Conditional Choquet Capacities on time and on uncertainty
Robert Kast, CNRS, LAMETA, Montpellier and IDEP, Aix-Marseille1, kast@supagro.inra.fr
André Lapied, Université Paul Cézanne, GREQAM and IDEP, Aix-Marseille, a.lapied@univcezanne.fr
Preliminary, 30 November 2007

JEL classification numbers: D 81, D 83, D 92, G 31.
Key words: Capacities, Comonotonicity, Product space and capacities, Conditional
Choquet integrals, Conditional capacities.

1. Introduction

Conditioning capacities has been a problem for many years. The pioneer’s work of Dempster(1967) and Shafer (1967) who presented the first formula opened the way to many researchamong which we can cite (forgetting many) Fagin and Halpern (1991) and Jaffray (1992) who
introduced another rule (the Full Bayesian Updating Rule), Cohen, Gilboa, Jaffray,
Schmeidler (1994) who compared the Bayesian and the Dempster-Shafer rule, etc.
Dempster (1994) used a different approach as he deduced the updating rule (the full Bayesian
updating rule or the Bayesian rule depending on the assumptions) from an implicit definitionof the conditional Choquet integral. Denneberg followed the usual presentation of theconditional Lebesgue integral (Expectation) in classical measure theory handbooks fromwhich Bayes rule for probabilities obtains. Dempster chose the definition : E[EY(X) – X] = 0
that is equivalent in the linear case to E[X – EY(X)] = 0 or to the more usual definition: E(X) =E[EY(X)]. Each of these formulas yields a different (implicit) definition of the conditional
Choquet integral, and, depending on the case, different updating rules (see Kast, Lapied andToqueboeuf (2007) for a general representation of these results). These defining equations
impose some consistency between conditional integrals and non-conditional ones: what is
usually called “dynamic consistency” but may take different expressions depending on themodel. More generally, all models have imposed some consistency axioms between a priori
measures and their updates given information. We know that under the Dynamic Consistency(DC) and the Consequentialism (C) axioms in their general formulation, the valuation will be
1This paper was achieved while Kast was an invited fellow at ICER, Torino. Financial and material support
from ICER, as well as facilities and contacts with colleagues of the mathematics department of the faculty of
Economics at Torino are thankfully acknowledged.

1

linear (see Sarin and Wakker (1998), Machina (1998), Ghirardato (2002), Lapied andToquebeuf (2007) and the relevant literature) and degenerate into additive or quasi additive
measures after several iterations. In this paper, we obtain a different result and non additive
updated capacities, this is because we weaken Consequentialism in the following sense:
Counterfactual events are given a zero measure, however, they may interfere with valuation
because that they may modify the payoffs ranking. Furthermore, this paper departs fromothers where, even though dynamic consistency is invoked, it cannot play its full role because
the models themselves are not really dynamical. By this we mean that the set of states that
represent the future seen from a present date and the information that may arrive “later” areleft without any reference to any real timing. In the practice of managing an investment or a
project and calculating its ex-ante value, the timing where actions have to be taken when
information arrives and may induce options to be exercised, and so on, are fundamental. So
that, Time, as well as Uncertainty, is an important part of our description of the future.

Furthermore, time is economically relevant, it is measured by discount factors: whether
market ones when a market for bonds exists, or individual ones (preference for present overfuture consumption). For instance, Koopman (1972) gave seminal conditions for thisvaluation to be additive (time separability). Then, Gilboa (1989) extended the model to the
non-additive case, following the extensions of Subjective Expected Utility models proposedby Schmeidler (1986 and 1989). Notice that Koopman, Gilboa as well as all their followers(notably, Chateauneuf and Rébillé (2004)) exclude uncertainty considerations.
However, in general, future payoffs are contingent on dates in Time, but also on states(events) of Uncertainty. Here again, axioms yielding additive properties to the representationof preferences were extended by Schmeidler (1989) and Gilboa and Schmeidler (1989) among
others to the non additive case (capacities). This is why in this paper, we decompose
preferences on future payoffs (contingent on futures states in a set Ω) as contingent on two
factors: uncertain states and dates. Formally, we write: Ω = S×T, where S stands for the set ofUncertain states and T represent the set of dates in the future Time under consideration.
Preferences of the decision maker could be defined on Ω, but most of the time they are better
known on S and on T and it is not clear how to define the global value of a cash flow X: Ω =S×T  R from the value of uncertain cash payoffs and the value of time contingent time
payoffs separately: this is the first step of this paper. The object of the paper is to define2dynamically consistent values and the updating of capacities measuring uncertainty and non
additive discount factors measuring time, when information arrives.
In part 2 of the paper, we specify the model : representation of preferences, information, andresults from the Ghirardato-Fubini theorem (Ghirardato 1997) on product spaces. In section 3
and 4 we concentrate on the conditioning of capacities on uncertain states and theconditioning of discount factors under a dynamic consistency and a model consistency
axioms. We verify that consequentialism is violated even though the updated measures are notadditive.

