Team Name:
USP
Team Members:
USP
A revolutionary gamified risk management platform that combines the AXiLe® Constructive Modelling Paradigm with AI-powered simulation to help organizations identify, assess, and mitigate risks before they become catastrophic failures. Through interactive scenarios guided by Harry, our AI Risk Simulator, users navigate complex risk landscapes while learning evidence-based decision-making and cross-domain systems thinking.
RiskyGames is a revolutionary gamified risk management platform that combines the AXiLe® Constructive Modelling Paradigm with AI-powered simulation to help organizations identify, assess, and mitigate risks before they become catastrophic failures. Through interactive scenarios guided by Harry, our AI Risk Simulator, users navigate complex risk landscapes while learning evidence-based decision-making and cross-domain systems thinking.
Harry is an advanced AI persona that embodies the AXiLe® Constructive Modelling Paradigm, capable of:
- Cross-Domain Risk Integration: Uses SmartMatter Framework® to connect risks across different knowledge domains
- Pattern Recognition: Leverages Open Reference Patterns (ORP) to identify risk patterns from historical failures
- Natural Language Processing: Communicates using AXiLe® Natural Pattern Language for precise risk articulation
- Premortem Facilitation: Guides users through "failure imagination" exercises
- Real-Time Assessment: Provides immediate feedback on risk management decisions
RiskyGames transforms traditional risk management training into engaging, immersive experiences where:
- Failure is Learning: Players explore "what if we failed?" scenarios without real-world consequences
- Pattern Discovery: Users discover risk patterns through gameplay rather than theoretical study
- Collaborative Problem Solving: Teams work together to solve complex multi-domain risk challenges
- Evidence-Based Decisions: All game mechanics reward evidence-driven thinking and decision-making
1. Sarah Chen - Risk Manager at Mid-Size Construction Company
- Age: 34, 8 years experience in risk management
- Challenges: Struggles to get executive buy-in for comprehensive risk assessments
- Goals: Wants to demonstrate risk management value through engaging presentations
- RiskyGames Use: Runs executive team through construction project failure simulations
2. Marcus Aboriginal Community Development Coordinator
- Age: 41, manages community infrastructure projects
- Challenges: Limited formal risk management training, complex cultural and environmental considerations
- Goals: Protect community from project failures while respecting traditional knowledge
- RiskyGames Use: Explores infrastructure project risks with cultural and environmental factors
3. Dr. Emily Rodriguez - University Risk Management Lecturer
- Age: 52, academic specializing in organizational risk
- Challenges: Students find traditional risk management theory boring and abstract
- Goals: Make risk management education engaging and practical
- RiskyGames Use: Uses platform for interactive risk management curriculum
Harry - The Risk Whisperer
- Personality: Wise, patient, slightly mischievous mentor who loves discovering hidden patterns
- Capabilities: Cross-domain risk analysis, historical pattern recognition, premortem facilitation
- Communication Style: Uses storytelling and metaphors to explain complex risk concepts
- Learning Approach: Continuously evolves based on user interactions and real-world risk outcomes
Alex - The Scenario Architect
- Personality: Creative, detail-oriented designer of complex risk scenarios
- Capabilities: Generates realistic multi-domain risk scenarios, adapts difficulty based on user skill
- Communication Style: Immersive storytelling that makes users feel they're living the scenario
- Learning Approach: Learns from user decisions to create increasingly challenging and realistic scenarios
The fictional city of Riverside decided to become Australia's first fully smart city, implementing IoT sensors, AI traffic management, smart energy grids, and digital citizen services. The project had a $200M budget and 3-year timeline.
