how life got complex

8122019 How Life Got Complex

httpslidepdfcomreaderfullhow-life-got-complex 128

2014 vol 28



ldquoVerily at the rst Chaos came to be but next wide-bosomed

Earth the ever-sure foundations of all the deathless ones whohold the peaks of snowy Olympus and dim Tartarus in the

depth of the wide-pathed Earth and Eros fairest among the

deathless gods who unnerves the limbs and overcomes the

mind and wise counsels of all gods and all men within them

From Chaos came forth Erebus and black Night but of Night

were born Aether and Day whom she conceived and bare

from union in love with Erebus And Earth rst bare starry Heaven equal to herself to cover her on every side and to be

an ever-sure abiding-place for the blessed godsrdquo mdash Hesiod

from the Theogony Part 2 translated by HG Evelyn-White

THE CLASSICAL UNIVERSE is made up brick by brick starting inthe void and culminating with the earth rom the emptinesso night that gave rise to day to the day that produces the

outward order o the heavens and finally to lie upon the groundTeTeogony is a story describing the origins o energy and matter andinormation in the orm o lie Te Teogony exemplifies humanityrsquosgreat surprise that the universe should have emerged rom chaosthat emptiness has not reigned eternal and that the earth shouldbe hospitable and supportive o multiorm sentience

Perspectives

TheComplexity of LifeBY DAVID KRAKAUER

DIRECTOR WISCONSIN INSTITUTEFOR DISCOVERY UNIVERSITY

OF WISCONSIN-MADISON

CO-DIRECTOR CENTER FOR COMPLEXITY

amp COLLECTIVE COMPUTATION

WISCONSIN INSTITUTE FOR DISCOVERY

UNIVERSITY OF WISCONSIN-MADISON

EXTERNAL PROFESSOR SANTA FE INSTITUTE

About the Santa Fe Institute

Founded in 1984 the Santa Fe Institute

is an independent nonprofit research

and education center that has pioneered the science of complex systems Its

missions are supported by philanthropic

individuals and foundations forward-

thinking partner companies and

government science agencies

The SFI Bulletin is published periodically to

keep SFIrsquos community informed of its scientific

work The Bulletin is available online either

as a web-based (html5) tablet publication

or as a pdf at wwwsantafeedubulletin

To receive email notifications of future

online issues please subscribe at

wwwsantafeedubulletin To request a

subscription to the printed edition please

email the editor at jdgsantafeedu

Editorial Staff

SFI Chair of the Faculty Jennifer Dunne

SFI Director of Communications John German

Art Director Paula Eastwood Eastwood Design

Design amp Function 8 Arms Creative

Printing Starline Printing

Published by the Santa Fe Institute

1399 Hyde Park Road

Santa Fe New Mexico 87501 USA

Phone 5059848800

Printed On

100 recycled fiber

100 post-consumer waste

Green-e certified

Low-VOC vegetable-based ink

wwwsantafeedu

COVER CHARLES DARWIN ADOC-PHOTOSART RE-SOURCE NEW YORK CIRCUIT SHUTTERSTOCKCOMCOMPOSITE PAULA EASTWOOD



April 2014 Santa Fe Institute Bulletin 1

ldquo Inorganic chemistry is essentiallysilent on the topic of biology We donot exist The theory of everything

is a theory of everything exceptof those things that theorize

rdquo

David Krakauer

David Krakauer

Afer almost three millennia our concerns are essentially the sameas those o this celebrated Greek armer and poet In just less than 14billion years the universe has generated rom nothing more than100 billion galaxies each o which contains on average 100 billionstars and around many o these stars a system o planets In our ownMilky Way tucked away in a local bubble o the Orion-Cygnus armo the galaxy 27000 light years rom the galactic center spins oursolar system home to eight planets ndash our small and dense and ourlarge and gaseous On one o these planets the third nearest the sun

we find lie o the best o our knowledge it is the only planet inour solar system supporting adaptive matter

From physical law we can derive essential properties o the sunthe elements and the planets Te incredible machinery o thetheories o gravity quantum mechanics and the standard model

give us significant insights into the observable structure in theuniverse Optimistically we can even deduce simple moleculesrom inorganic chemistry And then the theory machine stopsPhysics runs out o gas Chemistry dries up From the perspectiveo physics our own solar system or galaxy are not in any waydifferent rom those anywhere else in the universe Inorganicchemistry is essentially silent on the topic o biology We do notexist Te theory o everything is a theory o everything except othose things that theorize



2 Santa Fe Institute Bulletin Vol 28

Te projects described in this issue o the Bulletin are allefforts to grapple with some o the key ideas and conceptsrequired to understand living systems with an emphasis onevolution both biological and cultural We ask how overlong stretches o time successively more effective mechanismsor storing and processing inormation have been adaptivelyengineered and how these biological computing systems areused to predict model and control relevant states o noisy andliving environments We consider complexity through the lenso inormation processing we seek to quantiy how inormationis encoded in living systems (genomes and brains) and wesuggest estimates or upper bounds in adaptive inormation

capacity Some o the concepts relevant to understandingbiological complexity include hierarchy individuality criti-cality inormationuncertainty computation and sociality

Stanislaw Lem in his science fiction che-drsquoœuvre Solaris (1961) considers a planet swaddled by an inscrutable oceancapable o astonishing acts o reasoning So vastly more intel-ligent and powerul is the Ocean to the human explorers andscientists (Solarists) who dedicate their lives to its analysisand explication that humanity is orced to resign itsel toignorance over its ultimate mechanisms and motives I haveofen wondered whether Solaris is not a metaphor or lie onearth where the methods o the Solarists are the traditional




Cronus mutilates his father Uranus at the behest

of his mother Gaia in this scene from Greek

mythology featured in Hesiodrsquos Theogonymethods o science Solaris is a huge interconnected system oenergy and inormation flows that dey traditional methodso reduction Perhaps Solaris is waiting on complexity scienceinormation theory scaling network theory evolutionarydynamics and computation Perhaps we only stand a chanceo understanding complex lie when we approach it throughthe sciences o complexity ndash in which case consider thisissue o the Bulletin a temporary visa granting access to allo those restless explorers intent on the understanding o ourterrestrial Solaris




R ESEARCH on the origins development and dynam-ics o complexity in biological systems has been a coretopic o inquiry at the Santa Fe Institute since its

ounding 30 years ago in 1984 SFIrsquos scientists have workedto develop an understanding o a dizzying array o biological

phenomena rom the origins o lie to transitions rom single-to multi-cellularity to evolutionary innovation at different

levels o biological organization to the relationship betweenecological complexity and dynamical stabilityTe intertwined concepts o energy and inormation are

undamental to any understanding o biological complexityBiological systems are ar rom equilibrium requiring a constantflow o energy to maintain their organization and unction-ality Te processing and encoding o inormation providesa means or lie to manage and maintain energy acquisitionuse and dissipation

Te work o ormer resident proessors and current externalaculty members Jessica Flack and David Krakauer (and their manycolleagues) highlighted in this issue o the SFI Bulletin addressesthe intimate dance between energy and inormation processing inbiological systems ndash a dance they suggest gives rise to the complexmulti-scale structure we observe

Tis computational-thermodynamic view o biology provides

a powerul potentially generic ramework or understanding the properties o any kind o complex adaptive system including socio-economic systems

Sincerely

Jennier Dunne Chair o the Faculty Santa Fe Institute

Biological Systems Energy and Information




IMAGE CREDITS THIS PAGE FROM TOP BRAIN DRAWING HESSDESIGNWORKSCOM EARTH FROM SPACE NASA ROUSSEAU THE DREAM WIKIMEDIA COMMONSFACING PAGE PORTRAIT OF JENNIFER DUNNE MINESH BACRANIA PHOTOGRAPHY

SFI BULLETIN

VOL 28 APRIL 2014Contents

0 Perspectives The Complexity of Life

By DAVID KRAKAUER

4 Biological Systems Energy and Information

By JENNIFER DUNNE

6 Why Nature Went to the Trouble of CreatingYouBy NATHAN COLLINS

12 Lifersquos Information Hierarchy

By JESSICA FLACK

ldquo There is nothing that living things do that cannot be

understood from the point of view that they are made

of atoms acting according to the laws of physicsrdquo ndash R I C H A R D F E Y N M A N

12

6

0





April 2014 Santa Fe Institute Bulletin 7April 2014 Santa Fe Institute Bulletin 7

Y pestis the bacterium that causes bubonic

plague (aka The Black Death of the Middle Ages)

Lie on earth began some 35 billion

years ago not all that long afer the planet itsel first ormed and or 15

billion years it chugged along single-celledcreatures sel-replicating dividing diversi-ying Remarkably it took that long ndash 7500times longer than all o human history ndash orthe first multicellular lie to emerge and stilllonger or it to evolve into lie as we know it

Despite that the question manyresearchers ask isnrsquot what took so long

but rather why complex lie would haveevolved in the first place Consider thissingle-celled organisms make up more thanhal o the biomass on earth and even oneo the tiniest organisms ndash Y pestis betterknown as bubonic plague ndash can effortlesslythoughtlessly kill you

