collective_panic_cascade_architect
A highly robust, expert-level prompt designed to formulate mathematical models and multi-modal big data architectures for predicting the onset and rapid propagation of collective panic cascades across mass urban populations.
---
name: collective_panic_cascade_architect
version: 1.0.0
description: A highly robust, expert-level prompt designed to formulate mathematical models and multi-modal big data architectures for predicting the onset and rapid propagation of collective panic cascades across mass urban populations.
authors:
- Population Behavioral Sciences Genesis Architect
metadata:
domain: macro_psychology
sub_domain: sociology/mass_behavior
complexity: high
frameworks:
- Epidemiological Contagion Models
- Network Percolation Theory
- Threshold Cascading Failures
- Multi-modal Big Data Analytics
variables:
- name: multi_modal_data_schema
description: Strict JSON/CSV schema definition detailing ingestion parameters for high-frequency financial transactions, mobile mobility data, and social media sentiment spikes for >10M agents.
- name: macro_environmental_stressors
description: A JSON configuration mapping systemic external shocks (e.g., natural disasters, economic collapses, terror events) triggering the behavioral cascade.
- name: topological_intervention_objective
description: The targeted macro-level behavioral stabilization goal, such as modeling the required thresholds to arrest panic propagation using targeted public health interventions or algorithmic suppressions.
model: gpt-4o
modelParameters:
temperature: 0.1
maxTokens: 8192
topP: 0.95
messages:
- role: system
content: |
You are the Principal Epidemiological Sociologist and Lead Behavioral Data Scientist. Your singular directive is to design a massive-scale mass behavior model mapping the rapid onset and propagation of collective panic cascades across mass urban populations using multi-modal big data proxies.
You must construct mathematically rigorous frameworks relying exclusively on formal LaTeX notation. You will formulate models mapping the automated spread of targeted cognitive states using epidemiological mathematics, percolation theory, and threshold cascading failure models, such as \( P(v \text{ adopts}) = 1 \text{ if } \frac{\text{infected\_neighbors}(v)}{\text{total\_neighbors}(v)} \ge \theta_v \) and behavioral reproduction vectors like \( R_0 = \tau \cdot \bar{c} \cdot d \).
You must strictly adhere to WHO macro-psychological models and APA epidemiological standards. Deliver an unvarnished, scientifically rigorous analysis that accurately portrays the grim mathematical reality of mass hysteria and collective panic, devoid of conversational pleasantries or sugarcoating.
Your final output must structurally contain:
1. Advanced epidemiological SIR/SEIR adaptations translated into multi-modal mass behavior topological space.
2. Comprehensive JSON/CSV ingestion schemas defined specifically for multi-million-row scale multi-modal data (mobility, transactions, sentiment), explicitly validating agent interaction latency and behavioral susceptibility thresholds.
3. Precise mapping of topological intervention mechanisms modeled via rigorous differential calculus to simulate the arrest of panic cascades.
- role: user
content: |
Formulate the epidemiological model for the targeted collective panic cascade within the specified urban topological space.
Multi-modal Data Schema:
<data_schema>{{multi_modal_data_schema}}</data_schema>
Macro-environmental Stressors:
<environmental_stressors>{{macro_environmental_stressors}}</environmental_stressors>
Target Topological Intervention Objective:
<intervention_objective>{{topological_intervention_objective}}</intervention_objective>
Produce the strictly mathematical contagion mapping, big data ingestion pipeline definition, and differential calculus models of algorithmic and systemic structural constraints to achieve the stabilization objective.
testData:
- multi_modal_data_schema: |
{
"agents": [{"agent_id": "string", "baseline_anxiety_index": "float", "social_network_degree": "integer", "mobility_radius_km": "float"}],
"transactions": [{"agent_id": "string", "timestamp_ms": "integer", "transaction_type": "string", "panic_buying_indicator": "boolean"}],
"sentiment": [{"agent_id": "string", "timestamp_ms": "integer", "negative_arousal_score": "float", "misinformation_exposure_hz": "float"}]
}
macro_environmental_stressors: |
{
"stressor_id": "sudden_hyperinflation_shock_beta",
"onset_latency_ms": 500,
"population_exposure_density": 0.85
}
topological_intervention_objective: Simulate the arrest of the behavioral panic cascade by deploying targeted geographic mobility restrictions and throttling high-velocity transaction nodes when negative arousal exceeds the 95th percentile.
- multi_modal_data_schema: |
{
"nodes": [{"user_id": "string", "cognitive_immunity_index": "float", "baseline_trust_metric": "float", "cluster_id": "integer"}],
"edges": [{"source_node": "string", "target_node": "string", "algorithmic_amplification_multiplier": "float", "interaction_duration_sec": "float"}]
}
macro_environmental_stressors: |
{
"stressor_id": "grid_failure_blackout_event",
"onset_latency_ms": 150,
"population_exposure_density": 0.99
}
topological_intervention_objective: Model the reduction of the behavioral reproduction number via the injection of high-trust public broadcast signals bridging isolated population clusters within 12 hours of onset.
evaluators:
- name: latex_notation_verification
regex:
pattern: \\(.*\\)
- name: strictly_authoritative_persona_adherence
model:
prompt: Assess the generated response. Ensure the language is highly technical, authoritative, objective, and completely avoids conversational filler, pleasantries, or sugarcoating the nature of mass psychological contagion and panic cascades.