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Exactly $(T,L)$, $(M,C)$ and $(B,R)$ can be sustained as constant stage game outcomes. Their long-run average payoffs are $(6,6)$, $(4,4)$ and $(0,0)$, respectively. We assume the usual repeated game monitoring convention: both players observe each completed joint action before the next round. The source's colour convention puts the blue lower-left row payoff first and the red upper-right column payoff second.

For necessity, row action $B$ earns $(8,5,0)$ against $(L,C,R)$, so playing it forever guarantees a nonnegative long-run average payoff against every opposing pure strategy. Similarly, column action $R$ earns $(8,5,0)$ against $(T,M,B)$ and guarantees zero or more. Conversely, column action $R$ holds the row player's best possible stage payoff to $0$, and row action $B$ holds the column player's to $0$. Thus each player's minmax payoff is exactly $0$.

Any Nash equilibrium must give both players at least zero, an individually rational payoff in a repeated game. Every off-diagonal cell has a negative payoff for one player: $(T,C)$ and $(T,R)$ hurt row, $(M,L)$ and $(B,L)$ hurt column, $(M,R)$ hurts row and $(B,C)$ hurts column. Repeating any such cell forever therefore permits a profitable unilateral deviation to the zero-guaranteeing action. The three diagonal cells pass this necessary test.

For sufficiency, fix target $a^{\ast }=(T,L)$ or $(M,C)$. Each player's finite-state strategy has initial state $F$ and an absorbing state $P$. In $F$, output the player's component of $a^{\ast }$; remain in $F$ if the completed joint action equals $a^{\ast }$, and otherwise move to $P$. In $P$, row always outputs $B$ and column always outputs $R$, regardless of further observations. These are two-state grim trigger strategies; the separate machines are drawn below. The input is the observed joint action, so after any off-path history the prescribed continuation states agree.

If neither player deviates, the target is repeated forever. If a player first deviates at a finite round $\tau$, the opponent enters $P$ for all later rounds. Against column action $R$, every row action earns at most zero; against row action $B$, every column action earns at most zero. All payoffs are bounded, so the total reward before and at $\tau$, divided by the horizon, tends to zero. The deviator's upper limiting average is therefore at most $0$, below either target payoff $6$ or $4$. This proof tests arbitrary future deviations, not merely a single changed action. It also rules out improvement by deviations whose averages fail to converge, if the criterion is extended by an upper or lower limit.

For target $(B,R)$, one-state machines outputting $B$ and $R$ forever suffice: each is a best response to the other in every round. Their long-run average payoffs are zero and no unilateral deviation can improve them. In fact, the punishment is credible because $(B,R)$ is a stage Nash equilibrium, so the two positive-target constructions are also subgame perfect equilibria under the stated monitoring assumption. The general punishment mechanism is described in ^[ref].

“Outcomes of the stage game” here means a fixed action pair repeated forever. Allowing time sharing also sustains some average payoff vectors that are not stage cells: for example, a deterministic alternating cycle $(T,L),(M,C)$ gives $(5,5)$, with a two-phase finite-state strategy and the same permanent punishment. An off-diagonal cell may occur in a sustainable cycle; the impossibility above concerns its constant repetition, not every appearance on an equilibrium path. With finite-state pure strategies, an undisturbed path is eventually periodic, so its limiting cell frequencies are rational numbers.

Use the standard repeated game convention that the first round's joint action is observed before round two. The following original game tree represents simultaneous moves by making row move first in each round while hiding that current move from column. Terminal labels are total payoffs over both rounds, ordered row then column.

Formally, round one starts at a row decision with actions $T,B$. The two ensuing column nodes form one information set $I_2^0$, with available actions $L,R$; column cannot tell which current row action occurred. The four completed first-round histories are $H=\{TL,TR,BL,BR\}$. At each $h\in H$, round two begins at a separate row node. Its two successors, after row's current $T$ or $B$, form a column information set $I_2^h$. Distinct $h$ have distinct information sets: both players know the whole first-round outcome. There are sixteen terminal histories $(h,k)\in H^2$, whose total payoff is $u(h)+u(k)$, as displayed in the tree.

