Dynamic Decision-Making: A Recursive Model for Achieving Goals

In the ever-changing world of decision-making, it's easy to fall into the trap of thinking that choices are isolated events. But what if I told you that your decisions are actually part of a larger, recursive process? A process where each choice influences the next, and where your goals evolve based on both your actions and the ever-growing pool of information around you?

Let me introduce you to a dynamic decision-making model that brings all of this together. This model takes into account not only your initial goals but also the impact of previous decisions, the influence of time, and the information you gather along the way. It’s a recursive framework that simulates how we make decisions in real life. Strap in, because this is going to be deep and powerful.

The Equation:

Let’s get into the core equation behind this model:

\[ F_n = P_0 \cdot k F_{n-1} + m \left( T\left( f\left(I_n, I_\Delta\right) \right) + R\left(D_n, FM_n\right) \right) \]

Where:

Fₙ = Choice Taken:
This represents the decision or choice taken at the current moment. It’s the output of our decision-making process.
P₀ = Initial Goal:
This is the starting point—the goal or desired outcome we are aiming for.
kFₙ₋₁ = Effect of the Previous Moment on the Declared Goal:
This term shows how much the previous decision influences the current goal. Think of it as a feedback loop that adjusts your current goals based on past outcomes.
m = Rate Vector (time, etc.):
A vector that captures factors like time, motivation, and attention. It’s the acceleration of your decision-making process over time.
Iₙ = The Original Factual Information:
The initial data or facts that are available when you make your first decision.
IΔ = Facts Acquired Throughout the Process:
New information that is gathered as you progress through the decision-making process. This is where your decisions become more informed over time.
Dₙ = A Potential Choice (Vector of Choices):
The set of potential choices available at the current decision point. This could be multiple options that you are evaluating.
FMₙ = Subjective and Objective Assessments Performed on Dₙ:
These are evaluations of your choices, considering both subjective factors (personal preferences) and objective assessments (factual information, feedback).
R(Dₙ, FMₙ) = Information Gained from the Choosing Process:
Represents the knowledge gained through evaluating your choices. This includes new facts (IΔ) and insights from the process itself.

Parameter	Description
Fₙ	Choice Taken: The decision or choice made at the current moment.
P₀	Initial Goal: The starting point or desired outcome.
kFₙ₋₁	Effect of Previous Moment: How the prior decision influences the current goal.
m	Rate Vector: Factors like time, motivation, and attention shaping the process.
Iₙ	Original Factual Information: Initial data at the decision's start.
IΔ	Facts Acquired Throughout: New information gathered during the process.
Dₙ	Potential Choices: The set of options available at the decision point.
FMₙ	Subjective & Objective Assessments: Evaluations of choices using personal and factual inputs.
R(Dₙ, FMₙ)	Knowledge Gained: Insights and facts obtained from evaluating choices.

How It Works:

Now that you have the equation, let’s break down how this dynamic decision-making model works step by step.

1. Initial Goal (P₀)

Every decision starts with an initial goal (P₀)—the desired outcome you want to achieve. This is your baseline, your starting point.

Info

The goal here is clear: Start with a vision and set your sights on achieving it. Whether it's personal or professional, your starting goal sets the foundation for everything that follows.

2. Previous Moment Effect (kFₙ₋₁)

Your previous decisions have an impact on the current goal. The outcome of the last choice you made can influence how you set your current objective. This recursive loop means every decision is interconnected, influencing and adjusting future goals.

Tip

Think of it as a feedback loop—your past actions shape your present and future decisions.

3. Rate Vector (m)

This factor represents how fast or slow your decision-making process is influenced by time, motivation, and attention. It’s like the accelerator pedal in a car—the faster you go, the quicker your decisions are made.

# Rate vector - factors that influence decision speed
m = [time_factor, motivation, focus]

4. Factual Information (Iₙ and IΔ)

The initial factual information (Iₙ) is your starting point. But as you progress through the decision-making process, new facts (IΔ) are acquired. These facts alter the trajectory of your choices, making your decisions more informed as you go.

Note

Data drives decisions—the more facts you gather, the better your decisions will be. This model assumes that decisions evolve as more information is made available.

5. Choice Potential (Dₙ)

At any given moment, you have a set of potential choices (Dₙ) available to you. These choices form the basis of your decision-making process.

Pro Tip:
The more options you have, the more complex the decision-making process becomes. But a larger set of choices can lead to better opportunities if evaluated well.

6. Subjective and Objective Assessments (FMₙ)

Each potential choice is evaluated based on both subjective assessments (personal beliefs and values) and objective assessments (facts, data). It’s like balancing intuition with logic.

# Example: Objective vs Subjective Assessment
FMₙ = [subjective_assessment, objective_assessment]

7. Information Gain (R(Dₙ, FMₙ))

After you make a choice, you gain new information from the process itself. This knowledge comes from both the facts you acquire (IΔ) and the insights you gain through evaluating each choice.

Quick Fact:
The more you evaluate your choices, the more you learn. This learning loop drives your future decisions.

Putting It All Together:

The decision-making process starts with an initial goal (P₀). The influence of your previous choices (kFₙ₋₁) shapes the current decision. As you go, you gather more facts (Iₙ and IΔ) and evaluate your choices (Dₙ) through subjective and objective lenses (FMₙ). Each choice you make generates new information (R(Dₙ, FMₙ)), which informs your next decision.

Why This Model is Game-Changing:

This dynamic decision-making model reflects the way decisions work in the real world—nothing is static. Your goals, decisions, and the information you gather evolve continuously. This model helps you track how previous choices influence your future ones and how your decision-making process improves over time.

Success

Key takeaway: This model is recursive, self-adjusting, and scalable. The more decisions you make, the better your ability to achieve your goals becomes.

Conclusion:

This dynamic, recursive model for decision-making is groundbreaking. It combines past decisions, new information, and real-time evaluations to make smarter choices. By continuously refining your goals and incorporating fresh data, you can achieve your desired outcomes faster and more efficiently than ever before.

If you’re serious about improving your decision-making process, this model is your roadmap to success.

Call to Action:

Ready to apply this dynamic decision-making model to your own life or business? Start by identifying your initial goal (P₀) and apply this recursive framework to your decisions. Let us know how this model has helped you make smarter choices!