Month 6: Minor cybersecurity vulnerabilities discovered in traffic sensors
Month 12: Data privacy concerns raised by citizen advocacy groups
Month 18: Power grid integration caused unexpected brownouts
Month 24: Major data breach exposed 100,000 citizen records
Month 30: System-wide failure during emergency evacuation for bushfire
Month 36: Project terminated, $150M lost, public trust destroyed
Players navigating the ConnectedTown scenario would have:
- Discovered Hidden Connections: Harry would guide discovery of unexpected risk linkages between traffic, power, emergency, and social systems
- Cultural Risk Assessment: Alex would introduce Indigenous rights and community trust scenarios
- Cascade Modeling: Users would experience how cybersecurity failures cascade through interconnected smart city systems
- Stakeholder Mapping: Gamified stakeholder identification would reveal overlooked community groups
- Premortem Exercises: Regular "failure parties" where teams imagine creative failure scenarios
- Cross-Domain Learning: Users would learn how transportation risks connect to privacy, environmental, and social risks
Core Platform Development
- Develop Harry AI persona using advanced conversational AI and AXiLe® Constructive Modelling integration
- Create basic gaming engine with risk scenario simulation capabilities
- Build SmartMatter Framework® integration for cross-domain risk mapping
- Design user interface optimized for collaborative risk exploration
- Develop 5 foundational risk scenarios across different industries
AXiLe® Integration
- Implement Open Knowledge Reference Model (OKRM) for risk pattern library
- Create AXiLe® Natural Pattern Language integration for precise risk communication
- Build Open Reference Patterns (ORP) database for historical risk failures
- Design cross-domain knowledge mapping capabilities
- Test constructive modelling paradigm effectiveness in gaming context
Technical Infrastructure
- Cloud-based platform architecture supporting real-time collaboration
- Advanced AI natural language processing for Harry persona
- Secure data handling for organizational risk information
- Mobile-responsive design for accessibility across devices
- Integration APIs for future expansion and third-party connections
Validation and Testing
- Beta test with 3 partner organizations across different industries
- Validate AXiLe® paradigm effectiveness in gaming context
- Gather feedback from risk management professionals
- Test cross-cultural effectiveness with diverse Australian communities
- Refine Harry AI personality and interaction patterns
Advanced Gaming Features
- Develop 20+ complex multi-domain risk scenarios
- Add competitive and collaborative gaming modes
- Create adaptive difficulty based on organizational risk maturity
- Implement real-time premortem facilitation tools
- Build advanced analytics and progress tracking
AI Enhancement
- Enhance Harry with emotional intelligence and cultural competency
- Add Alex scenario architect AI for dynamic scenario generation
- Implement predictive risk modeling based on user decisions
- Create personalized learning pathways for different risk management roles
- Build advanced pattern recognition from user gameplay data
Professional Integration
- Create certification pathways recognized by risk management professional bodies
- Build integration with existing risk management frameworks (ISO 31000, COSO)
- Add compliance tracking for regulatory risk management requirements
- Develop industry-specific risk scenario libraries
- Create professional development credit systems
Market Expansion
- Launch with 100+ organizational customers
- Develop partnerships with risk management consulting firms
- Create educational institution licensing programs
- Build government agency pilot programs
- Establish presence at risk management conferences and professional events
Sophisticated AI Capabilities
- Implement advanced machine learning for real-world risk prediction
- Create AI-driven custom scenario generation based on organizational context
- Add natural language risk assessment and recommendation engines
- Build advanced cross-domain risk correlation analysis
- Develop predictive analytics for organizational risk trajectory
Ecosystem Integration
- Launch risk management service provider marketplace
- Create integration with real-world risk management tools and platforms
- Build API ecosystem for third-party risk management application integration
- Add real-time risk monitoring and alert systems
- Create community-driven risk scenario sharing platform
Advanced Gaming and Learning
- Develop VR/AR risk simulation experiences
- Create multiplayer massive risk scenario simulations
- Add AI-generated dynamic storytelling for immersive risk experiences
- Build advanced assessment and benchmarking against industry standards
- Implement peer learning and mentorship systems
Research and Development
- Partner with universities for risk management research
- Contribute to AXiLe® Constructive Modelling Paradigm development
- Publish research on gamified risk management effectiveness
- Create open-source components for broader risk management community
- Develop next-generation risk thinking methodologies
Widespread Adoption
- Achieve 1,000+ organizational customers across all Australian industries
- Launch government-wide risk management capability development program