Nature it seems doesnrsquot need youIndeed there isnrsquot any obvious reasonit would go to the trouble o creatingsomething as complex as a human beingcomplete with its differentiated organs and

top-down control systems And yet despiteour billion years o Naturersquos great ldquomehrdquohere you are alive multicellular complex

ndash even intelligent enough to ponder yourown existence

What was Nature thinking Accordingto one argument bacteria first boundtogether in colonies that enhanced cooper-ation and hence survival Eventually thosebacteria bound together physically as wellcreating the first multicellular lie Andso on

ldquoYou could say thatrsquos an answer but then you could go a bit urther and ask what isit exactly that makes you a better compet-

itorrdquo says David Krakauer who with SFIExternal Proessor Jessica Flack co-directs

the Wisconsin Institute or DiscoveryrsquosCenter or Complexity and CollectiveComputation or C4 and SFIrsquos Johnempleton Foundation-unded ldquoEvolutiono Complexity and Intelligence on Earthrdquoresearch project

ldquoWell yoursquore outsmarting everyone elserdquoKrakauer says

Simple versus complex

Despite its seeming indifference Naturedoes seem to have thought highly enough

Why Nature Went to the Trouble

of CreatinghellipYou BY NATHAN COLLINS

In a complex world where plants and animals and everything else are duking

it out to survive an organism stands to gain from becoming more complex

o complex structures to produce a ew othem and to have ratcheted up the complex-ity urther by embedding complex structures

within complex structures ndash animals withhearts and lungs and circulatory systems orgroups o people capable o building theirown social institutions

But why What purpose does it serveldquoWhy lie is hierarchically organized isnot at all obviousrdquo Flack says and howan organismrsquos or a societyrsquos complexity

relates to the complexity o its environ-ment remains unclear

Our anthropomorphized Nature mighthave started with one very simple idea

what Krakauer calls the reflection principle which presupposes that living thingscanrsquot be more complex than their environ-ments an idea rooted in experiments ldquoI

you take organisms and you place themin simpler environments they just throweverything [superfluous] away Tey losegenesrdquo Krakauer says

At the same time the world does seem toavor an intelligent creature Even the tiniestliving things need to be able to comprehend predict and react to their environmentsthatrsquos what allows them to outsmart eachother he says In a complex world where

plants and animals and everything else areduking it out to survive an organism standsto gain rom becoming more complex

Tat tension between simplicity andcomplexity is the starting point or C4

postdoctoral ellow Christopher Ellison




Watch David Krakauerrsquos interview at

wwwsantafeedulife

What does Nature care about hearts brains and other organs or for that

matter political parties ndash in other words structures within structures

ldquoWersquod like to understand the implica-tions this has or the environmentrdquo hesays ldquoFor example do simple or complexorganisms experience and live in simple orcomplex environmentsrdquo

Working with Flack and KrakauerEllison developed ldquoMarkov organismsrdquo

computer-simulated creatures that merge

insights rom biology with inormation processing techniques rom computerscience to help figure out how lie balancesthese trade-offs Rather than modeling real

organisms themselves he ocuses on howinormation flows in the ecological system

Itrsquos early days Ellison says but his simula-tions suggest that lie will ofen evolve tomatch its environmentrsquos complexity ndash findingsthat are in line with the reflection principlebut with some interesting caveats For one

thing evolving Markov organisms tend toovershoot their worldsrsquocomplexity and mighttake a long time to pruneunnecessary complexities

Teyrsquore also suscepti-ble to ldquobasis mismatchrdquo a

problem you know welli yoursquove ever tried to explain to a tourist howto get around in your hometown o youthere are just a ew steps but to the noviceitrsquos a complex process with many twists and

turns and every intersection represents a possible misdirection in sprawling cities likeLos Angeles a direction as simple as ldquotakeSunset to Vermont and turn lefrdquo becomesinfinitely complex Markov organisms arethe same i their way o solving a problem

doesnrsquot line up with how their environmentsconstructed it Ellisonrsquos simulations showMarkov organismsrsquo complexity keeps evolv-ing upward orever

But Ellisonrsquos inormation-centricapproach has some benefits One he says

ldquois that it attempts to answer the questiono how complexity evolves in an organ-ism-independent ashionrdquo meaning thatthe ideas apply equally well to anythingrom bacteria to politics Similarly Ellisonrsquosmethod allows the team to describe both anorganism and its environmentrsquos complexity

in the same terms because both derive romthe same underlying models o inormation

processing Surprisingly thatrsquos somethingew i any other researchers have done

Constructing predictability

So it appears Nature might avor multicel-lular lie i it affords a certain computational power not readily available to single-celledorganisms But what does Nature care abouthearts brains and other organs or or thatmatter political parties ndash in other words

structures within structureshe answer Flack says is that living

things like their worlds to be predictableand what makes cells and people more likelyto survive Nature avors

Much o the structure we observe in the world Flack says probably evolved becausestructure begets stability hence predictabilityGroups o genes cells or animals changetheir collective behaviors slowly compared

with individual genes cells or animals givingthe aster-moving individual components a

chance to anticipate changes more easilyBiologists call the idea that plants andanimals ndash and genes and organs and so on

ndash structure their environments to be morestable and predictable ldquoniche constructionrdquoand it usually applies to physical structurelike ants building nests But it also can beapplied to temporal structure

Politics offers perhaps a simple exampleEarly US politicians were explicit aboutdesigning Congress and the rest o govern-ment so that it would change gradually and




According to the reflection principle organisms are reflections of their environments Here the environment is represented as a prose excerpt from

Darwinrsquos Origin of Species wherein he contemplates an entangled bank filled with ldquoendless forms most beautiful and most wonderfulrdquo each evolved through natural selection Organism A matches the environmentrsquos complexity while Organism B is less complex than the environment and Organism C is

more complex than the environment The research team is asking when evolution satisfies or v iolates the reflection principle

be more stable Even when we complainabout our slow-moving government weare undoubtedly comorted by that verycharacteristic because slow is predictableAnd when our environment is predictable we know how to make sound decisions

So people construct relatively slow-mov-ing predictable institutions At the sametime those institutions help shape thebehavior o the individuals who createdthem points out C4 postdoctoral ellowPhilip Poon who is examining eedbackrom institutions to explain (in the govern-ment case) why or example Democrats andRepublicans seem to trade off controllingthe White House and Congress every ewterms A critical issue is o course howthat eedback works rom a mechanical

perspective ndash that is how the ways individ-uals perceive and understand institutionsinfluence their decision-making

Drawing on the theory o phase transi-tions the same one that explains whychanging a seemingly minor variable cansuddenly shif an entire system rom onestate to another Flack Krakauer and C4researcher and ormer SFI PostdoctoralFellow Bryan Daniels argue that hierarchi-cal structures bestow another advantageeicient inormation low rom the

collective to its individual parts Systems perched on the edge o a phase transitionare exquisitely sensitive so that a small orlocalized change ldquoleads to a large change inthe global dynamicsrdquo Daniels says Toughthat might seem unstable or chaotic systems

near the critical point where a phase transi-

tion begins are actually quite predictableGroups o macaque monkeys ndash one o

Flack and the teamrsquos earliest sources o data

and inspiration ndash are one system that appearsto be resting near a phase transition Whenmonkeys arenrsquot eeling especially aggres-sive Daniels says things are stable and ldquoi Isuddenly act out nothingrsquos going to happenhellipbut i [the group is] sitting at the critical

point then [my] contribution is always moreimportantrdquo and one extra monkey picking afight is enough to kick off a large-scale brawl

Below the critical point individualmonkeys act airly independently but rightat the transition their behavior is tightly

ldquoWhen monkeys arenrsquot feeling especially aggressive

things are stable but if the group is sitting at the

critical point one extra monkey picking a fight

is enough to kick off a large-scale brawlrdquo

coordinated and individual monkeys acttogether as one Tat Krakauer empha-sizes eases the flow o inormation romthe system as a whole to its constituent

parts making it all the more predictableNature is rie with examples one birdrsquos

sudden course correction changing the

direction o an entire flock alternatingDemocrat and Republican control oCongress and so on

Social circuitry

In the abstract Nature has good reasonto avor complexity and hierarchy ndash eachin its own way makes the tasks o compre-hension prediction and strategizingeasier But its a third concept circuitsthat grounds Flack and Krakauerrsquos teamand lays the practical oundation ormuch o their work

Usually the word ldquocircuitrdquo conjuresimages o the transistors microprocessors




Time series representation of the dis tribution of fight sizes in a macaque society Individuals who fought more than once are represented by a color

Grey squares represent individuals who fought only once An outburst by an individual wonrsquot normally tip the scales But when the system is perched at

a phase transition a brawl among many individuals can erupt

and lengths o copper wire that make upa computer Te analogy is apt Like anelectronic circuit individual components

ndash genes organs people ndash inormational-ly bind living things to orm a kind obiological or social computer In act the

circuit approach to describing a systemstems rom a hunch that the hierarchicalscales present throughout nature ldquoarise

