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

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.