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Equation 7 · The Type Chart Is a Payoff Matrix, and the Metagame Only Half-Solves It

What does this equation mean?

U(i,j)=log⁡2 ⁣(E(i,j)/E(j,i))U(i,j) = \log_2\!\big(E(i,j)/E(j,i)\big)

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Inputs and operationslog_2big(E(i,j)/E(j,i)big)
Result or conditionU(i,j)
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This equation states an equality: the expressions on both sides have the same value under the article’s assumptions. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.

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UU

Symbol U

U is part of the quantity the equation computes from the expression on the right.

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ii

Symbol i

i is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.

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jj

Symbol j

j is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.

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EE

Symbol E

E is an input to the expression that computes the quantity on the left.

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=

=

The expressions on both sides represent the same quantity under the stated assumptions.

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subscript

subscript

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

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How to interpret it

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What the article says around this equation

A matrix of pairwise advantages is an invitation to ask what a population that only cared about that matrix would converge on. Convert E into a genuine zero-sum payoff by taking a log-ratio: U(i,j) = log⁡2\log_2\!(\big(E(i,j)/E(j,i))\big) , which is antisymmetric by construction ( U(i,j) = -U(j,i) ) and reads in doublings of relative advantage — U(i,j)=2 means i ’s best attack into j outclasses j ’s best attack into i by a factor of four. This is a standard move in evolutionary game theory, and this piece does not pretend to invent the tool: this house has already used replicator dynamics to model real market selection and a matched null-model discipline to test claims of selection against chance,…
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A matrix of pairwise advantages is an invitation to ask what a population that only cared about that matrix would converge on. Convert E into a genuine zero-sum payoff by taking a log-ratio: U(i,j) = log⁡2\log_2\!(\big(E(i,j)/E(j,i))\big) , which is antisymmetric by construction ( U(i,j) = -U(j,i) ) and reads in doublings of relative advantage — U(i,j)=2 means i ’s best attack into j outclasses j ’s best attack into i by a factor of four. This is a standard move in evolutionary game theory, and this piece does not pretend to invent the tool: this house has already used replicator dynamics to model real market selection and a matched null-model discipline to test claims of selection against chance, and both apply directly here rather than needing to be rederived [ 16 , 17 ] .

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