Time

F i g h

t 1

F i g h

t 2

Individuals who fought more than once are represented by a color

Grey squares represent individuals who fought only once

Vd

Pa

Je

Cb

Yv

Ta

Sg

Qs

Po

Mv

Ip

Fp

Eo

Dh

Tr

Pr

Fc

Zu

Vf

Fp

Ec

Zu

Zr

Iw

Is

Fp

Fc

Cc

Yv

Cd

Ta

Cc

Qv

Ip

Cb

Pa

Eb

Sb

Jc

Je

Hp

Lr

Jc

Zu

Po

Dh

Vf

Eo

Eo

Cd

Pa

Dh

Hh

At

Zu

Yv

Th

Ta

Fn

through a process o collective computa-tionrdquo creating slow-changing predictablesocial and biological structures Flack says

Circuits are more than just an analo-gy though ndash theyrsquore the tools that bridgethe gap between individual and collective

behavior And in a scientific field whereitrsquos easy to avoid real data circuits are one

way Flack and Krakauerrsquos research group

makes sure their theories are a good matchto the real world ldquoOur group is committedto an empirical approachrdquo Flack says ldquoWebelieve that only when these measures aredeveloped with an understanding o thedata generated by real systems will they

be useulrdquoTe process o building circuits begins

by analyzing how a systemrsquos individual parts work together Using the macaquefight data Flack Krakauer and ormerSFI Omidyar Fellow Simon DeDeo (nowat Indiana University Bloomington) devel-oped a statistical method they dubbedinductive game theory to analyze howthe monkeys reacted to othersrsquo fights Teresulting social circuit Flack says servesas a detailed model ldquoor how the micro-

scopic behavior maps to the unctionallyimportant macroscopic eatures o socialstructurerdquo such as the distribution o fightsizes In other words to construct a socialor biological circuit is to understand how agroup builds and maintains stable predict-able inormation hierarchies

Te final step is to produce a simplifiedsocial circuit what the researchers call a

ldquocognitive effective theoryrdquo that accurate-ly predicts how groups behave Te aimis to extract the key sorts o interactions




responsible or power structures fight-sizedistributions or other macroscopic eaturesusing ldquowhat we know about individual orcomponent cognition to coarse grain orcompressrdquo social circuits Flack says Suchcompression is essential she says because

living things canrsquot base their decisions on what ever y other living thing is doing instead theyrsquore orced to pay attention to

just a ew patterns or details o whatrsquos goingon around them

Te key question here as collaboratorand Princeton University graduate studentEleanor Brush puts it is how little inor-mation individuals need to successullyoutsmart others

Accidental or inevitable

Answering questions like that one ndash or testingsome o the teamrsquos more abstract predic-tions ndash remains a central challenge Poonor example describes his studies o electioncycles and policy change as ldquotoy modelsrdquo ndashthey capture qualitative eatures o the datasuch as party switching but donrsquot stand upto more precise quantitative tests

Meanwhile Krakauer and others sayitrsquos not always clear how to test particularhypotheses such as the prediction that basismismatch leads to ever-increasing complexity

in living things ldquoWersquore still looking or somecompelling examplerdquo Ellison says ldquoPart othe issue is that one can ofen play the devilrsquosadvocate and call into question the examplerdquoone reason why his work to construct ormal

precise measures o complexity is so import-ant he says

New techniques or rapidly analyzinggenetic data Krakauer says might improvethe situation Combining those techniques

with laboratory-based ldquoexperimental evolutionrdquo

in which researchers study the effects o preciseenvironmental changes on small organismssuch as bacteria could help test some o theendeavorrsquos core ideas such as the reflection

principle or the role o phase transitionsAnother potential avenue is to use ldquodigital

sources like computer games where we cancontrol to a large extent the orm o the dataor the conditions under which they werecollectedrdquo Krakauer says

esting their theories is just one part o theteamrsquos ambitious aims Tey hope Flack says

People gather in Washington DC before Martin Luther King Jrrsquos ldquoI Have a Dreamrdquo speech on Aug 28 1963 to demand equalities

Individuals tend to move faster and be less predictable than the slow-moving institutions they create

to achieve nothing less than an understandingo why lie is organized the way it is rom thesmallest bacteria to the largest human insti-tutions Tat requires combining real-worldobservation and abstract mathematical theoryin novel and creative ways

And as i that wasnrsquot enough Krakauerhas one more question in mind is lie anaccident or is it inevitable And i lie isinevitable well are we alone

ldquoI itrsquos not a product o a series o random

accidents but therersquos an underlying law-likeregularity that would give us confidence inbelieving in the possibility o lie presenteverywhere in the universerdquo he says ldquoSo

when one asks why does it matter whetheritrsquos chance or necessity it matters i we care

whether wersquore alone are notrdquo

Nathan Collins is a feelance science writernew ather and film aficionado based inSan Francisco

ldquoLiving things canrsquot base their decisions on what

every other living thing is doing instead theyrsquore

forced to pay attention to just a few patternsrdquo




ldquohellipin mere Time all things follow

one another and in mere Space

all things are side by side it is

accordingly only by the combi-

nation of Time and Space thatthe representation of coexistence

arisesrdquo

mdash ARTHUR SCHOPENHAUER On the Fourfold Root

of the Principle of Sufficient Reason 1813

Yves Tanguy Indefinite Divisibility 1942




D I A C O M M O N S I N S E T H E N R I R O U S S E A U T H E

D R E A M 1 9 1 0 W I K I M E D I A C O M M O N S

Biological systems ndash rom cells totissues to individuals to societ-ies ndash are hierarchically organized

(eg Feldman amp Eschel 1982 Buss 1987Smith amp Szathmaacutery 1998 Valentineamp May 1996 Michod 2000 Frank2003) o many hierarchical organiza-tion suggests the nesting o components orindividuals into groups with these groupsaggregating into yet larger groups But this view ndash at least superficially ndash privilegesspace and matter over time and inorma-tion Many types o neural coding orexample require averaging or summing over neural firing ratesTe neuronsrsquo spatial location ndash that they are in proximity ndash iso course important but at least as important to the encoding

The explanation for the complex multi-scale structure of biological and social systems

lies in their manipulation of space and time to reduce uncertainty about the future

is their behavior in time Likewise insome monkey societies as I will discussin detail later in this review individualsestimate the uture cost o social inter-action by encoding the average outcomeo past interactions in special signals and

then summing over these signalsIn both examples inormation rom

events distributed in time as well as space(Figure 1) is captured with encodingsthat are used to control some behavioraloutput My collaborators and I in theCenter or Complexity amp Collective

Computation are exploring the idea that hierarchical organiza-tion at its core is a nesting o these kinds o unctional encodingsAs I will explain we think these unctional encodings result

LIFErsquoS INFORMATION HIERARCHY

BY JESSICA C FLACK

CO-DIRECTOR CENTER FOR COMPLEXITY amp COLLECTIVE COMPUTATION

WISCONSIN INSTITUTE FOR DISCOVERY UNIVERSITY OF WISCONSIN-MADISON


Jessica Flack

Number of spatial dimensions

N u m b e

r o f t i m e d i m e n s i o n s

0

0

1

2

3

4

5

1 2 3 4 5

UNSTABLE

TOO SIMPLE

UNPREDICTABLE (ELLIPTIC)

UNPREDICTABLE(ULTRAHYPERBOLIC)

TACHYONSONLY

UNSTABLE

Figure 1 The dimensionality of the time-space

continuum with properties postulated when x

does not equal 3 and y is larger than 1 Life on

earth exist s in three spatial dimensions and one

temporal dimension Biological systems effec-

tively ldquodiscretizerdquo time and space to reduce

environmental uncertainty by coarse-graining

and compressing environmental time series to

find regularities Components use the coarse-

grained descriptions to predict the future

tuning their behavior to their predictions




rom biological systemsmanipulating space andtime (Figure 2) to acilitateinormation extraction which in turn acilitatesmore efficient extraction

o energy h i s inormation

hierarchy appears to bea universal property obiological systems andmay be the key to one oliersquos greatest mysteries ndashthe origins o biologicalcomplexity In this essayI review a body o work byDavid Krakauer myseland our research group

that has been inspiredby many years o work atthe Santa Fe Institute (eg Crutchfield 1994 Gell-Mann1996 Gell-Mann amp Lloyd 1996 Fontana amp Buss 1996 West Brown amp Enquist 1997 Fontana amp Schuster 1998Ancel amp Fontana 2000 Stadler Stadler Wagner amp Fontana2001 Smith 2003 Crutchfield amp Goumlrnerup 2006 Smith2008) Our work suggests that complexity and the multi-scalestructure o biological systems are the predictable outcomeo evolutionary dynamics driven by uncertainty minimiza-tion (Krakauer 2011 Flack 2012 Flack Erwin Elliot ampKrakauer 2013)

Tis recasting o the evolutionary process as an inerentialone1 (Bergstrom amp Rosvall 2009 Krakauer 2011) is based onthe premise that organisms and other biological systems canbe viewed as hypotheses about the present and uture environ-ments they or their offspring will encounter induced rom thehistory o past environmental states they or their ancestors have

experienced (eg Crutchfield amp Feldman 2001 Krakauer ampZannotto 2009 Ellison Flack amp Krakauer in prep) Tis premise o course only holds i the past is prologue ndash that ishas regularities and the regularities can be estimated and evenmanipulated (as in niche construction) by biological systems ortheir components to produce adaptive behavior (Flack ErwinElliot amp Krakauer 2013 Ellison Flack amp Krakauer in prep)

