> 42:18.000
you double.

42:18.000 --> 42:20.000
Okay?

42:20.000 --> 42:21.000
>> Geoffrey?

42:21.000 --> 42:22.000
>> Yeah.

42:22.000 --> 42:23.000
>> Ten.

42:23.000 --> 42:24.000
>> Good, perfect.

42:24.000 --> 42:25.000
So this is kind of a mundane

42:25.000 --> 42:27.000
quantity, and that's like

42:27.000 --> 42:28.000
biology.

42:28.000 --> 42:29.000
Looks just like biology

42:29.000 --> 42:30.000
except it's .85

42:30.000 --> 42:33.000
instead of .75.

42:33.000 --> 42:34.000
But this is something that

42:34.000 --> 42:36.000
doesn't exist in biology.

42:36.000 --> 42:39.000
Wages, super-creative people,

42:39.000 --> 42:41.000
and you ask, how do those scale

42:41.000 --> 42:43.000
with the size of the city?

42:43.000 --> 42:45.000
And here they are.

42:45.000 --> 42:47.000
This is wages at the top, and

42:47.000 --> 42:50.000
this is super-creatives,

42:50.000 --> 42:51.000
so-called super-creatives by a

42:51.000 --> 42:53.000
man named Richard Florida.

42:53.000 --> 42:55.000
And what you see is there's more

42:55.000 --> 42:57.000
fluctuations in the data, but

42:57.000 --> 42:58.000
there's pretty good evidence of

42:58.000 --> 43:00.000
scaling.

43:00.000 --> 43:01.000
But, most importantly, the

43:01.000 --> 43:03.000
scaling is different than in

43:03.000 --> 43:04.000
biology.

43:04.000 --> 43:06.000
The slope of these are bigger

43:06.000 --> 43:07.000
than one rather than less than

43:07.000 --> 43:09.000
one, and this is critical.

43:09.000 --> 43:11.000
Namely, the slope of these you

43:11.000 --> 43:13.000
see is roughly about 1.15.

43:13.000 --> 43:15.000
So what that says is that if you

43:15.000 --> 43:19.000
double the size, instead of

43:19.000 --> 43:21.000
needing less per capita, you

43:21.000 --> 43:23.000
have more per capita, and I will

43:23.000 --> 43:25.000
come back to this in a moment.

43:25.000 --> 43:27.000
So here's wages, super-creative.

43:27.000 --> 43:29.000
Here's patents.

43:29.000 --> 43:31.000
This is some crude measure of

43:31.000 --> 43:33.000
the innovation of a city.

43:33.000 --> 43:36.000
The slope of this is also about

43:36.000 --> 43:37.000
1.15.

43:37.000 --> 43:40.000
This is the crime in Japan, a

43:40.000 --> 43:42.000
little bit bigger 1.15.

43:42.000 --> 43:44.000
This is police, tax receipts,

43:44.000 --> 43:47.000
construction, all about 1.15.

43:47.000 --> 43:48.000
And there they are, all plotted

43:48.000 --> 43:50.000
on one another, just a few of

43:50.000 --> 43:52.000
them to show a universality of

43:52.000 --> 43:55.000
income, GDP, crime, and patents.

43:55.000 --> 43:58.000
Very different things but they

43:58.000 --> 43:59.000
all scale in the same way with

43:59.000 --> 44:02.000
quite a lot of fluctuations, and

44:02.000 --> 44:03.000
the slope of that is roughly

44:03.000 --> 44:06.000
1.15.

44:06.000 --> 44:09.000
And we believe that underlying

44:09.000 --> 44:11.000
are the universality of social

44:11.000 --> 44:14.000
networks.

44:14.000 --> 44:16.000
The way we interact with each

44:16.000 --> 44:18.000
other is a kind of universal

44:18.000 --> 44:20.000
phenomenon whether you're in

44:20.000 --> 44:22.000
China, Japan, Columbia,

44:22.000 --> 44:24.000
United States, anywhere.

44:24.000 --> 44:26.000
The fundamentals of the social

44:26.000 --> 44:28.000
interaction is universal, and

44:28.000 --> 44:30.000
that is what is being manifested

44:30.000 --> 44:32.000
in these scaling laws.