The row player's five information sets are the initial singleton and the four singleton round-two nodes, one per $h$. The column player's five information sets are $I_2^0$ and the four two-node sets $I_2^h$. Thus each player must specify one binary choice at each of five information sets, including histories that its own first choice might prevent:Counting only the four possible two-round action sequences would miss the distinct contingent responses to the opponent's first action.

For a concrete pair of complete pure strategies, order the second-round histories as $(TL,TR,BL,BR)$ and useThe first entry is the initial action, and the four remaining entries prescribe the action after each first-round history. This induces the unique path $TL$ in round one followed by $BR$ in round two. Hence the total payoff isThe off-path entries are part of each pure strategy, even though this pair does not reach them.

For randomization, a mixed strategy is a probability distribution over the $32$ complete plans, with $31$ free probabilities in a general distribution. A behavioural strategy instead gives one binary-action probability at each of five information sets, requiring five parameters. This game has perfect recall: a player never forgets a previous own action or a first-round observation, and the only concealed action is the opponent's current simultaneous choice. Consequently neither kind of randomization has a greater attainable-payoff advantage: Kuhn's theorem gives realization-equivalent strategies against every fixed opposing strategy.^[ref]

The equivalence can be verified directly here. Given a behavioural strategy, sample all five contingent actions independently before play; the resulting product probability distribution over complete plans is an equivalent mixed strategy. Conversely, let $\mu$ be a mixed strategy for player $i$. Use its marginal probability for the initial action. At history $h$ containing own first action $a_i$, choose second action $b_i$ with conditional probabilitywhenever the denominator is positive; if it is zero, choose arbitrarily at that unreachable history. The opponent's first-round action is simultaneous and cannot depend on the sampled hidden plan beyond the chosen first action. Thus conditioning on $h$ preserves exactly these conditional probabilities of actions, and multiplying the first- and second-round factors reproduces each terminal probability. Correlations between prescriptions at mutually exclusive second-round histories have no further observable effect. Behavioural strategies offer the more compact representation, while mixed strategies describe a single random choice of a full plan. Imperfect information here does not mean imperfect recall.

Reading the lower-left number in each source cell as player 1's payoff, against an opponent playing $A$ the player's own payoffs from $A,B$ are $y,0$; against $B$ they are $x,x/2$. For $x>0$, identify $A$ with aggressive defection and $B$ with yielding cooperation. The standard payoffs then have valuesThe prisoner's dilemma ordering $T>R>P>S$ holds precisely for $x>0$ and $0<y<x/2$ with this labeling. For example, $x=4,y=1$ gives $T=4,R=2,P=1,S=0$. Each player then has the strictly dominant strategy $A$, although $(B,B)$ gives both a larger payoff than $(A,A)$. Here $2R=T+S$ as well, so the optional weak aggregate-payoff condition is met with equality.

For the Chicken payoff ordering, the collision outcome must be worse than yielding to an aggressive opponent: $T>R>S>P$. This holds for $x>0$ and $y<0$, for example $x=4,y=-1$. Against $B$, the best response is $A$ because $x>x/2$; against $A$, it is $B$ because $0>y$. Consequently $(A,B)$ and $(B,A)$ are the two pure strategy Nash equilibria, and mutual aggression is the worst outcome for both.

If action names may be exchanged, there is another prisoner's dilemma region: take $A$ as cooperation and $B$ as defection. Then $(T,R,P,S)=(0,y,x/2,x)$, so $x<0$ and $x/2<y<0$ also work, for example $x=-4,y=-1$. This relabeling explains why the first displayed region is not a necessary condition without an action convention. Equality at any of these boundaries destroys the strict ordering.

