gerhard j ager gerhard.jaeger@uni-tuebingengjaeger/lehre/ws0910/evolution/... · 2011. 3. 10. ·...
TRANSCRIPT
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Evolutionary game theory and language evolution
Gerhard [email protected]
November 26, 2009
Uni Tubingen
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Conceptualization of language evolution
prerequisites for evolutionary dynamics
replication
variation
selection
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Linguemes
“any piece of structure that can be independently learned andtherefore transmitted from one speaker to another” (Nettle1999:5)
Croft (2000) attributes the name lingueme to Haspelmath(Nettle calls them items)
Examples:
phonemesmorphemeswordsconstructionsidiomscollocations...
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Linguemes
Linguemes are replicators
comparable to genes
structured configuration of replicators
Biology: genotypeLinguistics: utterance
Croft:
The utterance is the genome!
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Evolution
Replication
(at least) two modes of lingueme replication:
acquisition
priming (Jager and Rosenbach 2005; Croft and Nettle wouldperhaps not agree)
Variation
linguistic creativity
reanalysis
language contact
...
Selection
social selection
selection for learnability
selection for primability
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Evolution
Replication
(at least) two modes of lingueme replication:
acquisition
priming (Jager and Rosenbach 2005; Croft and Nettle wouldperhaps not agree)
Variation
linguistic creativity
reanalysis
language contact
...
Selection
social selection
selection for learnability
selection for primability
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Evolution
Replication
(at least) two modes of lingueme replication:
acquisition
priming (Jager and Rosenbach 2005; Croft and Nettle wouldperhaps not agree)
Variation
linguistic creativity
reanalysis
language contact
...
Selection
social selection
selection for learnability
selection for primability
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Fitness
learnability/primability
selection against complexity
selection against ambiguity
selection for frequency
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EGT and pragmatics
Horn strategies: prototypical meanings tend to go with simpleexpressions and less prototypical meanings with complexexpressions.
(1) a. John went to church/jail. (prototypical interpretation)b. John went to the church/jail. (literal interpretation)
(2) a. I am going to marry you. (no indirect speech act)b. I will marry you. (indirect speech act)
(3) a. I need a new driller/cooker.b. I need a new drill/cook.
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Horn strategies
simple game:
players: speaker and hearertwo forms: f0 (short) and f1 (long)two meanings: m0 (frequent) and m1 (rare)speaker strategies: mappings from meanings to formshearer strategies: mappings from forms to meanings
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Speaker strategies
S1 : m0 7→ f0, m1 7→ f1: “Horn strategy”
S2 : m0 7→ f1, m1 7→ f0: “anti-Horn strategy”
S3 : m0 7→ f0, m1 7→ f0: “Smolensky strategy”
S4 : m0 7→ f1, m1 7→ f1: “anti-Smolensky strategy”
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Hearer strategies
H1 : f0 7→ m0, f1 7→ m1: “Horn strategy’
H2 : f0 7→ m1, f1 7→ m0: “anti-Horn strategy”
H3 : f0 7→ m0, f1 7→ m0: “Smolensky strategy”
H4 : f0 7→ m1, f1 7→ m1: “anti-Smolensky strategy”
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Utility of Horn games
whether communication works depends both on speakerstrategy S and hearer strategy H
two factors for functionality of communication
communicative success (“hearer economy”)
δm(S,H) ={
1 iff H(S(m)) = m0 else
least effort (“speaker economy”)
cost(f) . . .measure of complexity of expression
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Utility of Horn games
us/h(S,H) =∑m
pm × (δm(S,H)− cost(S(m)))
p . . . probability distribution over meanings
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Utility of Horn game
Let’s make up some numbers:
p(m0) = .75p(m1) = .25cost(f0) = .1cost(f1) = .2
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Utility of Horn game
H1 H2 H3 H4
S1 .875 -.125 .625 .125
S2 -.175 .825 .575 .25
S3 .65 .15 .65 .15
S4 .05 .55 .55 .05
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Utility of Horn game
H1 H2 H3 H4
S1 .875 -.125 .625 .125
S2 -.175 .825 .575 .25
S3 .65 .15 .65 .15
S4 .05 .55 .55 .05
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The problem of equilibrium selection
both Horn and anti-Horn are evolutionarily stable
EGT explains the aversion of natural languages againstsynonymy and ambiguity
preference for Horn not directly explainable in standard EGT
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The problem of equilibrium selection
rationalistic considerations favor Horn over anti-Horn:
Horn strategy is Pareto efficient (nobody can do better inabsolute terms)Horn strategy risk dominates anti-Horn (if you know thepopulation is in an equilibrium but you do not know in whichone, going for Horn is less risky than anti-Horn)
replicator dynamics favors Horn over anti-Horn:
complete random state evolves to Horn/Hornbasin of attraction of Horn is about 20 times as large as basinof attraction of anti-Horn (numerical approximation—doesanybody know how to do this analytically?)