44:32.000 --> 44:33.000
These scaling laws are for

44:33.000 --> 44:35.000
multiple socioeconomic

44:35.000 --> 44:36.000
quantities anywhere in the

44:36.000 --> 44:38.000
world.

44:38.000 --> 44:39.000
We've looked at data in Europe,

44:39.000 --> 44:41.000
United States, China, Japan,

44:41.000 --> 44:42.000
Latin America, and so forth.

44:42.000 --> 44:45.000
What is interesting to

44:45.000 --> 44:47.000
substantiate that is this graph

44:47.000 --> 44:48.000
which is very recent data which

44:48.000 --> 44:51.000
is the number of cell phone, the

44:51.000 --> 44:53.000
amount of people you call on

44:53.000 --> 44:56.000
your phone, on the average, as a

44:56.000 --> 44:58.000
function of city size.

44:58.000 --> 45:00.000
So this is a kind of way of

45:00.000 --> 45:02.000
tapping in to the network, the

45:02.000 --> 45:04.000
social network, and if this

45:04.000 --> 45:07.000
theoretical framework of the

45:07.000 --> 45:09.000
mathematics of that social

45:09.000 --> 45:12.000
network is the right one, the

45:12.000 --> 45:14.000
slope of this should be about

45:14.000 --> 45:15.000
the same as these other

45:15.000 --> 45:16.000
networks.

45:16.000 --> 45:18.000
And indeed, we could have

45:18.000 --> 45:19.000
plotted this on top of this, and

45:19.000 --> 45:21.000
it would fit right on top of it.

45:21.000 --> 45:23.000
So this is the number of people

45:23.000 --> 45:25.000
you talk to, on the average, as

45:25.000 --> 45:27.000
a function of your city size.

45:27.000 --> 45:30.000
So, I'm going to miss this.

45:30.000 --> 45:32.000
That's what I said a moment ago.

45:32.000 --> 45:34.000
And here's kind of a summary of

45:34.000 --> 45:35.000
this.

45:35.000 --> 45:37.000
The good, the bad, and the ugly.

45:37.000 --> 45:39.000
If you double the size of a city

45:39.000 --> 45:40.000
or if you look at a city that's

45:40.000 --> 45:42.000
twice as big as another city,

45:42.000 --> 45:44.000
then systematically across the

45:44.000 --> 45:48.000
globe, and you double the size,

45:48.000 --> 45:50.000
you can go from 50,000 to

45:50.000 --> 45:51.000
100,000 or 5 million to 10

45:51.000 --> 45:53.000
million, it doesn't matter where

45:53.000 --> 45:55.000
you begin to double the size,

45:55.000 --> 45:57.000
systematically, income, wealth,

45:57.000 --> 45:59.000
patents, colleges, creative

45:59.000 --> 46:01.000
people, police, crime, AIDS,

46:01.000 --> 46:03.000
flu, all of these things, the

46:03.000 --> 46:04.000
good, bad, and the ugly all go

46:04.000 --> 46:06.000
up together to the same degree.

46:06.000 --> 46:09.000
And, at the same time, you save

46:09.000 --> 46:12.000
about 15% on the infrastructure.

46:12.000 --> 46:15.000
Big cities are good, and the

46:15.000 --> 46:17.000
bigger ones are better still

46:17.000 --> 46:18.000
both at the collective level.

46:18.000 --> 46:21.000
They save on infrastructure per

46:21.000 --> 46:22.000
capita.

46:22.000 --> 46:24.000
I had a graph which I flipped

46:24.000 --> 46:25.000
through.

46:25.000 --> 46:26.000
They save on carbon footprint

46:26.000 --> 46:27.000
per capita.

46:27.000 --> 46:30.000
And at the individual level,

46:30.000 --> 46:33.000
everybody is attracted to cities

46:33.000 --> 46:35.000
by the first lot of those,

46:35.000 --> 46:36.000
income, wealth, number of

46:36.000 --> 46:38.000
patents, colleges, and the

46:38.000 --> 46:39.000
general buzz of city, and ignore

46:39.000 --> 46:40.000
the other bad things.