Represent satisfied and unsatisfied goals by payoffs one and zero. The second player's goal is an exclusive or: it is true precisely when $q\ne r$, independently of $p$. The first goal is false only when all three Boolean variables are false. The resulting payoff matrix is below; each boxed entry marks that player's best response to the opposing pure strategy, including ties:For player 1, choosing $r$ satisfies the goal against every opposing pure strategy. Choosing $\bar{r}$ does equally well except against $\bar{p}\bar{q}$, where it does worse. Thus $r$ is player 1's unique dominant strategy in the weak sense, and there is no strictly dominant strategy. The ties are why most first-player entries in both rows are boxed.

For player 2, either pure strategy with $q=0$ is a best response against $r$, while either with $q=1$ is a best response against $\bar{r}$. No one choice of $q$ works against both rows, so player 2 has no dominant strategy, and there is no dominant-strategy equilibrium. The value of $p$ does not affect player 2's own payoff.

The cells with both entries boxed give exactly four pure strategy Nash equilibria:All four give payoffs $(1,1)$. In particular, the weakly dominated choice $\bar{r}$ can occur in a Nash equilibrium because player 1 is already satisfied there and cannot strictly improve.

The succinct input is the players' variable-control sets, their goal propositional formulas, and a complete candidate Boolean valuation; an exponentially expanded payoff matrix is not part of the input. This representation is essential to candidate equilibrium checking in a Boolean game.

For membership in co-NP, consider the complement: the candidate is not a Nash equilibrium. A complexity certificate consists of a player $i$ and an alternative Boolean valuation of that player's controlled Boolean variables. Evaluate the current goal and the goal after this unilateral deviation, keeping every other player's variables fixed. A profitable unilateral deviation exists precisely when the current goal is false and the new goal is true. The certificate has at most the number of input variables plus the player index, and both propositional formula evaluations take polynomial time. Thus the complement belongs to NP, and the original decision problem belongs to co-NP.

For hardness, start with a propositional formula $F(X)$ and introduce a fresh Boolean variable $z$. Construct a one-player Boolean game in which that player controls $X\cup \{z\}$ and has goalChoose the candidate pure strategy assigning every variable false. Its payoff is zero because $z$ is false, regardless of $F$. If $F$ has a satisfying assignment, changing $X$ to that assignment and $z$ to true is a profitable unilateral deviation. If $F$ is an unsatisfiable Boolean formula, no unilateral deviation can satisfy the goal. ThereforeThe construction increases the formula length by only a constant and writes a candidate of linear length, so it is a polynomial-time many-one reduction. Unsatisfiability is co-NP-complete; combining hardness and membership proves that candidate Nash equilibrium checking for Boolean games is co-NP-complete. If a convention requires at least two players, add a player controlling a fresh unused variable and having the always-true goal; this changes neither direction of the proof.

Read the lower-left entry of each cell as player 1's payoff and the upper-right entry as player 2's payoff, as indicated by the source diagram. Against $L$, player 1 strictly prefers $B$ because $2\pi >1$; against $R$, player 1 strictly prefers $T$ because $3>2$. Against $T$, player 2 strictly prefers $R$ because $6>0$; against $B$, player 2 strictly prefers $L$ because $2\sqrt{2}>2$. Thus the pure strategy Nash equilibria are

For a fully mixed strategy Nash equilibrium, write $p=\mathrm{Pr}(T)$ and $q=\mathrm{Pr}(L)$. Player 1's expected payoffs from the two pure strategies areEquality, required when both pure strategies have positive probability, givesPlayer 2's corresponding expected payoffs areso equality givesBoth probabilities lie strictly between zero and one. Hence the unique fully mixed strategy Nash equilibrium iswith complementary probabilities on $B$ and $R$. Its expected payoffs are

These are all three Nash equilibria. There are no equilibria with just one player genuinely mixing: against each opposing pure strategy, the other player's best response is unique, so that player cannot mix. When both players mix, the two linear indifference equations have the unique solution just obtained. This is support enumeration for a bimatrix game with all possible supports checked.

The definition in this part uses a single Boolean valuation, so the relevant Nash equilibria are pure strategy equilibria of a Boolean game. Use its standard convention that the players' control sets form a set partition of the variables.

