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Tag-based indirect reciprocity by
incomplete social information
Naoki Masuda1 and Hisashi Ohtsuki2
The University of Tokyo, Japan
2
Harvard University
http://www.stat.t.u-tokyo.ac.jp/~masuda
1

Ref: Masuda & Ohtsuki, Proc. R. Soc. B, 274,
689-695 (2007).
Prisoner’s Dilemma
Opponent

Cooperate

Defect

Cooperate

(3, 3)

(0, 5)

Defect

(5, 0)

(1, 1)

Self

unique Nash equilibrium
A Prisoner’s Dilemma
• A donor may donate
cost c to benefit the
recipient by b (>c).
• If each player serves
as donor and
recipient in different
(random) pairings, the
game is symmetric
PD.

recipient

donor

C

(-c, b)

D

(0, 0)
(b > c)

C

D

C (b-c, b-c)

(-c, b)

D

(0, 0)

(b, -c)
Origins of altruism
• Kin selection
• Direct reciprocity
– Iterated Prisoner’s dilemma

•
•
•
•
•

Spatial reciprocity
Indirect reciprocity
Network reciprocity
Group selection
Others

• Is ‘helping similar others’ a viable (stable)
strategy?
An affirmative answer by
Riolo, Cohen & Axelrod, Nature 2001
• b=1.0, c=0.1
• Player i has

– Tag wi ∈ [ 0,1]
– Tolerance µi ∈ [ 0,1]

• i cooperates with j if w j − wi ≤ µi
• Players copy tag and tolerance of successful others.
• mutation:
– Random allocation of tag
– Neutral drift of tolerance

• Results of their numerical simulations of evol dynamics:
– Donation rate is maintained high (~ 75%).
– The mean tolerance level is small (0.01-0.03).
– With some sudden changes though.
However, rebuttal
by Roberts & Sherratt (Nature 2002)
Criticism 1
i was assumed to coopreate if
w j − wi ≤ µi &
µi ∈ [ 0,1]

Criticism 2
Neutral drift & µi ∈ [ 0,1]

A player cooperate with birds
with exactly the same feather

Random walk with
reflecting boundary

Cooperation is lost if µi ∈ [ 0,1] is
replaced by µi ∈ [ − 10 −6 ,1]

Positive bias. Why
mutation increases
generosity?
We establish a viable model of tagbased reciprocity.

[

]

µi ∈ − 10 −6 ,1
• Use a kind of

• q: prob that μj is public to others
• If player i gets to know μj <|wj-wi|, i does
not donate even if μi ≥|wj-wi|
• q=0 → eventually ALLD (μi <0)
• q=1 → eventually ALLC (μi takes max)
• No mutation of tags
2-tag model
• Same or different only.
tag
tolerance

wi ∈ [ 0,1]

[

]

µi ∈ − 10 ,1
−6

{

→ wi ∈ w , w
→

a

b

}

µi ∈ { − 1,0,1}

μ

phenotype

-1 no donate (D)
0

tag user

1

donate (C)
Payoffs of 6 subpopulations
tag = a

tag = b

h: assortativity
Replicator dynamics
• Symmetric case
– Full theoretical analysis
(global analysis)

• Asymmetric case
6 vars, 4 dim

note: no tag evolution

– Best-response theory
(local analysis only)
– Numerical simulations
Symmetric case

Small q
μ

phenotype

-1 no donate (D)
0

tag user

1

donate (C)

Intermediate q

Large q

c (1 − q )
A≡
< 1 ⇒ bq > c
( b − c) q
is the condition for tag users
to emerge.
With assortativity h

b = 1, c = 0.3

q = 0.5, h = 0

q = 0.5, h = 0.8
Asymmetric case (best response)
A=

p >A
b
1

p1b ≤ A

A − (1 − ( t + h − ht ) ) p1b
X=
.
t + h − ht

μ

Among 9 pure strategies, only (μa,μb)=(-1,-1), (1,0), (0,-1), (0,0), and (1,1) are viable.

c (1 − q )
,
( b − c) q

phenotype

-1 no donate (D)
0

tag user

1

donate (C)
Basin areas (numerical)
(1, 1)

(-1, -1)

μ

phenotype

-1 no donate (D)
0
(0, -1)
(-1, 0)

(0, 0)

q

tag user

1

donate (C)
Best response (continuous tag)
• Any μi = μ is ESS if
bq>c
• If μi is uniformly
distributed,

bq − c
µ opt =
,
( b − c) q
µ opt = 0,

( bq > c )
( bq ≤ c )

optimal μ
b/c=4

2

1.2
q
Numerical simulations
noiseless case
noisy case
μ
n = 800
b=1
c = 0.3
q
Conclusions
• Tag-based indirect reciprocity is viable
when publicity of tolerance is intermediate.
– Large publicity → cooperation prevails
– Small publicity → defection prevails

• Future work: network version?

