Category: Self Development

The Lifecycle of an Idea

Every idea begins as a seed—an untested concept with the potential to grow into something transformative. But as ideas evolve, they face challenges: rejection, entrenchment, and inertia.

Ideas Trees

Once an idea is introduced, it tends to grow along a single “trunk,” branching and evolving without often being reconsidered from its roots. This natural progression, while powerful, can lead to ideational inertia—where ideas expand on outdated assumptions. The solution? Periodically “replant” your ideas by revisiting them from first principles, breaking them down into their foundational truths, and reimagining them with a fresh perspective.

Simplicity Can be Deceptive

The Simplicity Paradox shows us that the best ideas are often simple, but achieving simplicity requires deep understanding. We resist simple solutions because they seem inadequate, but through exploration and discussion, we uncover their elegant depth. Whether it’s Einstein’s E=mc² or a company mission statement, simplicity that resonates always rests on layers of refined understanding.

Recognizing Biases That Shape Our Thinking

Our relationship with ideas is shaped by biases like initial rejection bias, idea entrenchment, and the reinvention fallacy. These biases push us to reject new ideas too quickly, cling to outdated ones, or reinvent systems unnecessarily. By recognizing these tendencies, we can create space for innovation and avoid the traps of unproductive thinking.

Tools for Rethinking and Refining Ideas

We’ve explored practical methods for breaking through cognitive rigidity:

• First Principles Thinking: Strip ideas down to their core components and rebuild them with clarity.
• Stop-Start-Continue Analysis: Ask what you should stop doing, start doing, or continue doing to keep ideas relevant and impactful.
• Mental Scaffolding: Help others understand your ideas by guiding them through the steps you took to reach the solution, building trust and comprehension.
• Discussion and Dialogue: Use collaborative conversations to unpack and validate ideas, transforming skepticism into shared understanding.

Communication Matters as Much as the Idea

The best ideas mean little if they aren’t effectively communicated. Whether you’re presenting to a team, writing a book, or sharing a vision, how you frame your ideas determines their reception. Crafting clear, relatable explanations—using analogies, storytelling, and visual aids—bridges the gap between insight and impact.

The Question is the Answer

Ideas don’t exist in isolation; they are born from well-posed questions. Whether through heuristic methods, prompt engineering, or reverse engineering, the quality of the question determines the power of the answer. The next time you approach a problem, focus on asking the right question to unlock new possibilities.

Moving Forward: Planting the Right Seeds

As you reflect on what you’ve learned, consider these final questions:

• Which of your current ideas might need “replanting”?
• Are there simple solutions you’ve dismissed that deserve a second look?
• How can you communicate your ideas more effectively to inspire understanding and action?

Ideas have the power to transform the world—but only when we give them the attention, care, and clarity they deserve. By thinking again, refining our thought processes, and communicating with purpose, we can cultivate ideas that truly stick.

This is a summary of my book, Think Again, on Amazon Kindle.

Heuristic thinking is the idea of focusing on the structure and process (the question or framework) as a guide to solutions, rather than just the solution itself. It emphasizes exploration, problem-solving, and efficiency.

Heuristic methods are strategies or mental shortcuts used to simplify problem-solving and decision-making. They often rely on practical techniques rather than exhaustive analysis, making them especially useful in complex or ambiguous situations.

A good heuristic often starts with the right question, guiding exploration toward efficient outcomes. It uses mental shortcuts or rules of thumb to frame problems and explore solutions.

Here are some common heuristic methods:

1. Rule of Thumb

• Definition: A simple guideline based on experience or general principles.
• Example: “If you’re unsure about a purchase, sleep on it before deciding.”
• Application: Quick decision-making in everyday life or business.

2. Trial and Error

• Definition: Testing multiple approaches until one works.
• Example: Trying different algorithms to optimize a process until the best one is found.
• Application: When a clear path is not known, this brute-force, iterative approach to problem-solving is a known method of problem solving that replaces thinking with trying.

3. Working Backward

• Definition: Starting with the desired outcome and tracing steps back to the present.
• Example: Solving a maze by beginning at the exit and working toward the entrance.
• Application: Useful in planning, goal-setting, and reverse engineering.

4. Reverse Engineering

• Definition: The process of deconstructing a system, product, or process to understand its components and how they work, often for replication or improvement.
• Example: “Analyzing a competitor’s website to learn their SEO strategy and applying similar techniques to your own site.”
• Application: Used in product design, competitive analysis, and problem-solving to gain insights or replicate functionality.

