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README.md

@@ -1,14 +1,44 @@
 ---
-title: TradingPlacesv1
-emoji: 📉
-colorFrom: purple
-colorTo: pink
+title: AI Trading Trust Experiment
+emoji: 📈
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 6.0.1
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
 license: mit
-short_description: TestJoshGame
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# AI Trading Trust Experiment
+
+A psychology research game studying trust in AI advice under varying conditions.
+
+## About
+
+This is a behavioral experiment disguised as a trading simulation. Participants:
+
+1. Configure an AI advisor's characteristics (confidence, explanation depth, risk tolerance)
+2. Receive AI-generated trading advice for fictional companies
+3. Make trading decisions (follow or ignore the AI)
+4. Rate their confidence in their decisions
+
+## Data Collected
+
+- Participant ID (anonymized)
+- AI tuning preferences (slider positions)
+- Trading decisions and response times
+- Confidence ratings
+- Portfolio outcomes
+
+## For Researchers
+
+Data is stored in SQLite (`experiment_data.db`). Download from the Space's Files tab.
+
+Tables:
+- `participants`: Session-level data
+- `decisions`: Turn-by-turn data with all variables
+
+## Fictional World
+
+All companies and countries are entirely fictional to prevent participants from using external knowledge.

export_data.py

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+"""
+Data Export Utility for AI Trading Trust Experiment
+Run this locally after downloading experiment_data.db from HF Spaces
+"""
+
+import sqlite3
+import pandas as pd
+from pathlib import Path
+
+def export_data(db_path: str = "experiment_data.db", output_dir: str = "exports"):
+    """Export all experiment data to CSV files for analysis."""
+    
+    Path(output_dir).mkdir(exist_ok=True)
+    conn = sqlite3.connect(db_path)
+    
+    # Export participants
+    participants_df = pd.read_sql_query("""
+        SELECT 
+            participant_id,
+            session_start,
+            session_end,
+            final_portfolio_value,
+            total_decisions,
+            ai_reliance_score,
+            completed
+        FROM participants
+    """, conn)
+    participants_df.to_csv(f"{output_dir}/participants.csv", index=False)
+    print(f"Exported {len(participants_df)} participants")
+    
+    # Export decisions
+    decisions_df = pd.read_sql_query("""
+        SELECT 
+            participant_id,
+            scenario_id,
+            scenario_order,
+            timestamp,
+            ai_confidence_setting,
+            ai_explanation_setting,
+            ai_risk_setting,
+            ai_advice_direction,
+            ai_advice_accuracy,
+            decision,
+            decision_amount,
+            confidence_in_decision,
+            response_time_ms,
+            scenario_outcome,
+            profit_loss,
+            portfolio_value_after,
+            followed_ai
+        FROM decisions
+    """, conn)
+    decisions_df.to_csv(f"{output_dir}/decisions.csv", index=False)
+    print(f"Exported {len(decisions_df)} decisions")
+    
+    # Create summary statistics
+    summary = decisions_df.groupby('participant_id').agg({
+        'followed_ai': ['sum', 'count', 'mean'],
+        'confidence_in_decision': 'mean',
+        'response_time_ms': 'mean',
+        'ai_confidence_setting': 'mean',
+        'ai_explanation_setting': 'mean',
+        'ai_risk_setting': 'mean',
+    }).round(2)
+    summary.columns = ['_'.join(col).strip() for col in summary.columns]
+    summary.to_csv(f"{output_dir}/summary_by_participant.csv")
+    print(f"Created summary statistics")
+    
+    # AI accuracy analysis
+    accuracy_analysis = decisions_df.groupby(['ai_advice_accuracy', 'followed_ai']).size().unstack(fill_value=0)
+    accuracy_analysis.to_csv(f"{output_dir}/ai_accuracy_analysis.csv")
+    print(f"Created AI accuracy analysis")
+    
+    conn.close()
+    print(f"\nAll files exported to {output_dir}/")
+
+def quick_stats(db_path: str = "experiment_data.db"):
+    """Print quick statistics from the database."""
+    conn = sqlite3.connect(db_path)
+    
+    # Participant count
+    result = pd.read_sql_query("SELECT COUNT(*) as n, SUM(completed) as completed FROM participants", conn)
+    print(f"\n=== Quick Stats ===")
+    print(f"Total sessions: {result['n'].iloc[0]}")
+    print(f"Completed sessions: {result['completed'].iloc[0]}")
+    
+    # Decision stats
+    result = pd.read_sql_query("""
+        SELECT 
+            AVG(followed_ai) * 100 as ai_follow_rate,
+            AVG(confidence_in_decision) as avg_confidence,
+            AVG(response_time_ms) / 1000 as avg_response_sec
+        FROM decisions
+    """, conn)
+    print(f"\nAI Follow Rate: {result['ai_follow_rate'].iloc[0]:.1f}%")
+    print(f"Avg Confidence: {result['avg_confidence'].iloc[0]:.1f}/100")
+    print(f"Avg Response Time: {result['avg_response_sec'].iloc[0]:.1f}s")
+    
+    # By AI accuracy
+    result = pd.read_sql_query("""
+        SELECT 
+            ai_advice_accuracy,
+            AVG(followed_ai) * 100 as follow_rate,
+            COUNT(*) as n
+        FROM decisions
+        GROUP BY ai_advice_accuracy
+    """, conn)
+    print(f"\nFollow rate by AI accuracy:")
+    for _, row in result.iterrows():
+        print(f"  {row['ai_advice_accuracy']}: {row['follow_rate']:.1f}% (n={row['n']})")
+    
+    conn.close()
+
+if __name__ == "__main__":
+    import sys
+    
+    if len(sys.argv) > 1 and sys.argv[1] == "stats":
+        quick_stats()
+    else:
+        export_data()

requirements.txt

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+gradio>=4.0.0