To predict outcomes and achieve goals, develop an accurate model of reality. This is best done by removing subjective emotions and sentiment, and only analyzing what can be tangibly observed.

Most people make poor decisions because they are trapped by emotions and view the world in simple binaries. A better approach is to map a situation's full complexity, understand its trade-offs, and recognize where others are getting stuck in their feelings, thus avoiding those same traps.

When modeling a complex issue like malaria bed nets, don't start with every variable. Begin with a simple model of the 5-6 core drivers. This makes the model easier to understand, hold in your head, and debug. Add complexity later, once the basic dynamics are established and validated.

Certain individuals have a proven, high success rate in their domain. Rather than relying solely on your own intuition or A/B testing, treat these people as APIs. Query them for feedback on your ideas to get a high-signal assessment of your blind spots and chances of success.

Manually analyzing 30 data points builds deep intuition and overcomes the tech industry's bias for big data. It's enough to distinguish a major signal (e.g., a 60% rate) from a minor one (10%) and inform immediate action without complex analysis.

Critics claim explicit models for big decisions are flawed. However, relying on intuition is just using an opaque, implicit model you can't scrutinize. An explicit model, even if imperfect, makes assumptions transparent and challengeable, which is superior to a 'gut feeling' that cannot be dissected or debated.

When making big decisions, a weighted factor model forces you to define and weigh your criteria (e.g., impact, salary). Surprisingly, the model often validates your pre-existing intuitive choice. Its value lies in providing data-driven confidence and clarity for the path you already suspected was best, rather than revealing an unexpected new answer.

Effective problem-solving uses a two-stage process modeled by chess grandmaster Magnus Carlsen. First, leverage intuition and pattern recognition ('gut feel') to generate a small set of promising options. Then, apply rigorous, logical analysis only to that pre-filtered set, balancing creativity with analytical discipline.

Instead of starting with available data, marketers should first identify and rank key business decisions by their potential financial impact. This decision-first approach ensures data collection and analysis efforts are focused on what truly drives business value, preventing 'analysis paralysis' and resource waste.