Membership follows from an existential choice of a strategy profile and a universal check of all unilateral deviations. Assign payoff one to satisfying a player's goal and zero otherwise. A strategy profile $\sigma$ is a Nash equilibrium precisely when, for every player $i$ and every alternative Boolean valuation ${\tau }_i$ of that player's variables,is true: either the goal is already satisfied or no deviation can satisfy it. Thus the requested property isThe strings encoding $\sigma$, $i$ and ${\tau }_i$ have polynomial length, and evaluating the bracketed propositional formulas takes polynomial time. Invalid encodings of a player index can simply be ignored by the test. This is the existential second level of the polynomial hierarchy, ${\Sigma }_2^{\mathrm{P}}$.

For hardness, reduce the canonical complete problem of deciding a two-block quantified Boolean formulaMake a two-player Boolean game: player 1 controls $X$ and has goal ${\gamma }_1=\top$; player 2 controls $Y$ and has goal ${\gamma }_2=\neg \psi$. Set the query formula to $\varphi =\psi$. The construction has size linear in the input formula.

If an assignment $x$ satisfies $\forall Y\psi (x,Y)$, combine it with any assignment $y$. Player 1 already satisfies its goal, and player 2 cannot satisfy $\neg \psi$ by any unilateral deviation. Thus $(x,y)$ is a Nash equilibrium satisfying the query.

Conversely, let $(x,y)$ be a Nash equilibrium satisfying $\psi$. Player 2 currently has payoff zero. If some alternative $y^{\prime }$ made $\psi (x,y^{\prime })$ false, that deviation would satisfy player 2's goal and increase its payoff to one. The Nash equilibrium condition therefore implies $\forall Y\psi (x,Y)$. This proves the required equivalence and the polynomial-time reduction.

Together, membership and hardness prove ${\Sigma }_2^{\mathrm{P}}$-completeness of existential equilibrium checking in a Boolean game. The reduction works with only two players and one constant goal.

Measure all payoffs in millions of dollars, in the order (Ann, Bob). Since both players maximize expected money, those numbers are already their expected utility scale.

In the extensive-form game, Ann moves first. Staying out terminates at $(2,6)$. Entering leads to Bob's observed decision between fighting, with terminal payoffs $(1,1)$, and acquiescing, with terminal payoffs $(3,3)$. Each decision node is its own information set, so this is a perfect-information game.

The corresponding normal-form game gives Ann the pure strategies $O$ (stay out) and $E$ (enter), and Bob the complete contingent pure strategies $F$ (fight if entry occurs) and $A$ (acquiesce if entry occurs). The payoff matrix isThe pure strategy Nash equilibria are $(O,F)$ and $(E,A)$. Against $F$, Ann prefers $O$; against $A$, Ann prefers $E$. When Ann stays out, Bob's contingent response does not affect his payoff, so $F$ is a best response there.

There is exactly one subgame perfect equilibrium, $(E,A)$. In the subgame following entry, Bob strictly prefers $3$ to $1$, so backward induction selects acquiescence; Ann then strictly prefers entering for $3$ to staying out for $2$. The threat supporting $(O,F)$ is not optimal in that one-shot subgame.

For infinite repetition, the paper specifies no discount factor. First take the long-run average payoff convention, with all past actions observed. The feasible payoff set is the convex hullAnn can guarantee $2$ in every stage by staying out; Bob can guarantee at least $3$ in every stage by acquiescing whenever entry occurs. These guarantees are also their minmax payoffs: fighting holds Ann's best possible stage payoff to $2$, and entering holds Bob's best possible stage payoff to $3$.

Therefore the sustainable Nash equilibrium average payoffs are exactly the feasible, weakly individually rational payoffs in a repeated game:This is the closed triangle with vertices $(2,3),(2,6),(3,3)$.