46:40.000 --> 46:42.000
The fact that they're coming

46:42.000 --> 46:43.000
along with it, hand in glove,

46:43.000 --> 46:45.000
there's more crime, disease, and

46:45.000 --> 46:47.000
so forth.

46:47.000 --> 46:50.000
Okay.

46:50.000 --> 46:51.000
So, the network dynamics is the

46:51.000 --> 46:53.000
one that dominates this again,

46:53.000 --> 46:55.000
and the networks dynamics has

46:55.000 --> 46:57.000
the following interesting

46:57.000 --> 47:00.000
phenomenon that if it's

47:00.000 --> 47:03.000
sub-linear scaling then we have

47:03.000 --> 47:05.000
this pace of life slowing with

47:05.000 --> 47:07.000
size, as I mentioned, but if

47:07.000 --> 47:09.000
it's super linear, bigger than

47:09.000 --> 47:11.000
one like we see in socioeconomic

47:11.000 --> 47:13.000
systems in cities and driven by

47:13.000 --> 47:16.000
the social networks, then the

47:16.000 --> 47:18.000
theory tells you the pace of

47:18.000 --> 47:20.000
life systematically increases

47:20.000 --> 47:22.000
with size.

47:22.000 --> 47:25.000
And so, the pace of life in

47:25.000 --> 47:26.000
New York is systematically

47:26.000 --> 47:28.000
faster than it is in Madison,

47:28.000 --> 47:31.000
which is systematically faster

47:31.000 --> 47:33.000
than it is in Espanola, which is

47:33.000 --> 47:35.000
a tiny town near Santa Fe

47:35.000 --> 47:37.000
in a systematic way.

47:37.000 --> 47:39.000
And here's some whimsical data

47:39.000 --> 47:41.000
to show you that.

47:41.000 --> 47:42.000
We've looked at many things.

47:42.000 --> 47:44.000
On the left is some data on

47:44.000 --> 47:45.000
heart rate versus body size.

47:45.000 --> 47:47.000
The one on the right is walking

47:47.000 --> 47:48.000
speed.

47:48.000 --> 47:50.000
I show you walking speed.

47:50.000 --> 47:51.000
People have actually measured

47:51.000 --> 47:53.000
walking speed perfect.

47:53.000 --> 47:55.000
And you can see it

47:55.000 --> 47:56.000
systematically rising, and it's

47:56.000 --> 47:58.000
in reasonably good agreement

47:58.000 --> 47:59.000
with the data.

47:59.000 --> 48:01.000
But why?

48:01.000 --> 48:03.000
Because walking, actually,

48:03.000 --> 48:04.000
walking in a city is a social

48:04.000 --> 48:05.000
phenomenon.

48:05.000 --> 48:07.000
Okay, so I'm going to miss this

48:07.000 --> 48:09.000
out, I think.

48:09.000 --> 48:12.000
Yes.

48:12.000 --> 48:13.000
So, this is kind of a summary,

48:13.000 --> 48:15.000
and I'm going to finish off in

48:15.000 --> 48:16.000
the last few minutes with the

48:16.000 --> 48:18.000
last part of this.

48:18.000 --> 48:20.000
So, unlike biology, super linear

48:20.000 --> 48:22.000
scaling, socioeconomic, which

48:22.000 --> 48:24.000
has to do with wealth creation

48:24.000 --> 48:26.000
and innovation and ideas,

48:26.000 --> 48:28.000
dominates, and so you get this

48:28.000 --> 48:30.000
kind of 15% rule, which I'm not

48:30.000 --> 48:32.000
going to talk about its origins

48:32.000 --> 48:34.000
here, that also leads to a

48:34.000 --> 48:36.000
systematic increase in the pace

48:36.000 --> 48:37.000
of life.