2. The model
We consider that a payoff is a measurable function X: Ω = S×T  R+ where S = {s1, … , sN}
represents the set of uncertain states to whom the payoffs are contingent and T = {1, … , T}
the set of uncertain dates, both with the sets of parts, 2S and 2T, as σ-algebras. Obviously, a
project may have negative payoffs and uncertainty and time may not be perceived as finite
sets, we just restrict the problem to this case in order to concentrate on the principles ofdynamic valuation, i.e. consistency of preferences with information arrivals.

2.1. Representation of preferences

Given we consider finite spaces, we can deal with simple models of representation ofpreferences, namely the generalisation (for finite sets) of de Finetti’s axioms by Diecidue andWakker (2002).

Definition 2.1: Two measurable functions X and Y on a set of states E are comonotonic iff
and for any two states e and e’ : [X(e) – X(e’)][Y(e) – Y(e’)] ≥ 0.
Definition 2.2: A comonotonic set of functions is such that all functions in this set are two by
two comonotonic (notice that in Rm a comonotonic set is a positive cone generated by mlinearly independent comonotonic characteristic functions).

Definition 2.3: A Book is a finite sequence of preferences between two measurable functions(cash flows) (bets or assets, for example): (Xi) , (Yi) , i=1…n, such that each Xi is preferred tothe corresponding Yi.

3

Definition 2.4: A comonotonic Book is formed of functions belonging to the samecomonotonic set.
Definition 2.5: A Dutch Book is a Book such that: Σ Xi < Σ Yi.
We can know state the basis three axioms that define the representations of preferences.

Axiom1: Preferences define a complete preorder on the set on measurable functions.
Axiom 2: There exists a certainty equivalent to any measurable function.
Axiom 3: Preferences allow no comonotonic Dutch Books.

Theorem 2.1 (Diecidue and Wakker, 2002):
For a preference relation on Rm satisfying axioms 1 and 2, such that for all X in Rm their exists
a constant equivalent CE(X)∈ R, the following three statements are equivalent:(i)

CE(X) is strictly monotonic, additive on comonotonic vectors and non necessarily
additive on non comonotonic vectors.

(ii)

There exists a unique capacity such that CE(X) is the integral of X with respect tothis measure.

(iii)

CE(X) is such that axiom 3 is satisfied.

If axiom 3 is replaced by de Finetti’s (1931):
Axiom 3’: Preferences allow no Dutch Books.
Then the a special case of theorem 2.1 obtains with CE additive and a probability as a special
case of a capacity.
The proof of the theorem relies mainly on Diecidue and Wakker’s result proving that No
Dutch Books (comonotonic or not) imply strict monotonicity of the constant equivalent.

The representation theorem yields the three value functions that we need to represent
preferences over the future: Ω. With X: Ω = S×T  R+ the constant equivalent of X definedby the theorem is:
V(X) = V(X) = ∫ΩX dΨ if Ψ is a capacity, ∫Ω4

X dP if P is additive.

V(X) is a present certainty equivalent.
Obviously, Ψ defines two marginal capacities: ν (or µ if it is additive) on RS and ρ (or π if it isadditive) on RT. From the previous representation theorem, we know that these measures
allow to represent preferences of the decision maker over RS and RT that satisfy the same
axioms as the preferences on RΩ.
The certainty equivalent of uncertain payoffs is:∀ζ:RS R+, E(ζ) = ∫Sζ(s) dν(s) if ν is a capacity, E(ζ) = ∫Sζ(s) dµ(s) if µ is additive.

The present equivalent of date contingent payoffs is:∀ξ:RT  R+, D(ξ) = ∫Tξ(t) dρ(t) if ρ is a capacity, D(ξ) = ∫Tξ(t) dπ(t) if π is additive.

