RavaTimes

Key Concepts

Scenario Projection

Scenario projection assists decision analysis by systematically exploring different options:

• Generates possible development paths for each option
• Identifies key tradeoffs
• Evaluates short-term and long-term impacts
• Annotates uncertainties and key assumptions

Dynamic and Personalized

RavaTimes adjusts projections based on your historical decision patterns:

• Adjusts analysis focus based on your risk preferences
• Uses analogies and frameworks you're familiar with
• Prioritizes dimensions you typically focus on
• Provides references based on past similar scenario choices

This personalization is weaker for new users and improves gradually with use.

How It Works

RavaTimes is a Transformer-based language model specifically trained for projection on real-world life scenarios.

Projection Process

For each option, the system:

1. Understands current situation and constraints
2. Generates possible development paths (typically 3-6 months)
3. Identifies key turning points and uncertainties
4. Evaluates impacts from multiple dimensions
5. Generates structured comparative analysis

Personalization

The system considers your history:

• Past decision choices
• Dimensions of focus and tradeoff patterns
• Risk preferences and values

This information influences the perspective and focus of projections but doesn't change the fundamental logical reasoning.

Confidence Level

Each part of the projection has an implicit confidence level:

• High confidence — Based on common sense and universal logic
• Medium confidence — Requires domain knowledge or reasonable assumptions
• Low confidence — Highly uncertain or dependent on multiple assumptions

The system explicitly annotates key assumptions and uncertainties in its output.

Typical Applications

Career Decisions

Current situation: 3 years at Company A, Senior Engineer
Option 1: Accept Company B offer (salary +30%, startup)
Option 2: Stay at Company A (stable, clear promotion path)
Evaluation dimensions: Income, learning, work-life balance, risk

Option 1 (Company B)
Short-term (0-6 months):
- Income increases 30%, work intensity rises significantly
- Steep learning curve, exposure to new tech stack
- Startup environment, processes not mature

Mid-term (6-18 months):
- If company grows well: equity value rises, fast promotion possible
- If funding difficulty: layoff risk, need to find new job
- Key uncertainty: next funding round

Option 2 (Company A)
Short-term: Income unchanged, predictable workload
Mid-term: ~40% chance of promotion to Staff, income +20%

Key Differences:
- Option 1: High risk, high reward; higher ceiling but more volatile
- Option 2: Stable and predictable; limited growth but low risk

Product Decisions

Current situation: SaaS product, 100 enterprise clients, team of 15
Option 1: Develop enterprise features (3 months, $150k)
Option 2: Optimize existing features to improve retention (1.5 months, $75k)
Evaluation dimensions: Revenue, customer satisfaction, resource consumption

Option 1 (Enterprise Features)
- 3-month dev cycle, delays other projects
- Success: Opens enterprise market, per-client value +3x
- Failure: Sunk cost $150k, missed optimization window
- Key uncertainty: True demand intensity from enterprise clients

Option 2 (Optimize Retention)
- Faster results (1.5 months)
- Expected churn rate drops from 15% to 10%
- But doesn't solve enterprise client acquisition

Tradeoff:
- Option 1 is strategic investment, may change business model
- Option 2 is tactical optimization, doesn't solve growth bottleneck

Resource Allocation

Current situation: R&D team of 10, 3 parallel projects
Option 1: Concentrate resources on Project A
Option 2: Distribute resources equally
Evaluation dimensions: Delivery speed, quality, team morale, failure risk

Option 1 (Concentrate Resources)
- Project A delivery compressed from 6 months to 3 months
- Projects B, C stall, may lose market timing
- If Project A fails: wasted resources, other projects fall behind
- High team pressure, affects morale

Option 2 (Equal Distribution)
- All three projects progressing but slower
- Reduces single-point failure risk
- But may not deliver any on time
- Context switching adds ~20% communication overhead

Key Question: Project A success probability is the decision key.
If >70%, concentrating resources is reasonable.
If <50%, spreading risk is safer.

Data Requirements

Input Requirements

Input Quality

Projection quality is directly affected by input quality.

Good Input

Current situation: Team of 5, monthly revenue $50k, growing 10%/month
Option 1: Hire 3 more people (cost $30k/month)
Option 2: Maintain status quo, optimize processes
Evaluation dimensions: Cost, growth speed, management burden

Unclear Input

Should I expand my team?

Limitations

Not a Prediction Model

RavaTimes is based on reasoning, not data-driven prediction:

• Cannot provide exact probabilities
• Cannot predict truly random or unprecedented events
• Cannot access real-time data or market information
• Projections are based on logic and knowledge, not statistical analysis

Depends on Input Quality

• Missing key information causes projection deviation
• Wrong assumptions lead to wrong conclusions
• Cannot proactively identify important factors not provided

Knowledge Boundaries

• Limited projection depth in highly specialized domains
• Industry-specific tacit knowledge may be missing
• Localization or culture-specific factors may be insufficiently considered

Time Span Limitations

Does Not Replace Professional Judgment

• Legal, financial, and medical decisions need professionals
• Moral judgments are made by the user
• High-risk decisions should seek multiple opinions
• The system does not bear decision responsibility

Personalization Limitations

• Personalization is weaker for new users
• User preferences change over time
• Should not over-rely on historical patterns

When Not to Use

Unsuitable Scenarios

• Requires precise probabilities or statistical prediction
• Highly specialized technical decisions
• Time-critical decisions (trading, emergency response)
• Life-critical decisions (medical diagnosis)
• Analysis requiring real-time data access
• Legal or compliance-related judgments

Use Other Tools Instead

Comparison with RavaPush

When to Use Which

• Need to choose between several options → RavaTimes
• Need to predict future values of a metric → RavaPush
• Need comprehensive analysis → Both combined

Best Practices

Provide Clear Input

Include specific information:

• Key facts about current situation
• Specific differences between each option
• Numbers (budget, time, headcount)
• Relevant constraints

Limit Number of Options

Recommended 2-4 options:

• 2 — Clear comparison
• 3 — Balanced analysis
• 4 — Approaching the limit
• 5+ — Information overload

Specify Evaluation Dimensions

Specify 3-5 most important dimensions; avoid "comprehensive analysis."

Iterative Refinement

After the first projection:

• Add missing information
• Deep-dive into a specific aspect
• Test assumption changes ("What if X changes")

Cross-Validation

Don't rely solely on projections:

• Discuss with experienced people
• Find similar cases
• Seek professional advice
• Compare with your own intuition

Integration in RavaAI

RavaTimes is one of three core components of the RavaAI reasoning engine:

Synergy Example

Question: Should we scale up the team

FAQ

How accurate are projections?

Projections are not predictions. They provide logic-based possible development paths, identification of key tradeoff points, and annotation of uncertainties. Don't expect "Option A will succeed," but rather "Option A needs X, Y to succeed; if it fails, it may be because of Z."

How many options can be projected?

Recommended 2-4. Too many causes information overload.

Will projections consider my personal situation?

Yes, the system considers your historical decision patterns and preferences. But personalization is weaker for new users and improves with use. Key information still needs to be explicitly stated.

Can projection parameters be modified?

Yes, iteratively:

• "What if the time limit changes to 6 months"
• "What if the budget increases by 50%"
• "Assume competitors are doing something similar"

Will projections give advice?

The system will point out tradeoff points and risks, stating "if you prioritize X, Option A is more suitable." The system won't say "you should choose A" or make value judgments for you.

How to know if projections are reliable?

Pay attention to annotated assumptions and uncertainties:

Privacy

• Input information is only used to generate the current projection
• Not used for model training
• Can be deleted at any time