48:37.000 --> 48:39.000
And the last thing that I'm

48:39.000 --> 48:40.000
going to talk about which is

48:40.000 --> 48:43.000
very satisfactory is that if

48:43.000 --> 48:44.000
this is true, which it is, and

48:44.000 --> 48:46.000
it comes from these networks,

48:46.000 --> 48:48.000
then I'm going to talk about

48:48.000 --> 48:50.000
growth and the idea of

48:50.000 --> 48:52.000
open-ended growth following from

48:52.000 --> 48:53.000
this because we're going to use

48:53.000 --> 48:55.000
the same kind of growth equation

48:55.000 --> 48:58.000
that, again, based on energy

48:58.000 --> 49:01.000
equivalent of the flow of energy

49:01.000 --> 49:03.000
through these systems leading to

49:03.000 --> 49:05.000
maintenance and growth, and

49:05.000 --> 49:06.000
that's what we had before for

49:06.000 --> 49:08.000
us.

49:08.000 --> 49:09.000
The sub-linear scaling leading

49:09.000 --> 49:11.000
to boundary growth, and if it's

49:11.000 --> 49:13.000
super linear scaling, there's a

49:13.000 --> 49:15.000
cartoon on the left, this leads

49:15.000 --> 49:18.000
to actually faster than

49:18.000 --> 49:20.000
exponential growth.

49:20.000 --> 49:22.000
And that's very satisfying.

49:22.000 --> 49:24.000
That's great because that's what

49:24.000 --> 49:26.000
we see, and that's what we

49:26.000 --> 49:27.000
apparently love or have loved

49:27.000 --> 49:29.000
for the last couple hundred

49:29.000 --> 49:30.000
years.

49:30.000 --> 49:32.000
And that's great.

49:32.000 --> 49:33.000
However, it has, if you believe

49:33.000 --> 49:34.000
the theory, this has a fatal

49:34.000 --> 49:36.000
flaw.

49:36.000 --> 49:38.000
And the fatal flaw is denoted by

49:38.000 --> 49:40.000
this line.

49:40.000 --> 49:41.000
And it's called, in the

49:41.000 --> 49:43.000
technical language, a finite

49:43.000 --> 49:45.000
time singularity.

49:45.000 --> 49:46.000
And to put it in very simple

49:46.000 --> 49:48.000
language, what it says is that

49:48.000 --> 49:50.000
in some finite time, this damn

49:50.000 --> 49:52.000
growth curve will go to infinite

49:52.000 --> 49:54.000
size, and that's obviously

49:54.000 --> 49:56.000
impossible.

49:56.000 --> 49:57.000
So this is kind of a Malthusian

49:57.000 --> 49:59.000
argument.

49:59.000 --> 50:00.000
Namely, at some stage, you're

50:00.000 --> 50:02.000
going to run out of whatever it

50:02.000 --> 50:03.000
is that's keeping the system

50:03.000 --> 50:05.000
alive, the resources, the

50:05.000 --> 50:07.000
energy, whatever it is, and the

50:07.000 --> 50:08.000
theory tells you what happens.

50:08.000 --> 50:09.000
It says as you go through this

50:09.000 --> 50:10.000
so-called singularity,

50:10.000 --> 50:12.000
you stagnate and collapse.

50:12.000 --> 50:13.000
That's terrible and we need to

50:13.000 --> 50:14.000
avoid that, and we have avoided

50:14.000 --> 50:16.000
it.

50:16.000 --> 50:18.000
And this is how we avoid it.

50:18.000 --> 50:20.000
We avoid it for the very reason

50:20.000 --> 50:23.000
that Malthus and Paul Ehrlich

50:23.000 --> 50:26.000
were rejected.

50:26.000 --> 50:28.000
That is, they did not take into

50:28.000 --> 50:30.000
account the fact that we

50:30.000 --> 50:31.000
innovate ourselves out of these

50:31.000 --> 50:33.000
problems.

50:33.000 --> 50:34.000
So I want to finish on this

50:34.000 --> 50:35.000
note.

50:35.000 --> 50:37.000
So here's the situation.

50:37.000 --> 50:38.000
This is, you start at some point

50:38.000 --> 50:41.000
and you start growing and you

50:41.000 --> 50:43.000
would hit this singularity and

50:43.000 --> 50:44.000
collapse.