I these premises are correct lie at its core is computa-tional and a central question becomes How do systems andtheir components estimate and control the regularity in theirenvironments and use these estimates to tune their strategiesI suggest that the answer to this question and the explanation

or complexity is that biological systems manipulate spatialand temporal structure to produce order ndash low variance ndash atlocal scales

UNCERTAINTY REDUCTION

Te story I want to tell starts with the observation that witheach new level o organization typically comes new unction-ality ndash a new eature with positive payoff consequences or thesystem as a whole or or its components (Flack Erwin Elliot

1 Tis idea is related to work on Maxwel lrsquos Demon (eg Krakauer2011 Mandal 983121uan amp Jarzynski 2013) and the Carnot cycle ( egSmith 2003) but we do not yet understand the mapping

Figure 2 Biological systems ndash from

(left to right ) Volvox colonies to slime

molds to animal societies to large-scale

ecosystems such as reefs t o human

cities ndash are hierarchically organized with

multiple functionally important time and

space scales All feature 1) components

with only partially aligned interests

exhibiting coherent behavior at the

aggregate level 2) components that turn

over and that co-exist in the sys tem at

varying stages of development

3) social structure that persists but

component behavior that fluctuates and

4) macroscopic variation in temporal

and spatial structure and coupling with

microscopic behavior which has func-

tional impl ications when the components

can perceive ndash in evolutionary develop-

mental or ecological time ndash regularities

at the macroscopic scale

1 2




amp Krakauer 2013) Policing in a pigtailed macaque group is anexample Once a heavy tailed distribution o power ndash defined asthe degree o consensus in the group that an individual can winfights (see Flack amp Krakauer 2006 Boehm amp Flack 2010 BrushKrakauer amp Flack2013) ndash becomes effec-

tively institutionalized(here meaning hard tochange) policing (anintrinsically costlystrategy) becomesaffordable at least tothose animals that sitin the tail o the powerdistribution thosesuper powerul monkeys who are rarely or never challenged when they break up fights (Flack de Waal amp Krakauer 2005Flack Girvan de Waal amp Krakauer 2006)

My collaborators and I propose that a primary driver o theemergence o new unctionality such as policing is the reduc-tion o environmental uncertainty through the constructiono nested dynamical processes with a range o characteris-tic time constants (Flack Erwin Elliot amp Krakauer 2013)Tese nested dynamical processes arise as components extractregularities rom ast microscopic behavior by coarse-grain-ing (or compressing) the history o events to which they havebeen exposed

Proteins or example can have a long hal-lie relative toRNA transcripts and can be thought o as the summed outputo translation Cells have a long hal-lie relative to proteins

and are a unction o the summed output o arrays o spatiallystructured proteins Both proteins and cells represent someaverage measure o the noisier activity o their constituentsSimilarly a pigtailed macaquersquos estimate o its power is a kind

o average measure othe collective percep-

tion in the groupthat the macaque iscapable o winningights and this is abetter predictor othe cost the macaque will pay during fightsthan the outcome oany single melee as

these outcomes can fluctuate or contextual reasons Tesecoarse-grainings or averages are encoded as slow variables(Flack amp de Waal 2007 Flack 2012 Flack Erwin Elliot

amp Krakauer 2013 see also Feret Danos Krivine Harner ampFontana 2009 or a similar idea) Slow variables may have aspatial component as well as a temporal component as in the protein and cell examples (Figure 6) or minimally only atemporal component as in the monkey example

As a consequence o integrating over abundant microscopic processes slow variables provide better predictors o the localuture configuration o a system than the states o the fluctuatingmicroscopic components In doing so they promote acceleratedrates o microscopic adaptation Slow variables acilitate adapta-tion in two ways Tey allow components to fine-tune theirbehavior and they ree components to search at low cost a larger

3 4

Slow variables provide better predictors

of the local future configuration of a

system than the states of the fluctuating

microscopic componentsrdquo




Figure 3 A sea urchin gene regulatory circuit The empirically derived circuit describes the Boolean rules for coordinat ing genes and

proteins to produce aspects of the sea urchinrsquos phenotype ndash in this case the position of cells in the endomesoderm at 30 hours since

fertili zation Edges indicate whet her a node induces a state change in another node here genes and proteins The circuit is a rigorous

starting point for addressing questions about the logic of development and its evolution In computational terms the input is the set of

relevant genes and proteins and the output is the target phenot ypic feature




space o strategies or extracting resources rom the environment(Flack 2012 Flack Erwin Elliot amp Krakauer 2013) Tis phenomenon is illustrated by the power-in-support-o-policingexample and also by work on the role o neutral networks in

RNA olding In the RNA case many different sequences canold into the same secondary structure Tis implies that overevolutionary time structure changes more slowly than sequencethereby permitting sequences to explore many configurationsunder normalizing selection (Fontana amp Schuster 1998 Schusteramp Fontana 1999 Ferrada amp Krakauer in prep)

NEW LEVELS OF ORGANIZATION

As an interaction history builds up at the microscopic levelthe coarse-grained representations o the microscopic behav-ior consolidate becoming or the components increasinglyrobust predictors o the systemrsquos uture state

We speak o a new organizational level when the systemrsquoscomponents rely to a greater extent on these coarse-grained orcompressed descriptions o the systemrsquos dynamics or adaptivedecision-making than on local fluctuations in the microscop-ic behavior and when the coarse-grained estimates made bycomponents are largely in agreement (Krakauer Bertschinger

Ay Olbrich amp Flack in review) Te idea is that convergence onthese ldquogood-enoughrdquo estimates underlies non-spurious correlatedbehavior among the components Tis in turn leads to an increasein local predictability (eg Flack amp de Waal 2007 Brush Krakaueramp Flack 2013) and drives the construction o the inormationhierarchy (Note that increased predictability can seem the producto downward causation in the absence o careul analysis o thebottom-up mechanisms that actually produced it)

THE STATISTICAL MECHANICS amp THERMODYNAMICS OF BIOLOGY

Another way o thinking about slow variables is as a unctionallyimportant subset o the systemrsquos potentially many macroscop-ic properties An advantage o this recasting is that it builds a

bridge to physics which over the course o its maturation as afield grappled with precisely the challenge now beore biology understanding the relationship between behavior at the individ-ual or component level and behavior at the aggregate level

In physics

As discussed in Krakauer amp Flack (2010) the debate in physicsbegan with thermodynamics ndash an equilibrium theory treatingaggregate variables ndash and came to a close with the maturationo statistical mechanics ndash a dynamical theory treating micro-scopic variables

Termodynamics is the study o the macroscopic behavior

o systems exchanging work and heat with connected systems ortheir environment Te our laws o thermodynamics all operateon average quantities defined at equilibrium ndash temperature pressure entropy volume and energy Tese macroscopic variables exist in undamental relationships with each other asexpressed or example in the ideal gas law Termodynamics isan extremely powerul ramework as it provides experimentalists

with explicit principled recommendations about what variablesshould be measured and how they are expected to change relativeto each other but it is not a dynamical theory and offers noexplanation or the mechanistic origins o the macroscopic

variables it privileges Tis is the job o statistical mechanics By

providing the microscopic basis or the macroscopic variablesin thermodynamics statistical mechanics establishes the condi-tions under which the equilibrium relations are no longer validor expected to apply Te essential intellectual technologiesbehind much o statistical mechanics are powerul tools orcounting possible microscopic configurations o a system andconnecting these to macroscopic averages

In biology

Tis brie summary o the relation between thermodynam-ics and statistical mechanics in physics is illuminating or tworeasons On the one hand it raises the possibility o a potentially

This ldquoUp to 30 Hour Overviewrdquoprimarily shows the endomesoderm network architectureas it exists after 21 hours with the additon of all PMC components starting at 6 hours

the inclusion of the DeltandashNotch signal from PMC to Veg2 the presence of Wnt8 in Veg2

Endoderm the nBndashTCF and Otx inputs into Blimp1 in NSM and Gene X in the NSM the

latter four of these features are no longer present by 21 hours Consult the other models

to see all the network elements and interactions in he correct temporal context

Copyright copy 2001ndash2011 Hamid Bolouri and Eric Davidson




deep division between physical and biological systems So arndash and admittedly biology is young ndash biology has had only limit-ed success in empirically identiying important macroscopic properties and deriving these rom first principles rooted in physical laws or deep evolved constraints2 Tis may be the casebecause many o the more interesting macroscopic properties

are slow variables that result rom the collective behavior oadaptive components and their unctional value comes romhow components use them making them undamentally subjec-tive (see Gell-Mann amp Lloyd 1996 or more on subjectivity)and perhaps even nonstationary 3

On the other hand the role o statistical mechanics in physics suggests a way orward I we have intuition about which macroscopic properties are important ndash that is whichmacroscopic properties are slow variables ndash and we can getgood data on the relevant microscopic behavior we can proceedby working upward rom dynamical many-body ormalismsto equilibrium descriptions with a ew avored macroscopicdegrees o reedom (Levin Grenell Hastings amp Perelson1997 Krakauer amp Flack 2010 Krakauer et al 2011 GintisDoebeli amp Flack 2012)