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Dynamics starting from random state
0
0.2
0.4
0.6
0.8
1
S1 S2 S3 S4
0
0.2
0.4
0.6
0.8
1
H1 H2 H3 H4
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The evolution of differentialcase marking
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Ways of argument identification
transitivity may lead to ambiguity
the woman that Maria knows the woman that knows Maria
die Frau, die Maria kennt
three ways out1 word order2 agreement3 case
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the woman that he knows the woman that knows him
die Frau, die er kennt die Frau, die ihn kennt
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Suppose one argument is a pronoun and one is a noun (or aphrase)
{I, BOOK, KNOW}both conversants have an interest in successful communication
case marking (accusative or ergative) is usually more costlythan zero-marking (nominative)
speaker wants to avoid costs
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speaker strategies hearer strategies
always case mark the object ergative is agent(accusative) and accusative object
always case mark the agent pronoun is agent(ergative)
case mark the object pronoun is objectif it is a pronoun
pronoun is agentunless it is accusative
......
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Statistical patterns of language use
four possible clause types
O/p O/n
A/p he knows it he knows the book
A/n the man knows it the man knows the book
statistical distribution (from a corpus of spoken English)
O/p O/n
A/p pp = 198 pn = 716
A/n np = 16 nn = 75
pn � np
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functionality of speaker strategies and hearer strategiesdepends on various factors:
How often will the hearer get the message right?How many case markers does the speaker need per clause —on average?
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speaker strategies that will be considered
agent is pronoun agent is noun object is pronoun object is noun
e(rgative) e(rgative) a(ccusative) a(ccusative)
e e a z(ero)
e e z a
e e z z
e z a a
... ... ... ...
z e z z
z z a a
z z a z
z z z a
z z z z
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hearer strategies:
strict rule: ergative means “agent”, and accusative means“object”elsewhere rules:
1 SO: “The first phrase is always the agent.”2 pA: “Pronouns are agents, and nouns are objects.”3 pO: “Pronouns are objects, and nouns are agents.”4 OS: “The first phrase is always the object.”
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The game of case
strategy space and utility function are known
probability of meaning types can be estimated from corpusstudy
hard to estimate how the complexity of a case morphemecompares to its benefit for disambiguation from the speakerperspective
parameterized utility function
u(S,H) =∑m
pm × (δm(S,H)− k × cost(S(m)))
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Utility of case marking
let us assume k = .1
Speaker Hearer strategiesstrategies SO pA pO OS
eezz 0.90 0.90 0.90 0.90zzaa 0.90 0.90 0.90 0.90ezaz 0.85 0.85 0.85 0.85zeza 0.81 0.81 0.81 0.81zeaz 0.61 0.97 0.26 0.61ezzz 0.86 0.86 0.87 0.86zezz 0.54 0.89 0.54 0.54zzaz 0.59 0.94 0.59 0.59zzza 0.81 0.81 0.82 0.81zzzz 0.50 0.85 0.15 0.50
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Utility of case marking
let us assume k = .1
Speaker Hearer strategiesstrategies SO pA pO OS
eezz 0.90 0.90 0.90 0.90zzaa 0.90 0.90 0.90 0.90ezaz 0.85 0.85 0.85 0.85zeza 0.81 0.81 0.81 0.81zeaz 0.61 0.97 0.26 0.61ezzz 0.86 0.86 0.87 0.86zezz 0.54 0.89 0.54 0.54zzaz 0.59 0.94 0.59 0.59zzza 0.81 0.81 0.82 0.81zzzz 0.50 0.85 0.15 0.50
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Utility of case marking
only one evolutionarily stable state: zeaz/pA (split ergative)
very common among Australian aborigines languages
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Non-strict Nash equilibria
Why are non-strict Nash Equilibria unstable?
Dynamics without mutation
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Non-strict Nash equilibria
Why are non-strict Nash Equilibria unstable?
