# Description of Simulated Fear Dynamics Data

Published: 26 February 2024| Version 1 | DOI: 10.17632/3c542bxz65.1
Contributor:
Sunil Maria Benedict

## Description

The provided code generates synthetic data to simulate the dynamics of fear under the influence of external and decision-making forces. Let's delve into the components and describe the characteristics of the generated data: System Components: Mass of Fear Element: mass: A constant representing the mass of the fear element. In physics, mass influences how an object responds to external forces. Time Array: Time: An array representing time, ranging from 0 to 10 with steps of 0.1. This defines the temporal aspect of the simulation. Forces: external_force: Simulated external force, represented by the sine function over time. It models environmental factors impacting fear. decision_force: Simulated decision-making force, a linear function of time. It represents an illustrative force related to decision-making. 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 based on the external and decision-making forces. Visualisation: The simulated fear dynamics are visualised through two subplots: Forces Influencing Fear Dynamics: External force and decision force are plotted against time. This provides insight into how these forces vary over the simulation duration. Fear Dynamics with Decision-Making: Fear element position is plotted against time. This plot illustrates how the fear element's position evolves over time, considering both external and decision-making forces. Key Takeaways: The simulation combines external and decision-making forces to model fear dynamics. Visualisation helps understand the impact of forces on fear over time. This is a simplified model, and specific force functions can be adjusted for more realistic simulations. Note: In a real-world scenario, such simulations can be more complex, considering various factors and interactions influencing fear dynamics. This model provides a foundational understanding for more intricate simulations.

## 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 (simple for illustration) decision_force = -0.1 * time # Fear dynamics simulation 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]) / 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, 6)) plt.subplot(2, 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(2, 1, 2) plt.plot(time, fear_position, label='Fear Element Position') plt.title('Fear Dynamics with Decision-Making') plt.xlabel('Time') plt.ylabel('Position') plt.tight_layout() plt.show()

## Institutions

CMR Group of institutions

## Categories

Decision Analysis, Fear, Fear Conditioning, Decision Making