A STATISTICAL MECHANICS-COMPUTER SCIENCE-

INFORMATION THEORETIC HYBRID APPROACH

Te most common approach to studying the relationship betweenmicro and macro in biological systems is perhaps dynamicalsystems and more specifically pattern ormation (or examplessee Sumpter 2006 Ball 2009 Couzin 2009 Payne et al 2013)However i as we believe the inormation hierarchy results rombiological components collectively estimating regularities in theirenvironments by coarse-graining or compressing time series data

a natural (and complementary) approach is to treat the microand macro mapping explicitly as a computation

Elements of computation in biological systems

Describing a biological process as a computation minimallyrequires that we are able to speciy the output the input andthe algorithm or circuit connecting the input to the output(Flack amp Krakauer 2011 see also Mitchell 2010 Valiant2013) A secondary concern is how to determine when thedesired output has been generated In computer sciencethis is called the termination criterion or halting problemIn biology it potentially can be achieved by constructing

nested dynamical processes with a range o timescales withthe slower timescale processes providing the ldquobackgroundrdquoagainst which a strategy is evaluated (Flack amp Krakauer 2011)as discussed later in this paper in the section on Couplings

2 Te work on scaling in biological systems shows a funda mental relation-ship between mass and metabolic rate and this relationship can be derivedfrom the biophysics (eg West Brown amp Enquist 1997) Bettencourt and West are now investigatin g whether simi lar fund amental relat ionships can beestablished for macroscopic properties of human social systems like cities (eg Bettencourt Lobo Helbing Kuhnert amp West 2007 Bettencourt 2013)

3 With the important caveat that in biology the utility of a macroscop-ic property as a predictor will likely increase as consensus among thecomponents about the estimate increases effectively reducing the subjec-tivity and increasing stationarity (see also Gell-Mann amp Lloyd 1996)

1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud

Figure 4 Cognitive effective theories for one macroscopic property of

a macaque society the distribution of fight sizes (a) To reduce circuit

complexity we return to the raw time series data and remove as much noise

as possible by compressing the data In the case of our macaque dataset this

reveals which individuals and subgroups are regular and predictable conflict

participants We then search for possible strategies in response to these

regular and predictable individuals and groups This approach returns a famil y

of circuits (b is an example) each of which has fewer nodes and edges than

the full circuit (c) These circuits are simpler and more cognitively parsimo-

nious We then test the reduced circuits against each other in simulation

to determine how well they recover the target macroscopic properties

a

b

c




A macroscopic property can be said to be an output oa computation i it can take on values that have unctionalconsequences at the group or component level i it is the resulto a distributed and coordinated sequence o component inter-actions under the operation o a strategy set and i it is a stableoutput o input valuesthat converges (termi-nates) in biologicallyrelevant time (Flackamp Krakauer 2011)Examples studiedin biology include

aspects o vision suchas edge detection(eg Olshausen ampField 2004) phenotypic traits such as the average positiono cells in the developing endomesoderm o the sea urchin(eg Davidson 2010 Peter amp Davidson 2011) switchingin biomolecular signal-transduction cascades (eg SmithKrishnamurthy Fontana amp Krakauer 2011) chromatinregulation (eg Prohaska Stadler amp Krakauer 2010) andsocial structures such as the distribution o fight sizes ( eg DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) and the distribution

o power in monkey societies (eg Flack 2012 Flack ErwinElliot amp Krakauer 2013)

Te input to the computation is the set o elements imple-menting the rules or strategies As with the output we do nottypically know a priori which o many possible inputs is relevant

and so we must makean inormed guessbased on the proper-ties o the outputIn the case o the seaurchinrsquos endomeso-derm we might start

with a list o genes thathave been implicatedin the regulation o

cell position In the case o the distribution o fight sizes in amonkey group we might start with a list o individuals partic-ipating in fights

Reconstructing the microscopic behavior

In biological systems the input plus the strategies constitutethe systemrsquos microscopic behavior Tere are many approach-es to reconstructing the systemrsquos microscopic behavior Temost powerul is an experiment in which upstream inputs to a

Figure 5 A comparison of Markov organisms in two environments a Markov environment (lef t) and a non-Markov environment (right) In the top t wo plots

organismal complexity is plotted against time for each organism (organisms are represented by varying colors) and for many different sequences of 500

environmental observations the bold red line shows the average organismal complexity which in the Markov environment tends toward the environmental

complexity and in the non-Markov environment exceeds it In the bot tom plots the probability that a random organism has order k is plotted against time

4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500

Non-Markov Environment εinfinMarkov Environment ε3

Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key

t (Generations)t (Generations)

In all biological systems there are

multiple components interacting and

simultaneously coarse graining to make

predictions about the futurerdquo




Phospholipidbilayer

target component are clamped off and the output o the targetcomponent is held constant Tis allows the experimentalistto measure the target componentrsquos specific contribution tothe behavior o a downstream component (Pearl 2010) Tistype o approach is used to construct gene regulatory circuitsmapping gene-gene and gene-protein interactions to pheno-typic traits (Figure 3)

When such experiments are not possible causal relation-ships can be established using time series analysis in whichclamping is approximated statistically (Ay 2009 Pearl 2010)My collaborators and I have developed a novel computationaltechnique called Inductive Game Teory (DeDeo Krakauer amp

Flack 2010 Flack amp Krakauer 2011 Lee Daniels Krakaueramp Flack in prep) that uses a variant o this statistical clamp-ing principle to extract strategic decision-making rules gamestructure and (potentially) strategy cost rom correlationsobserved in the time series data

Collective computation through stochastic circuits

In all biological systems o course there are multiple compo-nents interacting and simultaneously coarse graining to make

predictions about the uture Hence the computation is inher-ently collective A consequence o this is that it is not sufficientto simply extract rom the time series the list o the strategies

in play We must also examine how different configurations ostrategies affect the macroscopic output One way these config-urations can be captured is by constructing Boolean circuitsdescribing activation rules as illustrated by the gene regulato-ry circuit shown in Figure 3 which controls cell position (theoutput) at thirty hours rom ertilization in the sea urchin (Peteramp Davidson 2011) In the case o our work on micro to macromappings in animal societies we describe the space o micro-scopic configurations using stochastic ldquosocialrdquo circuits (Figure4) (DeDeo Krakauer amp Flack 2010 Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep)

Nodes in these circuits are the input to the computation

As discussed above the input can be individuals or subgroupsor they can be defined in terms o component properties likeage or neurophysiological state A directed edge between twonodes indicates that the ldquoreceiving noderdquo has a strategy or theldquosending noderdquo ndash and the edge weight can be interpreted asthe above-null probability that the sending node plays thestrategy in response to some behavior by the receiving node ina previous time step Hence an edge in these circuits quantifiesthe strength o a causal relationship between the behaviors o asending and receiving node

Sometimes components have multiple strategies in theirrepertoires Which strategy is being played at time t may

Figure 6 The cell can be thought of as a slow variable to the extent it is a function of the summed output of arrays of spatially

structured proteins and has a long half-life compared to its proteins Features that ser ve as slow variables provide better

predictors of the local future configuration of a sys tem than the sta tes of the fluctuating microscopic components We propose

tha t when detec tab le b y t he s ys tem or i ts com ponents s low var iab les can reduce env ironm ental uncer tai nt y an d b y increas ing

predictability promote accelerated rates of microscopic adaptation




vary with context Tese meta-strategies can be capturedin the circuit using different types o gates speciying howa componentrsquos myriad strategies combine (see also FeretDavis Krivine Harmer amp Fontana 2009) By varying thetypes o gates andor the strength o causal relationships we end up with multiple alternative circuits ndash a amily o

circuits ndash all o which are consistent with the microscopicbehavior albeit with different degrees o precision (LeeDaniels Krakauer amp Flack in prep) Each circuit in theamily is essentially a model o the micro-macro relationshipand so serves as a hypothesis or how strategies combineover nodes (inputs) to produce to the target output (LeeDaniels Krakauer amp Flack in prep) We test the circuitsagainst each other in simulation to determine which canbest recover the actual measured macroscopic behavior oour system

Cognitive effective theories for collective computation

Te circuits describing the microscopic behavior can be compli-

cated with many ldquosmallrdquo causes detailed as illustrated by the generegulatory circuit shown in Figure 3 Te challenge ndash once wehave rigorous circuits ndash is to figure out the circuit logic (Flackamp Krakauer 2011see also Feret DavisKrivine Harmer ampFontana 2009)

here are many wa ys to ap pr oac hthis problem Ourapproach it is to build

whatrsquos called in physics

an eective theory acompact descriptiono the causes o a macroscopic property Effective theories oradaptive systems composed o adaptive components requirean additional criterion beyond compactness As discussedearlier in this essay components in these systems are tuningtheir behaviors based on their own effective theories ndash coarse-grained rules (see also Feret Davis Krivine Harmer amp Fontana2009) ndash that capture the regularities (Daniels Krakauer ampFlack 2012) I we are to build an effective theory that explainsthe origins o unctional space and time scales ndash new levels oorganization ndash and ultimately the inormation hierarchy the

effective theory must be consistent with component models omacroscopic behavior as these models through their effects onstrategy choice drive that process In other words our effectivetheory should explain how the system itsel is computing

