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The Matching Hypothesis. Jeff Schank PSC 120. Mating. Mating is an evolutionary imperative Much of life is structured around securing and maintaining long-term partnerships. Physical Attractiveness. Focus on physical attractiveness may have basis in “good genes” hypothesis - PowerPoint PPT Presentation
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The Matching Hypothesis
Jeff SchankPSC 120
Mating
• Mating is an evolutionary imperative• Much of life is structured around securing and
maintaining long-term partnerships
Physical Attractiveness
• Focus on physical attractiveness may have basis in “good genes” hypothesis– Features associated with PA may be implicit
signals of genetic fitness• Social Psychology: How does physical
attractiveness influence mate choice?
The Matching Paradox
• Everybody wants the most attractive mate• BUT, couples tend to be similar in attractiveness
• r = .4 to .6 (Feingold, 1988; Little et al., 2006)
Matching Paradox
• How does this similarity between partners come about?
• How is the observed population-level regularity generated by the decentralized, localized interactions of heterogeneous autonomous individuals? (That’s a mouthful!)
Kalick and Hamilton (1986)
• Previously, many researchers assumed people actively sought partners of equal attractiveness (the “matching hypothesis”)
• Repeated studies showed no indication of this, but rather a strong preference for the most attractive potential partners
• ABM showed that matching could occur with a preference for the most attractive potential partners
The Model
• Male and female agents– Only distinguishing feature is attractiveness
• Randomly paired on “dates”• Choose whether to accept date as mate• Mutual acceptance coupling• “Attractiveness” can represent any one-
dimensional measure of mate quality
The Model: Decision Rules
• Rule 1: Prefer the most attractive partner• Rule 2: Prefer the most similar partner• CT Rule: Agents become less “choosy” as they
have more unsuccessful dates – Acceptance was certain after 50 dates.
The Model: Decision Rules more Formally
• Rule 1: Prefer the most attractive partner
• Rule 2: Prefer the most similar partner
• CT Rule: Agents become less “choosy” as they have more unsuccessful dates – Acceptance was certain
after 50 dates.
Model Details
• Male and Female agents (1,000 of each)• Each agent randomly assigned an “attractiveness”
score, which is an integer between 1-10• Each time step, each unmated male was paired
with a random unmated female for a “date”• Each date accepted/rejected partner using
probabilistic decision rule• If mutual acceptance, the pair was mated and left
the dating pool
Problem: Model not Parameterized
•
•
•
Model Parameterized
• Male and Female agent (1,000 of each) Nm (males) and Nf (females)
• Each agent randomly assigned an “attractiveness” score, which is an integer between 1 – 10 A random number between 1 – Max(A)
What Can We Do?
• Replicate the model and check the original results– Are there any other interesting things to check
out?• Modify the model
– Check robustness of findings– Increase realism and see what happens
Replication
Rule 1 Rule 2Kalick and Hamilton r .55 .83
Mean r .61 .83
95% Confidence Interval (.57-.65) (.78-.87)
95% confidence interval means 95% of simulations had results in this range.
Mathematical Structure of Decision Rules
• Qualitative difference easy to explain:– Accept a mate with a probability that increases an
agent’s objective maximizing: attractiveness (Rule 1) or similarity (Rule 2)
• There are many functions that could fit this description– Why a 3rd-order power function?– What is the probability of finding
a mate? – Is this the same for each rule?
Mathematical Structure of Decision Rules
1 2 3 4 5 6 7 8 9 10
-0.05
-4.16333634234434E-17
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Rule 1
Rule 2
Attractiveness
Pro
babi
lity
of M
atch
ing
Rule 1 Rule 2 Rule 30
0.1
0.2
0.3
Ave
rage
Pro
babi
lity
A B
Choice of Exponent n
• K & H used a 3rd-order power function with no explanation
• The assumption is that the exact nature of the function, including the value of the exponent, is unimportant
Choice of Exponent n
0 1 2 3 4 50
0.2
0.4
0.6
0.8
1
Rule 1
Rule 2
n
Pop
ulat
ion
Cor
rela
tion
Space and Movement
• Usually, agents are paired completely randomly each turn– Spatial structure can facilitate the evolution of cooperation
(Nowak & May, 1992; Aktipis, 2004)
– Foraging: Different movement strategies vary in search efficiency and behave differently in various environmental conditions (Bartumeus et al., 2005; Hills, 2006)
• Agents were placed on 200x200 grid (bounded) allowing them to move probabilistically
• Could interact with neighbors only within a radius of 5 spaces
Space and Movement
Zigzag Brownian
Space and Movement
Rule 1 Rule 20
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
OriginalZigZagBrownian
Pop
ulat
ion
Cor
rela
tion
Space and Movement
• Movement strategies and spatial structure influence mate choice dynamics
• Population density should influence speed of finding mates, as well as likelihood of finding an optimal mate
• Suggests the evolution of strategies to increase dating options (e.g., rise in Internet dating)
• Provides new opportunities for asking questions about individual behavior and population dynamics
Conclusions
• By modifying any number of the parameters, either decision rule can generate almost any desired correlation
• The Matching Paradox remains unresolved by Kalick and Hamilton’s (1986) ABM
• It is important to evaluate the effects of parameter values and environmental assumptions of a model