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The Matching Hypothesis

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

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The Matching Hypothesis

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  1. The Matching Hypothesis Jeff Schank PSC 120

  2. Mating • Mating is an evolutionary imperative • Much of life is structured around securing and maintaining long-term partnerships

  3. 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?

  4. 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)

  5. 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!)

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. 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

  11. Problem: Model not Parameterized •  •  • 

  12. 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)

  13. 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

  14. Replication 95% confidence interval means 95% of simulations had results in this range.

  15. 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 findinga mate? • Is this the same for each rule?

  16. Mathematical Structure of Decision Rules A B

  17. 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

  18. Choice of Exponent n

  19. 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

  20. Space and Movement Zigzag Brownian

  21. Space and Movement

  22. 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

  23. 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

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