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Add lean source and binary files (part 61)
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import Mathlib
namespace OAI
namespace DFVSGames.Foundations.Games
open scoped BigOperators
noncomputable section
structure FiniteDistribution (Ω : Type*) [Fintype Ω] where
weight : Ω → ℝ
nonnegative : ∀ x, 0 ≤ weight x
normalized : ∑ x, weight x = 1
namespace FiniteDistribution
variable {Ω Γ : Type*} [Fintype Ω] [Fintype Γ]
def expectation (μ : FiniteDistribution Ω) (f : Ω → ℝ) : ℝ :=
∑ x, μ.weight x * f x
def probability (μ : FiniteDistribution Ω) (event : Ω → Bool) : ℝ :=
∑ x, if event x then μ.weight x else 0
theorem probability_nonnegative (μ : FiniteDistribution Ω) (event : Ω → Bool) :
0 ≤ μ.probability event := by
apply Finset.sum_nonneg
intro x _
split
· exact μ.nonnegative x
· exact le_rfl
theorem probability_le_one (μ : FiniteDistribution Ω) (event : Ω → Bool) :
μ.probability event ≤ 1 := by
rw [← μ.normalized]
apply Finset.sum_le_sum
intro x _
split
· exact le_rfl
· exact μ.nonnegative x
@[simp] theorem probability_true (μ : FiniteDistribution Ω) :
μ.probability (fun _ => true) = 1 := by
simpa [probability] using μ.normalized
@[simp] theorem probability_false (μ : FiniteDistribution Ω) :
μ.probability (fun _ => false) = 0 := by
simp [probability]
theorem probability_mono (μ : FiniteDistribution Ω) {event event' : Ω → Bool}
(h : ∀ x, event x = true → event' x = true) :
μ.probability event ≤ μ.probability event' := by
apply Finset.sum_le_sum
intro x _
by_cases hx : event x = true
· simp [hx, h x hx]
· simp [hx]
split
· exact μ.nonnegative x
· exact le_rfl
def pushforward (μ : FiniteDistribution Ω) (f : Ω → Γ) : FiniteDistribution Γ := by
classical
exact
{ weight := fun y => ∑ x, if f x = y then μ.weight x else 0
nonnegative := fun y => Finset.sum_nonneg fun x _ => by
split
· exact μ.nonnegative x
· exact le_rfl
normalized := by
rw [Finset.sum_comm]
simpa using μ.normalized }
theorem probability_pushforward (μ : FiniteDistribution Ω) (f : Ω → Γ)
(event : Γ → Bool) :
(μ.pushforward f).probability event = μ.probability (fun x => event (f x)) := by
classical
simp only [probability, pushforward]
calc
_ = ∑ y, ∑ x, if f x = y then (if event y then μ.weight x else 0) else 0 := by
apply Finset.sum_congr rfl
intro y _
by_cases hy : event y = true <;> simp [hy]
_ = _ := by rw [Finset.sum_comm]; simp
def transport (μ : FiniteDistribution Ω) (equiv : Ω ≃ Γ) : FiniteDistribution Γ where
weight y := μ.weight (equiv.symm y)
nonnegative y := μ.nonnegative (equiv.symm y)
normalized := by rw [equiv.symm.sum_comp, μ.normalized]
theorem probability_transport (μ : FiniteDistribution Ω) (equiv : Ω ≃ Γ)
(event : Γ → Bool) :
(μ.transport equiv).probability event = μ.probability (fun x => event (equiv x)) := by
unfold probability transport
exact Fintype.sum_equiv equiv.symm _ _ (fun _ => by simp)
def iid (μ : FiniteDistribution Ω) (n : Nat) : FiniteDistribution (Fin n → Ω) where
weight x := ∏ i, μ.weight (x i)
nonnegative x := Finset.prod_nonneg fun i _ => μ.nonnegative (x i)
normalized := by rw [← Fintype.sum_pow, μ.normalized, one_pow]