50:44.000 --> 50:47.000
So somewhere along here you

50:47.000 --> 50:49.000
better change something because

50:49.000 --> 50:53.000
this growth was done in the

50:53.000 --> 50:55.000
paradigm of whatever the major

50:55.000 --> 50:56.000
innovation is.

50:56.000 --> 50:57.000
It could be the discovery of

50:57.000 --> 50:58.000
iron.

50:58.000 --> 51:00.000
It could be the discovery of

51:00.000 --> 51:01.000
coal.

51:01.000 --> 51:02.000
It could be oil.

51:02.000 --> 51:03.000
It could be the invention of

51:03.000 --> 51:04.000
computers.

51:04.000 --> 51:06.000
The invention of IT, but

51:06.000 --> 51:08.000
something major that has a kind

51:08.000 --> 51:09.000
of paradigm shift and changes

51:09.000 --> 51:10.000
everything.

51:10.000 --> 51:12.000
So, this suggests that the way

51:12.000 --> 51:14.000
out of this dilemma of collapse

51:14.000 --> 51:17.000
is that you innovate somewhere

51:17.000 --> 51:19.000
here, and you start over again.

51:19.000 --> 51:21.000
You reset the clock, and then

51:21.000 --> 51:23.000
you can go on merrily.

51:23.000 --> 51:24.000
And, of course, you're going to

51:24.000 --> 51:26.000
hit another finite time

51:26.000 --> 51:27.000
singularity and collapse unless

51:27.000 --> 51:28.000
you innovate again.

51:28.000 --> 51:30.000
So you have to keep innovating.

51:30.000 --> 51:31.000
So there's a kind of theorem if

51:31.000 --> 51:33.000
you like.

51:33.000 --> 51:35.000
But if you want to continue to

51:35.000 --> 51:37.000
have open-ended growth, you have

51:37.000 --> 51:39.000
to have cycles of innovation.

51:39.000 --> 51:40.000
Well, people have been saying

51:40.000 --> 51:41.000
things like that for a long

51:41.000 --> 51:43.000
time.

51:43.000 --> 51:44.000
Here's the difference, going

51:44.000 --> 51:46.000
back to what we said: as you

51:46.000 --> 51:49.000
grow and you get bigger, the

51:49.000 --> 51:51.000
pace of life increases.

51:51.000 --> 51:53.000
So, first of all, the pace of

51:53.000 --> 51:54.000
life is increasing.

51:54.000 --> 51:55.000
The second thing is, it turns

51:55.000 --> 51:57.000
out the theory says yes you can

51:57.000 --> 51:58.000
do this, but the time to go from

51:58.000 --> 52:01.000
here to here necessarily has to

52:01.000 --> 52:02.000
be, and systematically, a lot

52:02.000 --> 52:05.000
shorter than the time from here

52:05.000 --> 52:08.000
to here.

52:08.000 --> 52:10.000
So that you can innovate, you

52:10.000 --> 52:11.000
have these cycles of innovation,

52:11.000 --> 52:12.000
but they must come quicker and

52:12.000 --> 52:14.000
quicker.

52:14.000 --> 52:15.000
Okay?

52:15.000 --> 52:17.000
So, the image is we're on this

52:17.000 --> 52:19.000
treadmill that is going faster

52:19.000 --> 52:21.000
and faster, which is difficult

52:21.000 --> 52:24.000
to begin with, but at some

52:24.000 --> 52:26.000
stage, you've got to jump from

52:26.000 --> 52:27.000
that treadmill onto another

52:27.000 --> 52:28.000
treadmill that's going even

52:28.000 --> 52:30.000
faster.

52:30.000 --> 52:31.000
And then very soon, you've got

52:31.000 --> 52:32.000
to jump onto another one, and

52:32.000 --> 52:34.000
you've got to kind of keep doing

52:34.000 --> 52:36.000
this so you have this kind of

52:36.000 --> 52:37.000
double acceleration.

52:37.000 --> 52:38.000
And the question is, do you

52:38.000 --> 52:39.000
suffer a kind of global heart

52:39.000 --> 52:41.000
attack from that?