Dynamics with mutation
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Utility of case marking
If speakers get lazier...
k = 0.45
Speaker Hearer strategiesstrategies SO pA pO OS
eezz 0.550 0.550 0.550 0.550zzaa 0.550 0.550 0.550 0.550ezaz 0.458 0.458 0.458 0.458zeza 0.507 0.507 0.507 0.507zeaz 0.507 0.863 0.151 0.507ezzz 0.545 0.538 0.553 0.545zezz 0.505 0.861 0.148 0.505zzaz 0.510 0.867 0.154 0.510zzza 0.539 0.531 0.547 0.539zzzz 0.500 0.849 0.152 0.500
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Utility of case marking
If speakers get lazier...
k = 0.45
Speaker Hearer strategiesstrategies SO pA pO OS
eezz 0.550 0.550 0.550 0.550zzaa 0.550 0.550 0.550 0.550ezaz 0.458 0.458 0.458 0.458zeza 0.507 0.507 0.507 0.507zeaz 0.507 0.863 0.151 0.507ezzz 0.545 0.538 0.553 0.545zezz 0.505 0.861 0.148 0.505zzaz 0.510 0.867 0.154 0.510zzza 0.539 0.531 0.547 0.539zzzz 0.500 0.849 0.152 0.500
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Utility of case marking
... and lazier ...
k = 0.53
Speaker Hearer strategiesstrategies SO pA pO OS
eezz 0.470 0.470 0.470 0.470zzaa 0.470 0.470 0.470 0.470ezaz 0.368 0.368 0.368 0.368zeza 0.436 0.436 0.436 0.436zeaz 0.483 0.839 0.127 0.483ezzz 0.473 0.465 0.480 0.473zezz 0.497 0.854 0.141 0.497zzaz 0.494 0.850 0.137 0.494zzza 0.476 0.468 0.484 0.476zzzz 0.500 0.848 0.152 0.500
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Utility of case marking
... and lazier ...
k = 0.53
Speaker Hearer strategiesstrategies SO pA pO OS
eezz 0.470 0.470 0.470 0.470zzaa 0.470 0.470 0.470 0.470ezaz 0.368 0.368 0.368 0.368zeza 0.436 0.436 0.436 0.436zeaz 0.483 0.839 0.127 0.483ezzz 0.473 0.465 0.480 0.473zezz 0.497 0.854 0.141 0.497zzaz 0.494 0.850 0.137 0.494zzza 0.476 0.468 0.484 0.476zzzz 0.500 0.848 0.152 0.500
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Utility of case marking
... and lazier...
k = 0.7
Speaker Hearer strategiesstrategies SO pA pO OS
eezz 0.300 0.300 0.300 0.300zzaa 0.300 0.300 0.300 0.300ezaz 0.177 0.177 0.177 0.177zeza 0.287 0.287 0.287 0.287zeaz 0.431 0.788 0.075 0.431ezzz 0.318 0.310 0.326 0.318zezz 0.482 0.838 0.126 0.482zzaz 0.457 0.814 0.101 0.457zzza 0.343 0.335 0.350 0.343zzzz 0.500 0.848 0.152 0.500
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Utility of case marking
... and lazier...
k = 0.7
Speaker Hearer strategiesstrategies SO pA pO OS
eezz 0.300 0.300 0.300 0.300zzaa 0.300 0.300 0.300 0.300ezaz 0.177 0.177 0.177 0.177zeza 0.287 0.287 0.287 0.287zeaz 0.431 0.788 0.075 0.431ezzz 0.318 0.310 0.326 0.318zezz 0.482 0.838 0.126 0.482zzaz 0.457 0.814 0.101 0.457zzza 0.343 0.335 0.350 0.343zzzz 0.500 0.848 0.152 0.500
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Utility of case marking
...
k = 1
Speaker Hearer strategiesstrategies SO pA pO OS
eezz 0.000 0.000 0.000 0.000zzaa 0.000 0.000 0.000 0.000ezaz −0.160 −0.160 −0.160 −0.160zeza 0.024 0.024 0.024 0.024zeaz 0.340 0.697 −0.016 0.340ezzz 0.045 0.037 0.053 0.045zezz 0.455 0.811 0.099 0.455zzaz 0.394 0.750 0.037 0.394zzza 0.106 0.098 0.144 0.106zzzz 0.500 0.848 0.152 0.500
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Utility of case marking
...
