Fear Factors and decision making parameters

Published: 26 February 2024| Version 1 | DOI: 10.17632/4x8gcvzp6f.1
Sunil Maria Benedict


Description of Synthetic Data: Exploring Fear Factors and Decision-Making Parameters The provided code generates synthetic data to simulate the interaction between fear factors and decision-making parameters. Let's break down the components and describe the characteristics of the generated data: Parameters: Fear Factors: k_stimuli: Weight for the intensity of stimuli. k_past: Weight for the impact of past experiences. k_uncertainty: Weight for the degree of uncertainty. Decision-Making Parameters: k_utility: Weight for perceived utility. k_risk: Weight for perceived risk. k_reward: Weight for anticipation of reward. Synthetic Data: The synthetic data is generated for 100 data points for each of the following variables: Fear Factors: intensity_of_stimulus: Random values representing the intensity of stimuli. impact_of_past_experiences: Random values representing the impact of past experiences. degree_of_uncertainty: Random values representing the degree of uncertainty. Decision-Making Parameters: perceived_utility: Random values representing perceived utility. perceived_risk: Random values representing perceived risk. anticipation_of_reward: Random values representing anticipation of reward. Calculation of Fear and Decision-Making: Fear (fear) and Decision-Making Outcome (decision) are calculated based on the provided weights and generated synthetic data. These calculations represent a simplified model of how fear and decision-making might be influenced by various factors. Visualisation: The generated data is visualised using scatter plots for both fear and decision-making outcomes. Each point on the scatter plots corresponds to a data point, providing a visual representation of the relationship between fear factors and decision-making parameters. Data Analysis: The correlation coefficient between fear and decision-making is calculated. This coefficient quantifies the degree of linear relationship between the two variables. The resulting value provides insights into how fear and decision-making parameters are associated in the synthetic dataset. Key Takeaway: This synthetic dataset and analysis offer a simulated exploration into the relationships between fear factors and decision-making parameters. The visualisations and correlation coefficient provide a preliminary understanding of how these elements might interact in a simplified model.


Steps to reproduce

import numpy as np import matplotlib.pyplot as plt # Define parameters k_stimuli = 0.5 k_past = 0.7 k_uncertainty = 0.3 k_utility = 0.6 k_risk = 0.8 k_reward = 0.9 # Generate synthetic data intensity_of_stimulus = np.random.rand(100) impact_of_past_experiences = np.random.rand(100) degree_of_uncertainty = np.random.rand(100) perceived_utility = np.random.rand(100) perceived_risk = np.random.rand(100) anticipation_of_reward = np.random.rand(100) # Calculate Fear and Decision-Making outcomes fear = k_stimuli * intensity_of_stimulus + k_past * impact_of_past_experiences + k_uncertainty * degree_of_uncertainty decision = k_utility * perceived_utility - k_risk * perceived_risk + k_reward * anticipation_of_reward # Visualization plt.figure(figsize=(12, 6)) plt.subplot(2, 1, 1) plt.scatter(np.arange(100), fear, label='Fear') plt.title('Fear Factors') plt.legend() plt.subplot(2, 1, 2) plt.scatter(np.arange(100), decision, label='Decision-Making Outcome') plt.title('Decision-Making Parameters') plt.legend() plt.tight_layout() plt.show() # Data Analysis correlation_coefficient = np.corrcoef(fear, decision)[0, 1] print(f"Correlation Coefficient between Fear and Decision: {correlation_coefficient}")


CMR Group of institutions


Computational Mathematics, Data Modeling, Fear, Decision Making