Here is a direct sufficiency argument for this particular game. Choose a publicly known sequence of stage outcomes whose frequencies give the target point. Before any observed deviation, follow that sequence. After an Ann deviation, Bob fights forever, so Ann can thereafter obtain at most $2$ per stage. After a Bob deviation, Ann enters forever, so Bob can thereafter obtain at most $3$ per stage. A finite prefix has no effect on long-run average payoff, so no unilateral deviation improves a target with $a\ge 2,b\ge 3$. Rational frequencies can be implemented periodically; arbitrary real frequencies can be implemented by a deterministic sequence with convergent frequencies. Thus even the weak boundary is covered here. In particular, staying out forever and entry followed by acquiescence forever are sustainable; fighting forever is not.

For discounted payoffs $(1-\delta ){\sum }_{t\ge 0}{\delta }^t{u}_i^t$, the folk theorem for repeated games instead gives the usual patience qualification: every feasible point with $a>2,b>3$ can be sustained for all sufficiently large $\delta <1$. It does not classify the whole region for an unspecified fixed $\delta$. The two one-shot Nash equilibrium outcomes remain sustainable for every $\delta$.^[ref]

Finally consider the particular history-dependent pair in the paper. Initially Bob has never acquiesced, so Ann stays out. That preserves the same condition forever, giving payoffs $(2,6)$. The pair is a Nash equilibrium. Holding Bob's strategy fixed, Ann cannot induce an acquiescence: he fights whenever she enters, and his rule never changes unless he has previously acquiesced. Every entry would give her $1$ instead of $2$, so no unilateral deviation helps. Holding Ann's strategy fixed, Bob never reaches a decision node because she stays out; changing his strategy alone cannot create a history in which he actually acquiesces. Hence he also cannot improve.

This direct argument works for both discounted and long-run average payoffs. The outcome gives Ann exactly her minmax payoff and Bob more than his, so it is compatible with the folk theorem for repeated games. A version requiring strict gains above both minmax payoffs is a sufficient condition, and cannot be used to rule out this boundary example.

A player has perfect recall when the information sets never erase that player's previously available information or earlier chosen actions. With perfect recall, randomizing once over complete pure strategies and using suitable independent randomizations at information sets can induce the same outcome distributions.

Without perfect recall, one information set may be encountered twice on the same play path. A behavioural strategy then uses the same probabilities with fresh draws at both encounters; its outcome distribution need not be attainable by any mixed strategy over deterministic plans.

Two people can each have a clear incentive to choose an outcome that leaves both worse off. The prisoner's dilemma isolates this tension between individual best responses and Pareto efficiency. For example, two partners can each save effort by withholding a promised contribution, yet both would prefer a successful joint project to mutual withholding. The model applies when each person's payoffs really have the ordering below; the names “cooperate” and “defect” alone do not establish a prisoner's dilemma.

In the symmetric two-player normal-form game, each player chooses cooperation $C$ or defection $D$ simultaneously, without observing the other's current choice. The four payoffs are the mutual-cooperation reward $R$, mutual-defection punishment $P$, sole-defector temptation $T$, and sole-cooperator payoff $S$, with $T>R>P>S$. Defection is a strictly dominant strategy: it beats cooperation against either opposing pure strategy. This definition and the resulting one-shot Nash equilibrium are discussed in Steven Kuhn, Prisoner's Dilemma, Stanford Encyclopedia of Philosophy, section 1.

Take the concrete payoffs $T=5$, $R=3$, $P=1$, $S=0$. Each entry below gives the row player's payoff followed by the column player's payoff:

For the row player, switching from $C$ to $D$ improves the payoff from $3$ to $5$ against $C$, and from $0$ to $1$ against $D$. The column player has the same comparisons, so $D$ is a strictly dominant strategy for each. Even against a mixed strategy choosing $C$ with probability $q$, the expected payoff advantage is $2q+(1-q)=1+q>0$. Thus cooperation cannot appear with positive probability in a best response, and $(D,D)$ is the unique one-shot Nash equilibrium, including mixed strategies.

At $(D,D)$, a unilateral deviation to $C$ lowers the deviator's payoff from $1$ to $0$. A joint change to $(C,C)$ instead raises both payoffs from $1$ to $3$: mutual cooperation Pareto dominates mutual defection, making the Nash equilibrium fail Pareto efficiency. A Nash equilibrium tests unilateral deviations, so it need not maximize shared gains. This is the useful lesson: improving the available outcome may require changing incentives, rather than telling each participant to choose a better best response.