k = 1
Speaker Hearer strategiesstrategies SO pA pO OS
eezz 0.000 0.000 0.000 0.000zzaa 0.000 0.000 0.000 0.000ezaz −0.160 −0.160 −0.160 −0.160zeza 0.024 0.024 0.024 0.024zeaz 0.340 0.697 −0.016 0.340ezzz 0.045 0.037 0.053 0.045zezz 0.455 0.811 0.099 0.455zzaz 0.394 0.750 0.037 0.394zzza 0.106 0.098 0.144 0.106zzzz 0.500 0.848 0.152 0.500
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Taking stock
zeaz/pAsplit ergative
zzaz/pA ezzz/pOdifferential object marking inverse DOM
—
zezz/pA zzza/pOdifferential subject marking inverse DSM
zzzz/pA zzza/pOno case marking
zzzz/pA
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Taking stock
zeaz/pAsplit ergativeAustralian languages
zzaz/pA ezzz/pOdifferential object marking inverse DOM
—
zezz/pA zzza/pOdifferential subject marking inverse DSM
zzzz/pA zzza/pOno case marking
zzzz/pA
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Taking stock
zeaz/pAsplit ergativeAustralian languages
zzaz/pA ezzz/pOdifferential object marking inverse DOMEnglish, Dutch, ... —
zezz/pA zzza/pOdifferential subject marking inverse DSM
zzzz/pA zzza/pOno case marking
zzzz/pA
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Taking stock
zeaz/pAsplit ergativeAustralian languages
zzaz/pA ezzz/pOdifferential object marking inverse DOMEnglish, Dutch, ... —
zezz/pA zzza/pOdifferential subject marking inverse DSMseveral caucasian languages
zzzz/pA zzza/pOno case marking
zzzz/pA
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Taking stock
zeaz/pAsplit ergativeAustralian languages
zzaz/pA ezzz/pOdifferential object marking inverse DOMEnglish, Dutch, ... —
zezz/pA zzza/pOdifferential subject marking inverse DSMseveral caucasian languages
zzzz/pA zzza/pOno case markingChinese, Thai
zzzz/pA
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Taking stock
zeaz/pAsplit ergativeAustralian languages
zzaz/pA ezzz/pOdifferential object marking inverse DOMEnglish, Dutch, ... —
zezz/pA zzza/pOdifferential subject marking inverse DSMseveral caucasian languages Nganasan
zzzz/pA zzza/pOno case markingChinese, Thai
zzzz/pA
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Taking stock
only very few languages are not evolutionary stable in thissensezzaa: Hungarian, ezza: Parachi, Yazguljami (Iranianlanguages), eeaa: Wangkumara
curious asymmetry: if there are two competing stable states,one is common and the other one rare
similar pattern as with Horn vs. anti-Horn
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Alle equilibria are stable, butsome equilibria are more stable
than others.
Stochastic EGT
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Random mutation and stability
idealizations of standard Evolutionary Game Theory
populations are (practically) infinitemutations rate is constant and low
better model (Young 1993; Kandori, Mailath and Rob 1993)
finite populationmutation is noisy
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Consequences of finite population model
every mutation barrier will occasionally be taken
no absolute stability
if multiple Strict Nash Equilibria coexist, system will oscillatebetween them
some equilibria are more stable than others
system will spend most of the time in most robustly stablestate
stochastically stable states
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A particular model
discrete time/finite population version of replicator dynamics
mutations occur rarely (most generations have no mutants atall)
if mutation occurs, each individual in this generation has sameprobability to be a mutant
mutation frequency and mutation rate equal for bothpopulations
each strategy is equally likely for a mutant (within itspopulation)
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The formulas
∆xi
∆t= xi(ui − uA) +
∑j
Zji − Zij
n
∆yi
∆t= yi(ui − uB) +
∑j
Zji − Zij
n
xi: frequency of speaker strategy i
yi: frequency of hearer strategy i
ui: expected utility of strategy i
uR: average utility of entire R-population
Zij : random variable; distributed according to the binomialdistribution b(pij , bxinc)pij : probability that an i-individual mutates to strategy j
n: population size
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The formulas
∆xi
∆t= xi(ui − uA) +
∑j
Zji − Zij
n
∆yi
∆t= yi(ui − uB) +
∑j
Zji − Zij
n
xi: frequency of speaker strategy i
yi: frequency of hearer strategy i
ui: expected utility of strategy i
uR: average utility of entire R-population
Zij : random variable; distributed according to the binomialdistribution b(pij , bxinc)pij : probability that an i-individual mutates to strategy j
n: population size
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The formulas
∆xi
∆t= xi(ui − uA) +
∑j
Zji − Zij
n
∆yi
∆t= yi(ui − uB) +
∑j
Zji − Zij
n
xi: frequency of speaker strategy i