- Create national risk management skill development initiative
- Build integration with university curricula across multiple disciplines
- Establish RiskyGames as standard tool for major project risk assessment
Advanced Ecosystem
- Launch AI-powered risk consultancy services using platform insights
- Create real-world risk prediction and early warning systems
- Build national risk intelligence sharing network
- Add crisis response simulation and training capabilities
- Create international expansion framework for other countries
Innovation and Leadership
- Establish research center for gamified learning and risk management
- Create innovation lab for next-generation risk management technologies
- Build thought leadership platform for risk management community
- Launch risk management innovation challenges and competitions
- Create venture capital fund for risk management technology startups
Enterprise Subscriptions
- Starter Package: $500-2,000/month for small organizations (50-200 employees)
- Professional Package: $2,000-10,000/month for medium organizations (200-1,000 employees)
- Enterprise Package: $10,000-50,000/month for large organizations (1,000+ employees)
- Government Agency Licensing: $50,000-500,000/year for government departments and agencies
Training and Certification
- Individual Certification: $299-999 per person for RiskyGames Risk Management Certification
- Corporate Training Programs: $10,000-100,000 for comprehensive organizational risk training
- Train-the-Trainer Programs: $25,000-75,000 for internal training capability development
- University Course Licensing: $10,000-50,000/year per educational institution
Professional Services
- Risk Assessment Consulting: $5,000-25,000/day for AI-enhanced risk assessments using platform insights
- Custom Scenario Development: $50,000-500,000 for organization-specific risk scenarios
- Risk Strategy Consulting: $10,000-50,000/day for organizational risk strategy development
- Crisis Simulation Services: $25,000-200,000 for large-scale crisis response simulations
Marketplace and Partnerships
- Service Provider Marketplace: 10-20% commission on risk management service referrals
- Technology Integration Partnerships: Revenue sharing with risk management software providers
- Insurance Industry Partnerships: Revenue sharing for improved risk management leading to reduced claims
- Consulting Firm White-Label Licensing: $100,000-1,000,000/year for major consulting firms
Data and Intelligence
- Anonymous Risk Intelligence Reports: $10,000-100,000/year for industry risk trend analysis
- Benchmarking Services: $5,000-25,000 for organizational risk management maturity assessment
- Research Data Licensing: Academic and government research partnerships
- Predictive Risk Analytics: $25,000-250,000/year for real-world risk prediction services
Innovation and Expansion
- Government Innovation Contracts: $1M-20M for developing national risk management capabilities
- International Licensing: $5M-50M for adapting platform to other countries' regulatory and cultural contexts
- IP Licensing: License gaming-based learning methodologies to other education and training sectors
- Research and Development Grants: Government and academic funding for risk management innovation
Year 1: $500K revenue (100 organizations × $5K average annual subscription)
Year 2: $2.5M revenue (300 organizations + consulting services + certification programs)
Year 3: $8M revenue (750 organizations + major government contracts + international expansion)
Year 4: $20M revenue (1,500+ organizations + research partnerships + marketplace commissions)
Year 5: $50M+ revenue (National adoption + international licensing + ecosystem revenue)
The Brisbane Metro Rail expansion project aimed to connect outer suburbs with the city center using autonomous electric buses in dedicated lanes. Budget: $1.2B, Timeline: 5 years, Expected ridership: 100,000+ daily passengers.
Janet Williams - Project Director
- Background: 15 years infrastructure experience, confident in traditional project management
- Fatal Assumption: Believed proven bus technology in dedicated lanes was low-risk
- RiskyGames Journey: Would discover through gaming how technology, community, environmental, and political risks interconnect
Dr. Ahmed Hassan - Environmental Impact Assessor
- Background: Environmental scientist focused on emissions and noise impacts
- Fatal Oversight: Didn't consider flood risk integration with bus lane elevation changes
- RiskyGames Journey: Would learn through cross-domain scenarios how environmental risks cascade into operational and financial risks
Maria Santos - Community Engagement Coordinator
- Background: Community relations specialist, focused on consultation compliance
- Fatal Gap: Conducted consultation but didn't identify Indigenous cultural significance of route
- RiskyGames Journey: Would experience cultural risk scenarios and learn stakeholder mapping techniques
Harry AI Analysis: "I'm detecting pattern similarity to the Berlin Brandenburg Airport failure cascade. Your technology confidence is masking stakeholder and environmental risk interconnections."
Month 6: Community protests over bus lane placement through heritage area
- RiskyGames Learning: Cultural heritage risk assessment gaming scenarios
- Harry Insight: "This pattern matches the Sydney Light Rail heritage conflicts. Have you mapped all cultural stakeholders?"
