DecisionGuard: Predictive Decision Error Detection System

A neuropsychology-based AI system that detects cognitive biases in real-time, predicts decision error probability, and learns from outcomes.

What Problem Does This Solve?

Most people make predictable mistakes in decisions due to cognitive biases:

Loss aversion — avoiding losses more aggressively than pursuing gains (costs traders billions)
Recency bias — recent outcomes distort judgment (why you panic-sell after losses)
Overconfidence — believing you're better than you are (startup failures, bad hires)
Anchoring — first number you hear distorts all subsequent judgments
Time pressure — rushing decisions increases error rate

These aren't character flaws. They're hardwired neuroscience. But you can detect when they're active and make better choices.

DecisionGuard catches cognitive biases before you make decisions and learns your personal bias patterns over time.

Core Architecture

The Five Components

1. Context Analyzer

Measures your current cognitive state:

Time of day (decision quality varies by circadian rhythm)
Recent decision outcomes (recency bias activation)
Decision frequency (cognitive load/fatigue)
Emotional state

Neuropsych basis: Decision-making quality peaks ~9am, dips post-lunch, recovers in late afternoon. Recent losses make you risk-averse; recent wins make you overconfident.

2. Bias Detector

Identifies which cognitive biases are likely active given your context and decision.

Tracks:

Loss aversion
Recency bias
Overconfidence
Anchoring bias
Sunk cost fallacy
Availability heuristic

Each bias has a "probability score" (0-1) based on triggers detected.

3. Error Predictor

Estimates probability you'll regret this decision, accounting for:

Time pressure (increases errors by 30%)
Your confidence level (low confidence often correlates with errors)
Decision stakes (high stakes amplify consequences)
Active biases (each active bias adds ~15% error probability)
Cognitive load (making many decisions = fatigue = worse decisions)

Output: A single error probability (0-1) + reasoning.

4. Decision Logger

Records:

What you decided
Your context and state at the time
Predicted biases
Predicted error probability
Later: the actual outcome

Stores everything locally in SQLite.

5. Learning Engine

Over time, learns:

Which biases actually correlate with bad outcomes (for you specifically)
Your personal decision-making patterns
Which contexts lead to poor decisions
How to improve accuracy of future predictions

Installation

Requirements

Python 3.8+
No external dependencies for core system
Flask (optional, for web dashboard)

Setup

# Clone or download the code
cd decision_guard

# Install (optional for dashboard)
pip install flask

# Make executable
chmod +x decision_system.py dashboard.py

That's it. Everything else uses Python stdlib.

Usage

Quick Start: Analyze a Decision

python decision_system.py analyze

You'll be prompted:

Decision type (financial/career/relational/medical/code/other): financial
What decision are you facing? Should I accept this job offer?
How confident are you? (1-10): 6
Are you under time pressure? (y/n): y
Current emotional state: anxious
Decision stakes (low/medium/high/critical): high

Output:

====================================================================
DECISION GUARD: Bias Detection & Error Prediction
====================================================================

Decision ID: #1

YOUR DECISION:
  Decision Type: financial
  Description: Should I accept this job offer?
  Stakes: high
  Time Pressure: true
  Your Confidence: 6/10

ERROR PROBABILITY: 0.68
RISK LEVEL: HIGH

ACTIVE COGNITIVE BIASES:
  • loss_aversion (high): 75%
    Triggered by: recent_losses, high_stakes, emotional:anxious
  • time_pressure_bias (high): 70%
    Triggered by: time_pressure, decision_overload
  • anchoring (medium): 52%
    Triggered by: time_pressure

RECOMMENDATIONS:
  ⏸ DELAY: You're under time pressure. Sleep on this decision for 24 hours if possible.
  💭 LOSS BIAS: You may be overly cautious due to recent losses. Consider if this is realistic.
  🧠 COGNITIVE FATIGUE: You've made many decisions recently. This one might benefit from a fresh mind.

====================================================================
Remember decision ID #1 to log outcome later.
====================================================================

Log Outcomes

After you make the decision and see the result:

python decision_system.py outcome

Decision ID: 1
Outcome (success/failure/neutral/pending): success
Any notes about what happened? Took the job, turned out well. Was more anxious than necessary.
✓ Outcome recorded. System is learning from this.

View Decision History

python decision_system.py history

=====================================================================
DECISION HISTORY
=====================================================================
Total Decisions: 5
Success Rate: 60.0%

  #1 | 2024-01-15 | financial
       Should I accept this job offer?...
       Outcome: success (Error prob: 68%)

  #2 | 2024-01-14 | career
       Switch teams or stay put?...
       Outcome: failure (Error prob: 52%)
       Status: (Error prob: 45%)

Web Dashboard

python dashboard.py

Then open: http://localhost:5000

Dashboard shows:

Total decisions tracked
Success rate
Most common biases
Decision history with risk levels
Bias tags for each decision
Visual error probability bars

How It Works: The Science

The Neuroscience Behind Biases

Every cognitive bias has a neurobiological mechanism:

Loss Aversion (~2x loss sensitivity vs. gains)

