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Express the skill level of knowledge worker as an n-dimensional non-negative real number (for simplicity, you may start with one dimension).
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Distribution of skill levels of goods (= knowledge worker) in the market
- Generally not uniformly distributed
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Distribution of skill levels required by buyers in the market
- Generally not uniformly distributed
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Assume that the quantity of goods satisfying the required skill level is inversely proportional to the price
- In reality, buyers compete with each other in an auction-like manner to determine the utility that the buyer can obtain from the commodity, subject to other constraints, but the model is too complex to approximate.
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For example, if there are many “jobs in the market” where “around 30 skills are enough” and “jobs requiring 90+ skills”, the graph of skills and compensation should look like the graph in [A model where only the top players in the market get utility.
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Only the right end of the graph would be higher if “only the top people are hired.”
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And by trying to draw a graph like this, you’ve implicitly assumed that the skill level is one-dimensional.
- Actually n-dimensional, with requirement levels set in multiple dimensions
- Then the phenomenon of “not the top on any axis, but highly regarded” can be expressed.
- There is a trade-off relationship between skills due to the finite amount of time required to acquire them. On the graph, this is expressed in the form of constraint, i.e., skill1 + skill2 is less than or equal to a certain value.
- ref: Budget Constraint Lines and Non-Discrimination Curves - Instant EconomicsBudget Constraint LinesNon-Discrimination Curves
- There is a trade-off relationship between skills due to the finite amount of time required to acquire them. On the graph, this is expressed in the form of constraint, i.e., skill1 + skill2 is less than or equal to a certain value.
- Aiming to maximize each skill leads to a red ocean
- Surprisingly unfocused with respect to mixing multiple skills.
- When the dimension is high, contrary to intuition, the mixed region is wide (Curse of the dimension).
- When N dimensions are N, there are N regions that are top for each axis, but N(N - 1)/2 regions that are top for the combination of two axes. mixed regions are more numerous when N is 4 or more dimensions.
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