In most economic models these representations are assumed to be known and the problem isto define a representation of preferences overs RS×T that is consistent with the previous ones.
Two obvious candidates are:
X: Ω = S×T  R+,

DE(X) = ∫T

[ ∫X(s,t) dν(s)] dρ(t)ED(X) = ∫[∫X(s,t) dρ(t) ] dν(s).

S

S

T

and:

However, it is not the case that Fubini’s theorem applies and the two candidates yield thesame result V(X), if the measures are not additive. This why, in section 2.3, we shall invoke
the Ghirardato-Fubini theorem that will allow us to investigate separately the effect of
information arrivals on both E, D and on V.

In the special case where de Finetti’coherence axiom (axiom 3’) is satisfied, the cash flows’valuation representing the DM’ preferences (constant equivalent) is called the Net Present
Value. Indeed, in this case we have:
NPV(X) = DE(X) = ∫

T

[ ∫

S

X(s,t) dµ(s)] dπ(t)= ∫

S

[∫

T

X(s,t) dπ(t) ] dµ(s) = ED(X)The equalities are obtained because of Fubini’s theorem applied to Lebesgue integrals.
Integrating informational values in the valuation of a cash flow is straightforward: if some
information arrives at some date τ , it is valued at that date by the conditional valuation, sayEτ and the original cash flow is replaced (is equivalent) by the cash flow: (X1, …, Xτ-1,
Eτ(X),0, …, 0)) then discounted according to the usual conditions in dynamic programming.
The aim of this paper is to extend this result, as far as it can be done, to non-additive
valuations.

52.2 Information

Taking future flexibilities and options in an investment or a project into account amounts tointegrate the value of the options to modify it in the project’s present value. The option is
exercised in accordance with information arrivals of the type [Y=i], i∈I. Indeed, when
information [Y=i] obtains, the DM may modify its preferences over the project’s payoffs andhence its valuation. For instance, its aversion to uncertainty (convex capacity) may be
reduced, or increased depending on the type of information (“good” or “bad” news). An otherexample may be that, the DM learning it has more wealth available, its preferences for present
consumption may change.
Let us consider a filtration on 2S: F = {F0, … , FT} with F0 = {∅, S} ⊂ F1 ⊂ … ⊂ FT = 2S.
Information at date t = 1, … , T is given by an Ft-measurable function Yt that defines apartition It of Ft with elements [Yt=it]. For the sake of simplifying notations, we’ll write:
It = (it1,…,itM (t) ) where for j = 1, … , M(t), itj ∈ It and itj stands for the set [s∈S ; Yt(s)∈ itj ].
We assume preferences and conditional preferences satisfy the (Sarin and Wakker (1998)):

Axiom 4 (Model Consistency): Preferences on uncertain payoffs and preferences conditional
on information satisfy the same axioms, in our case:
The preferences conditional on information are represented by: Vit, Eit and Dit ≡ Dt that are
Choquet integrals with respect to capacities: Ψit, νit and πit ≡ πt. (MC)The conditional Choquet integrals (and the corresponding conditional capacities) have to be
defined, at least implicitly, by some Dynamic Consistency requirements.
Such a consistency of valuation with information arrivals can be questioned this way (wedrop the time index in the following given we refer here to static models):
If, for some it, Vit(X) ≥ Vit(X’) can we have: V(X) < V(X’) ?
The answer is yes, there are cases where we could. For instance, assume the set [Y=i]excludes the set on which X < X’, so that on any set in σ([Y=i]), X ≥ X’, then, if preferences
are monotonic we could have a contradiction between unconditional and conditional
valuations2. However we need not have one because all the i’s are possible and the decision
maker may still take into account payoffs for which X < X’ and then not prefer X to X’. But if,

2

For instance it would be the case if the decision maker’s preferences satisfy consequentialim.

6for all i’s, we had X = X’ on [Y=i]c, then, consistency with information arrivals will imply
that:∀it∈I, Vit (X) ≥ Vit(X’) ⇔ V(X) ≥ V(X’).
This equivalence (under the previous condition) is the way Karni and Schmeidler (1983)3, forinstance, expressed Dynamic Consistency (they did it in terms of preferences instead ofvalues as we did and they limited information to a unique value).
We’ll require a similar condition, as expressed for example by Nishimura and Osaki (2003):

Axiom 5 (Dynamic Consistency):∀ τ = 1, … , T–1, ∀ X, X' such that: Xt(s) = X't(s), ∀ t = 0, … , τ–1, ∀ s ∈ S, ∀X, X’,[∀iτ, ∈ Iτ Viτ (X) ≥ Viτ(X’)] ⇒ V(X) ≥ V(X’).