Month 12: Flooding exposes inadequate drainage design for elevated bus lanes
- RiskyGames Learning: Environmental-infrastructure risk integration scenarios
- Harry Insight: "Climate change patterns suggest 1-in-100 year floods now occur every 25 years. Your infrastructure assumptions need updating."
Month 18: Autonomous bus technology fails safety certification due to pedestrian detection issues
- RiskyGames Learning: Technology overconfidence scenarios and backup planning exercises
- Harry Insight: "The Tesla Autopilot pattern suggests overconfidence in autonomous vehicle technology. What's your fallback plan?"
Month 24: State government changes, new transport minister cancels project
- RiskyGames Learning: Political risk gaming with electoral cycle modeling
- Harry Insight: "Political risk patterns show infrastructure projects are vulnerable during electoral transitions. How have you built cross-party support?"
Month 30: $800M spent, project abandoned, public transport crisis continues
- RiskyGames Learning: Complete failure analysis and lessons learned integration
- Harry Insight: "This cascade demonstrates the AXiLe® principle of interconnected domains. Each individual risk was manageable, but their interaction created system failure."
Premortem Gaming Sessions
- Month 1: Project team plays "Brisbane Metro Failure Party" scenario
- Harry guides team through systematic failure imagination across all risk domains
- Team discovers cultural heritage conflicts through Indigenous rights gaming scenario
- Environmental risk gaming reveals flood vulnerability and climate change impacts
- Political risk scenarios expose electoral cycle vulnerability
Cross-Domain Risk Discovery
- Technology-Environment Connection: Gaming reveals how bus technology interacts with Australian weather patterns
- Community-Political Link: Scenarios show how community opposition creates political risk
- Financial-Cultural Integration: Gaming demonstrates how cultural conflicts escalate into budget blowouts
- Infrastructure-Climate Nexus: Users discover how climate change affects infrastructure assumptions
AI-Enhanced Risk Intelligence
- Harry continuously analyzes team decisions against historical failure patterns
- Alex generates new scenarios based on emerging risks discovered during gameplay
- AI provides real-time coaching on evidence-based decision making
- System builds organizational risk intelligence that improves over time
Stakeholder Gaming
- Multi-perspective scenarios where team members play different stakeholder roles
- Indigenous community representative scenarios reveal cultural significance
- Environmental activist scenarios expose overlooked environmental risks
- Political opposition scenarios reveal electoral vulnerabilities
- Community resistance scenarios teach effective engagement strategies
Natural Language Risk Assessment
User: "Our solar farm project seems pretty straightforward - just panels in a field."
Harry: "Interesting! Let me show you the Ivanpah Solar failure pattern. Solar projects intersect with wildlife migration, Indigenous cultural sites, grid integration, and community economics. Ready to explore these connections?"
Pattern Recognition and Historical Learning
- Analyzes user decisions against database of real-world project failures
- Identifies emerging risk patterns from current global events and trends
- Provides real-time coaching based on successful risk management strategies
- Learns from user interactions to improve future risk guidance
Cross-Domain Integration Using AXiLe® Framework
- Maps user project risks across multiple knowledge domains simultaneously
- Uses SmartMatter Framework® to identify unexpected risk connections
- Applies Open Reference Patterns to benchmark against historical successes and failures
- Communicates insights using AXiLe® Natural Pattern Language for precision
Alex's Scenario Architecture
- Generates infinite variations of risk scenarios based on user organization profile
- Adapts scenario complexity based on team risk management maturity
- Creates personalized scenarios reflecting user industry and geographic context
- Learns from user decisions to create increasingly challenging scenarios
Real-World Data Integration
- Incorporates current events and emerging risks into gaming scenarios
- Uses Australian government open data for realistic regulatory and compliance scenarios
- Integrates climate change data for environmental risk accuracy
- Includes real demographic and economic data for stakeholder scenarios
Organizational Risk Trajectory Modeling
- Analyzes gameplay patterns to predict organizational risk blind spots
- Identifies team collaboration patterns that increase or decrease risk awareness
- Provides predictive analytics on organizational risk management maturity development
- Generates personalized learning pathways for continuous risk skill development
Industry Risk Intelligence
- Aggregates anonymous gameplay data to identify industry-wide risk patterns
- Provides early warning of emerging risks affecting multiple organizations
- Creates industry benchmarking for risk management capabilities
- Facilitates cross-industry risk lesson sharing
RiskyGames occupies a unique position at the intersection of:
- Serious Gaming and Professional Development
- Academic Research and Practical Application
- Individual Learning and Organizational Transformation
- Risk Management and Innovation Enablement
- Australian Context and Global Applicability
RiskyGames represents a revolutionary approach to risk management that combines cutting-edge AI, academic research, and engaging gaming to create organizational capabilities that prevent failures before they happen. By integrating the AXiLe® Constructive Modelling Paradigm with immersive gaming experiences, we're creating a new category of professional development that transforms how Australians think about and manage risk.
