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A Game Theory-Based Model for Predicting Depression due to Frustration in Competitive Environments.

R Loula1, L H A Monteiro1,2

  • 1Universidade Presbiteriana Mackenzie, PPGEEC, São Paulo SP, Brazil.

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This study introduces a game theory model to predict depression from workplace frustration. It uses social network payoffs and individual earnings to forecast depression prevalence in UK adults.

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Area of Science:

  • Computational social science
  • Behavioral economics
  • Psychological modeling

Background:

  • Competitive environments can lead to frustration and depression.
  • Social connections and perceived payoffs influence individual well-being.
  • Formal education may impact labor market outcomes and mental health.

Purpose of the Study:

  • To develop a computational model forecasting depression prevalence.
  • To investigate depression as a consequence of frustration in competitive labor markets.
  • To analyze the role of social networks and individual payoffs in depression onset.

Main Methods:

  • A spatially structured game theory model, akin to the prisoner's dilemma, was employed.
  • The model incorporates payoffs from the labor market, considering formal academic education.
  • Depression onset is modeled based on the difference between personal and neighbors' average payoffs, using distinct thresholds for men and women.

Main Results:

  • The model accurately predicts depression prevalence by tuning payoff thresholds.
  • Socially connected individuals' payoffs significantly influence their propensity for depression.
  • The model highlights the critical role of relative economic standing in a competitive environment.

Conclusions:

  • Game theory provides a robust framework for modeling depression linked to workplace frustration.
  • Social comparison and relative economic status are key factors in the development of depression.
  • The model offers insights into mental health disparities potentially influenced by gender-specific thresholds.