yi: frequency of hearer strategy i
ui: expected utility of strategy i
uR: average utility of entire R-population
Zij : random variable; distributed according to the binomialdistribution b(pij , bxinc)pij : probability that an i-individual mutates to strategy j
n: population size
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The formulas
∆xi
∆t= xi(ui − uA) +
∑j
Zji − Zij
n
∆yi
∆t= yi(ui − uB) +
∑j
Zji − Zij
n
xi: frequency of speaker strategy i
yi: frequency of hearer strategy i
ui: expected utility of strategy i
uR: average utility of entire R-population
Zij : random variable; distributed according to the binomialdistribution b(pij , bxinc)pij : probability that an i-individual mutates to strategy j
n: population size
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The formulas
∆xi
∆t= xi(ui − uA) +
∑j
Zji − Zij
n
∆yi
∆t= yi(ui − uB) +
∑j
Zji − Zij
n
xi: frequency of speaker strategy i
yi: frequency of hearer strategy i
ui: expected utility of strategy i
uR: average utility of entire R-population
Zij : random variable; distributed according to the binomialdistribution b(pij , bxinc)pij : probability that an i-individual mutates to strategy j
n: population size
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The formulas
∆xi
∆t= xi(ui − uA) +
∑j
Zji − Zij
n
∆yi
∆t= yi(ui − uB) +
∑j
Zji − Zij
n
xi: frequency of speaker strategy i
yi: frequency of hearer strategy i
ui: expected utility of strategy i
uR: average utility of entire R-population
Zij : random variable; distributed according to the binomialdistribution b(pij , bxinc)pij : probability that an i-individual mutates to strategy j
n: population size
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The formulas
∆xi
∆t= xi(ui − uA) +
∑j
Zji − Zij
n
∆yi
∆t= yi(ui − uB) +
∑j
Zji − Zij
n
xi: frequency of speaker strategy i
yi: frequency of hearer strategy i
ui: expected utility of strategy i
uR: average utility of entire R-population
Zij : random variable; distributed according to the binomialdistribution b(pij , bxinc)pij : probability that an i-individual mutates to strategy j
n: population size
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A simulation
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Horn anti-Horn
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Stochastic stability
punctuated equilibria
long periods of dynamic stability alternate with shorttransition periods
in the long run, more time in Horn state (67% vs. 26% inanti-Horn)
simulation suggests that Horn is stable while anti-Horn is not
can this be proved?
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Analytic considerations
Simple recipes for finding stochastically stable state in 2×2games
not easily extrapolated to larger games
basic idea:
calculate the height of the invasion barrier of each ESSthe ESSs with maximal invasion barrier is stochastically stable
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Analytic considerations
invasion barrier = amount of mutations necessary to push thesystem into the basin of attraction of another ESS
Horn ⇒ anti-Horn: 50%
anti-Horn ⇒ Horn: 47.5%
Hence:
Horn strategy is the only stochastically stablestate
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Stochastic evolution of case marking
k = 0.45competition between zzaz/pA and ezzz/pOevolution of speaker population:
0
0.2
0.4
0.6
0.8
1
0 100 200 300 400 500 600 700 800 900 1000
zzaz ezzz
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Stochastic evolution of case marking
k = 0.45competition between zzaz/pA and ezzz/pOevolution of hearer population:
0
0.2
0.4
0.6
0.8
1
0 100 200 300 400 500 600 700 800 900 1000
AO OA
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Analysis
invasion barriers:
differential object marking: 45.2%inverse differential subject marking: 2.06%
Differential object marking is stochastically stable;inverse differential subject marking is not.
likewise, differential subject marking is stochastically stablewhile inverse differential object marking is not.
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Stochastically stable states
zeaz/pAsplit ergativeAustralian languages
zzaz/pAdifferential object markingEnglish, Dutch, ...
zezz/pAdifferential subject markingseveral caucasian languages
zzzz/pAno case markingChinese, Thai
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Conclusion
out of 4× 16 = 64 possible case marking patterns only fourare stochastically stable
vast majority of all languages that fit into this categorizationare stochastically stable
precise numbers are hard to come by though
linguistic universals can be result of evolutionary pressure inthe sense of cultural evolution
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