The additional inequality $2R\ge T+S$ says that mutual cooperation gives each player at least as much average payoff as equal-time alternation between the two asymmetric outcomes. It is a further assumption, not a consequence of $T>R>P>S$. Here it holds strictly: $6>5$, and alternating exploitation gives each player average payoff $5/2<3$. This comparison concerns equal-time averages, not every discounted sequence. See Kuhn, section 3, on the additional cardinal-payoff condition.

A pure strategy is a definite, complete plan for a player's choices. In a game with one simultaneous choice, it selects one available action with certainty: heads or tails in matching pennies, for example. In a sequential game it specifies an action at every situation where that player could be called upon to move, including situations never reached when the plan is followed. The plan can use only information available to the player: indistinguishable decision situations must prescribe the same action. An action at one such situation is only part of the plan.

If $S_i$ is player $i$'s set of pure strategies, the payoff $u_i(s_i,s_{-i})$ depends on the whole strategy profile. Thus the same pure strategy can be a best response against one opposing choice and perform poorly against another. A dominant strategy is the stronger case in which it is a best response against every opposing strategy profile.

The replicator equation says that a strategy grows in frequency when its expected payoff exceeds the population's current mean payoff. It is an ordinary differential equation for a composition, rather than for total population size. The subtraction of the mean is what makes relative success matter: even a type growing in absolute number can shrink as a fraction if other types grow faster.

Let $x_i(t)$ be the frequency of pure strategy $i$, with $n$ available pure strategies andIn a symmetric bimatrix game, let the payoff matrix $A$ have entry $a_{ij}$ equal to the payoff to strategy $i$ against $j$. The other player's payoff in that encounter is $a_{ji}$: symmetry between player roles does not require $A=A^T$. Uniform random encounters give the expected payoffs and their population meanAfter choosing the time unit so that the growth-rate scale is one, the continuous-time replicator equation isHere a payoff is measured on a scale proportional to net growth per unit time, not merely as an arbitrary preference ranking. More generally one can specify locally Lipschitz frequency-dependent payoffs $f_i(x)$ directly; the same equation with $\bar{f}={\sum }_i{x}_i{f}_i$ then defines a unique frequency trajectory. The linear pairwise model and its reproductive interpretation are given in Taylor and Jonker (1978), sections 2–3; the formulas here also follow by the following direct normalization.

Suppose the number $N_i$ of type $i$ obeys ${\dot{N}}_i=N_i[b(t)+f_i(x)]$, where the baseline $b(t)$ is identical for all types. Set $N={\sum }_i{N}_i>0$ and $x_i=N_i/N$. The quotient rule givesThus the common baseline cancels. Adding the same constant to every entry of $A$ leaves the replicator equation unchanged; multiplying $A$ by a positive constant changes only the speed along trajectories. The same payoff matrix can therefore describe frequency evolution at different growth-rate baselines and time scales.

The probability simplex is invariant in forward time. On ${\sum }_i{x}_i=1$,Normalization alone would not exclude negative frequencies. Nonnegativity follows as well: along a solution,An absent type remains absent, and a positive frequency remains positive at every finite time. For the fixed payoff matrix model, bounded rates on the compact probability simplex give global forward solutions. Frequencies can still tend to zero as $t\to \infty$. Each face is invariant, so this model has no spontaneous introduction of new pure strategies; mutation or outside introductions would require additional terms. For two present types the logarithm of their frequency ratio satisfies $\frac{d}{dt}\log (x_i/x_j)=f_i-f_j$, directly expressing the accumulated payoff advantage.

