memes & changing language
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FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Memes & Changing language
Richard K. Belew• Computational analyses of cultural artifacts• Computational models of evolution, learning
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Marta Kutas’ poem
•
repeat again and againdate, mate & procreateor merely carve it on a slateengrave itpaint it on cave wall or a plateor anywhere at allimpress it on any medium via any pressmechanical or electricalor some other -icalnot yet designedand release the ideafrom your mindand create for it a new fate.Imitate and pass it onidentical,similaror slightly modified.Then it's been codifiedand will survive.Perhaps far longerthan any genenow alive.
ImitateCopysmooth or choppyneat or a little sloppyreproduceduplicaterepeat in principle, mimememe - a cultural geneplaying the evolutionary gamewith similar toolswith similar rulesand much the same aimreplicateduplicate
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Summary• Key terms
• Replication rate• Fitness• Mutation• Intention• Temporal/spatial distribution• Neutral drift
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Summary (cont)• Evolution is a powerful adaptive process• Cultures certainly change• Do “selectionist” models offer the best accounts?• Vast amounts of new evidence becoming
available• The interactions between culture and biological
evolution will only increase in the future
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Evolution
• Charles Darwin (1809 -1882)• Definitions• Basic algorithm• GA at a glance
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Charles Darwin (1809 -1882)
• Replication• Mutation• Selection
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Definitions
• Individual xi = (0,1)L ; string of L bits• Population X = xi ; |X| = P, size of
population• Generation Xt ; population at a point
in time
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Basic algorithmCreate X0, randomlydo ; “Generation”
forall xi in XEvaluate fitness f(xi) in R
Determine num. offspring g(xi) in J ; ∑ g(xi) = PForm Xt+1 = g(xi) copies of xiApply genetic operators Ω to Xt+1
until Xt+1 has converged ; (X ≈ Xt+1)
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
GA at a glance
101010111010100010010111010100101001001011011011010101010101001001001011011110110101010101101011010101101010010101010110
0.2560.6730.3540.5430.4290.8670.2170.3160.6320.5640.5360.5650.4560.9310.101
<f(x)>=0.496
011112012111030
101010001001011101010010100100101101101111011011010100100100101101001011011110110101010101101011101001011010010110100101
101010001001011101010010100100101101001111011011010100100110101101001011011110110101010101100111101010011010010110100101
Mutation
Crossover
Xt Xt+1f(x) g(x)
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Evolution, Development,Learning, Culture
• Adaptation as sine qua non of cognitive system
• Adaptation to invariants of environmentat varying time-scales
• Cultural change• Evolution as primary adaptive system• Interaction among adaptive mechanisms• Abstract model• Computational modeling - benefits
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Adaptation as sine qua non of cognitive system
What is invariant in adaptive systems will depend on the timeintervals during which we observe them. There are at leastthree time scales of relevance to such systems,corresponding to three different forms of adaptation. - On the shortest time scale intelligent --- hence adaptive ---systems continually change their behavior in the course ofsolving each problem situation they encounter.... - On a somewhat longer time scale, intelligent systemsmake adaptations that are preserved and remain available formeeting new situations successfully.... - On the longest time scale, intelligent systems evolve.
“Cognitive Science: The newest science of the artificial”H. Simon, 1980
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Adaptation to invariants of environmentat varying time-scales
Environmentalvariation
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Cultural change
• Slower than individuals can change• But faster than evolution can adapt
Environmentalvariation
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Evolution as primary adaptivesystem
• Chronologically• (The?) defining characteristic of living systems• Emergence of subsequent adaptive mechanisms
• Ontogenesis• Learning• Culture
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Interaction among adaptivemechanisms
• Only models subsuming multiple adaptivemechanisms can speak to interactions amongthem
• But the constituent models (eg, of evolution,learning, etc.) will necessarily be simplified
• Confusions abound• “Inheritance of acquired characteristics” [Lamarck]• “Ontogeny recapitulates phylogeny” [Haeckel]• “Nature” XOR “nurture”?• “Nativist” XOR “empiricist”?• “Race predicts IQ”
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Abstract modelL(ph)
L
gM
g'g"
f( ph)...
L
L
d(g)
ph Ph
G$ d-1(g) ?