The opportunity is enormous: Every major project failure, every innovation abandoned due to risk aversion, every community conflict over development represents a failure of risk management thinking. RiskyGames provides the tools and skills to turn these failures into successes, creating a more innovative, resilient, and collaborative Australian economy.
Join us in revolutionizing risk management through the power of gaming, AI, and evidence-based thinking. Together, we can build an Australia where better questions lead to brighter futures.
src/
├── components/
│ ├── ai/ # Harry AI persona components
│ ├── modules/ # Interactive learning modules
│ ├── ui/ # Reusable UI components
│ └── gamification/ # Gamification features
├── data/
│ └── mock/ # AXiLe mock data and scenarios
├── hooks/ # Custom React hooks
├── services/ # API and business logic
├── types/ # TypeScript type definitions
└── utils/ # Utility functions
Dashboard (src/components/ui/Dashboard.tsx)
ScenarioPlayer (src/components/modules/ScenarioPlayer.tsx)
HarryAI (src/components/ai/HarryAI.tsx)
AXiLe Data Layer (src/hooks/useAXiLe.tsx)
Main Menu Structure:
- 🏠 Dashboard: Main landing page with overview metrics and progress tracking
- 🎭 Scenarios: Interactive risk management game scenarios
- 📊 Analytics: Advanced analytics and visualizations
- 🧠 AI Assistant (Harry): Always available via the brain icon in bottom-left corner
Getting Started Flow:
1. Start at Dashboard: Overview of your learning journey and current stats
2. Browse Scenarios: Click "Play Scenarios" or navigate to scenarios section
3. Select a Scenario: Choose from available risk management scenarios
4. Interactive Gameplay: Make decisions, get AI guidance, earn points
5. Review Progress: Return to Dashboard to see improvements
npm start: Start development servernpm run build: Build for productionnpm test: Run test suitenpm run eject: Eject from Create React App (not recommended)src/types/axile.tssrc/data/mock/axileMockData.tsEdit the harryAIResponses object in src/data/mock/axileMockData.ts to customize Harry's responses.
Users will develop skills in:
- Risk identification and assessment
- Mitigation strategy planning
- Decision making under uncertainty
- Crisis management
- Evidence-based reasoning
- Cross-domain thinking
The application implements the AXiLe® (Advanced eXpert Intelligence Learning environment) framework with comprehensive data structures and intelligent leveraging of risk management concepts.
typescript
// Located in src/types/axile.ts
interface OKRMStructure {
objectives: Objective[]; // Business objectives with priorities and statuses
keyResults: KeyResult[]; // Measurable outcomes with progress tracking
risks: Risk[]; // Identified risks with probability/impact matrices
mitigations: Mitigation[]; // Risk treatment strategies with effectiveness metrics
}
Data Leveraging:
- Dashboard Analytics: OKRM data drives progress charts and risk level indicators
- Scenario Generation: Objectives create realistic business contexts for gameplay
- AI Guidance: Harry uses OKRM relationships to provide contextual advice
- Cross-Reference Analysis: Risks are automatically linked to affected objectives
typescript
interface NaturalPattern {
name: string; // Pattern identification
domain: string; // Business domain (Technology, Finance, etc.)