theorem probability_iid_all (μ : FiniteDistribution Ω) (event : Ω → Bool) (n : Nat) :
(μ.iid n).probability (fun x => decide (∀ i, event (x i) = true)) =
μ.probability event ^ n := by
classical
unfold probability iid
rw [Fintype.sum_pow]
apply Finset.sum_congr rfl
intro x _
simp only [decide_eq_true_eq]
by_cases h : ∀ i, event (x i) = true
· simp [h]
· rw [ite_eq_right h]
obtain ⟨i, hi⟩ := not_forall.mp h
symm
apply Finset.prod_eq_zero (Finset.mem_univ i)
simp [hi]
end FiniteDistribution
abbrev Strategy (Q₁ Q₂ A₁ A₂ : Type*) := (Q₁ → A₁) × (Q₂ → A₂)
structure Game (Q₁ Q₂ A₁ A₂ : Type*) [Fintype Q₁] [Fintype Q₂]
[Fintype A₁] [Fintype A₂] where
questions : FiniteDistribution (Q₁ × Q₂)
accepts : Q₁ → Q₂ → A₁ → A₂ → Bool
namespace Game
variable {Q₁ Q₂ A₁ A₂ : Type*}
[Fintype Q₁] [Fintype Q₂] [Fintype A₁] [Fintype A₂]
def wins (G : Game Q₁ Q₂ A₁ A₂) (strategy : Strategy Q₁ Q₂ A₁ A₂)
(questions : Q₁ × Q₂) : Bool :=
G.accepts questions.1 questions.2 (strategy.1 questions.1) (strategy.2 questions.2)
def success (G : Game Q₁ Q₂ A₁ A₂) (strategy : Strategy Q₁ Q₂ A₁ A₂) : ℝ :=
G.questions.probability (G.wins strategy)
theorem success_nonnegative (G : Game Q₁ Q₂ A₁ A₂)
(strategy : Strategy Q₁ Q₂ A₁ A₂) : 0 ≤ G.success strategy :=
G.questions.probability_nonnegative _
theorem success_le_one (G : Game Q₁ Q₂ A₁ A₂)
(strategy : Strategy Q₁ Q₂ A₁ A₂) : G.success strategy ≤ 1 :=
G.questions.probability_le_one _
def simulatedStrategy {R₁ R₂ B₁ B₂ : Type*}
(questionMap₁ : Q₁ → R₁) (questionMap₂ : Q₂ → R₂)
(answerMap₁ : Q₁ → B₁ → A₁) (answerMap₂ : Q₂ → B₂ → A₂)
(strategy : Strategy R₁ R₂ B₁ B₂) : Strategy Q₁ Q₂ A₁ A₂ :=
(fun x => answerMap₁ x (strategy.1 (questionMap₁ x)),
fun y => answerMap₂ y (strategy.2 (questionMap₂ y)))
theorem success_le_of_localSimulation {R₁ R₂ B₁ B₂ : Type*}
[Fintype R₁] [Fintype R₂] [Fintype B₁] [Fintype B₂]
(G : Game Q₁ Q₂ A₁ A₂) (H : Game R₁ R₂ B₁ B₂)
(questionMap₁ : Q₁ → R₁) (questionMap₂ : Q₂ → R₂)
(answerMap₁ : Q₁ → B₁ → A₁) (answerMap₂ : Q₂ → B₂ → A₂)
(questionLaw : H.questions =
G.questions.pushforward (fun q => (questionMap₁ q.1, questionMap₂ q.2)))
(acceptance : ∀ x y a b,
H.accepts (questionMap₁ x) (questionMap₂ y) a b = true →
G.accepts x y (answerMap₁ x a) (answerMap₂ y b) = true)
(strategy : Strategy R₁ R₂ B₁ B₂) :
H.success strategy ≤
G.success (simulatedStrategy questionMap₁ questionMap₂ answerMap₁ answerMap₂ strategy) := by
unfold success
rw [questionLaw, FiniteDistribution.probability_pushforward]
apply FiniteDistribution.probability_mono
intro questions h
exact acceptance questions.1 questions.2 _ _ h
def tupleQuestionEquiv (n : Nat) :
(Fin n → Q₁ × Q₂) ≃ (Fin n → Q₁) × (Fin n → Q₂) where
toFun questions := (fun i => (questions i).1, fun i => (questions i).2)
invFun questions := fun i => (questions.1 i, questions.2 i)
left_inv _ := rfl
right_inv _ := rfl
def repetition (G : Game Q₁ Q₂ A₁ A₂) (n : Nat) :
Game (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂) := by
classical
exact
{ questions := (G.questions.iid n).transport (tupleQuestionEquiv n)
accepts := fun x y a b => decide (∀ i, G.accepts (x i) (y i) (a i) (b i) = true) }
