Business Algorithms
1) First Principles Thinking
Algorithm:
Step 1: Identify the problem or goal.
Step 2: Break down the problem into its most basic, fundamental elements (assume nothing).
Step 3: Question all assumptions and strip them away until you reach the core principles.
Step 4: Rebuild the solution from these fundamental truths, without relying on existing methods or precedents.
Mathematical representation (conceptually):
Let P be the problem, and F(x) be the assumptions.
F(x)=x1+x2+x3+⋯+xnF(x) = x_1 + x_2 + x_3 + \dots + x_nF(x)=x1+x2+x3+⋯+xn
Strip away assumptions:
F′(x)=P(where P is the most fundamental understanding of the problem)F'(x) = P \quad (\text{where } P \text{ is the most fundamental understanding of the problem})F′(x)=P(where P is the most fundamental understanding of the problem)
Build new solution based on principles F'(x).
2) Dream Big. Insanely Big
Algorithm:
Step 1: Define a goal G without any limitations or constraints.
Step 2: Multiply G by a factor of 10 or more (G' = 10G).
Step 3: Identify the biggest constraints C and actively work to remove or bypass them.
Step 4: Develop a strategy that scales the resources, knowledge, and actions required to match G'.
Mathematical representation:
Let G be the original goal.
G′=10GG' = 10GG′=10G
Where G' is the "insanely big" version of the goal.
3) Create a Mission-Driven Culture
Algorithm:
Step 1: Define the mission M that represents the core values and objectives of the business.
Step 2: Align every individual’s task T_i (for all employees, i=1, 2, ..., n) to the mission M.
Step 3: Incentivize tasks T_i that directly contribute to M.
Step 4: Regularly evaluate the progress towards M by assessing the total sum of contributions.
Mathematical representation:
Let T_i be the task of employee i and C(M) the contribution to the mission:
C(M)=∑i=1nTiwhere each Ti aligns with mission MC(M) = \sum_{i=1}^{n} T_i \quad \text{where each } T_i \text{ aligns with mission } MC(M)=i=1∑nTiwhere each Ti aligns with mission M
4) Become Obsessed with Systems
Algorithm:
Step 1: Identify key processes P in your business or life.
Step 2: Break each process into repeatable components R_i.
Step 3: Optimise each component R_i for efficiency, scalability, and automation.
Step 4: Continuously monitor and iterate on these systems to maintain peak performance.
Mathematical representation:
Let S be the system and R_i be each repeatable component:
S=∑i=1nRiS = \sum_{i=1}^{n} R_iS=i=1∑nRi
Optimise each R_i to improve S, where:
∂S∂Ri=0(locally optimised)\frac{\partial S}{\partial R_i} = 0 \quad \text{(locally optimised)}∂Ri∂S=0(locally optimised)
5) Live Life Like It Is a Video Game
Algorithm:
Step 1: Treat each challenge C_i as a level or quest to be completed.
Step 2: Earn experience points XP for completing each challenge C_i.
Step 3: Use XP to level up in specific skill areas S_j.
Step 4: Continuously set higher-level challenges C', which require more XP but yield greater rewards.
Mathematical representation:
Let XP_i be experience points earned from challenge C_i:
Total XP=∑i=1nXPi\text{Total XP} = \sum_{i=1}^{n} XP_iTotal XP=i=1∑nXPi
Where levelling up occurs when:
Level∝∑i=1nXPi\text{Level} \propto \sum_{i=1}^{n} XP_iLevel∝i=1∑nXPi
Higher-level challenges require more XP:
C′(requires higher XP thresholds for completion).C' \quad (\text{requires higher XP thresholds for completion}).C′(requires higher XP thresholds for completion).