5. Simplification

• Definition: Breaking a problem into smaller, more manageable components.
• Example: Solving a complex math equation by addressing its parts individually.
• Application: Used in project management, coding, or strategic planning.

6. Pattern Recognition

• Definition: Identifying recurring structures or relationships to make decisions.
• Example: Recognizing that a certain customer segment always responds to discounts.
• Application: Analytics, marketing, and machine learning.

7. Anchoring

• Definition: Relying heavily on an initial piece of information to make subsequent decisions.
• Example: Setting a high initial price in negotiations to influence the final outcome.
• Application: Negotiation, pricing strategies, and decision-making.

8. Availability Heuristic

• Definition: Basing decisions on information that is readily available or recent.
• Example: Judging the likelihood of a plane crash based on a recent news story.
• Application: Risk assessment and quick judgment calls.

9. Divide and Conquer

• Definition: Splitting a problem into smaller, independent tasks to solve them efficiently.
• Example: Sorting algorithms like Merge Sort in computer science.
• Application: Algorithm design, management, and troubleshooting.

10. Means-End Analysis

• Definition: Identifying the difference between the current state and the goal, then taking steps to reduce the gap.
• Example: Planning a route to minimize travel time between two cities.
• Application: Used in AI, logistics, and strategic decision-making.

11. The Pareto Principle (80/20 Rule)

• Definition: Focusing on the 20% of efforts that produce 80% of results.
• Example: Identifying key customers who generate most of a company’s revenue.
• Application: Business prioritization, time management, and productivity.

12. Satisficing

• Definition: Choosing an option that is “good enough” rather than optimal.
• Example: Selecting the first hotel that meets your budget and location needs.
• Application: Quick decision-making under time constraints.

13. Analogies and Metaphors

• Definition: Using a similar situation or system to understand or solve a problem.
• Example: Comparing software bugs to diseases to think of “cures” (fixes).
• Application: Creative problem-solving, education, and brainstorming.

14. Gut Instinct (Intuition)

• Definition: Making decisions based on feelings or past experience rather than analysis.
• Example: A seasoned investor deciding to buy a stock based on a “hunch.”
• Application: Decision-making in uncertain or fast-moving scenarios.

15. Hill-Climbing Heuristic

• Definition: Making choices that seem to lead directly toward the goal, step by step.
• Example: Solving a puzzle by completing the edges first, then working inward.
• Application: Problem-solving in design, strategy, or iterative processes.

16. Elimination by Aspects

• Definition: Narrowing down options by sequentially removing those that don’t meet criteria.
• Example: Choosing a car by first eliminating models outside your budget, then filtering by fuel efficiency, etc.
• Application: Decision-making in purchasing or recruitment.

17. Cognitive Restructuring

• Definition: Reframing the way you see a problem to uncover new solutions.
• Example: Viewing a business failure as an opportunity to learn and pivot.
• Application: Psychology, leadership, and creative innovation.

18. Occam’s Razor

• Definition: A philosophical principle that favors the simplest explanation or solution with the fewest assumptions.
• Example: “If your car won’t start, first check if it’s out of gas before assuming there’s a complex mechanical issue.”
• Application: Used in scientific reasoning, diagnostics, and theoretical problem-solving to eliminate unnecessary complexity and focus on the most likely explanation.

These methods allow for flexibility and adaptability, which are crucial when clear solutions are not readily apparent. Each heuristic has strengths and limitations, but they can be combined or tailored to fit specific scenarios.

This is chapter 7 of Think Again, my book on Amazon Kindle.

The Question is the Answer

Oftentimes the question is more important than the answer.

“If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.” – Albert Einstein.

This quote emphasizes the importance of asking the right questions and deeply understanding the problem before rushing to a solution. It aligns with the idea that the majority of the answer lies in properly defining and framing the problem.

Let’s take a look at this through the lens of AI chat programs.

With AI chat programs, the value has shifted from the solution to the question being asked, which is called the prompt. Compared to the question, the answer is relatively free. It’s the question that matters the most because it informs the answer. In that way, the question is the answer.