def repeatStrategy (strategy : Strategy Q₁ Q₂ A₁ A₂) (n : Nat) :
Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂) :=
(fun x i => strategy.1 (x i), fun y i => strategy.2 (y i))
theorem success_repeatStrategy (G : Game Q₁ Q₂ A₁ A₂)
(strategy : Strategy Q₁ Q₂ A₁ A₂) (n : Nat) :
(G.repetition n).success (repeatStrategy strategy n) = G.success strategy ^ n := by
unfold success
change ((G.questions.iid n).transport (tupleQuestionEquiv n)).probability _ = _
rw [FiniteDistribution.probability_transport]
exact G.questions.probability_iid_all (G.wins strategy) n
@[simp] theorem repetition_question_weight (G : Game Q₁ Q₂ A₁ A₂) (n : Nat)
(questions : (Fin n → Q₁) × (Fin n → Q₂)) :
(G.repetition n).questions.weight questions =
∏ i, G.questions.weight (questions.1 i, questions.2 i) := rfl
@[simp] theorem repetition_accepts_iff (G : Game Q₁ Q₂ A₁ A₂) (n : Nat)
(x : Fin n → Q₁) (y : Fin n → Q₂) (a : Fin n → A₁) (b : Fin n → A₂) :
(G.repetition n).accepts x y a b = true ↔
∀ i, G.accepts (x i) (y i) (a i) (b i) = true := by
classical
simp [repetition]
def coordinateWin (G : Game Q₁ Q₂ A₁ A₂) {n : Nat}
(strategy : Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂))
(coordinate : Fin n) (questions : (Fin n → Q₁) × (Fin n → Q₂)) : Bool :=
G.accepts (questions.1 coordinate) (questions.2 coordinate)
(strategy.1 questions.1 coordinate) (strategy.2 questions.2 coordinate)
def selectedWins (G : Game Q₁ Q₂ A₁ A₂) {n : Nat}
(strategy : Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂))
(selected : Finset (Fin n)) (questions : (Fin n → Q₁) × (Fin n → Q₂)) : Bool := by
classical
exact decide (∀ i ∈ selected, G.coordinateWin strategy i questions = true)
theorem selectedWins_mono (G : Game Q₁ Q₂ A₁ A₂) {n : Nat}
(strategy : Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂))
{selected selected' : Finset (Fin n)} (h : selected ⊆ selected') :
(G.repetition n).questions.probability (G.selectedWins strategy selected') ≤
(G.repetition n).questions.probability (G.selectedWins strategy selected) := by
apply FiniteDistribution.probability_mono
intro questions hwin
simp only [selectedWins, decide_eq_true_eq] at hwin ⊢
intro i hi
exact hwin i (h hi)
@[simp] theorem selectedWins_empty (G : Game Q₁ Q₂ A₁ A₂) {n : Nat}
(strategy : Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂))
(questions : (Fin n → Q₁) × (Fin n → Q₂)) :
G.selectedWins strategy ∅ questions = true := by
simp [selectedWins]
theorem selectedWins_univ (G : Game Q₁ Q₂ A₁ A₂) {n : Nat}
(strategy : Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂)) :
G.selectedWins strategy Finset.univ = (G.repetition n).wins strategy := by
classical
funext questions
simp [selectedWins, coordinateWin, repetition, wins]
end Game
end
end DFVSGames.Foundations.Games
namespace DFVSGames.Foundations.Games
open scoped BigOperators
noncomputable section
namespace Game
variable {Q₁ Q₂ A₁ A₂ : Type*}
[Fintype Q₁] [Fintype Q₂] [Fintype A₁] [Fintype A₂]
[Nonempty A₁] [Nonempty A₂]
def value (G : Game Q₁ Q₂ A₁ A₂) : ℝ := by
classical
exact Finset.univ.sup' Finset.univ_nonempty G.success
theorem success_le_value (G : Game Q₁ Q₂ A₁ A₂)
(strategy : Strategy Q₁ Q₂ A₁ A₂) : G.success strategy ≤ G.value := by