We begin the search or cognitively principled effective theoriesusing what we know about component cognition to inorm how

we coarse-grain and compress the circuits (Flack amp Krakauer 2011Lee Daniels Krakauer amp Flack in prep) Tis means taking intoaccount given the available data the kinds o computations compo-nents can perorm and the error associated with these computationsat the individual and collective levels given component memorycapacity and the quality o the ldquodata setsrdquo components use to estimate

regularities (Krakauer Flack DeDeo amp Farmer 2010 Flack ampKrakauer 2011 Daniels Krakauer amp Flack 2012 Ellison Flackamp Krakauer in prep all building on Gell-Mann 1996)

As we refine our understanding o the micro-macro mappingthrough construction o cognitive effective theories we alsorefine our understanding o what time series data constitute the

ldquorightrdquo input ndash and hence the building blocks o our systemAnd by investigating whether our best-perorming empiri-cally justified circuits can also account or other potentiallyimportant macroscopic properties we can begin to establish which macroscopic properties might be undamental and what their relation is to one another ndash the thermodynamicso biological collectives

Couplings information flow and macroscopic tuning

Troughout this essay I have stressed the importance o slowness(effective stationarity) or prediction Slowness also has costshowever Consider our power example Te power structuremust change slowly i individuals are to make worthwhileinvestments in strategies that work well given the structurebut it cannot change too slowly or it may cease to reflect theunderlying distribution o fighting abilities on which it is based

and hence cease to bea good predictor ointeraction cost (Flack2012 Flack ErwinElliot amp Krakauer2013) Te question

we must answer is what is the optimalcoupling between

macroscopic andmicroscopic change

and can systems by manipulating how components are organizedin space and time get close to this optimal coupling

One approach to this problem is to quantiy the degeneracyo the target macroscopic property and then perturb the circuitsby either removing nodes up- or down-regulating node behav-ior or restructuring higher order relationships (subcircuits) todetermine how many changes at the microscopic level need tooccur to induce a state change at the macroscopic level

Another approach is to ask how close the system is to acritical point ndash that is how sensitive the target macroscopic

property is to small changes in parameters describing the micro-scopic behavior Many studies suggest that biological systemso all types sit near the critical point (Mora amp Bialek 2011)A hypothesis we are exploring is that sitting near the critical point means that important changes at the microscopic scale will be visible at the macroscopic scale (Daniels Krakauer ampFlack in prep) O course this also has disadvantages as it meanssmall changes can potentially cause big institutional shifsundermining the utility o coarse-graining and slow variablesor prediction (Flack Erwin Elliot amp Krakauer 2013)

I balancing trade-offs between robustness and predictionon the one hand and adaptability to changing environments

A hypothesis we are exploring is thatsitting near the critical point means that

important changes at the microscopic scalewill be visible at the macroscopic scalerdquo




on the other can be achieved by modulating the couplingbetween scales (Flack Hammerstein amp Krakauer 2012 FlackErwin Elliot amp Krakauer 2013) we should be able to make predictions about whether a system is ar rom near or at thecritical point based on whether the data suggest that robustnessor adaptability is more important given the environment and itscharacteristic timescale (Daniels Krakauer amp Flack in prep)Tis presupposes that the system can optimize where it sits withrespect to the critical point implying active mechanisms ormodulating the coupling We are working to identiy plausi-ble mechanisms using a series o toy models to study how thetype o eedback rom the macroscopic or institutional level

to the microscopic behavior influences the possibility o rapidinstitutional switches (Poon Flack amp Krakauer in prep seealso Sabloff in prep or related work on the rise o the state inearly human societies)

COMPLEXITY

Tis essay covers a lot o work so allow me to summarize Isuggested that the origins o the inormation hierarchy lie inthe manipulation o space and time to reduce environmentaluncertainty I urther suggested that uncertainty reduction ismaximized i the coarse-grained representations o the data thecomponents compute are in agreement (because this increases

the probability that everyone is making the same predictionsand so tuning the same way) As this happens the coarse-grainedrepresentations consolidate into robust slow variables at theaggregate level creating new levels o organization and givingthe appearance o downward causation

I proposed that a central challenge lies in understanding what the mapping is between the microscopic behavior andthese new levels o organization (How exactly do everyonersquoscoarse grainings converge) I argued that in biology a hybridstatistical mechanics-computer science-inormation theoreticapproach (see also Krakauer et al 2011) is required to establishsuch mappings Once we have cognitively principled effective

theories or mappings we will have an understanding o howbiological systems by discretizing space and time produceinormation hierarchies

Where are we though with respect to explaining the originso biological complexity

Te answer we are moving toward lies at the intersectiono the central concepts in this essay I evolution is an iner-ential process with complex lie being the result o biologicalsystems extracting regularities rom their environments toreduce uncertainty a natural recasting o evolutionary dynam-ics is in Bayesian terms (Ellison Flack amp Krakauer in prep)Under this view organism and environment can be interpreted




as k-order Markov processes and modeled using finite-statehidden Markov models (Figure 5) Organisms update priormodels o the environment with posterior models o observedregularities We are exploring how the Markov order (a proxyor memory) o organisms changes as organisms evolve tomatch their environment quantiying fit to the environment

with model selection We use inormation-theoretic measuresto quantiy structure Our approach allows us to evaluate thememory requirements o adapting to the environment given itsMarkov order quantiy the complexity o the models organ-isms build to represent their environments and quantitativelycompare organismal and environmental complexity as ourMarkov organisms evolve We hypothesize that high degrees ocomplexity result when there is regularity in the environmentbut it takes a long history to perceive it and an elaborate modelto encode it (Ellison Flack amp Krakauer in prep)

Acknowledgements

Tis essay summarizes my view o the pa st present and predicted uture o the core research program at the Centeror Complexity amp Collective Computation (C4) In additionto our current collaborators ndash Nihat Ay Dani Bassett KarenPage Chris Boehm and Mike Gazzaniga ndash and the supersmart students and postdoctoral ellows Eleanor Brush BryanDaniels Simon DeDeo Karl Doron Chris Ellison the lateanya Elliot Evandro Ferrada Eddie Lee and Philip Poon who have carried out much o this work on a daily basis I amdeeply grateul to the Santa Fe Institute or its support overthe years and to the Santa Fe olks whose ideas have providedinspiration First and oremost this includes David Krakauermy main collaborator Other significant SFI influences include

Jim Crutchfield Doug Erwin Walter Fontana Lauren AncelMeyers Geoffrey West Eric Smith Murray Gel l-Mann BillMiller David Padwa and Cormac McCarthy I am indebtedto Ellen Goldberg or making possible my first postdoctoral position at SFI Final ly much o this research would not be possible without the generous financial support provided bythe John empleton Foundation through a grant to SFI tostudy complexity and a grant to C4 to study the mind-brain

problem a National Science Foundation grant (0904863) anda grant rom the US Army Research Laboratory and the USArmy Research Office under contract number W911NF-13-1-0340

ReferencesAncel L W amp W Fontana 2000 Plasticity evol983158ability andmodularity in RNA J Exp Zoology (Molec amp Devel Evol) 288242-283

Ay N 2009 A refinement o the common cause principle DiscreteAppl Math 157 2439ndash2457

Ball P 2009 Naturersquos patterns A tapestry in three parts OxordUK Oxord University Press

Bergstrom C amp M Rosvall 2009 Te transmission sense oinormation Biol amp Phil 26 159ndash176

Bettencourt L M A 2013 he origins o scaling in cities Science340 1438-1441

Bettencourt L M A J Lobo D Helbing C Kuhnert amp GB West 2007 Growth inno983158ation scaling and the pace o liein cities PNAS 104 7301-7306

Boehm C amp J C Flack 2010 he emergence o simple andcomplex power structures through niche construction In he So-cial Psychology o Power ed A Guinote amp K Vescio 46-86New York Guilord Press

Brush E R D C Krakauer amp J C Flack 2013 A amilyo algorithms or computing consensus about node state romnetwork data PLOS Comp Biol 9 e1003109

Buss L W 1987 he evolution o individuality Princeton NJPrinceton University Press 224 p

Couzin I D 2009 Collective cognition in animal groupsrends Cog Sci 13 36ndash43

Crutchield J P 1994 he calculi o emergence Computationdynamics and induction Physica D 75 11-54

Crutchield J P amp D P Feldman 2001 Synchronizing to theen983158ironment Inormation-theoretic constraints on agent learn-ing Adv Compl Sys 4 251ndash264

Crutchield JP amp O Goumlrnerup 2006 Objects that makeobjects he population dynamics o structural complexity J RoySoc Interace 22 345-349

Daniels B D C Krakauer amp J C Flack 2012 Sparse code oconlict in a primate society PNAS 109 14259ndash14264

Daniels B D C Krakauer amp J C Flack nd Conlict tuned tomaximum inormation low In preparation