For two pure strategies, write the composition as $(x,1-x)$, where $x$ is the frequency of strategy 1, and letTheir payoff difference and the resulting scalar ordinary differential equation areIn the interior $0<x<1$, the sign of the payoff difference determines the direction of motion. The endpoints are always equilibrium points, and an additional interior equilibrium point exists precisely when the affine payoff difference has a zero strictly between them. At a simple interior zero $x_{\ast }$, $F^{\prime }(x_{\ast })=x_{\ast }(1-x_{\ast })\Delta f^{\prime }(x_{\ast })$: a negative value gives asymptotic stability, a positive value gives instability. At the endpoints $F^{\prime }(0)=b-d$ and $F^{\prime }(1)=c-a$, with stability understood relative to $[0,1]$. A zero derivative requires examining the actual flow rather than this strict sign test.

For an original coordination game, imagine two conventions that reward matching, with convention 1 giving the larger matching reward. ChooseThe crossing is $x_{\ast }=2/5$. Below it, convention 1 loses frequency; above it, convention 1 gains frequency. The endpoint equilibrium points $0$ and $1$ have one-sided asymptotic stability, while $2/5$ is unstable: the corresponding derivatives are $-2,-3$ and $6/5$, respectively. Every initial $0<x(0)<2/5$ tends to $0$, every initial $2/5<x(0)<1$ tends to $1$, and exactly $x(0)=2/5$ stays there. Even the convention with the larger matching reward can disappear if it starts below the threshold. This is bistability, not selection of a universally best convention.

The compositions giving a symmetric Nash equilibrium in this coordination game are the two pure conventions and the mixed composition $(2/5,3/5)$. Both pure conventions are evolutionarily stable strategies because a mutant convention has strictly lower payoff against the pure resident. The mixed symmetric Nash equilibrium is not an evolutionarily stable strategy: against that resident both conventions tie, but after a pure convention-1 mutant is introduced its payoff advantage is $5(x-2/5)>0$. Equal payoffs at a symmetric Nash equilibrium need not provide resistance to invasion or asymptotic stability.

For an original coexistence model, choose payoffs making each convention advantageous when sufficiently rare:Here $x_{\ast }=3/4$ attracts every initial composition with both types present: $\dot{x}>0$ below it and $\dot{x}<0$ above it. The interior derivative is $F^{\prime }(3/4)=-3/4$, while $F^{\prime }(0)=3$ and $F^{\prime }(1)=1$ make both endpoints unstable to introduction of the other type. The endpoints nevertheless remain stationary if the other type is exactly absent; the attraction statement excludes those boundary initial states.

The sole symmetric Nash equilibrium in this coexistence model is $p=(3/4,1/4)$. It is also an evolutionarily stable strategy. For any different mixed strategy $q=(y,1-y)$, the first comparison in the two-condition criterion for evolutionary stability ties because $Ap=(3/2,3/2)$. Its second comparison is strictly positive:Thus restoring frequency feedback can protect a mixture even though all present types earn equal payoffs at the mixture. Coexistence here means different individuals using different pure strategies; it does not require every individual to randomize with a mixed strategy.

Frequency evolution need not increase the mean payoff. In the coexistence example $\bar{f}=5x-4x^2$, whose maximum occurs at $x=5/8$, whereas the attracting composition is $3/4$. For $5/8<x<3/4$, the frequency $x$ increases while $\frac{d}{dt}\bar{f}=(5-8x)\dot{x}<0$. Individual payoff advantages drive the equation; maximizing collective mean payoff is a different optimization problem.

More generally, a rest point only equalizes payoffs among present types: $x_i>0$ implies $(Ax{)}_i=x^{T}Ax$. A symmetric Nash equilibrium additionally requires $(Ax{)}_i\le x^{T}Ax$ for every absent type. All vertices are rest points even when an absent type could invade. For finite symmetric bimatrix games, a rest point with Lyapunov stability on the full probability simplex is a symmetric Nash equilibrium; an interior trajectory that converges has a symmetric Nash equilibrium as its limit. These are conditional results, not a convergence theorem for every trajectory. See Cressman and Tao (2014), Theorem 1.

