1. The Matching Hypothesis
Jeff Schank
PSC 120

2. Choosing (a) Mate(s) Mating is an evolutionary imperative
Much of life is structured around securing and maintaining long-term partnerships
Choosing a mate is species and mating system dependent
There are several factors that go into choosing a mate that depend, in part, on the mating system
Today, we will focus on a type of human monogamous mating system

3. Physical Attractiveness
In culturally more recent human mating systems, individuals exercise considerable choice in mates
One important component is physical attractiveness (PA), which 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?

4. The Matching Paradox
Assumption: Everybody wants the most attractive mate
BUT, couples tend to be similar in attractiveness
r = .4 to .6 (Feingold, 1988; Little et al., 2006)

6. Matching Paradox How does this similarity between partners come about?
That is, if people prefer the most attractive, how do they end up partnered with individuals of similar physical attractiveness?

7. 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
They developed an ABM and showed that matching could occur with a preference for the most attractive potential partners

8. Their Model Male and female agents Randomly paired on “dates”
Only distinguishing feature is attractiveness
Randomly paired on “dates”
Choose whether to accept date as mate
Mutual acceptance  partnering
“Attractiveness” represents a one-dimensional measure of mate quality

9. 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.

10. 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.

11. Statistical Properties of Decision RulesB
The probability of matching into a couple on the first date (d = 1) using each decision rule, assuming an equal distribution of all agent types. A: The probability as a function of individual attractiveness. B: Average probability across all agent types for the different decision rules.

12. Problem: Model not Parameterized

13. Model Details Male and Female agents (1,000 of each)
Each agent was randomly assigned an “attractiveness” score, which is an integer between 1-10
On 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

14. 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

15. Replication Rule 1 Rule 2 Kalick and Hamilton r .55 .83 Mean r .61
95% Confidence Interval
( )
( )
95% confidence interval means 95% of simulations had results in this range.

16. Choice of Exponent n
K & H used a 3rd-order power function with no explanation
What happens when we change the power?

17. Choice of Exponent n

18. 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 200 x 200 grid (bounded) allowing them to move probabilistically
Could interact with neighbors only within a radius of 5 spaces

19. Space and Movement
Zigzag
Brownian

20. Space and Movement

21. 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

22. Conclusions
The Matching Paradox still remains and is more complicated than we thought
There are parameter values for which either rule can match the data
Likely both rules are too simple