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A binary relation $R$ on $A$ is a subset of $A\times A$. Its properties are quantified over elements of $A$:

Thus an equivalence relation is a binary relation that is simultaneously reflexive, symmetric and transitive. The equivalence class of $a$ is the set of all elements related to it:Antisymmetry permits diagonal pairs and does not mean that a binary relation fails to be a symmetric relation.

Check each partial order axiom. For every $(a,b)$, $a\le a$ and $b\ge b$, so $S$ is a reflexive relation. If both $(a,b)S(c,d)$ and $(c,d)S(a,b)$, the first coordinates satisfy $a\le c\le a$ and the second coordinates satisfy $b\ge d\ge b$. Therefore $a=c$ and $b=d$, which proves $S$ is an antisymmetric relation.

Finally, if $(a,b)S(c,d)$ and $(c,d)S(e,f)$, then $a\le c\le e$ and $b\ge d\ge f$. Hence $(a,b)S(e,f)$, making $S$ a transitive relation. All three axioms hold, so $S$ is a partial order. It is a product order, with the usual order on the first coordinate and the reversed order on the second.

A binary relation $R$ on $A$ is a transitive relation precisely when $(aRb\text{and}bRc)\Rightarrow aRc$ for all $a,b,c\in A$.

Read the initial universal quantifiers as governing the conjunction of irreflexivity and transitivity. Explicitly, the intended first-order sentence assertsThus $Q$ is an irreflexive relation, a transitive relation, and a serial relation.

Suppose a first-order model had a finite nonempty domain of cardinality $N$. Choose $a_0$. Seriality supplies successive elements $a_1,\dots ,a_N$ with $Q(a_i,a_{i+1})$. By the pigeonhole principle, $a_i=a_j$ for some $i<j$. Repeated transitivity along that part of the chain gives $Q(a_i,a_j)=Q(a_i,a_i)$, contradicting irreflexivity. Hence there is no finite nonempty model. This is the infinite-model property of a serial strict partial order.

An infinite model exists: take domain $\mathbb{N}$ and $Q(x,y)$ to mean $x<y$. It is irreflexive and transitive, and $y=x+1$ witnesses seriality for every $x$.

The quantifier-scope convention matters: giving each printed universal quantifier only the immediately following atomic scope would leave the transitivity implication with free variables and would not express the claimed theorem. The explicit parenthesisation above records the reading under which the source's requested assertion is valid.

The least reflexive relation and transitive relation containing $R$. Here $R^0$ is the identity relation. Membership means that a finite sequence of zero or more $R$-steps connects the ordered pair. Adding paths can create reverse connections that were absent in $R$, so initial antisymmetry need not be preserved.

A binary relation is serial when every domain element has at least one outgoing related element. A serial relation may have cycles; combining seriality with an irreflexive relation and a transitive relation instead forces a nonempty domain to be infinite, by the infinite-model property of a serial strict partial order.

The transitive closure of a relation connects $x$ to $y$ when a finite nonempty chain of $R$-steps leads from $x$ to $y$. Concatenation proves transitivity, and induction along a chain shows that any transitive relation containing $R$ contains $R^{+}$. This concerns closure of a relation, rather than the transitive closure of a set under membership.

For a finite outcome space, complete lottery preferences, a transitive relation, continuity of lottery preferences and the independence axiom for lottery preferences are equivalent to an expected utility representation of the displayed form. The representing outcome utility function is unique up to a positive affine function. The theorem is associated with John von Neumann and Oskar Morgenstern.

A useful proof construction selects a best outcome $a$ and a worst outcome $b$. If all outcomes are indifferent, a constant utility function suffices. Otherwise assign $u(a)=1$, $u(b)=0$. Continuity of lottery preferences makes each outcome indifferent to a mixture $u(\omega )a+(1-u(\omega ))b$, and mixture monotonicity makes the coefficient unique. Substitution of indifferent lotteries and reduction of compound lotteries turn any lottery into a best–worst mixture with best-outcome probability ${\sum }_{\omega }L(\omega )u(\omega )$. Mixture monotonicity then ranks all such mixtures. The reverse direction follows by taking expected values; positive affine functions preserve every comparison.^[ref]