Davidson E H 2010 Emerg ing propert ies o animal gene reg u-latory networks Nature 468 911ndash920

DeDeo S D C Krakauer amp J C Flack 2010 Inductive game theory and the dynamics of animal conflict PLOS Comp Biol 6 e1000782

Ellison C JC Flack amp D C Krakauer nd On inerentialevolution and the complexity o lie In preparation

Feldman M amp I Eschel 1982 On the theory o parent-offspringconflict A two-locus genetic model Amer Natur119 285ndash292

Feret J V Danos J Krivine R Harmer amp W Fontana2009 Internal coarse-graining o molecular sy stems PNAS 1066453ndash6458

Ferrada E amp D C Krakauer nd he Simon modularity principle In preparation

Flack J C 2012 Multiple time-s cales and the developmentaldynamics o social systems Phil rans Roy Soc B Biol Sci367 1802ndash1810

Flack J C amp D C Krakauer 2006 Encoding power in com-munication networks Amer Natur 168 E87ndash102

Flack J C amp F B M de Waal 2007 Context modulates signalmeaning in primate communication PNAS 104 1581-1586

Flack J C amp D C Krakauer 2011 Challenges or complexitymeasures A perspective rom social dynamics and collective socialcomputation Chaos 21 037108ndash037108

Flack J C F B M de Waal amp D C Krakauer 2005 Social structure robustness and policing cost in a cognitively sophisticat-ed species Am Natur 165 E126ndash39




Flack J C P Hammerstein amp D C Krakauer 2012 Robustness in biolog ical and social s ystems In Evolution andthe mechanisms o decision-making ed P Hammerstein amp JStevens 129-151 Cambridge MI Press

Flack J C D Erwin Elliot amp D C Krakauer 2013imescales symmetry and uncertainty reduction in the originso hierarchy in biological systems In Cooperation and its evolu-tion

ed K Sterelny R Joyce B Calcott amp B Fraser 45ndash74Cambridge MI Press

Flack J C M Girvan F B M de Waal amp D C Krakauer2006 Policing stabilizes construction o social niches in pri-mates Nature 439 426ndash429

Fontana W amp L W Buss 1996 Te barrier o objects Fromdynamical systems to bounded organizations In Boundaries andbarriers ed J Casti amp A Karlqvist 56-116 Reading M AAddison-Wesley

Fontana W amp P Schuster 1998 Continuity in evolution Onthe nature o transitions Science 280 1451ndash1455

Frank S A 2003 Repression o competition and the evolutiono cooperation Evolution 57 693-705

Gell-Mann M 1996 Fundamental sources o unpredictabil-ity alk presented at conerence o the same name lthttp

www-physicsmpsohio-stateedu~p erryp633_sp07articlesundamental-sources-o-unpredictabilitypdgt Accessed01102014

Gell-Mann M amp S Lloyd 1996 Inormation measures effec-tive complexity and total inormation Complexity 2 44ndash53

Gintis H M Doebeli amp J C Flack 2012 Te evolution ohuman cooperation Cliodynamics J Teor amp Math Hist 3

Krakauer D C 2011 D arwinian d emons evolutionary complexity and inormation maximization Chaos 21 037110

Krakauer DC N Bertschinger N Ay E Olbrich J CFlack Te inormation theory o individuality In What is an

Individual eds L Nyhart amp S Lidgard University o Chica-go Press In review

Krakauer D C amp J C Flack 2010 Better living through physics Nature 467 661

Krakauer D C amp P Zanotto 2009 Viral individuality amplimitations o the lie concept In Protocells Bridging non-liv-ing and living matter ed M A Rasmussen et al 513ndash536Cambridge MI Press

Krakauer D C J C Flack S DeDeo amp D Farmer 2010 Intelligentdata analysis o intelligent systems IDA 2010 LNCS 6065 8ndash17

Krakauer D C J P Collins D Erwin J C Flack W Fon-

tana M D Laubichler S Prohaska G B West amp P Stadler2011 Te challenges and scope o theoretical biology J TeorBiol 276 269-276

Lee E B Daniels D C Krakauer amp J C Flack nd Cog-nitive effective theories or probabilistic social circuits mapping

strateg y to social structure In preparation

Levin S A B Grenell A Hastings amp A S Perelson 1997 Mathematical and computational chal lenges in populationbiology and ecosystems science Science 275 334ndash343

Mandal D H 983121uan amp C Jarzynski 2013 Maxwellrsquos refig-erator An exactly sol983158able model Phys Rev Lett 111 030602

Michod R E 2000 Darwinian dynamics Evolutionary transitions in itness and individualit y Princeton NJ PrincetonUniversity Press

Mitchell M 2010 Biological computation Working Paper2010-09-021 Santa Fe Institute Santa Fe NM

Mora amp W Bialek 2011 Are biological systems poi sed atcriticality J Stat Phys 144 268ndash302

Olshausen B amp D Field 2004 Sparse coding o sensory inputsCurr Opin Neurobiol 14 481ndash487

Payne S L Bochong Y Cao D Schaeer M D Ryser amp LYou 2013 emporal control o sel-organized pattern ormationwithout morphogen gradients in bacteria Mol Sys Biol 9 697

Pearl J 2010 Causality 2nd ed Cambridge MA CambridgeUniversity Press

Peter I S amp E H Davidson 2011 A gene regulatory networkcontrolling the embryonic speciication o endoderm Nature 474635-639

Poon P J C Flack amp D C Krakauer nd Niche construc-tion and institutional switching via adaptive learning rules In

preparation

Prohaska S J P F Stadler amp D C Krakauer 2010 Inno983158ationin gene regulation he case o chromatin computation J heorBiol 265 27ndash44

Sablo J A (ed) nd he rise o archaic states New perspec-tives on the development o complex societies Santa Fe InstituteIn preparation

Schuster P amp W Fontana 1999 Chance and necessity inevolution Lessons rom RNA Physica D Nonlinear Phenomena133 427ndash452

Smith E 2003 Sel-organization rom structural re rigerationPhys Rev E 68 046114

Smith E 2008 hermodynamics o natural selection I Energy low an d the limits on organiz ation J heor Biol httpdx

doiorg101016jjtbi200802010 P DF

Smith E S Krishnamurthy W Fontana amp D C Krakauer2011 Nonequilibrium phase transitions in biomolecular signaltransduction Phys Rev E 84 051917

Smith J M amp E Szathmaacutery 1998 he major transitions inevolution Oxord UK Oxord University Press

Stadler B M R P F Stadler G Wagner amp W Fontana 2001he topology o the possible Formal spaces underlying patterns oevolutionary change J heor Biol 213 241-274

Sumpter D J 2006 he principles o collective animal be-

haviour Phil rans Roy Soc Lon d B 361 5ndash22Valentine J amp R May 1996 Hierarchies in biolog y and paleon-tology Paleobiology 22 23ndash33

Valiant L 2013 Probably approximately correct New YorkBasic Books

West G B J H Brown amp B J Enquist 1997 A gene ral model or the origin o allometric scaling laws in biology Science 276

122-126




More than 70 of our annual

operating budget comes from

private sources - forward-thinking

individuals corporations and

foundations If you believe

complex systems science can

generate the insights that matter

most for science and society makea gift today Visit our website at

wwwsantafeedusupport and

help us achieve great things

ITrsquoS SIMPLE

Support

ComplexityScience





ldquoVerily at the rst Chaos came to be but next wide-bosomed

Earth the ever-sure foundations of all the deathless ones whohold the peaks of snowy Olympus and dim Tartarus in the

depth of the wide-pathed Earth and Eros fairest among the

deathless gods who unnerves the limbs and overcomes the

mind and wise counsels of all gods and all men within them

From Chaos came forth Erebus and black Night but of Night

were born Aether and Day whom she conceived and bare

from union in love with Erebus And Earth rst bare starry Heaven equal to herself to cover her on every side and to be

an ever-sure abiding-place for the blessed godsrdquo mdash Hesiod

from the Theogony Part 2 translated by HG Evelyn-White

THE CLASSICAL UNIVERSE is made up brick by brick starting inthe void and culminating with the earth rom the emptinesso night that gave rise to day to the day that produces the

outward order o the heavens and finally to lie upon the groundTeTeogony is a story describing the origins o energy and matter andinormation in the orm o lie Te Teogony exemplifies humanityrsquosgreat surprise that the universe should have emerged rom chaosthat emptiness has not reigned eternal and that the earth shouldbe hospitable and supportive o multiorm sentience