(DC)

Or, in terms of preferences: [∀iτ ∈ Iτ , X f X'] ⇒ X f X'.
≈ iτ

≈In order to address the problem of consistently conditioning V, D and E when V = DE or V =ED, we invoke the following
2.3 Ghirardato-Fubini theorem
The DM has preferences on RΩ that are represented by a Choquet integral with respect to acapacity Ψ on 2Ω:- ∀ X ∈ RΩ V(X) = ∫ X dΨ .
ΩAs Ω = S × T, Ψ yields two marginal capacities, say: ν on 2S and ρ on 2T. In turn, these twocapacity measures define values of uncertain states-contingent payoffs and of future dates
contingent payoffs. These value functions represent preferences over RS and RT that satisfy thesame axioms than preferences on RΩ, so they are represented again by Choquet integrals thatdefine:- ∀ t ∈ T, Xt ≈ t E(Xt) = ∫Xt dν .

- ∀ s ∈ S, Xs ≈ s D(Xs) = ∫Xs dρ .

S
T

Mixing up the marginals and the value function representing preferences on RS and RT by
introducing a hierarchy between the two components that has to be justified, we can define:3

But see also : Sarin and Wakker (1998), Machina (1998) and Ghirardato (2002).

7

- ∀ X ∈ RR×T

ED(X) = E[D(X)] = ∫S[∫

X(s,t) dρ(t)] dν(s).

- ∀ X ∈ RR×T

DE(X) = D[E(X)] = ∫[∫X(s,t) dν(s)] dρ (t) .

T

T
S

These value functions define two orders of preferences that have the same properties as theothers and can be represented by: ED(X) = ∫ X dΨ1 and DE(X) = ∫ X dΨ2 . In general,ΩΩ

Ψ1, Ψ2 and Ψ will not coincide but in some particular cases that we shall consider.
As we shall see, the hierarchy between preferences on time and on uncertain states can bejustified by some hedging properties, then, in these cases, it will be possible to show thecoherence of the different preferences and of the measures they define.
Now let us recall some definitions introduced by Ghirardato (1997):

Definition 2.3 (Slice-comonotonicity):- X ∈ RS × T is T-slice (resp. S-slice) comonotonic, if for all t in T, its t-sections on RS (resp. forall s in S, its s-sections on RT) are comonotonic.
- X ∈ RS × T is slice comonotonic, if all its t-sections and its s-sections are comonotonic.
- A set F ⊂ RS×T= RΩ is said to be comonotonic if all the s-sections of its characteristic
function 1F are comonotonic, which is equivalent to: all its t-sections are also comonotonic
and then to: 1F is slice comonotonic.
Lemma 2.3: For any T-slice comonotonic X such that ∀t∈T, Xt∈Ck where Ck is acomonotonic class, k∈{1, … , N!}, then ν is represented by a probability measure µk and∀t∈T, E(Xt) = ∫ Xt dν = ∫ Xt dµk.
SSFor any S-slice comonotonic X such that ∀s∈S, Xs∈Ch where Ch is a comonotonic class,h∈{1, … , T!}, then ρ is represented by a probability measure πh and∀s∈S, D(Xs) = ∫ Xs dρ = ∫TT

Xs dρh.

Proof: If X is T-slice comonotonic, ∀ t ∈ T, X(.,t) belongs to some comonotonic class, say Ck,
k=1, … ,N! of RS. A comonotonic class Ck is generated by a basis of linearly independent
comonotonic characteristic functions of sets Ak1⊂ Ak2⊂ … ⊂ AkN = RS. Then, because we
assumed all payoffs to be non negative, if Xt is in the comonotonic class Ck :N

∃ (α1t, …, αNt)∈RS+, Xt = ∑ αit 1A ik .
i=1

8

Furthermore, we know that a capacity ν on 2S is additive on each comonotonic class so that:∀ Ck, k=1, … ,N! ∃µk additive so that:∀ Xt∈Ck, E(Xt) = ∫NS

Xt dν = ∑ αit ν(A ik )= ∫S

i=1

Xt dµk.

