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Multilevel Hidden Markov Models for Behavioral Data: A Hawk-and-Dove Experiment.

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This study introduces a multilevel hidden Markov model to analyze behavioral data from the Hawk-Dove game. Results show that how possession is obtained and possession itself significantly influence aggressive "Hawk" behaviors.

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Area of Science:

  • Behavioral Economics
  • Statistical Modeling
  • Game Theory

Background:

  • Behavioral data from economic experiments, particularly the Hawk-Dove game, exhibit complex heterogeneity.
  • Understanding latent factors influencing player decisions is crucial for accurate behavioral modeling.

Purpose of the Study:

  • To introduce a unified framework for a multilevel hidden Markov model (HMM) incorporating covariates, autoregression, and endogenous initial conditions.
  • To analyze behavioral data from a Hawk-Dove game experiment, focusing on factors influencing aggressive (Hawk) strategies.

Main Methods:

  • Fitting a multilevel logistic regression model for repeated player behavior measurements nested within groups.
  • Integrating discrete random effects at group and player levels, with Markovian sequences for player-level effects.
  • Employing a computationally feasible expectation-maximization algorithm for parameter estimation.

Main Results:

  • The developed model effectively handles multilevel unobserved factors and observed covariates.
  • Initial possession and the method of acquiring possession (treatment manipulation) were identified as key determinants of Hawk strategy adoption.
  • Significant time-dependence in player behavior was observed and captured by Markovian random effects.

Conclusions:

  • The proposed multilevel hidden Markov model provides a robust framework for analyzing complex behavioral data with hierarchical structures.
  • Possession and its acquisition method are critical drivers of aggressive behavior in the Hawk-Dove game.
  • The model's ability to capture time-dependence enhances the understanding of dynamic strategic interactions.