52:41.000 --> 52:42.000
[LAUGHTER]

52:42.000 --> 52:44.000
So, is this sustainable?

52:44.000 --> 52:46.000
Obviously not, ultimately.

52:46.000 --> 52:48.000
And this is not mine at all.

52:48.000 --> 52:50.000
This is someone, I don't know

52:50.000 --> 52:51.000
who he is, but he did it and he

52:51.000 --> 52:53.000
showed it was great.

52:53.000 --> 52:56.000
Oh, this is something I saw in

52:56.000 --> 52:58.000
an English newspaper a week ago

52:58.000 --> 53:00.000
which I thought illustrated the

53:00.000 --> 53:02.000
point a little bit.

53:02.000 --> 53:05.000
So here's something, some of you

53:05.000 --> 53:07.000
may have heard the idea of

53:07.000 --> 53:09.000
singularity from this, what I

53:09.000 --> 53:12.000
consider, little bit looney work

53:12.000 --> 53:14.000
of Ray Kurzweil.

53:14.000 --> 53:16.000
I think it's looney, but he did

53:16.000 --> 53:18.000
some wonderful things of

53:18.000 --> 53:19.000
collecting data.

53:19.000 --> 53:21.000
I don't agree with a lot of

53:21.000 --> 53:22.000
this, but what he's plotted here

53:22.000 --> 53:24.000
is that here's the paradigm

53:24.000 --> 53:26.000
shift.

53:26.000 --> 53:28.000
That is, how long it took to

53:28.000 --> 53:30.000
create each one of these

53:30.000 --> 53:33.000
paradigms versus how long ago it

53:33.000 --> 53:34.000
happened.

53:34.000 --> 53:36.000
Okay?

53:36.000 --> 53:39.000
And what you see is it takes

53:39.000 --> 53:40.000
shorter and shorter times to

53:40.000 --> 53:43.000
make these shifts, and it's

53:43.000 --> 53:45.000
happening faster and faster.

53:45.000 --> 53:47.000
And he drew this.

53:47.000 --> 53:48.000
All kinds of people have done

53:48.000 --> 53:50.000
things like this.

53:50.000 --> 53:51.000
I'm going to finish up.

53:51.000 --> 53:54.000
And, whoops, and interestingly

53:54.000 --> 53:56.000
enough, that straight line, if

53:56.000 --> 53:58.000
you take this theoretical

53:58.000 --> 53:59.000
framework that I just told you

53:59.000 --> 54:01.000
about, this continuous

54:01.000 --> 54:03.000
innovation and jumping from one

54:03.000 --> 54:04.000
treadmill to another, it

54:04.000 --> 54:06.000
predicts, almost exactly, that

54:06.000 --> 54:07.000
orange line.

54:07.000 --> 54:09.000
So that's data kind of

54:09.000 --> 54:10.000
supporting it.

54:10.000 --> 54:12.000
So I'm going to finish with

54:12.000 --> 54:13.000
that, and I'm going to finish

54:13.000 --> 54:15.000
with one last image which is

54:15.000 --> 54:17.000
this just to give you a sense of

54:17.000 --> 54:19.000
things in terms of energy.

54:19.000 --> 54:21.000
Our metabolic rate, I didn't

54:21.000 --> 54:22.000
point that out on the graph.

54:22.000 --> 54:23.000
You remember that graph of

54:23.000 --> 54:24.000
metabolic rate?

54:24.000 --> 54:26.000
Our metabolic rate in watts is

54:26.000 --> 54:27.000
about 90 watts.

54:27.000 --> 54:29.000
A little bit less, women versus

54:29.000 --> 54:30.000
men.

54:30.000 --> 54:31.000
But roughly it's about 90 watts.

54:31.000 --> 54:33.000
We're a light bulb.

54:33.000 --> 54:34.000
One of these bloody light bulbs

54:34.000 --> 54:37.000
is equivalent to one of us.

54:37.000 --> 54:39.000
So, turning a light bulb off,

54:39.000 --> 54:41.000
it's like your mother telling

54:41.000 --> 54:42.000
you if you turn it off, you help

54:42.000 --> 54:44.000
starving people in Sudan or

54:44.000 --> 54:45.000
somewhere.