classical
exact Finset.le_sup' G.success (Finset.mem_univ strategy)
theorem value_le_iff (G : Game Q₁ Q₂ A₁ A₂) (bound : ℝ) :
G.value ≤ bound ↔ ∀ strategy : Strategy Q₁ Q₂ A₁ A₂, G.success strategy ≤ bound := by
classical
simp [value, Finset.sup'_le_iff]
theorem exists_optimal_strategy (G : Game Q₁ Q₂ A₁ A₂) :
∃ strategy : Strategy Q₁ Q₂ A₁ A₂, G.success strategy = G.value := by
classical
obtain ⟨strategy, _, h⟩ := Finset.exists_mem_eq_sup'
(s := (Finset.univ : Finset (Strategy Q₁ Q₂ A₁ A₂))) Finset.univ_nonempty G.success
exact ⟨strategy, h.symm⟩
theorem value_nonnegative (G : Game Q₁ Q₂ A₁ A₂) : 0 ≤ G.value := by
obtain ⟨strategy, h⟩ := G.exists_optimal_strategy
rw [← h]
exact G.success_nonnegative strategy
theorem value_le_one (G : Game Q₁ Q₂ A₁ A₂) : G.value ≤ 1 :=
(G.value_le_iff 1).2 G.success_le_one
theorem randomized_success_le_value (G : Game Q₁ Q₂ A₁ A₂)
{Seed : Type*} [Fintype Seed] (seedLaw : FiniteDistribution Seed)
(strategies : Seed → Strategy Q₁ Q₂ A₁ A₂) :
seedLaw.expectation (fun seed => G.success (strategies seed)) ≤ G.value := by
unfold FiniteDistribution.expectation
calc
_ ≤ ∑ seed, seedLaw.weight seed * G.value := by
apply Finset.sum_le_sum
intro seed _
exact mul_le_mul_of_nonneg_left (G.success_le_value _) (seedLaw.nonnegative seed)
_ = G.value := by rw [← Finset.sum_mul, seedLaw.normalized, one_mul]
omit [Nonempty A₁] [Nonempty A₂] in
theorem success_repetition_zero (G : Game Q₁ Q₂ A₁ A₂)
(strategy : Strategy (Fin 0 → Q₁) (Fin 0 → Q₂) (Fin 0 → A₁) (Fin 0 → A₂)) :
(G.repetition 0).success strategy = 1 := by
have h : (G.repetition 0).wins strategy = fun _ => true := by
funext questions
simp [wins, repetition]
unfold success
rw [h, FiniteDistribution.probability_true]
@[simp] theorem value_repetition_zero (G : Game Q₁ Q₂ A₁ A₂) :
(G.repetition 0).value = 1 := by
obtain ⟨strategy, h⟩ := (G.repetition 0).exists_optimal_strategy
rw [← h]
exact G.success_repetition_zero strategy
theorem pow_value_le_repetition_value (G : Game Q₁ Q₂ A₁ A₂) (n : Nat) :
G.value ^ n ≤ (G.repetition n).value := by
obtain ⟨strategy, h⟩ := G.exists_optimal_strategy
rw [← h, ← G.success_repeatStrategy strategy n]
exact (G.repetition n).success_le_value (repeatStrategy strategy n)
theorem value_le_of_localSimulation {R₁ R₂ B₁ B₂ : Type*}
[Fintype R₁] [Fintype R₂] [Fintype B₁] [Fintype B₂]
[Nonempty B₁] [Nonempty B₂]
(G : Game Q₁ Q₂ A₁ A₂) (H : Game R₁ R₂ B₁ B₂)
(questionMap₁ : Q₁ → R₁) (questionMap₂ : Q₂ → R₂)
(answerMap₁ : Q₁ → B₁ → A₁) (answerMap₂ : Q₂ → B₂ → A₂)
(questionLaw : H.questions =
G.questions.pushforward (fun q => (questionMap₁ q.1, questionMap₂ q.2)))
(acceptance : ∀ x y a b,
H.accepts (questionMap₁ x) (questionMap₂ y) a b = true →
G.accepts x y (answerMap₁ x a) (answerMap₂ y b) = true) :
H.value ≤ G.value := by
apply (H.value_le_iff _).2
intro strategy
exact (G.success_le_of_localSimulation H questionMap₁ questionMap₂
answerMap₁ answerMap₂ questionLaw acceptance strategy).trans (G.success_le_value _)
end Game
end
end DFVSGames.Foundations.Games
namespace DFVSGames.Foundations.Games
open scoped BigOperators
noncomputable section
namespace FiniteDistribution
