We show that the set of priors in the representation of Choquet expectation is the one of equivalent martingale measures under some conditions, when given capacity is submodular. It is proven via Peng’s g -expectation and related topics. A starting point for a mathematical definition of risk is simply as standard deviation. The more risk we take, the more we stand to lose or gain. Standard deviation (or volatility) is a kind of simple risk measure. Different families of risk measures have been proposed in literature like coherent, convex, spectral risk measures, conditional value-at-risk etc. and discussed to measure or quantify the market risks in theoretical and practical perspectives. Risk measures are also linked to insurance premiums. Markowitz [18] used the standard deviation to measure the market risk in his portfolio theory but his method doesn’t tell the difference between the positive and the negative deviation. Artzer et al. [1 , 2] proposed a coherent risk measure in an axiomatic approach, and formulated the representation theorems. Fritelli [11] proposed sublinear risk measure to weaken coherent axioms. Heath [14] firstly studied the convex risk measures and Föllmer & Schied [8 , 9 , 10] and Frittelli & Rosazza Gianin [12] extended them to general probability spaces. They had weakened the conditions of positive homogeneity and subadditivity by replacing them with convexity. There exist stochastic phenomena like Allais paradox and Ellsberg paradox which can not be dealt with linear mathematical expectation in economics. So Choquet [4] introduced a nonlinear expectation called Choquet expectation which applied to many areas such as statistics, economics and finance. Choquet expectation is equivalent to the convex(or coherent) risk measure if given capacity is submodular. But Choquet expectation has a difficulty in defining a conditional expectation. Peng [21] introduced a nonlinear expectation, g -expectation which is a solution of a nonlinear backward stochastic differential equation. It’s easy to define conditional expectation with Peng’s g -expectation (see papers [5 , 13 , 15 , 17 , 20 , 22] for related topics). In this paper, we show that the set of priors in the representation of Choquet expectation is the one of equivalent martingale measures under some conditions, when the distortion is submodular. That is, if a capacity c is submodular, then we have the representation where Q_c := { Q ∈ M _1,f : Q [ A ] ≤ c ( A ) ∀ A ∈ F_T }. There is no specific explanation in the literature for the structure of the set Q_c . It is worthy of examining it. By using g -expectation and related topics, we’ll show that for some density generator set Θ ^g . This paper consists of as follows. Introduction is given in section 1. Definitions of Choquet expectation( or integral) and risk measures are stated in section 2. Definition of Peng’s g -expectation and related topics are given in section 3. The set of priors in the representation of Choquet expectation is discussed and the main Theorem 4.4 is given in section 4. In this section, we give definitions of Choquet expectation( or integral) and coherent( or convex) risk measures. Let (Ω,( F_t ) _t∈[0,T] , P ) be the given filtered probability space. Definition 2.1. A set function c : F → [0, 1] is called monotone if and normalized if The monotone and normalized set function is called a capacity . A monotone set function is called submodular or 2- alternating if Two real functions X and Y defined on Ω are called comonotonic if A class of function X is said to be comonotonic if for every pair ( X , Y ) ∈ X × X , X and Y are comonotonic. Definition 2.2. Let ψ : [0, 1] → [0, 1] be increasing function with ψ (0) = 0 and ψ (1) = 1. The set function is called distortion of P with respect to the distortion function ψ . The c_ψ defined in Definition 2.2 becomes normalized monotone function. The notion of integral with respect to a capacity is due to Choquet [4] . Definition 2.3. Let c : F → [0, 1] be monotone and normalized set function. The Choquet integral or concave distortion risk measure of X ∈ L ² ( F_T ) with respect to c is defined as The following is the definition of coherent risk measure of which concept is borrowed from one of norm. Definition 2.4. A coherent risk measure ρ : X → ℝ is a mapping satisfying for X , Y ∈ X (1) ρ ( X ) ≥ ρ ( Y ) if X ≤ Y (monotonicity), (2) ρ ( X + m ) = ρ ( X ) − m for m ∈ ℝ (translation invariance), (3) ρ ( X + Y ) ≤ ρ ( X ) + ρ ( Y ) (subadditivity), (4) ρ ( λX ) = λρ ( X ) for λ ≥ 0 (positive homogeneity). The subadditivity and the positive homogeneity can be relaxed to a weaker quantity, i.e. convexity which means diversification should not increase the risk. A convex risk measure ρ :→ ℝ is a functional satisfying monotonicity, translation invariance and convexity. Definition 2.5. Choquet integral of the loss is defined as where c is a capacity. Choquet integral of the loss ρ : X → ℝ satisfies monotonicity, cash invariance, positive homogeneity and the others. (1) ∫ λdc = λ for constant λ (constant preserving). (2) If X ≤ Y , then ∫ (− X ) dc ≥ ∫ (− Y ) dc (monotonicity). (3) For λ ≥ 0, ∫ λ (− X )dc = λ ∫ (− X ) dc (positive homogeneity). (4) If X and Y are comonotone functions, then . (5) If c is submodular or concave function, then In this section, the definition of g-expectation is given. Let g : Ω×[0, T ]×ℝ×ℝ ⁿ → ℝ be a function that g ↦ g ( t , y , z ) is measurable for each ( y , z ) ∈ ℝ×ℝ ⁿ and satisfy the following conditions Theorem 3.1 ( [21] ) . For every terminal condition ξ ∈ L ² ( F_T ) := L ² (Ω, F_T , P ) the following backward stochastic differential equation has a unique solution Definition 3.2. For each ξ ∈ L ² ( F_T ) and for each t ∈ [0, T ] g −expectation of X and the conditional g −expectation of X under F_t is respectively defined by where y_t is the solution of the BSDE (3.2). 3.1. Two sets of probability measures, and Let g be independent of y and g ( t , y , 0) = 0. We define two sets of probability measures on the measurable space (Ω, F_T ), where t ∈ [0, T ] and Θ ^g is defined as Let’s see properties of set of priors, Q_c as in (1.1). Set Then is a P -martingale since . Also is a P -density on F_T since . A probability measure Q^θ on (Ω, F ) is equivalent to P , where Q^θ is defined as We can easily see that Q_c is convex and weakly compact in L ¹ (Ω, F , P ). For every deterministic τ ∈ [0, T ] and every B ∈ F_τ , where denotes the restriction of Q ³ to F_τ (See the paper [23] for details). If θ ∈ Θ ^g , i.e. θ_t · z ≤ g ( t , z ), then we have θ_t · z ≤ | g ( t , z )| ≤ K | z | and so | θ_t | ≤ K by taking z = θ_t . The Girsanov transformation implies that there exists a probability measure Q^θ on the space (Ω, F_t ) such that and , t ∈ [0, T ] is a Q^θ -Brownian motion. The two prior sets, and are the same set under some conditions. Theorem 3.3 ( [16] ) . Let g be independent of y and satisfy the conditions (3.1a) and (3.1c). Then Definition 3.4. Let g be independent of y and satisfy the conditions (3.1a) and (3.1c). The generator g is said to be sublinear with respect to z if for a ≥ 0, z ₁ , z ₂ ∈ ℝ ^d Theorem 3.5 ( [16] ) . Let g be independent of y and satisfy the conditions (3.1a) and (3.1c). Then ∀ ξ ∈ L ² (Ω, F_t , P ) if and only if g is sublinear with respect to z . The following theorem is about the equivalent properties on the Choquet integral with respect to a capacity c . Theorem 4.1 ( [7] ) . For the Choquet integral with respect to a capacity c, the followings are equivalent . (1) ρ ( X ) := ∫ (− X ) dc is a convex risk measure on L ² ( F_T ). (2) ρ ( X ) := ∫ (− X ) dc is a coherent risk measure on L ² ( F_T ). (3) For Q_c := { Q ∈ M _1,f : Q [ A ] ≤ c ( A ) ∀ A ∈ F_T }, where M _1,f := M _1,f (Ω, F ) is the set of all finitely additive normalized set functions Q : F → [0, 1]. (4) The set function c is submodular . In this case , Q_c = Q_max , where Q_max := { Q ≪ P : α_min ( Q ) = 0} is in the representation of the convex risk measure Peng’s g-expectation provides various features. We will use the properties of g-expectation to investigate the set of prior, Q_c . The classical mathematical expectation can be represented by the Choquet expectation if g is linear function of z . The following theorem deals with the one-dimensional Brownian motion case, and y , z ∈ ℝ. Theorem 4.2 ( [3] ) . Suppose that g satisfies the conditions (3.1a), (3.1b) and (3.1c) . Then there exists a Choquet expectation whose restriction to L ² (Ω, F , P ) is equal to a g-expectation if and only if g is independent of y and is linear in z, i.e. there exists a continuous function ν_t such that Set g ( y , z , t ) = ν_tz through the last of this paper. Then Choquet expectation is equal to g -expectation by Theorem 4.2. I.e., there exist a capacity c_g such that If we take ξ = I_A for A ∈ F in (4.2), then the capacity c_g satisfies Then we can prove that c_g is submodular. Theorem 4.3. The capacity c_g in (4.2) is submodular . Proof . By Dellacherie’s theorem in Dellacherie [6] , Choquet expectation on L ² (Ω, F , P ) is comonotonic additive. That is, if Ɛ_g is Choquet expectation, then we have Ɛ_g [ ξ + η ] = Ɛ_g [ ξ ] + Ɛ_g [ η ] whenever ξ and η are comonotonic. Note that I _A∪B and I _A∩B is a pair of comonotone functions for all A , B ∈ F . Hence comonotonicity and subadditivity of Ɛ_g imply So the proof is done.    ☐ Since c_g is submodular, by Theorem 4.1 we have the representation where Q_cg := { Q ∈ M _1,f : Q [ A ] ≤ c_g ( A ) ∀ A ∈ F_T }. The following is the main theorem. Theorem 4.4. where Q_cg is the prior set in the representation (4.3) . Notice that is defined as where t ∈ [0, T ] and Θ ^g is defined as Proof . Since has the same expression as becomes Q_cg . Since g ( y , z , t ) = ν_tz is independent of y and satisfy the conditions (3.1a) and (3.1c), = by Theorem 3.3. Therefore, we have .    ☐ In fact, for the linear function g ( t , y , z ) = ν_tz , let us consider the BSDE The above differential equation (4.4) is reduced to By Girsanov’s Theorem, -Brownian motion under Q^ν defined as Therefore we have the relations which means g -expectation is a classical mathematical expectation.