Similarly, if X is S-slice comonotonic, then ∀ s ∈ S, X(s,.) belongs to some comonotonic class,say Ch, h=1, … ,T! of RT that is generated by a basis of linearly independent comonotonic
characteristic functions of sets Bh1⊂ Bh2 ⊂ … ⊂ BhT = RT andT∃ (β1s, …, βTs)∈RT+, Xs = ∑ βis 1B ih .
i=1

Furthermore, we know that a capacity ρ on T is additive on each comonotonic class so that:∀ Ch, h = 1, … ,T!

∃πh additive such that:∀ Xs∈Ch, D(Xs) = ∫Xs dρ = ∑ βis ρ(B ih ) = ∫

T

T

T

i=1

Xs dπh.

In order to be able to use some of Ghirardato (1997) results, we need to introduce a new
axiom on preferences that insure that preferences on RΩ, RS and RT are consistent. Consistencyof marginal preferences on state or on date contingent payoffs, and global preferences on cash
flows can be expressed by: The measures ν, ρ and Ψ are such that Ψ can be reconstructedfrom ν and ρ. Obviously, this is requiring too much in general, there are someintertwinements between state and date contingency that may induce some preferences to bemodified when future payoffs are seen as whole. However, when no hedging possibilities areavailable neither on uncertain payoffs nor on certain cash flows, we can impose someconsistence of the DM’s behaviour (notice that the hedging argument is the one used to justify
the comonotonic additivity, or comonotonic independence, or No comonotonic Dutch books
axiom).

Axiom 6 (Comonotonic Consistency):
If F is a comonotonic subset of Ω = S×T, and (Ft)t∈T and (Fs)s∈S are its projections, i.e.: Ft ={s∈S / 1F(s, t)= 1}, Fs = {t∈T / 1F(s, t) = 1} then preferences must be such that:[ ∀t∈T E(1F(., t)) = ν(Ft) and ∀s∈S D(1F(s, .)= ρ(Fs)] ⇒ V(1F) = Ψ(F).

9Obviously, the axiom is always satisfied by definition of the marginals if F = A×A’, A ⊂ S, A’⊂ T. Assume the axiom is not satisfied by a DM, for example, we have for some trajectory s,a measure ρ’ on Time ρ≠ρ’ with ρ’ convex while ρ is not. This would mean that the DM ismore time-variations averse when confronted to a certain time contingent cash flow trajectorys than it is if the payoffs were part of a state and date contingent flow. Obviously, that could
be acceptable if F were not comonotonic, but being so, it offers no possibilities for hedging
time-variations so that the two different measures are inconsistent.

Proposition 2.3.1: Under the comonotonic consistency axiom, the condition that Ghirardato(1997) dubbed “the Fubini property” is satisfied: For any comonotonic subset F of 2 S×T,Ψ(F) = D(ν[{s∈S / (s,t)∈F]) = E(ρ[{t∈T / (s,t)∈F]), or:∫S×T 1F(s,t) dψ(s,t) = ∫T dρ(t) ∫S 1F(s,t) dν(s) = ∫S dν(s) ∫T 1F(s,t) dρ(t).
Proof: Notice that ∫ 1F(s,.) dν(s) is comonotonic with any of the 1F(.,t), t∈T, so that thereS

exists an additive probability πh on T such that:∫T [ ∫S 1F(s,t) dν(s)]dρ(t) = ∫T [ ∫S 1F(s,t) dν(s)]dπh(t) and∀s∈S ∫ 1F(s,t) dρ(t) = ∫ 1F(s,t) dπh(t).
T

But ∫

T

T

1F(.,t) dπh(t) is comonotonic with any of the 1F(s,.), s∈S so that there exists anadditive probability µk on S such that :∫S [ ∫T 1F(s,t) dπh(t)]dν(s) = ∫S [ ∫T 1F(s,t) dπh(t)]dµk(s).
Fubini theorem applies and ∫ [ ∫S

T

1F(s,t) dπh(t)]dµk(s) = ∫T[ ∫ 1F(s,t) dµk(s)]dπh(t).
SThis yields the second equality of the lemma.
The first equality obtains because µk and πh define a product probability, say Φj on S×T, butΦj is an additive representation of Ψ valid on the comonotonic class Cj, j=1, …, (N×T)! (inRΩ) to which 1F belongs. But this is true for any comonotonic class, so the Φj’s define Ψ.
QEDNow, Ghirardato’s theorem (in fact, his lemma 3) yields the following decomposition ofpreferences on RS×T and preferences on RS and on RT:

10

Proposition 2.3.2 (Ghirardato): Under the comonotonic consistency axiom, if preferences
on RS×T are represented by V (defined by capacity Ψ) and preferences on RS by E (capacity ν)and on RT by D (capacity ρ), we have:
1- If X in RS×T is T-slice comonotonic, then: V(X) = E[D(X)].
Furthermore, for any comonotonic class Ck in RS containing all the comonotonic t-sections ofX, there exists a probability distribution µk defining an additive representation Ek of
preferences on Ck such that for any X’ with all its comonotonic t-sections in Ck:
V(X’) = Ek[D(X’)].
2- If X in RS×T is S-slice comonotonic, then: V(X) = D[E(X)].
Furthermore, for any comonotonic class Ch containing all the comonotonic s-sections of X,
there exists a probability distribution πh defining an additive representation Dh of preferences
on Ch such that for any X’ with all its comonotonic s-sections in Ch:
V(X’) = Dh[E(X’)].
3- If X in RS×T is slice comonotonic, then: V(X) = E[D(X)] = D[E(X)].

Interpretations:
The first two results are lemma 3 of Ghirardato (1997). The additive representation (valid
only on one comonotonic class) is interpreted this way:
For the first one, consider a model consistent with Gilboa’s (1989) idea in which time ismeasured by an non decreasing, non negative and bounded measure (a capacity in our specialcase). In this model, uncertainty has not been taken into account of. Now, if we add it at eachdate, we obtain our model. However, because all the uncertain variables are comonotonic,
comonotonic additivity applies and we only need to know the probability distribution that
represents it on each comonotonic class. This can, but need be to be, extended to the whole
space of uncertain variables, assuming then that de Finetti’s coherence axiom applies.
The second formula is the usual discounted expected (here in the sense of a Choquet integral)
payoffs. Notice that discount factors (probabilities, here) depend on the comonotonic class inwhich no hedging of time variations can be obtained.
The last result is the famous Ghirardato-Fubini theorem applied to our model.

11In the two next section, we shall address the problem of conditioning NPV expressed in termsof Choquet integrals and derive some results about conditional capacities using the
Ghiraradato-Fubini theorem.

3. Conditional valuation of S-slice comonotonic cash payoffs
In this section, we consider S-slice comonotonic cash payoffs X: S × T  R+, i.e. payoffs suchthat their time variations along trajectories go all in the same way and hence can't be hedged.
From Ghirardato theorem (proposition 2.3.2 , part 2) preferences of the DM are represented
for all cash payoffs in a comonotonic class Ch in RT, by V = DhE where Dh is linear,
expressing the fact that for the cash payoffs at stake the DM is time variations neutral. From
now on, we’ll drop the h index. Notice that, while Eit and νit as well as Dit ≡ Dt and πit ≡ πthave to be defined, at least implicitly by Dynamic Consistency requirements.

We assume X is a F–measurable process, and in order to introduce the present (as a date 0 thathas no other role than defining an eventual present (hence certain) cash amount:X0(s1) =…= X0(sN) = X0) the set of all possible cash flows generated by X is (we use the same
notation X for X(S × T)):

 X 0 (s1 ) … X T (s1) 
X = …
…  with, ∀ t ∈ T, ∀ s ∈ S, Xt(s) ≥ 0 and X0(s1) =…= X0(sN) = X0.
 X 0 (sN ) … X T (sN )
V (X) = ∑ [ ∑ X t (s)∆ν (s)]π (t) = ∑ E t (X)π (t) ,t ∈T s∈St ∈T

where: ∀ t ∈ T, E t (X) = ∑ X t (s)∆ν (s) , with the usual notation for a Choquet integral:s∈Sif, for instance, X(s1) ≤…≤ X(sN), then: ∆ν(sn) = ν(sn ∪…∪ sN) – ν(sn+1 ∪…∪ sN), sN+1 = ∅.
Define: E 0 (X) … E T (X)

EC(X) =  …
… , E 0 (X) … E T (X)with π(0) = 1,we have:
V[EC(X)] = V(X).
Therefore, EC(X) is a certainty equivalent of X.