Amygdala + insula activation on potential losses
Triggers risk-averse behavior
Worse after recent losses (sensitized)

Recency Bias (last thing disproportionately influences judgment)

Working memory + availability heuristic
Recent outcomes are vivid and easy to recall
Activates when making similar decisions

Overconfidence (misjudging your own ability)

Ventromedial prefrontal cortex (vmPFC) overestimates control
After successes, vmPFC gets overactive
Time pressure makes it worse (can't access doubt networks)

Anchoring (first number you hear distorts subsequent judgment)

Numerical priming of semantic networks
Number becomes "activation baseline" for subsequent judgments
Especially strong under cognitive load or time pressure

Why Timing Matters

Decision-making quality varies by circadian rhythm:

8-11am: Peak decision quality (frontal lobe function peaks)
11am-2pm: Post-lunch dip (glucose drop, melatonin rise)
2-4pm: Afternoon recovery
After 6pm: Evening fatigue (decision quality declines)

This isn't willpower. It's neurotransmitter fluctuation.

Why Recent Outcomes Distort Judgment

Recency bias activates because:

Recent memories are more vivid (still in working memory)
Amygdala tags recent outcomes as "important"
Pattern-matching circuits (hippocampus) are primed to repeat recent patterns

After a loss: risk aversion kicks in (you're trying to protect against a repeat) After a win: overconfidence kicks in (you think you've figured something out)

Subsystems & Architecture

Core Subsystems

DecisionGuard (Main Orchestrator)
├── ContextAnalyzer
│   ├── Time of day analyzer
│   ├── Recency tracker (recent outcomes)
│   └── Decision frequency monitor (cognitive load)
├── BiasDetector
│   ├── Loss aversion detector
│   ├── Overconfidence detector
│   ├── Recency bias detector
│   ├── Anchoring detector
│   ├── Sunk cost detector
│   └── Availability heuristic detector
├── ErrorPredictor
│   └── Probabilistic error estimation (Bayesian)
├── DecisionDatabase
│   ├── Decision storage (SQLite)
│   ├── Outcome logging
│   └── Pattern learning
└── LearningEngine (Future)
    ├── Per-user bias calibration
    ├── Outcome prediction refinement
    └── Personalized recommendations

Each component:

Works independently (testable)
Can be improved separately
Feeds into the orchestrator

Example: Real Decision Walkthrough

Scenario

You're a trader who just lost $50K on a bad trade. A new opportunity shows up. You have 2 hours to decide.

What Happens

Your Input:

Decision type: financial
Description: Liquidate bonds to buy tech stocks?
Confidence: 7/10
Time pressure: yes
Emotional state: anxious (after loss)
Stakes: high

ContextAnalyzer detects:

It's 3pm (afternoon recovery period, quality factor 0.9)
Recent outcomes: 2 losses, 1 win last 24hrs (loss-averse state)
Decision frequency: 6 decisions in last 2 hours (cognitive load high)

BiasDetector activates:

Loss aversion: 0.80 (triggered by: recent_losses + high_stakes + emotional:anxious)
Recency bias: 0.75 (triggered by: high_decision_frequency + recent_losses)
Overconfidence: 0.40 (confidence level 7 triggers slight overconfidence)
Anchoring: 0.50 (time pressure present)

ErrorPredictor calculates:

Base error rate: 30%
Time pressure multiplier: ×1.3
Confidence factor: 0.93 (your 7/10 confidence helps slightly)
Stakes multiplier: ×1.2 (high stakes)
Bias multiplier: ×1.45 (3 active biases)
Cognitive load: ×1.2 (making many decisions)
Final error probability: 0.68 (68% chance of regret)

System recommends:

⏸ DELAY — You're under time pressure. Sleep on this 24 hours if possible.
💭 LOSS BIAS — Recent losses are making you risk-averse. Is this realistic?
🧠 COGNITIVE FATIGUE — You've made many decisions. Fresh perspective needed.

Building on This: Future Components

Planned Extensions

1. Personal Bias Calibration

Tracks which predictions actually matched outcomes
Updates model per user (people have different baseline biases)
"You're susceptible to loss aversion (75th percentile), but overconfidence (15th percentile)"

2. Decision Type Specialization

Different domains have different error patterns
Traders are loss-averse but good at recency
Startups are overconfident but bad at sunk costs
System learns your domain-specific weaknesses

3. Emotional State Tracking

Integrates voice analysis, text sentiment, physiological signals (if available)
More accurate than self-reported mood
"Your vocal stress increased 40% — anxiety bias likely active"

4. Social & Team Dynamics

Group decision-making has different biases than individual
Tracks "who in your team is likely to be loss-averse right now"
Recommends decision structure (devil's advocate, pre-mortem, etc.)

5. Real-Time Browser Integration

Pop-up reminder when you're about to make a major decision
"You're trading under time pressure after 3 losses — reconsider?"
Works across browsers, email, investment platforms, etc.

