1. How does LifeScript use algorithms to optimize life choices and improve goal achievement rates? What are the fundamental principles?

   LifeScript uses an AI decision engine and behavioral science models to transform vague life goals into quantifiable, executable scientific paths. Its core principle is: like a "navigation system" predicting route traffic—it scans the health of your goals across 7 dimensions, combines this with a case database, predicts your goal success rate, and optimizes your action plan in real-time.

   How is the "Goal Achievement Rate Prediction" as reliable as a weather forecast?

   Calculates the success rate (0%-100%) based on dynamically weighted real data:

   Data Dimension	Weight	Role
   Your Execution Health	40%	Task completion rate/emotional stability directly impacts results
   Success Rate of Similar Users	30%	References 87% success paths of users with similar backgrounds
   External Environmental Factors	10%	Industry trends/policy changes (e.g., tight job market)
   ...	...	...

   How does "Goal Setting Health" help avoid blind effort?

   This is an AI + algorithm comprehensive check-up report on goal scientificity (*0-100* points), addressing common issues from 6 dimensions:

   Dimension	Problem Solved	Example
   Phased Focus	Too many goals (overload)	Setting "Promotion + Fitness + Side Business" simultaneously → Alert for scattered energy
   Core Value Alignment	Goals misaligned with true needs	"High-intensity socializing" but actual preference for solitude → Low alignment score
   Conflict Analysis	Time/resource self-consumption	"2 hours daily gym" vs. "Overtime project" conflict
   ...	...	...

   Why are goals divided into 4 stages? How does the system intelligently advance through them?

   Imitates the natural progression of experts achieving goals, addressing the pain point of "quitting halfway":

   1. Startup Break down big goals into small tasks (e.g., "Obtain certification")
   2. Growth😗* Strengthen core abilities (e.g., "Manage a team of 3")
   3. Reinforcement😗* Tackle key results (e.g., "Independently manage a project")
   4. Maintenance: Internalize into habits (e.g., "Automate weekly review")

   Intelligent Progression Rules: ✅ Automatic Upgrade: Complete 90% of phase tasks + Health Score ≥ 75 points (e.g., Unlock "Reinforcement Phase" courses after "Improvement Phase" fitness goals are met) ❌ Unexpected Downgrade: Fall back to consolidation if task completion rate < 50% for 1 consecutive week (e.g., Frequently skipping workouts reverts to "Improvement Phase")

   What is the purpose of the "High/Medium/Low Contribution" labels next to tasks?

   Helps you focus on the 20% key tasks that create 80% of the results:

   · 🔴 High Contribution (Must Prioritize): → Directly impacts goal outcome (Failure causes >15% progress drop) → e.g., "Submit promotion presentation report"

   · 🔵 Medium Contribution (Try to Complete): → Indirectly supports the goal (e.g., "Collect industry case studies")

   · ⚪ Low Contribution (Adjustable): → Optimization tasks (e.g., "Update weekly report template") Design Philosophy: Respects Cognitive Load Theory (Stanford Research: Multitasking reduces efficiency by 20%), enforcing ≤ 3 core actions per day.

   Is there scientific support for these features?

   Core Principles:

   1. Goal Setting Theory (Locke & Latham): Specific goals increase success rate by 230%
   2. Implementation Intention Theory (Gollwitzer): Phased breakdown triples completion rates for activities like "fitness"
   3. Self-Determination Theory (Deci & Ryan): Value-aligned goals increase persistence rate by 68%
   4. MIT Human Dynamics Laboratory: Resource conflict models optimize multi-goal management

Related papers

1. Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation. American Psychologist, 57 (9), 705–717.
2. Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54 (7), 493–503.
3. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12 (2), 257–285.
4. Newport, C. (2016). Deep Work: Rules for Focused Success in a Distracted World . Grand Central Publishing.
5. Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. PNAS, 106 (37), 15583–15587.
6. Sheeran, P., et al. (2021). Goal pursuit: Current state of the science and future directions. Nature Reviews Psychology, 1 , 236–248.
7. Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11 (4), 227–268.
8. Kahneman, D. (1973). Attention and Effort . Prentice-Hall.
9. Pink, D. H. (2011). Drive: The Surprising Truth About What Motivates Us . Riverhead Books.