This is because the quality of the output heavily depends on the clarity, context, and precision of the prompt, which is what some people now call “prompt engineering”, which contains the following attributes:

1. Framing the Problem: The way you phrase your prompt often determines whether the AI fully understands the intent behind your request. A well-structured prompt leads to more relevant and actionable responses.
2. Iterative Refinement: Crafting AI prompts is often an iterative process, much like problem-solving. You refine the input until it aligns with the desired output, emphasizing the importance of the question.
3. Creativity and Precision Balance: Sometimes, the creativity or flexibility of an AI’s solution stems from how open-ended or detailed the prompt is. The better the question (or prompt), the more nuanced or insightful the output can be (like an “idea tree”).

In the world of AI, crafting the perfect prompt has become an art and science of its own, echoing the idea that the solution’s quality depends largely on how well the problem is framed. It’s not just about what the AI can do but how effectively it’s asked to do it.

Questions as Functions

Let’s look at another example in mathematics. Imagine a function, which is agnostic to the output. Compared to the function, the answer is relatively free. However, oftentimes the function itself was difficult to create.

In mathematics, crafting a function can often be much harder than simply using it to produce an output. Take Einstein’s equation, E=mc², for example. It looks incredibly simple, but it contains a depth of understanding that embodies a large sum of mathematical principles. This is similar to how asking the right question or crafting the perfect prompt for AI leads to better answers.

1. Function as the “Question”

• In mathematics, a function represents a structured way of mapping inputs to outputs. However, defining the function often requires deep understanding, creativity, and problem-solving to ensure it models the desired relationship or behavior correctly. Similarly, creating a prompt or framing a problem for AI requires clarity and precision, as it determines how the “mapping” (or solution) will work.
• The function doesn’t “care” about the output—it’s a generalized mechanism. Its value lies in its ability to transform various inputs effectively.

2. Input as Context

• Once the function exists, the inputs determine the outputs. The challenge of crafting the function mirrors the effort in refining the prompt. A robust function (or prompt) will handle a wide variety of inputs gracefully, just as a well-posed question will yield rich, meaningful answers.
• The output of a function is often trivial to compute once the function is defined. Similarly, generating an answer from an AI becomes straightforward once the question is properly framed. The hard part is designing the system (or question) that leads to that ease.

4. Higher-Order Functions and Flexibility

• In mathematics, higher-order functions (functions that take other functions as input) are incredibly powerful but also abstract. These resemble the art of refining prompts for AI to create meta-level queries or cascading effects, where the crafting process is even more intricate than the outputs themselves.

Why This Matters

The intellectual and creative weight of problem solving often lies in defining “the question” (the function or the prompt) rather than solving individual instances. Once the question, function, or system is in place, solutions can be generated repeatedly with minimal effort.

This underscores the idea that the solution is often secondary to the process of structuring the problem—whether in mathematics, AI, or any other creative or problem-solving domain.

Let’s take a look at a few philosophical, scientific, and problem-solving methodologies:

1. Socratic Method / Inquiry-Based Learning

• Definition: A form of inquiry and discussion where asking the right questions leads to deeper understanding and discovery. This is an educational approach where questions drive the learning process, encouraging learners to explore, investigate, and construct knowledge.
• Relevance: In the Socratic method, the question itself is often more important than the answer, as it shapes the journey of exploration and critical thinking. Inquiry-Based Learning emphasizes that the quality of the question determines the depth and relevance of the discovery. The idea is that the answer lies in crafting the right question.

2. Epistemological Pragmatism

• Definition: A philosophy that prioritizes practical inquiry and problem-solving. The formulation of a question or problem is key to uncovering truth or utility. It’s like a  “function” for evaluating truth, where the input is an idea, belief, or hypothesis, and the output is determined by how well it works or aligns with practical outcomes.
• Relevance: Epistemological Pragmatism often focuses on actionable questions, suggesting that answers are less important than how the question frames the pathway to discovery or progress. If the idea consistently works and adapts under real-world conditions, it’s considered “true” within the scope of its application.

3. First Principles Thinking

• Definition: Breaking down a problem into its most fundamental parts to rebuild understanding from the ground up. This method is often used by Elon Musk and is famously key to how he decided to start SpaceX, reasoning that the sum of the parts and the labor to make them were less expensive than buying a pre-built rocket.
• Relevance: By asking foundational questions, this approach ensures that the framework (or “function”) is correct, leading to solutions that are both accurate and scalable. It is also occasionally a remedy for “planting a new idea tree”.

This is chapter 6 of my book, Think Again, on Amazon Kindle.

In order to understand an idea, people need to work through the layers of understanding in a simplified, but similar way to how you first did when you discovered the solution. This allows them to test each step along the way until they reach a deeper proof that confirms the idea’s validity. This process is akin to constructing a logical proof, even if it’s done intuitively rather than formally. It is “standing on the shoulders of giants”.

