A MARKOV CHAIN SIMULATION FOR ANALYZING THE INFLUENCE OF KNOWLEDGE ON DECISION-MAKING SYSTEM AND LONG-TERM HAPPINESS

Abstract

This study employs a Markov chain model to simulate the influence of knowledge on daily decision-making and long-term happiness. In the simulation, each individual is assumed to make 6,500 decisions per day over a 10-year period, with a total of 1,000 individuals categorized according to their levels of knowledge and awareness. Knowledge serves as a variable that influences the transition probabilities between good and bad decisions, as well as the emotional responses to those decisions (gratitude or regret). The simulation results indicate that individuals with higher levels of knowledge and awareness tend to maintain more stable happiness compared to those with lower levels of knowledge. The Markov chain model proves to be effective in mapping the dynamics of decision-making behavior and can be utilized as an analytical tool for developing behavior-based decision support systems in the context of industrial engineering.