The rock paper scissors payoff matrix gives an explicit obstruction to universal convergence:Since $A^T=-A$, $x^{T}Ax=0$, and in the interior $\frac{d}{dt}\log (x_1{x}_2{x}_3)={\sum }_i(Ax{)}_i=0$. Thus $x_1{x}_2{x}_3$ is conserved. Its maximum on the probability simplex occurs only at $(1/3,1/3,1/3)$, the unique symmetric Nash equilibrium, so an interior trajectory starting elsewhere cannot converge to that composition. Every other interior product level is a closed curve without a rest point, giving a periodic orbit. The existence of these cycles is also discussed in Cressman and Tao (2014), Example 1. The useful lesson is to inspect the specified payoff feedback and dynamical system, rather than infer convergence from the existence of an evolutionarily stable strategy or a Nash equilibrium.

The normal-form game played once in each round of a repeated game. Its actions and payoffs are distinct from the repeated interaction's history-dependent pure strategies and aggregate payoffs. The same stage game can produce different incentive constraints under a finite sum, discounting or long-run average payoff criterion.

A strategy profile specifies one strategy for every player. A pure strategy strategy profile lies in the Cartesian product $S_1\times \cdots \times S_n$ of the players' strategy sets. A mixed strategy strategy profile specifies one probability distribution ${\sigma }_i$ over each $S_i$. Write $s_{-i}$ or ${\sigma }_{-i}$ for the choices of all players except player $i$.

The support of a finite mixed strategy consists of the pure strategies assigned positive probability. At a Nash equilibrium, every action in the support is a best response. In a game satisfying nondegeneracy of a bimatrix game, the two equilibrium supports have equal size.

A finite game is symmetric when all players have the same pure strategies and permuting the players permutes their payoffs. In the anonymous special case, a player's payoff depends on their own action and the multiset of opposing actions. A symmetric equilibrium exists even when there are more than two players, by the gain-map proof of symmetric equilibrium.

A unilateral deviation changes only one player's strategy, holding the other players' strategies fixed. It is profitable if it strictly increases that player's payoff, or expected payoff for mixed strategies. The comparison does not allow the opponents to adjust in response to the deviation. In a sequential game, however, holding an opponent's complete pure strategy fixed still allows their prescribed actions to differ after different observed histories.

A pure strategy is very weakly dominated by another strategy when its payoff is no greater against every opposing pure strategy profile, with no strict advantage required anywhere. The definition permits payoff-equivalent strategies and even domination by the original strategy itself. A weakly dominated strategy instead has a strict inequality for at least one opposing strategy profile.

In a binary game, suppose ${\sigma }_i$ very weakly dominates $s_i$ and assigns positive probability to a different pure action. At every opposing strategy profile where $s_i$ pays one, all actions in the support of a mixed strategy must also pay one. Where $s_i$ pays zero, their payoffs are automatically no smaller. Therefore every distinct supported action very weakly dominates $s_i$. If the mixed domination is weak with a strict improvement somewhere, choose a supported action paying one at a profile where $s_i$ pays zero; that action also supplies a strict improvement. The requirement of nontrivial support is indispensable for the very-weak version.

A strategy giving at least as large a payoff as every alternative against every opponent strategy profile. Under the convention distinguishing weak dominance from complete payoff equivalence, require a strict advantage somewhere. In a two-action finite game, it suffices to check the opponent's pure strategies: expected payoff differences against an opponent's mixed strategy are weighted averages of those pure-action comparisons.

In a zero-sum game, the players' payoffs sum to zero at every pure strategy strategy profile. For two players this means $u_2=-u_1$: maximizing the column player's payoff is minimizing the row player's. A single payoff matrix $A$ therefore specifies a finite two-player matrix game, and independent mixed strategies $x,y$ give expected payoffs $x^{\mathsf{T}}Ay$ and $-x^{\mathsf{T}}Ay$.

Matching pennies and the usual rock paper scissors are two-player zero-sum games. The asymmetric coordination example is not: its total payoff is $5$ when choices match and $0$ otherwise. Applying a two-player minimax theorem to general bimatrix games would discard this possibility of shared gains. For a two-player zero-sum game, however, optimal mixed strategies guarantee the value of a zero-sum game, and pairing any two opposing optimal strategies gives a Nash equilibrium.