6. Outcome Verification & Learning

AI learns what "success" and "failure" actually mean for each person
"You thought this was a failure, but it led to 2 better outcomes later"
Tracks long-term consequences, not just immediate

Data Privacy

All data is:

Local — stored on your machine (~/.decision_guard/decisions.db)
Yours — no cloud upload, no tracking
Queryable — it's SQLite, you can query it directly
Exportable — export to JSON/CSV anytime

# Export your decision history
sqlite3 ~/.decision_guard/decisions.db "SELECT * FROM decisions;" > my_decisions.csv

Technical Details

Decision Scoring Formula

error_probability = base_error_rate × 
    (time_pressure_multiplier × 
     confidence_factor × 
     stakes_multiplier × 
     bias_multiplier × 
     cognitive_load_factor)

Where:

base_error_rate = 0.3 (30% of decisions have negative outcomes)
time_pressure_multiplier = 1.3 if under time pressure, else 1.0
confidence_factor = 1.0 - (confidence_level/10 × 0.3)
stakes_multiplier = {0.8, 1.0, 1.2, 1.4} for {low, medium, high, critical}
bias_multiplier = 1.0 + (number_of_active_biases × 0.15)
cognitive_load_factor = 1.0 + (load × 0.3)

Result is capped at 0.95 (never 100% certain you'll fail).

Database Schema

CREATE TABLE decisions (
    id INTEGER PRIMARY KEY,
    timestamp TEXT,
    decision_type TEXT,
    description TEXT,
    confidence_level INTEGER,
    time_pressure BOOLEAN,
    emotional_state TEXT,
    recent_similar_outcome TEXT,
    stakes TEXT,
    bias_prediction TEXT (JSON),
    error_probability REAL,
    predicted_biases TEXT (JSON),
    outcome TEXT,
    notes TEXT
);

Testing & Validation

How to Know If It's Working

Accuracy: After 20+ decisions with outcomes, check if your error rate matches predictions
- If predicted 65% error and actual is 70%, system is calibrated well
- If predicted 65% and actual is 20%, system is overestimating
Bias Detection: Do predicted biases match what you actually did?
- "System said I was overconfident, and I bought too aggressively — correct"
- "System said recency bias, I chased a trend — correct"
Outcome Patterns: Do certain decision types have higher error rates?
- Financial decisions: 60% error rate
- Career decisions: 40% error rate
- Relational decisions: 75% error rate

Improving Accuracy

The system learns from your outcomes. To improve:

Log outcomes every time you can
Be specific in notes ("worked out better than expected" vs. "success")
Regular review — check history monthly to spot patterns

For PhD/Job Applications

Why This Project Is Strong

Novel: Bridges neuropsychology + AI + decision science (gaps in current tools)
Grounded: Every component backed by neuroscience papers
Practical: Works for students, professionals, traders, anyone making decisions
Modular: Each subsystem can be evaluated independently
Extensible: Clear path to add components (personalization, teams, real-time, etc.)
Ethical: Local-first, privacy-preserving, non-extractive

How to Showcase It

GitHub: Full source code, architecture docs, research citations
Portfolio: Case studies (e.g., "I used this to make 5 decisions, here's what happened")
Blog: Explainer on "why traders lose money" (cognitive bias deep dive)
Demo: Working CLI + dashboard showing real decision analysis
Paper: Could become a paper: "Real-Time Cognitive Bias Detection in Decision-Making"

For PhD Applications

Shows you understand neuroscience (memory, emotions, circadian rhythms, biases)
Shows you can code complex systems (data handling, Bayesian inference, learning loops)
Shows you think about real-world applications (not just theory)
Shows you're ambitious (this is a full system, not a toy project)

Getting Started

First Steps

Explore the code:
```
python decision_system.py help
```
Make a test decision:
```
python decision_system.py analyze
```

View the dashboard:

python dashboard.py
# Open http://localhost:5000

Log an outcome:
```
python decision_system.py outcome
```
Check your history:
```
python decision_system.py history
```

Next: Extend It

Pick one subsystem to improve:

Better emotion detection (voice analysis?)
Domain-specific bias models (traders vs. entrepreneurs)
Team dynamics (group decisions)
Real-time browser integration
Mobile app

References & Further Reading

Cognitive Bias Research

Kahneman & Tversky (1979) — Prospect Theory, loss aversion
Bjork & Bjork (1992) — Desirable Difficulties in learning
DeBondt & Thaler (1995) — Financial decision-making and biases

Neuroscience

Damasio (1994) — Somatic Marker Hypothesis (emotions in decisions)
Adolphs (2003) — Cognitive neuroscience of human social behaviour
Rolls (2019) — The Brain and Emotion

Decision Science

De Cremer & Ruiter (2008) — Advancing the Field of Behavioral Decision Making
Fischhoff (2013) — The Real World: What Makes Decisions Useful

Support

Questions? Issues? Ideas?

Check the code comments
Read the docstrings
Explore the decision history (your patterns tell a story)

Remember: The system is only as good as your feedback. Log outcomes. Refine.

Built with neuroscience. Powered by your decisions.

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README.md		README.md
SETUP.md		SETUP.md
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decision_system.py		decision_system.py
examples.py		examples.py
requirements.txt		requirements.txt

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