variable {Ω Γ Q A : Type*} [Fintype Ω] [Fintype Γ] [Fintype Q] [Fintype A]
theorem eq_of_weight_eq {μ ν : FiniteDistribution Ω}
(h : ∀ x, μ.weight x = ν.weight x) : μ = ν := by
cases μ with
| mk w hw hs =>
cases ν with
| mk w' hw' hs' =>
have he : w = w' := funext h
cases he
rfl
def table [DecidableEq Q] (responses : Q → FiniteDistribution A) :
FiniteDistribution (Q → A) := by
classical
exact
{ weight := fun answers => ∏ q, (responses q).weight (answers q)
nonnegative := fun answers => Finset.prod_nonneg fun q _ =>
(responses q).nonnegative (answers q)
normalized := by
rw [← Fintype.prod_sum]
simp only [FiniteDistribution.normalized, Finset.prod_const_one] }
theorem expectation_table_eval [DecidableEq Q] (responses : Q → FiniteDistribution A)
(q₀ : Q) (h : A → ℝ) :
(table responses).expectation (fun answers => h (answers q₀)) =
(responses q₀).expectation h := by
classical
change (∑ answers : Q → A,
(∏ q, (responses q).weight (answers q)) * h (answers q₀)) =
∑ a, (responses q₀).weight a * h a
calc
_ = ∑ answers : Q → A,
∏ q, (responses q).weight (answers q) *
(if q = q₀ then h (answers q) else 1) := by
apply Finset.sum_congr rfl
intro answers _
rw [Finset.prod_mul_distrib]
simp
_ = ∏ q, ∑ a, (responses q).weight a * (if q = q₀ then h a else 1) :=
(Fintype.prod_sum (fun q a =>
(responses q).weight a * (if q = q₀ then h a else 1))).symm
_ = ∏ q : Q, if q = q₀ then (∑ a, (responses q₀).weight a * h a) else 1 := by
apply Finset.prod_congr rfl
intro q _
by_cases hq : q = q₀
· subst q
simp
· simp [hq, (responses q).normalized]
_ = _ := by simp
theorem table_eval_pushforward [DecidableEq Q]
(responses : Q → FiniteDistribution A) (q₀ : Q) :
(table responses).pushforward (fun answers => answers q₀) = responses q₀ := by
classical
apply eq_of_weight_eq
intro a
calc
_ = (table responses).expectation
(fun answers => if answers q₀ = a then 1 else 0) := by
simp [pushforward, expectation, mul_ite]
_ = (responses q₀).expectation (fun b => if b = a then 1 else 0) :=
expectation_table_eval responses q₀ (fun b => if b = a then (1 : ℝ) else 0)
_ = _ := by simp [expectation, mul_ite]
def product (μ : FiniteDistribution Ω) (ν : FiniteDistribution Γ) :
FiniteDistribution (Ω × Γ) where
weight x := μ.weight x.1 * ν.weight x.2
nonnegative x := mul_nonneg (μ.nonnegative _) (ν.nonnegative _)
normalized := by
rw [Fintype.sum_prod_type]
simp_rw [← Finset.mul_sum, ν.normalized, mul_one]
exact μ.normalized
theorem expectation_product (μ : FiniteDistribution Ω) (ν : FiniteDistribution Γ)
(f : Ω × Γ → ℝ) :
(μ.product ν).expectation f =
μ.expectation (fun x => ν.expectation (fun y => f (x,y))) := by
simp only [expectation, product, Fintype.sum_prod_type, Finset.mul_sum, mul_assoc]
theorem expectation_comm (μ : FiniteDistribution Ω) (ν : FiniteDistribution Γ)
(f : Ω → Γ → ℝ) :
μ.expectation (fun x => ν.expectation (f x)) =
ν.expectation (fun y => μ.expectation (fun x => f x y)) := by
unfold expectation
simp_rw [Finset.mul_sum]
rw [Finset.sum_comm]
apply Finset.sum_congr rfl
intro y _
apply Finset.sum_congr rfl
intro x _
exact mul_left_comm _ _ _
theorem expectation_congr (μ : FiniteDistribution Ω) {f g : Ω → ℝ}