54:45.000 --> 54:47.000
So it's ridiculous having all

54:47.000 --> 54:48.000
these lights on, frankly.

54:48.000 --> 54:50.000
They're us.

54:50.000 --> 54:51.000
We are extraordinarily

54:51.000 --> 54:53.000
efficient.

54:53.000 --> 54:55.000
That 1,800-2,000 food calories a

54:55.000 --> 54:57.000
day is unbelievably efficient.

54:57.000 --> 54:59.000
It may seem like a lot of food,

54:59.000 --> 55:01.000
but it's just a light bulb.

55:01.000 --> 55:04.000
However, and incidentally, that

55:04.000 --> 55:07.000
metabolic rate is the metabolic

55:07.000 --> 55:10.000
rate we should have for a mammal

55:10.000 --> 55:13.000
of our size.

55:13.000 --> 55:14.000
That's what we should have.

55:14.000 --> 55:15.000
Okay?

55:15.000 --> 55:17.000
And if you add in activity,

55:17.000 --> 55:19.000
hunting and gathering, which is

55:19.000 --> 55:21.000
the way we evolved or versions

55:21.000 --> 55:23.000
there of, that number changes

55:23.000 --> 55:25.000
from 100 to about 250, the

55:25.000 --> 55:26.000
amount of energy we do to hunt

55:26.000 --> 55:28.000
and gather and so on.

55:28.000 --> 55:30.000
And that also scales the active

55:30.000 --> 55:32.000
metabolic rate for mammals.

55:32.000 --> 55:34.000
So that's what we "should have."

55:34.000 --> 55:35.000
Then we started talking to one

55:35.000 --> 55:37.000
another.

55:37.000 --> 55:40.000
We discovered economies of scale

55:40.000 --> 55:41.000
to form communities, and then we

55:41.000 --> 55:42.000
started to innovate.

55:42.000 --> 55:44.000
And once we innovated,

55:44.000 --> 55:45.000
we created all of this [###]

55:45.000 --> 55:47.000
around us, all this wonderful

55:47.000 --> 55:48.000
quality of life and standard of

55:48.000 --> 55:50.000
living that we'd like to have

55:50.000 --> 55:51.000
for everybody.

55:51.000 --> 55:53.000
And we can ask, what is our real

55:53.000 --> 55:54.000
metabolic rate?

55:54.000 --> 55:55.000
What is our social metabolic

55:55.000 --> 55:57.000
rate of having cars, and lights,

55:57.000 --> 55:58.000
and buildings, and all the rest

55:58.000 --> 56:00.000
of the stuff?

56:00.000 --> 56:02.000
That number goes from about

56:02.000 --> 56:06.000
90 watts to 11,000 watts.

56:06.000 --> 56:07.000
And you can turn it around, this

56:07.000 --> 56:09.000
has been done on this graph as I

56:09.000 --> 56:10.000
did here, and ask, how big is

56:10.000 --> 56:12.000
it?

56:12.000 --> 56:13.000
What big an animal are we?

56:13.000 --> 56:16.000
This is about a dozen elephants.

56:16.000 --> 56:18.000
Each one of us in this room and

56:18.000 --> 56:19.000
there are seven billion people

56:19.000 --> 56:21.000
of us on this planet who all

56:21.000 --> 56:22.000
want to be like us, and there's

56:22.000 --> 56:24.000
three billion more coming in the

56:24.000 --> 56:26.000
next 30-40 years, and they all

56:26.000 --> 56:27.000
want to be like us

56:27.000 --> 56:30.000
using 11,000 watts.

56:30.000 --> 56:32.000
So this is an enormous problem,

56:32.000 --> 56:34.000
and I cannot answer the

56:34.000 --> 56:35.000
question, what is the future

56:35.000 --> 56:36.000
and what does it bring?

56:36.000 --> 56:38.000
But I can say

56:38.000 --> 56:39.000
that I'm a pessimist.

56:39.000 --> 56:41.000
So I'll finish on that.

56:41.000 --> 56:45.000
[APPLAUSE]