When an answer seems overly simple, people may instinctively doubt it because they haven’t worked through the “proofs” that make the simplicity credible. As they reason through discussion, they’re effectively piecing together the mental  steps that satisfy their need for validation. This pattern reflects the cumulative nature of knowledge—each new idea builds upon what is already understood, creating a foundation for accepting new truths.

Mental scaffolding and informal proofs of understanding can provide the structure that makes it easier for others to accept new ideas based on previously accepted knowledge.

To lead a person up to an idea (rather than presenting it outright as a solution) is to build up to it with a mental scaffolding, like an informal math proof, to lead the listener through the process you took to get to the answer. Formal math proofs build upon axioms, but in other areas of life, we don’t have the benefit of those axioms so we have to rely on other building blocks.

In some ways, all reasoning relies on a foundation of prior knowledge and collective beliefs. This concept of mental scaffolding and informal proofs provides us a mechanism to build complex ideas from simpler ones (like the process I’m doing right now in this book).

But mental scaffolding and informal proofs are just one way to help you disseminate your ideas and prevent initial rejection. Here are some other strategies counteract these biases:

1. Start from First Principles – First Principles thinking periodically breaks down ideas into their core components, questioning assumptions and working back from foundational truths (similar to Working Backwards or Reverse Engineering, which will be discussed later). By resetting to first principles, they can “replant” the Idea Tree when needed, creating space for innovation.

2. Build in Discussion and Dialogue – Discussing ideas with others can not only satisfy their need for understanding and ultimately accepting the answer, but it also aligns with one of the the 48 Laws of Power by Robert Greene, “Law 31: Control the Options: Get Others to Play with the Cards You Deal,” which emphasizes the importance of framing choices and influencing decisions before they are publicly debated or decided upon.

3. Seek Elegant Simplicity, not Complex Compromise – Encourage readers to actively seek solutions that are both simple and profound. Explain that simplicity doesn’t mean sacrificing quality; rather, it requires distilling the core of the solution. Recognizing elegant simplicity allows us to see the strength in straightforward answers.

The main idea is to occasionally reflect on how you engage with ideas. Challenge yourself and others to think differently about simplicity and to approach solutions with a mind open to the elegance within straightforward answers.

This is chapter 5 in my book, Think Again, on Amazon Kindle.

Have you ever had an idea rejected at first, only to have someone come back to it later and realize its truth? This can be due to The Simplicity Paradox or Initial Rejection Bias.

Initial rejection bias is the phenomenon where the first idea is dismissed by a person or group simply because it is first. And when coupled with the innate suspicion that something simple must be incomplete or inadequate, it further moves a person to reject it.

This can happen with a partner when choosing a name for a baby or with a group of people suggesting places to eat. Often the first thing suggested is rejected due to initial rejection bias.

“The tallest poppy gets cut down” is a proverb that describes the common psychological and social behavior that is often rooted in status quo bias, fear of standing out, or social conformity. Oftentimes the initial proposal is dismissed simply because it’s the first to “stand out.” Additionally, there’s anchoring resistance, where groups avoid the first idea because they worry it will set a fixed direction (a “trunk”) too early.

People often believe the “best” idea must come after careful deliberation. While this is sometimes true, this can lead to good ideas being thrown out in favor of further brainstorming, even when the first suggestion might have been sound.

Only when the initial rejection bias is removed is the group able to accept it, because they have understood what the solution means. Only through exploration and discussion can the group unpack the “deceptive depth” of the simple solution, revealing that its elegance actually stems from a well-rounded understanding—thus overcoming the initial bias.

Knowledge or understanding is often built layer by layer, drawing on foundational ideas that have already been established—similar to how formal proofs build upon axioms and previously proven theorems. In informal reasoning, people rely on a kind of mental scaffolding or internal “proof” process where they validate new ideas based on established beliefs, past experiences, or accepted truths.

Occam’s Razor asserts that simplicity often holds the best answer, yet highlights how simplicity must first prove its merit before being accepted. The idea is to reason to a solution through proofs in a “standing on the shoulders of giants” type of way. We’ll cover that in the next chapter.

This is chapter 4 in the book, Think Again, on Amazon Kindle.

Have you ever been part of a group that dismissed an answer because it seemed too simple?

