Game Theory & Decision
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Shapley Value (2-player)

Fair share of the total from a player's marginal contributions.

Formula

ϕi=12(v(i)+[v(N)−v(j)])\phi_i = \tfrac{1}{2}(v(i) + [v(N) - v(j)])

Variables

φ_iPlayer i's fair share
vValue of a coalition
NGrand coalition

Example

Splits joint gains by each player's average contribution.

How it works

The 2-player Shapley value assigns each player the average of their marginal contribution across the two possible orders in which the players can join a coalition. For player i, that means averaging what they are worth alone, v(i), and what they add when joining the other player, v(N) - v(j). This works because it fairly splits the total surplus while respecting symmetry, so neither player is favored just because of the order in which contributions are counted.

When to use it

Use this when two agents, firms, or partners jointly create value and you want a principled way to divide the total payoff based on contribution rather than bargaining power. It appears in cooperative game theory, profit-sharing, cost allocation, and any two-player setting where the value of alone vs together can be measured.

Worked example, step by step

Two consultants, A and B, can earn revenue from projects alone or together. Let v(A) = 30, v(B) = 50, and v(AB) = 100. For A, the Shapley value is φ_A = 1/2 [v(A) + (v(AB) - v(B))] = 1/2 [30 + (100 - 50)] = 1/2 [30 + 50] = 1/2 × 80 = 40. For B, the Shapley value is φ_B = 1/2 [v(B) + (v(AB) - v(A))] = 1/2 [50 + (100 - 30)] = 1/2 [50 + 70] = 1/2 × 120 = 60. Check: 40 + 60 = 100, so the full coalition value is fully and fairly allocated.

Common mistakes to avoid

  • Using the grand coalition value twice instead of averaging each player's standalone and join-in contribution
  • Subtracting the wrong player's solo value when computing the marginal contribution term
  • Forgetting that the two-player Shapley values should add up exactly to v(N)

Did You Know?

The Shapley value, another Nobel-winning idea, is now used to explain individual feature impact in machine learning.

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Reviewed & maintained by the FormulaWon editorial teamLast reviewed September 2026