(h : ∀ x, f x = g x) : μ.expectation f = μ.expectation g := by
unfold expectation
apply Finset.sum_congr rfl
intro x _
rw [h x]
end FiniteDistribution
namespace Game
variable {Q₁ Q₂ A₁ A₂ : Type*}
[Fintype Q₁] [Fintype Q₂] [Fintype A₁] [Fintype A₂]
[DecidableEq Q₁] [DecidableEq Q₂]
def responseTableLaw (responses₁ : Q₁ → FiniteDistribution A₁)
(responses₂ : Q₂ → FiniteDistribution A₂) :
FiniteDistribution (Strategy Q₁ Q₂ A₁ A₂) := by
classical
exact (FiniteDistribution.table responses₁).product (FiniteDistribution.table responses₂)
def stochasticSuccess (G : Game Q₁ Q₂ A₁ A₂)
(responses₁ : Q₁ → FiniteDistribution A₁)
(responses₂ : Q₂ → FiniteDistribution A₂) : ℝ :=
G.questions.expectation (fun q =>
(responses₁ q.1).expectation (fun a =>
(responses₂ q.2).expectation (fun b =>
if G.accepts q.1 q.2 a b then 1 else 0)))
omit [DecidableEq Q₁] [DecidableEq Q₂] in
theorem success_eq_question_expectation (G : Game Q₁ Q₂ A₁ A₂)
(strategy : Strategy Q₁ Q₂ A₁ A₂) :
G.success strategy = G.questions.expectation
(fun q => if G.wins strategy q then 1 else 0) := by
simp [success, FiniteDistribution.probability,
FiniteDistribution.expectation, mul_ite]
theorem table_success_eq_stochastic (G : Game Q₁ Q₂ A₁ A₂)
(responses₁ : Q₁ → FiniteDistribution A₁)
(responses₂ : Q₂ → FiniteDistribution A₂) :
(responseTableLaw responses₁ responses₂).expectation G.success =
G.stochasticSuccess responses₁ responses₂ := by
classical
unfold stochasticSuccess
calc
_ = (responseTableLaw responses₁ responses₂).expectation
(fun strategy => G.questions.expectation
(fun q => if G.wins strategy q then 1 else 0)) :=
FiniteDistribution.expectation_congr _ (G.success_eq_question_expectation)
_ = G.questions.expectation (fun q =>
(responseTableLaw responses₁ responses₂).expectation
(fun strategy => if G.wins strategy q then 1 else 0)) :=
FiniteDistribution.expectation_comm _ _ _
_ = _ := by
apply FiniteDistribution.expectation_congr
intro q
rw [responseTableLaw, FiniteDistribution.expectation_product]
change (FiniteDistribution.table responses₁).expectation
(fun answers₁ => (FiniteDistribution.table responses₂).expectation
(fun answers₂ =>
if G.accepts q.1 q.2 (answers₁ q.1) (answers₂ q.2) then 1 else 0)) = _
calc
_ = (FiniteDistribution.table responses₁).expectation
(fun answers₁ => (responses₂ q.2).expectation
(fun b => if G.accepts q.1 q.2 (answers₁ q.1) b then 1 else 0)) := by
apply FiniteDistribution.expectation_congr
intro answers₁
exact FiniteDistribution.expectation_table_eval responses₂ q.2
(fun b => if G.accepts q.1 q.2 (answers₁ q.1) b then (1 : ℝ) else 0)
_ = _ := FiniteDistribution.expectation_table_eval responses₁ q.1
(fun a => (responses₂ q.2).expectation
(fun b => if G.accepts q.1 q.2 a b then (1 : ℝ) else 0))
variable [Nonempty A₁] [Nonempty A₂]
theorem stochasticSuccess_le_value (G : Game Q₁ Q₂ A₁ A₂)
(responses₁ : Q₁ → FiniteDistribution A₁)
(responses₂ : Q₂ → FiniteDistribution A₂) :
G.stochasticSuccess responses₁ responses₂ ≤ G.value := by
classical
rw [← G.table_success_eq_stochastic]
exact G.randomized_success_le_value (responseTableLaw responses₁ responses₂) id
theorem exists_deterministic_ge_stochastic (G : Game Q₁ Q₂ A₁ A₂)
