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Extracting multistage screening rules from online dating activity data.

Elizabeth Bruch1, Fred Feinberg2, Kee Yeun Lee3

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This study introduces a statistical model to analyze online decisions, revealing that people use "deal breakers" to filter choices in multi-stage decision-making processes like online dating.

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Area of Science:

  • Decision Science
  • Cognitive Science
  • Behavioral Economics

Background:

  • Understanding complex decision-making processes is crucial.
  • Existing models often struggle with exploratory behavior and multi-stage choices.
  • Online activity data offers a rich source for studying real-world decisions.

Purpose of the Study:

  • To develop a statistical framework for analyzing multi-stage decision-making using online activity data.
  • To identify the use and impact of noncompensatory screening strategies.
  • To understand how individuals, particularly men and women, evaluate options at different decision stages.

Main Methods:

  • Development of a discrete choice model incorporating exploratory behavior and multiple decision stages.
  • Estimation of the model using deidentified activity data from 1.1 million online dating interactions.
  • Application of nonparametric methods to account for heterogeneity in decision-making.

Main Results:

  • Individuals employ 'deal breakers' (noncompensatory screeners) to eliminate alternatives.
  • Mate evaluation differs significantly across decision stages and between genders.
  • Heterogeneity in decision-making persists even after controlling for observable attributes.

Conclusions:

  • The proposed statistical framework effectively models complex, multi-stage choices.
  • Noncompensatory screening is a prevalent strategy in online decision-making.
  • Individual and group differences play a significant role in mate selection and other large-ticket item searches.