Description of Enhanced fear dynamic data

Published: 26 February 2024| Version 1 | DOI: 10.17632/k5zwjj8mf3.1
Sunil Maria Benedict


Description of Enhanced Fear Dynamics Data: The provided code generates synthetic data to simulate the dynamics of fear, incorporating more complex forces, including external forces, decision-making forces, and a damping force that opposes motion. Let's explore the characteristics of the generated data: System Components: Mass of Fear Element: mass: A constant representing the mass of the fear element. Mass influences the response to forces. Time Array: time: An array representing time, ranging from 0 to 10 with steps of 0.1. It defines the temporal aspect of the simulation. Forces: external_force: Simulated external force, represented by the sine function over time. Models environmental factors impacting fear. decision_force: More complex decision-making force, involving an exponential and sinusoidal function. Illustrates a nuanced force related to decision-making. damping_force: Damping force opposes motion and is calculated based on the absolute value and sign of external force, along with fear velocity. Fear Dynamics Simulation: The fear dynamics are simulated using numerical integration. The fear element's position (fear_position) and velocity (fear_velocity) are updated over time, considering external force, decision force, and damping force. Visualisation: The simulated fear dynamics are visualised through three subplots: Forces Influencing Fear Dynamics: External force and decision force are plotted against time, providing insight into how these forces vary over the simulation duration. Damping Force Opposing Motion: Damping force, opposing motion, is plotted against time. It showcases how the damping force acts against the fear element's velocity. Fear Dynamics with Decision-Making and Damping: Fear element position is plotted against time. This plot illustrates how the fear element's position evolves over time, considering external and decision forces along with damping. Key Takeaways: The simulation is enhanced with a more intricate decision-making force and the inclusion of a damping force. Visualisation aids in understanding how these forces collectively influence fear dynamics. Damping force adds a realistic touch by simulating an opposing force to motion. Note: This model provides a more sophisticated representation of fear dynamics, considering additional forces. Adjusting force functions allows for customisation based on specific scenarios or experimental data.


Steps to reproduce

import numpy as np import matplotlib.pyplot as plt # Constants mass = 1.0 # Mass of fear element time = np.arange(0, 10, 0.1) # Time array # External force (e.g., environmental factors) external_force = np.sin(time) # Decision-making force (more complex for illustration) decision_force = -0.1 * np.exp(-0.1 * time) * np.sin(0.5 * time) # Damping force (opposes motion) damping_force = -0.1 * np.sqrt(np.abs(external_force)) * np.sign(external_force) * np.abs(fear_velocity) # Fear dynamics simulation with damping fear_position = np.zeros_like(time) fear_velocity = np.zeros_like(time) for i in range(1, len(time)): acceleration = (external_force[i] + decision_force[i] + damping_force[i]) / mass fear_velocity[i] = fear_velocity[i - 1] + acceleration * 0.1 fear_position[i] = fear_position[i - 1] + fear_velocity[i] * 0.1 # Plotting plt.figure(figsize=(10, 8)) plt.subplot(3, 1, 1) plt.plot(time, external_force, label='External Force') plt.plot(time, decision_force, label='Decision Force') plt.title('Forces Influencing Fear Dynamics') plt.xlabel('Time') plt.ylabel('Force') plt.legend() plt.subplot(3, 1, 2) plt.plot(time, damping_force, label='Damping Force', color='red') plt.title('Damping Force Opposing Motion') plt.xlabel('Time') plt.ylabel('Force') plt.legend() plt.subplot(3, 1, 3) plt.plot(time, fear_position, label='Fear Element Position') plt.title('Fear Dynamics with Decision-Making and Damping') plt.xlabel('Time') plt.ylabel('Position') plt.tight_layout()


CMR Group of institutions


Computational Mathematics, Decision Analysis, Computational Physics, Fear, Fear Conditioning, Decision Making