While simplicity is often the hallmark of great ideas, it’s not always easy to recognize. Simple solutions can appear deceptively shallow, leaving us feeling that something so straightforward must lack depth. This bias against simplicity is powerful; in many cases, we reject the simplest answers before fully understanding their value. I call this The Simplicity Paradox.

Once an idea is accepted, it becomes entrenched, but, when ideas are still in their “seed” stage, they’re often brushed aside, especially if they appear too simple.

This phenomenon has been referred to in other areas as “elegant simplicity” or “deceptive simplicity”, which is similar to the heuristic, “Occam’s Razor”, which is where simpler explanations are often preferred, but only after fully understanding the complexities involved.

The Simplicity Paradox states that simplicity often masks the underlying complexity and effort required to truly understand and express something. The paradox lies in the idea that achieving simplicity often demands a deep, complex journey of learning and refinement.

A person with deep understanding can make even complex ideas seem straightforward in their essence. Albert Einstein: “If you can’t explain it simply, you don’t understand it well enough.” (SEE E=mc².) Blaise Pascal, a French mathematician, physicist, and inventor once said, “I have made this letter longer than usual because I lack the time to make it shorter.” 

Richard Feynman,an American theoretical physicist, also spoke about this idea, emphasizing clarity and simplicity as markers of genuine understanding. Feynman emphasized the importance of truly understanding a subject in order to explain it clearly, and he developed what’s known as the Feynman Technique for learning. He believed that if you can’t explain something in simple terms, then you don’t fully understand it.

In his teaching, Feynman encouraged people to break down concepts to the simplest language possible. He argued that, when you really grasp something, you can communicate it without jargon, in a way that anyone can understand. His approach was to keep digging deeper until every aspect of a concept could be explained simply, showing that true mastery means seeing through the complexity to the underlying simplicity.

I remember when I first started a job at a software company and I asked my manager what our software did. He simply said, “It’s field service software.” Not knowing what that meant, I asked him to explain it and then when I, explaining it to others, it would take me around 5 minutes to explain it until one day I too just began saying “It’s field service software.” This is because I finally had the depth of understanding as to what those words mean.

Language itself embodies this paradox, as each word seems straightforward but carries layers of meaning shaped by collective agreement and individual interpretation. It’s as if each word is a distilled vessel of thought, simple on the surface but rich in the depth and history of human understanding.

But what happens when you know you’re right, but your idea is still rejected outright?

This is chapter 3 of Think Again, available on Amazon Kindle.

Have you ever had someone who didn’t understand why something was the way it was so they wanted to rebuild it? The act of rebuilding it helps them understand it, and they end up building something similar. Oftentimes, as a business analyst I was asked to build something only to discover that it had already been built sometime in the past (I called that an “archelogical find”).

And the solution is temporary because the next person or group to encounter that problem or system will have to learn it too. If the first person or group had just learned or kept the original system operating, it wouldn’t have needed to be rebuilt. I call this the “reinvention fallacy”.

The reinvention fallacy is  the act of reinventing something to understand it, which can often take the same amount of effort (or more). This is because they either won’t take the time to learn it or they have a bias that because they don’t understand a system, it must be wrong.

Here are a few cognitive biases and psychological tendencies that explain why this happens:

1. Ego-Centric Bias: The assumption that because you don’t understand something, it must be flawed or poorly designed. This bias leads to dismissing the value of existing systems in favor of creating new ones.
2. Not-Invented-Here (NIH) Syndrome: A tendency to distrust or undervalue solutions or systems created by others and instead prioritize rebuilding or creating something from scratch, even if the existing solution is adequate.
3. Curse of Knowledge (Inverse): This occurs when someone lacking knowledge assumes the system is overly complex or broken, rather than recognizing their own learning gap.
4. Action Bias: A preference for taking action (e.g., rebuilding a system) over inaction (e.g., learning the existing one), even if action isn’t necessarily the optimal solution. This bias can create a false sense of productivity.
5. Dunning-Kruger Effect: In its early stages, this effect could explain why someone underestimates the complexity of an existing system and believes they can create something better without fully understanding the original.
6. Reinvention Bias: This is the “grass is always greener” tendency to favor starting over rather than learning or adapting what’s already there, driven by the mistaken belief that rebuilding will lead to better outcomes or deeper understanding.

But what happens when an idea is first getting started? Oftentimes there is just as much resistance to an idea first getting established. We’ll cover that in the next few chapters.

This is chapter 2 of Think Again, available on Amazon Kindle.