Solution

ID: past-exam-of-the-mathematics-course-of-the-university-of-cambridge/2007/iii/paper-38/3/solution

Let be the number of open edges, the number of connected components in the open subgraph, counting isolated vertices, and . Summing the joint weight over spins requires a constant spin on every open component. There are exactly choices. Thus the edge marginal is
This is the random-cluster measure, and the joint normalizing constant is .
Summing instead over edge configurations factors independently over edges. For a fixed spin configuration the resulting weight is
since . The factor is independent of the spins. Hence the spin marginal is exactly the ferromagnetic Potts measure in the convention
Its partition function satisfies .
Conditionally on , edge states are independent: an edge with different endpoint spins is closed with probability one, while one with equal endpoint spins is open with probability . Conditionally on , color its open components independently and uniformly from . Equivalently, every compatible spin configuration has conditional probability , and all incompatible configurations have probability zero. These are the two conditional laws of the Edwards-Sokal coupling.
If belong to the same open component, their colors agree surely; otherwise the two independent component colors agree with probability . Therefore
Rearranging proves the spin-connectivity identity for the Potts model:
Phase transitions refer to infinite-volume limits, not a single finite graph. The identity shows that connectivity is exactly the excess spin agreement above its independent-color baseline. More directly, use a wired random-cluster boundary condition and fix the corresponding boundary spin to color . Its component has color , while every finite component has a uniform color. In the compatible infinite-volume coupling,
Consequently normalized spontaneous magnetization is
The onset of a wired infinite cluster and the onset of spontaneous magnetization in the Potts model are the same transition under , giving . This also transfers any jump or continuity of that order parameter between the two descriptions; it makes no assumption about behavior exactly at the critical point.

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