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Computational modeling -benefits
• Algorithmically precise characterization oftheories
• More expressive than other mathematicallanguages (eg, FOPL)
• Models provide insight into statisticalobservations- Mechanistic account underlying variables’
interactions
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Computational modeling -benefits (cont)
• Models as interdisciplinary communicationdevices- Composition of sub-models developed by
domain experts
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Computational modeling -issues
• Appropriate level of abstraction?• Traditional models
• Veridical relation between features of model andobservables
• Minimal models• Simplest demonstration of phenonmenom• Eg, culture = non-genetic inheritance [Boyd & Richerson]• Eg, learning = multiple guesses [Hinton & Nowlan]
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Computational modeling -issues (cont)
• Video games• Demonstration of expressive adequacy• Eg, SimLife
• NB: GA typically used as engineers’ optimizationtechnique• But appears adaptationist in biological context
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Memes: Cultural analog of gene• “Replicator in the soup of human culture, ... unit
of cultural transmission, of imitation”• Dawkins’76, Chap11 (revised 1989)
• D. Campbell (1974): Evolutionary Epistemology• Other biologically inspired models of culture
• Cavalli-Sforza & Feldman (1981)• Boyd & Richerson (1985)
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Algorithm1. Divide your piece in half2. Optional: Add ‘graffiti’• If you do, add the SAME graffiti to all instances on half• You can add different notes to the two halves3. Pass your two halves to two neighbors• Odd: Front/Back• Even: Left/Right4. SELECT: your favorite ONE from the TWOsheets passed you• (Wad up the other; we’ll collect)Go to #1
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Evolution of symbols
• A Brief History• Ancient history ...• ... which ‘evolved’ to exploit a coincidental
mneumonic• ... which becomes a modern bumper sticker!• Perhaps an entire species of bumper stickers!• Survey data T. Lessl]
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
A Brief History
The fish symbol has been used throughout history by pagan andearth-based religions. Ancient Goddesses in China, Egypt andIndia were represented by the fish symbol. In ancient Greece thefish symbol represented the Goddess of Love. Venus, the ancientRoman empire's Goddess of Love was also represented by a fishsymbol. She was so revered that Christian authorities insisted ontaking over the symbol. The Christians revised the symbol'sassociated mythology to fit their own purposes. Today, the fishsymbol has "evolved" yet again to represent Evolution, Scienceand Political Freedom; as well as all of the topics on our emblemspage.
• According to EvolveFish.com!
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Ancient history ...JesusChristGod’sSonSavior------Fish
• In the beginning there was Christian grafftti
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Perhaps an entire species ofbumper stickers!
• http://www.evolvefish.com/fish/emblems.html
DARW IN
Jesus lives!
DARW IN Jesus!
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Survey• 140 cars with “fish with legs” DarwinFish on
their bumpers given surveys• “[Lessl] considered using a survey on the Internet, but there is
no way to be sure respondents actually have the fish on theircars. So he set out on foot in parking lots in several states,looking for the symbols and leaving questionnaires underwindshield wipers. He asked three simple questions:”• Why did you put this emblem on your car?• What does the Darwin fish mean to you?• What audience did you hope to reach?
• 51 surveys returned• 66% self-identified as Christian
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Lessl’s interpreation "By inserting Darwin's name in to the place on the fish iconusually reserved for Christ, the Icthus symbol is rituallyprofaned," said Lessl, "which is to say, emptied of its religiousmeaning. By putting Darwin's name where Christ's wouldtraditionally go, the Darwin fish does not assert, as one mightthink, that science is salvation and that Darwin is its prophet. Forthe majority of those who display this emblem, Darwin's roleseems to be that of anti-Messiah. This is more like theinversion rituals of carnival, where some drunken peasant isdressed up as the king. Its purpose is not to elevate the peasantbut to make fun of the king."
•
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Dawkins’ data!• “If the meme is a
scientific idea... a roughmeasure of its survivalvalue could be obtainedby [its citation count]” -1976
• 1989 notes, citations to[Hamilton’64] used asevidence ofEXPONENTIAL growth
• In distinction to simplyCUMULATIVE citationrates, ala influentialother influential texts
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Dan Dennett • What kind of biological analogy?
• Virus?• Symbiotic host/guest
• Parasite: negative effect on host• Commensal: neutral• Mutual: positive
• “Genes hold culture on a leash” [E. O. Wilson]
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Dan Dennett (cont)• Intentional stance (on cultural analysis)
• “analyzing the flux of events in terms of agentsand their (rational) actions and reactions”
• “a meme is an information packet with anattitude”
• Intention + biology = genetic engineering! [P.Kitcher, Lives to Come]
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
A literature-based model ofscientific change
• Science needs to be published• Theories are expressed as arguments• In terms of shared “keywords”• Published record• Available evidence• Lexical dynamics• Temporal feature selection for text categorization
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Available evidence
• Text• Authorship• Citation patterns• Meta-data
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DynamicIDFWeighting
• Up-weighting“seminal”
• “Historicalreview”
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Towards “tacit” language
• Use ofARTIFICIALINTELLIGENCEwithin AIT
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SIGIR corpus• Assoc Computing Machinery • Special Interest Group for Information Retrieval
0102030405060708090
10019
8719
8819
8919
9019
9119
9219
9319
9419
9519
9619
9719
9819
9920
0020
0120
02
No. d
ocum
ents
1987 2002
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
Replicated on other corpora
Bothconferenceproceedingsand journals
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Text classification
•Classifier
0.28 Sports
0.06 Weather
0.17 Business0.34 Entertainment0.11 Politics
Classifier
0.48 Sports0.03 Weather
0.12 Business0.27 Entertainment
0.06 Politics
Summer 1984
…word57
los angelesword59
…
…word57
los angelesword59
…
FOA © rik@cs.ucsd.edu22 Jan 03CogSci 1
ACM’s classification of SIGIR Category NDocE. Data 9F. Theory Of Computation 8G. Mathematics Of Computing 8H.2. Database Management 36H.3.1. Content Analysis And Indexing 89H.3.3/Clustering 18H.3.3/Information Filtering 15H.3.3/Query Formulation 50H.3.3/Retrieval Models 135H.3.3/Search Process 34H.3.4. Systems And Software 18H.3.5. Online Information Services 8H.5. Information Interfaces 17I.2. Artificial Intelligence 42I.5. Pattern Recognition 14I.7. Document And Text Processing 10J. Computer Applications 8
•
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