frequency: number; // Historical occurrence rate
historicalOccurrences: HistoricalOccurrence[];
relatedRisks: string[]; // Connected risk IDs
}
Data Leveraging:
- Predictive Learning: Historical patterns inform future risk assessments
- Cross-Domain Insights: Patterns from one domain applied to others (e.g., tech market saturation → SaaS customer acquisition)
- Evidence-Based Decisions: Users access historical data to support their choices
- Pattern Recognition Training: Scenarios test ability to identify recurring risk patterns
```typescript
interface SmartMatterNode {
type: 'risk' | 'mitigation' | 'objective' | pattern';
relationships: Relationship[]; // Causal connections between entities
}
interface Relationship {
type: 'causes' | 'mitigates' | 'impacts' | 'correlates';
strength: number; // Connection strength (0-1)
confidence: number; // Statistical confidence in relationship
}
```
Data Leveraging:
- Risk Cascade Analysis: Understanding how one risk triggers others
- Mitigation Effectiveness: Track which strategies work best for specific risk types
- System Thinking: Visualize complex interdependencies in risk landscapes
- Scenario Complexity: Generate realistic multi-factor crisis events
typescript
// Example from src/data/mock/axileMockData.ts
const mockGameScenarios: GameScenario[] = [
{
id: 'scenario-001',
title: 'The Startup Expansion Crisis',
initialRisks: [mockRisks[0], mockRisks[1]], // Market Saturation + Currency Risk
events: [
{
trigger: 'time_based',
description: 'Major competitor announces similar product...',
impact: [
{
type: 'risk_increase',
targetId: 'risk-001', // References actual OKRM risk
magnitude: 0.2
}
]
}
]
}
];
Real-World Application:
- Scenarios pull from actual business objectives (revenue growth, compliance, efficiency)
- Events reflect real historical patterns (market saturation cycles, regulatory changes)
- Decision consequences directly modify OKRM structures
- AI responses reference specific risk categories and mitigation strategies
typescript
interface Choice {
requiredEvidence: string[]; // What data supports this decision
consequences: EventImpact[]; // How choice affects OKRM structures
cost: number; // Resource implications
}
Data Integration:
- Historical Context: Choices require evidence from Natural Pattern database
- Cost-Benefit Analysis: Real financial implications based on mitigation data
- Outcome Prediction: Consequences calculated using relationship strengths
- Learning Reinforcement: Better decisions reward understanding of AXiLe® principles
typescript
const harryAIResponses = {
riskIdentified: "Great observation! You've identified a significant risk. Now, let's think about the potential impact and probability. What evidence supports your assessment?",
goodMitigation: "Excellent mitigation strategy! Your approach addresses the root cause and provides measurable outcomes. Consider also thinking about early warning indicators."
};
Contextual Intelligence:
- Pattern Recognition: Harry references specific Natural Patterns when advising
- OKRM Integration: Advice considers current objective priorities and risk levels
- Historical Learning: Responses incorporate lessons from past occurrences
- Adaptive Guidance: AI adjusts complexity based on user's demonstrated AXiLe® understanding
User Action → OKRM State Update → Pattern Matching → Consequence Calculation → UI Feedback
↓ ↓ ↓ ↓ ↓
Scenario Events → Risk Recalculation → Historical Analysis → Score Update → Harry Response
The application uses comprehensive mock data (src/data/mock/axileMockData.ts) that represents:
- 150+ interconnected data points across OKRM structures
- Realistic business scenarios from Fortune 500 experiences
- Statistical relationships between risks, mitigations, and outcomes
- Historical pattern libraries spanning multiple industries and time periods
- Evidence trails linking decisions to supporting data sources
This creates a rich learning environment where every user action is informed by AXiLe® principles, making abstract risk management concepts tangible through interactive gameplay.
This is a prototype/MVP implementation. For production use, consider:
- Adding comprehensive testing
- Implementing proper error handling
- Adding accessibility features
- Optimizing performance
- Adding security measures
This project is a demonstration/prototype for educational purposes.
Developed with: React + TypeScript + AXiLe® Framework
AI Assistant: Harry - Your Risk Management Mentor
Learning Approach: Gamified, Interactive, Evidence-Based
Solving problems before they become problems
Eligibility: Open to everyone. Submissions should use at least one government data source.
Go to Challenge | 9 teams have entered this challenge.