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Tag-based indirect reciprocity

  • 1. Tag-based indirect reciprocity by incomplete social information Naoki Masuda1 and Hisashi Ohtsuki2 The University of Tokyo, Japan 2 Harvard University http://www.stat.t.u-tokyo.ac.jp/~masuda 1 Ref: Masuda & Ohtsuki, Proc. R. Soc. B, 274, 689-695 (2007).
  • 2. Prisoner’s Dilemma Opponent Cooperate Defect Cooperate (3, 3) (0, 5) Defect (5, 0) (1, 1) Self unique Nash equilibrium
  • 3. A Prisoner’s Dilemma • A donor may donate cost c to benefit the recipient by b (>c). • If each player serves as donor and recipient in different (random) pairings, the game is symmetric PD. recipient donor C (-c, b) D (0, 0) (b > c) C D C (b-c, b-c) (-c, b) D (0, 0) (b, -c)
  • 4. Origins of altruism • Kin selection • Direct reciprocity – Iterated Prisoner’s dilemma • • • • • Spatial reciprocity Indirect reciprocity Network reciprocity Group selection Others • Is ‘helping similar others’ a viable (stable) strategy?
  • 5. An affirmative answer by Riolo, Cohen & Axelrod, Nature 2001 • b=1.0, c=0.1 • Player i has – Tag wi ∈ [ 0,1] – Tolerance µi ∈ [ 0,1] • i cooperates with j if w j − wi ≤ µi • Players copy tag and tolerance of successful others. • mutation: – Random allocation of tag – Neutral drift of tolerance • Results of their numerical simulations of evol dynamics: – Donation rate is maintained high (~ 75%). – The mean tolerance level is small (0.01-0.03). – With some sudden changes though.
  • 6. However, rebuttal by Roberts & Sherratt (Nature 2002) Criticism 1 i was assumed to coopreate if w j − wi ≤ µi & µi ∈ [ 0,1] Criticism 2 Neutral drift & µi ∈ [ 0,1] A player cooperate with birds with exactly the same feather Random walk with reflecting boundary Cooperation is lost if µi ∈ [ 0,1] is replaced by µi ∈ [ − 10 −6 ,1] Positive bias. Why mutation increases generosity?
  • 7. We establish a viable model of tagbased reciprocity. [ ] µi ∈ − 10 −6 ,1 • Use a kind of • q: prob that μj is public to others • If player i gets to know μj <|wj-wi|, i does not donate even if μi ≥|wj-wi| • q=0 → eventually ALLD (μi <0) • q=1 → eventually ALLC (μi takes max) • No mutation of tags
  • 8. 2-tag model • Same or different only. tag tolerance wi ∈ [ 0,1] [ ] µi ∈ − 10 ,1 −6 { → wi ∈ w , w → a b } µi ∈ { − 1,0,1} μ phenotype -1 no donate (D) 0 tag user 1 donate (C)
  • 9. Payoffs of 6 subpopulations tag = a tag = b h: assortativity
  • 10. Replicator dynamics • Symmetric case – Full theoretical analysis (global analysis) • Asymmetric case 6 vars, 4 dim note: no tag evolution – Best-response theory (local analysis only) – Numerical simulations
  • 11. Symmetric case Small q μ phenotype -1 no donate (D) 0 tag user 1 donate (C) Intermediate q Large q c (1 − q ) A≡ < 1 ⇒ bq > c ( b − c) q is the condition for tag users to emerge.
  • 12. With assortativity h b = 1, c = 0.3 q = 0.5, h = 0 q = 0.5, h = 0.8
  • 13. Asymmetric case (best response) A= p >A b 1 p1b ≤ A A − (1 − ( t + h − ht ) ) p1b X= . t + h − ht μ Among 9 pure strategies, only (μa,μb)=(-1,-1), (1,0), (0,-1), (0,0), and (1,1) are viable. c (1 − q ) , ( b − c) q phenotype -1 no donate (D) 0 tag user 1 donate (C)
  • 14. Basin areas (numerical) (1, 1) (-1, -1) μ phenotype -1 no donate (D) 0 (0, -1) (-1, 0) (0, 0) q tag user 1 donate (C)
  • 15. Best response (continuous tag) • Any μi = μ is ESS if bq>c • If μi is uniformly distributed, bq − c µ opt = , ( b − c) q µ opt = 0, ( bq > c ) ( bq ≤ c ) optimal μ b/c=4 2 1.2 q
  • 16. Numerical simulations noiseless case noisy case μ n = 800 b=1 c = 0.3 q
  • 17. Conclusions • Tag-based indirect reciprocity is viable when publicity of tolerance is intermediate. – Large publicity → cooperation prevails – Small publicity → defection prevails • Future work: network version?