(responses₁ : Q₁ → FiniteDistribution A₁)
(responses₂ : Q₂ → FiniteDistribution A₂) :
∃ strategy : Strategy Q₁ Q₂ A₁ A₂,
G.stochasticSuccess responses₁ responses₂ ≤ G.success strategy := by
obtain ⟨strategy, h⟩ := G.exists_optimal_strategy
refine ⟨strategy, ?_⟩
rw [h]
exact G.stochasticSuccess_le_value responses₁ responses₂
end Game
end
end DFVSGames.Foundations.Games
namespace DFVSGames.Foundations.Games.FiniteDistribution
open scoped BigOperators
noncomputable section
theorem iid_pushforward {Ω Γ : Type*} [Fintype Ω] [Fintype Γ]
(μ : FiniteDistribution Ω) (f : Ω → Γ) (n : Nat) :
(μ.pushforward f).iid n =
(μ.iid n).pushforward (fun answers i => f (answers i)) := by
classical
apply eq_of_weight_eq
intro y
simp only [iid, pushforward]
rw [Fintype.prod_sum]
apply Finset.sum_congr rfl
intro x _
by_cases h : (fun i => f (x i)) = y
· have hp : ∀ i, f (x i) = y i := congrFun h
simp [hp]
· rw [ite_eq_right h]
have hn : ¬ ∀ i, f (x i) = y i := fun hp => h (funext hp)
obtain ⟨i, hi⟩ := not_forall.mp hn
apply Finset.prod_eq_zero (Finset.mem_univ i)
simp [hi]
def uniform (Ω : Type*) [Fintype Ω] [Nonempty Ω] : FiniteDistribution Ω where
weight _ := 1 / (Fintype.card Ω : ℝ)
nonnegative _ := div_nonneg zero_le_one (Nat.cast_nonneg _)
normalized := by
have hn : (Fintype.card Ω : ℝ) ≠ 0 :=
Nat.cast_ne_zero.mpr Fintype.card_ne_zero
simp only [Finset.sum_const, Finset.card_univ, nsmul_eq_mul, one_div]
exact mul_inv_cancel₀ hn
theorem iid_uniform {Ω : Type*} [Fintype Ω] [Nonempty Ω] (n : Nat) :
(uniform Ω).iid n = uniform (Fin n → Ω) := by
classical
apply eq_of_weight_eq
intro x
simp [iid, uniform, Nat.cast_pow, one_div]
theorem expectation_uniform {Ω : Type*} [Fintype Ω] [Nonempty Ω] (f : Ω → ℝ) :
(uniform Ω).expectation f = (∑ x, f x) / (Fintype.card Ω : ℝ) := by
unfold expectation uniform
rw [← Finset.mul_sum]
simp [div_eq_mul_inv, mul_comm]
end
end DFVSGames.Foundations.Games.FiniteDistribution
namespace DFVSGames.Repetition
open DFVSGames.Foundations.Games
open scoped BigOperators
noncomputable section
variable {Q₁ Q₂ A₁ A₂ R₁ R₂ B₁ B₂ : Type*}
[Fintype Q₁] [Fintype Q₂] [Fintype A₁] [Fintype A₂]
[Fintype R₁] [Fintype R₂] [Fintype B₁] [Fintype B₂]
def IsProjection (G : Game Q₁ Q₂ A₁ A₂) : Prop :=
∀ x y a b b', G.accepts x y a b = true →
G.accepts x y a b' = true → b = b'
theorem IsProjection.of_accepts_iff (G : Game Q₁ Q₂ A₁ A₂)
(project : Q₁ → Q₂ → A₁ → A₂)
(h : ∀ x y a b, G.accepts x y a b = true ↔ project x y a = b) :
IsProjection G := by
intro x y a b b' hb hb'
exact ((h x y a b).mp hb).symm.trans ((h x y a b').mp hb')
theorem IsProjection.repetition {G : Game Q₁ Q₂ A₁ A₂}
(hG : IsProjection G) (n : Nat) : IsProjection (G.repetition n) := by
intro x y a b b' hb hb'
have h₁ := (G.repetition_accepts_iff n x y a b).mp hb
have h₂ := (G.repetition_accepts_iff n x y a b').mp hb'
funext i
exact hG (x i) (y i) (a i) (b i) (b' i) (h₁ i) (h₂ i)
def ofProjection (questions : FiniteDistribution (Q₁ × Q₂))
(project : Q₁ → Q₂ → A₁ → A₂) : Game Q₁ Q₂ A₁ A₂ := by
classical
exact { questions := questions, accepts := fun x y a b => decide (project x y a = b) }
@[simp] theorem ofProjection_accepts_iff
