'Estimat - Values and Data’s For Starters'- A Necessary Proposal?

post by Gabriel Brito (gabriel-brito) · 2024-11-14T14:37:57.692Z · LW · GW · 0 comments

Contents

  1. PROBLEM 
    1.1 Health and Well-being
    1.2 Behavioral Economics and Our Choices
  2. SOLUTIONS
    2.1 Practical Models for Better Decisions
    2.2 Awareness and Mindfulness
    2.3 Learning from the Best Decision-Makers
  3. Proposal: 'Estimat - Values and Data’s For Starters'
    3.1 Key Components
    3.2 Practical Learning Modules
    3.3 Implementation Methodology
  4. Expected Benefits
    4.1 Short-Term:
    4.2 Long-Term:
  Conclusion and Personal Reflection
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1. PROBLEM 

In today’s digital era, teenagers face a dual challenge: processing vast amounts of information and maintaining emotional well-being. Traditional educational models, focused on memorization and mechanical procedures, might be better suited to preparing students for complex decision-making in an increasingly uncertain world.

As part of a non-profit Latín American education initiative, Jacominesp, I seek to connect with members os LessWrong who are interested in collaborating on the development of a program that integrates rational thinking, data analysis, and personal development for young people. Would there be interest in participating or contributing ideas?

1.1 Health and Well-being

World Health Organization (WHO) statistics indicate that in 2019, nearly a billion people, including 14% of teenagers globally, were affected by some mental disorder. World Health Organization

Moreover, it’s estimated that over 20% of adolescents worldwide suffer from mental health disorders, with suicide being the second leading cause of death among youth aged 15 to 19. UNICEF

In terms of education, it’s been observed that although the school curriculum emphasizes abstract concepts, it often lacks approaches that connect these concepts with practical applications in everyday life. This can hinder students from making better life decisions. Educrea

1.2 Behavioral Economics and Our Choices

Research by Daniel Kahneman and Amos Tversky has revolutionized our understanding of decision-making. Their studies show how our values and identity influence our choices, especially in complex or long-term decisions, and highlight the need for models to help us reason better in today’s world—even when linking decisions to self-knowledge and well-being.

2. SOLUTIONS

2.1 Practical Models for Better Decisions

Several good models have been developed to help us make better decisions:

  1. Probability-based predictions (Bayesian Method)
  2. OODA Decision Cycle (Observe, Orient, Decide, Act)
  3. SMART Goals (Specific, Measurable, Achievable, Relevant, and Time-bound)
  4. First Principles Method

2.2 Awareness and Mindfulness

Mindfulness practice has been shown to be useful for making more conscious decisions. By developing the ability to observe and label our impulses and emotions in the present moment, we reduce automatic reactions and increase mental clarity. This can help us distinguish momentary impulses from deeper goals.

2.3 Learning from the Best Decision-Makers

The Good Judgment Community Forecasting Project, funded by DARPA, studied the characteristics of top forecasters ("superforecasters") who can best use information for decision-making. These individuals demonstrated:

  1. Strong analytical thinking
  2. Intellectual curiosity
  3. Flexibility in adjusting opinions
  4. Probability-based focus
  5. Humility and eagerness to learn

3. Proposal: 'Estimat - Values and Data’s For Starters'

By combining intellectual and emotional approaches through the lens of information theory and fuzzy mathematics, we can establish measurable values.

3.1 Key Components

  1. Personal Scoring System (PSS)
  2. Self-evaluation per activity through quantifiable metrics
  3. Goal tracking with specific indicators
  4. Continuous data-driven feedback

3.2 Practical Learning Modules

  1. Applied probability in everyday decisions and emergency situations
  2. Personal data analysis
  3. Optimization of routines and habits
  4. Quantified emotional management

3.3 Implementation Methodology

Phase 1: Diagnosis

  1. Initial assessment of motivations, goals, and routines
  2. Initial bias assessment
  3. Initial assessment of mathematical competencies
  4. Establishing baseline data

Phase 2: Development

  1. Weekly interactive workshops
  2. Guided personal projects
  3. Peer mentorship

Phase 3: Monitoring

  1. Continuous evaluation through key indicators
  2. Personalized adjustments based on progress
  3. Structured feedback

4. Expected Benefits

4.1 Short-Term:

  1. Improved analytical skills
  2. Reduced anxiety in decision-making
  3. Increased self-awareness

4.2 Long-Term:

  1. Development of critical thinking
  2. Greater emotional resilience
  3. Enhanced planning skills

Conclusion and Personal Reflection

During my exploration and application of these and other decision-making models, I encountered a recurring limitation: they are often too abstract and subjective, making systematic and measurable implementation challenging. This observation led me to develop a more mathematical and quantifiable approach for my own self-knowledge and decision-making process.

By applying information theory principles to my personal development, I find indications that I can:

  1. Reduce ambiguity in progress assessment
  2. Set more objective parameters to measure success
  3. Create a more replicable and verifiable system

I see an approach using information theory and fuzzy mathematics could be valuable for teenagers. I’m curious if other community members have experienced similar limitations with traditional models and if they would see utility in a more quantitative self-knowledge system.

Would a framework combining data analysis with practical tools for personal development be useful for teenagers?

Would there be interest from LessWrong community in help this type of proposal that combines rationality, data analysis, and personal development?

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