Perspectives

TheComplexity of LifeBY DAVID KRAKAUER

DIRECTOR WISCONSIN INSTITUTEFOR DISCOVERY UNIVERSITY

OF WISCONSIN-MADISON

CO-DIRECTOR CENTER FOR COMPLEXITY

amp COLLECTIVE COMPUTATION

WISCONSIN INSTITUTE FOR DISCOVERY

UNIVERSITY OF WISCONSIN-MADISON


About the Santa Fe Institute

Founded in 1984 the Santa Fe Institute

is an independent nonprofit research

and education center that has pioneered the science of complex systems Its

missions are supported by philanthropic

individuals and foundations forward-

thinking partner companies and

government science agencies

The SFI Bulletin is published periodically to

keep SFIrsquos community informed of its scientific

work The Bulletin is available online either

as a web-based (html5) tablet publication

or as a pdf at wwwsantafeedubulletin

To receive email notifications of future

online issues please subscribe at

wwwsantafeedubulletin To request a

subscription to the printed edition please

email the editor at jdgsantafeedu

Editorial Staff

SFI Chair of the Faculty Jennifer Dunne

SFI Director of Communications John German

Art Director Paula Eastwood Eastwood Design

Design amp Function 8 Arms Creative

Printing Starline Printing

Published by the Santa Fe Institute

1399 Hyde Park Road

Santa Fe New Mexico 87501 USA

Phone 5059848800

Printed On

100 recycled fiber

100 post-consumer waste

Green-e certified

Low-VOC vegetable-based ink

wwwsantafeedu

COVER CHARLES DARWIN ADOC-PHOTOSART RE-SOURCE NEW YORK CIRCUIT SHUTTERSTOCKCOMCOMPOSITE PAULA EASTWOOD






rdquo

David Krakauer

David Krakauer




























Sincerely







SFI BULLETIN



By DAVID KRAKAUER


By JENNIFER DUNNE



By JESSICA FLACK




12

6

0










































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Social circuitry












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BY JESSICA C FLACK




Jessica Flack


N u m b e


0

0

1

2

3

4

5

1 2 3 4 5

UNSTABLE

TOO SIMPLE



TACHYONSONLY

UNSTABLE















































1 2














3 4

























In physics







In biology























1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience








rdquo

David Krakauer

David Krakauer




























Sincerely







SFI BULLETIN



By DAVID KRAKAUER


By JENNIFER DUNNE



By JESSICA FLACK




12

6

0










































wwwsantafeedulife




















































Social circuitry












Time

F i g h

t 1

F i g h

t 2



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Je

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Ta

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Fp

Ec

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Iw

Is

Fp

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Yv

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Ta

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Cb

Pa

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Jc

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Eo

Cd

Pa

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Hh

At

Zu

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Ta

Fn

















































arisesrdquo


















BY JESSICA C FLACK




Jessica Flack


N u m b e


0

0

1

2

3

4

5

1 2 3 4 5

UNSTABLE

TOO SIMPLE



TACHYONSONLY

UNSTABLE















































1 2














3 4

























In physics







In biology























1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience


























Sincerely







SFI BULLETIN



By DAVID KRAKAUER


By JENNIFER DUNNE



By JESSICA FLACK




12

6

0










































wwwsantafeedulife




















































Social circuitry












Time

F i g h

t 1

F i g h

t 2



Vd

Pa

Je

Cb

Yv

Ta

Sg

Qs

Po

Mv

Ip

Fp

Eo

Dh

Tr

Pr

Fc

Zu

Vf

Fp

Ec

Zu

Zr

Iw

Is

Fp

Fc

Cc

Yv

Cd

Ta

Cc

Qv

Ip

Cb

Pa

Eb

Sb

Jc

Je

Hp

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Vf

Eo

Eo

Cd

Pa

Dh

Hh

At

Zu

Yv

Th

Ta

Fn

















































arisesrdquo


















BY JESSICA C FLACK




Jessica Flack


N u m b e


0

0

1

2

3

4

5

1 2 3 4 5

UNSTABLE

TOO SIMPLE



TACHYONSONLY

UNSTABLE















































1 2














3 4

























In physics







In biology























1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience







SFI BULLETIN



By DAVID KRAKAUER


By JENNIFER DUNNE



By JESSICA FLACK




12

6

0










































wwwsantafeedulife




















































Social circuitry












Time

F i g h

t 1

F i g h

t 2



Vd

Pa

Je

Cb

Yv

Ta

Sg

Qs

Po

Mv

Ip

Fp

Eo

Dh

Tr

Pr

Fc

Zu

Vf

Fp

Ec

Zu

Zr

Iw

Is

Fp

Fc

Cc

Yv

Cd

Ta

Cc

Qv

Ip

Cb

Pa

Eb

Sb

Jc

Je

Hp

Lr

Jc

Zu

Po

Dh

Vf

Eo

Eo

Cd

Pa

Dh

Hh

At

Zu

Yv

Th

Ta

Fn

















































arisesrdquo


















BY JESSICA C FLACK




Jessica Flack


N u m b e


0

0

1

2

3

4

5

1 2 3 4 5

UNSTABLE

TOO SIMPLE



TACHYONSONLY

UNSTABLE















































1 2














3 4

























In physics







In biology























1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience












































wwwsantafeedulife




















































Social circuitry












Time

F i g h

t 1

F i g h

t 2



Vd

Pa

Je

Cb

Yv

Ta

Sg

Qs

Po

Mv

Ip

Fp

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Tr

Pr

Fc

Zu

Vf

Fp

Ec

Zu

Zr

Iw

Is

Fp

Fc

Cc

Yv

Cd

Ta

Cc

Qv

Ip

Cb

Pa

Eb

Sb

Jc

Je

Hp

Lr

Jc

Zu

Po

Dh

Vf

Eo

Eo

Cd

Pa

Dh

Hh

At

Zu

Yv

Th

Ta

Fn

















































arisesrdquo


















BY JESSICA C FLACK




Jessica Flack


N u m b e


0

0

1

2

3

4

5

1 2 3 4 5

UNSTABLE

TOO SIMPLE



TACHYONSONLY

UNSTABLE















































1 2














3 4

























In physics







In biology























1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience




























Social circuitry












Time

F i g h

t 1

F i g h

t 2



Vd

Pa

Je

Cb

Yv

Ta

Sg

Qs

Po

Mv

Ip

Fp

Eo

Dh

Tr

Pr

Fc

Zu

Vf

Fp

Ec

Zu

Zr

Iw

Is

Fp

Fc

Cc

Yv

Cd

Ta

Cc

Qv

Ip

Cb

Pa

Eb

Sb

Jc

Je

Hp

Lr

Jc

Zu

Po

Dh

Vf

Eo

Eo

Cd

Pa

Dh

Hh

At

Zu

Yv

Th

Ta

Fn

















































arisesrdquo


















BY JESSICA C FLACK




Jessica Flack


N u m b e


0

0

1

2

3

4

5

1 2 3 4 5

UNSTABLE

TOO SIMPLE



TACHYONSONLY

UNSTABLE















































1 2














3 4

























In physics







In biology























1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience












Time

F i g h

t 1

F i g h

t 2



Vd

Pa

Je

Cb

Yv

Ta

Sg

Qs

Po

Mv

Ip

Fp

Eo

Dh

Tr

Pr

Fc

Zu

Vf

Fp

Ec

Zu

Zr

Iw

Is

Fp

Fc

Cc

Yv

Cd

Ta

Cc

Qv

Ip

Cb

Pa

Eb

Sb

Jc

Je

Hp

Lr

Jc

Zu

Po

Dh

Vf

Eo

Eo

Cd

Pa

Dh

Hh

At

Zu

Yv

Th

Ta

Fn

















































arisesrdquo


















BY JESSICA C FLACK




Jessica Flack


N u m b e


0

0

1

2

3

4

5

1 2 3 4 5

UNSTABLE

TOO SIMPLE



TACHYONSONLY

UNSTABLE















































1 2














3 4

























In physics







In biology























1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience









































arisesrdquo


















BY JESSICA C FLACK




Jessica Flack


N u m b e


0

0

1

2

3

4

5

1 2 3 4 5

UNSTABLE

TOO SIMPLE



TACHYONSONLY

UNSTABLE















































1 2














3 4

























In physics







In biology























1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience

















BY JESSICA C FLACK




Jessica Flack


N u m b e


0

0

1

2

3

4

5

1 2 3 4 5

UNSTABLE

TOO SIMPLE



TACHYONSONLY

UNSTABLE















































1 2














3 4

























In physics







In biology























1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience






































1 2














3 4

























In physics







In biology























1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience
















3 4

























In physics







In biology























1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience























In physics







In biology























1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience


















1000

0100

0010

0001

4 6 8 10 12 14i (Fight Size)

L o n g

F r a c t i o n N ( i ) N ( gt 2 )

Ud

Fo

Cd

Qs

Fp

EoCd + Qs

Eo + FoEo+Qs

Cd+Fp

Fp+Qs

Eo+Fp

Fo+Fp

Cd+Eo

Cd+FoCd+Ud

Qs+Ud

Fo+Qs

Fo+Ud

Eo+Ud

Fp+Ud












a

b

c

















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience



















4

3

2

1

0

1

3 4

1 2

1 4

0

0 100 200 300 400 500 0 100 200 300 400 500


Statistical

Complexity

Probability

Ratio

C μ

( x 0 t k ) ( b i t s )

P ( Ο k )

O1

O2

O3

O4

ε j

Key









Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience






Phospholipidbilayer


















































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience





































COMPLEXITY












Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience








Acknowledgements
































































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience





































preparation














122-126














ITrsquoS SIMPLE

Support

ComplexityScience
















ITrsquoS SIMPLE

Support

ComplexityScience



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