(questions : FiniteDistribution (Q₁ × Q₂))
(project : Q₁ → Q₂ → A₁ → A₂) (x : Q₁) (y : Q₂) (a : A₁) (b : A₂) :
(ofProjection questions project).accepts x y a b = true ↔ project x y a = b := by
classical
simp [ofProjection]
theorem ofProjection_isProjection (questions : FiniteDistribution (Q₁ × Q₂))
(project : Q₁ → Q₂ → A₁ → A₂) : IsProjection (ofProjection questions project) :=
IsProjection.of_accepts_iff _ project (ofProjection_accepts_iff questions project)
def productQuestionEquiv :
((Q₁ × Q₂) × (R₁ × R₂)) ≃ ((Q₁ × R₁) × (Q₂ × R₂)) where
toFun q := ((q.1.1, q.2.1), (q.1.2, q.2.2))
invFun q := ((q.1.1, q.2.1), (q.1.2, q.2.2))
left_inv _ := rfl
right_inv _ := rfl
def product (G : Game Q₁ Q₂ A₁ A₂) (H : Game R₁ R₂ B₁ B₂) :
Game (Q₁ × R₁) (Q₂ × R₂) (A₁ × B₁) (A₂ × B₂) where
questions := (G.questions.product H.questions).transport productQuestionEquiv
accepts x y a b := G.accepts x.1 y.1 a.1 b.1 && H.accepts x.2 y.2 a.2 b.2
@[simp] theorem product_question_weight
(G : Game Q₁ Q₂ A₁ A₂) (H : Game R₁ R₂ B₁ B₂)
(q : (Q₁ × R₁) × (Q₂ × R₂)) :
(product G H).questions.weight q =
G.questions.weight (q.1.1, q.2.1) * H.questions.weight (q.1.2, q.2.2) := rfl
@[simp] theorem product_accepts_iff
(G : Game Q₁ Q₂ A₁ A₂) (H : Game R₁ R₂ B₁ B₂)
(x : Q₁ × R₁) (y : Q₂ × R₂) (a : A₁ × B₁) (b : A₂ × B₂) :
(product G H).accepts x y a b = true ↔
G.accepts x.1 y.1 a.1 b.1 = true ∧ H.accepts x.2 y.2 a.2 b.2 = true := by
simp [product]
theorem IsProjection.product {G : Game Q₁ Q₂ A₁ A₂} {H : Game R₁ R₂ B₁ B₂}
(hG : IsProjection G) (hH : IsProjection H) : IsProjection (product G H) := by
intro x y a b b' hb hb'
have h₁ := (product_accepts_iff G H x y a b).mp hb
have h₂ := (product_accepts_iff G H x y a b').mp hb'
exact Prod.ext (hG _ _ _ _ _ h₁.1 h₂.1) (hH _ _ _ _ _ h₁.2 h₂.2)
def productStrategy (s : Strategy Q₁ Q₂ A₁ A₂) (t : Strategy R₁ R₂ B₁ B₂) :
Strategy (Q₁ × R₁) (Q₂ × R₂) (A₁ × B₁) (A₂ × B₂) :=
(fun q => (s.1 q.1, t.1 q.2), fun q => (s.2 q.1, t.2 q.2))
theorem success_productStrategy (G : Game Q₁ Q₂ A₁ A₂) (H : Game R₁ R₂ B₁ B₂)
(s : Strategy Q₁ Q₂ A₁ A₂) (t : Strategy R₁ R₂ B₁ B₂) :
(product G H).success (productStrategy s t) = G.success s * H.success t := by
unfold Game.success
change ((G.questions.product H.questions).transport productQuestionEquiv).probability _ = _
rw [FiniteDistribution.probability_transport]
change (G.questions.product H.questions).probability
(fun q => G.wins s q.1 && H.wins t q.2) = _
simp only [FiniteDistribution.probability, FiniteDistribution.product]
rw [Fintype.sum_prod_type, Finset.sum_mul_sum]
apply Finset.sum_congr rfl
intro q _
apply Finset.sum_congr rfl
intro r _
by_cases hq : G.wins s q = true <;>
by_cases hr : H.wins t r = true <;> simp [hq, hr]
theorem mul_value_le_product_value [Nonempty A₁] [Nonempty A₂]
[Nonempty B₁] [Nonempty B₂]
(G : Game Q₁ Q₂ A₁ A₂) (H : Game R₁ R₂ B₁ B₂) :
G.value * H.value ≤ (product G H).value := by
obtain ⟨s, hs⟩ := G.exists_optimal_strategy
obtain ⟨t, ht⟩ := H.exists_optimal_strategy
rw [← hs, ← ht, ← success_productStrategy]
exact (product G H).success_le_value (productStrategy s t)
end
end DFVSGames.Repetition
end OAI