12

From the assumption of Model Consistency we have the same type of value functions for agiven information at some time τ.
∀ τ ∈ T, ∀ iτ ∈ Iτ, V iτ (X) = ∑ [ ∑ X t (s)∆ν iτ (s)]π τ (t) = ∑ E tiτ (X)π τ (t) ,t ∈T s∈St ∈T

where: ∀ t = 0,…,τ – 1, πτ(t) = 0, πτ(τ) = 1, ∀ t = τ,…,T, E tiτ (X ) = ∑ X t (s)∆ν iτ (s) ,s∈Sand define: E iτ (X ) … E iτ (X )
T τ
EC iτ (X) =  …
… ,
 E iτ (X ) … E iτ (X )
T
 τ

with:

V iτ [EC iτ (X )] = V iτ (X).

3.1. Dynamic consistency

In section 2, we have introduced a weak definition of Dynamic Consistency (Axiom 5), whichgave a link between unconditional and conditional valuations.

Proposition 3.1.1: (DC) implies: ∀ τ = 1,…,T–1, ∀ t = τ,…,T,∑ [ ∑ X t (s)∆ν iτ (s)]∆ν (iτ ) = ∑ X t (s)∆ν (s)iτ ∈Iτ s∈S

(3.1)

s∈S

Proof: ∀ τ = 1,…,T–1, define Zτ by the following:∀ t = 0,…,τ–1, ∀ s ∈ S, Xt(s) = Zτt(s), andl∀ t = τ,…,T, ∀ s ∈ S, ∃ l ∈ {1, … , M(τ)}, such that s ∈ iτl and then Z tτ (s) = E tiτ (X).
T

∀ iτ ∈ Iτ, V (Z ) = ∑[ ∑ E ti (X)∆ν iτ (i)]π τ (t) .
iτ

τ

t=τ i ∈IτSuppose w.l.o.g. that, for a date t = τ,…,T:
m(τ )

1

E tiτ (X) ≤ … ≤ E tiτ

M (τ )(X ) ≤ … ≤ E tiτ

(X ), for which iτ = iτm (τ ) .

Then:

∑
i ∈Iτ

E ti (X)∆ν iτ (i) =M (τ )lm(τ )∑ E tiτ (X )[ν iτm(τ )(iτ1 ∪…∪ iτl ) − ν iτ

l=1

13

(iτ1 ∪…∪ iτl−1 )]m (τ )−1

m(τ )

l

∑ E tiτ (X)[ν iτ

=

m(τ )

(iτ1 ∪…∪ iτl ) − ν iτ(iτ1 ∪…∪ iτl−1 )]

l=1
m(τ )

+ E tiτM (τ )

+

m(τ )

m(τ )

(iτ1 ∪…∪ iτm (τ ) ) − ν iτ(X)[ν iτm(τ )l

∑ E tiτ (X)[ν iτ

l=m (τ )+1(iτ1 ∪…∪ iτm (τ )−1 )]m(τ )(iτ1 ∪…∪ iτl ) − ν iτ(iτ1 ∪…∪ iτl−1 )] .

With the normalisation of conditional capacities:i ⊂ A ⇒ νi(A) = 1, i ∩ A = ∅ ⇒ νi(A) = 0,m(τ )∑ E ti (X)∆ν iτ (i) = E tiτi ∈Iτ(X) = E tiτ (X) .

It follows that:T∀ iτ ∈ Iτ, V iτ (Z τ ) = ∑ E tiτ (X)π τ (t) = V iτ (X).
t=τTherefore, with (DC), we have: V (Z τ ) = V (X) , which implies:τ −1T

t=0 s∈S

t=τ iτ ∈Iτ∑[ ∑ X t (s)∆ν (s)]π (t) + ∑[ ∑ E tiτ (X)∆ν (iτ )]π (t) = ∑ [ ∑ X t (s)∆ν (s)]π (t),t ∈T s∈S

and then:T∑[ ∑E tiτ (X)∆ν (iτ )]π (t) =t=τ iτ ∈Iτ

T∑[ ∑ X t (s)∆ν (s)]π (t) .

t=τ s∈S

This equality is satisfied for any X, and then it should be true for each date t:∑ E tiτ (X)∆ν (iτ ) = ∑ [ ∑ X t (s)∆ν iτ (s)]∆ν (iτ ) = ∑ X t (s)∆ν (s)iτ ∈Iτ

iτ ∈Iτ s∈S

(3.1)

s∈S

QED

3.2 Updating capacities

Relation (3.1) is a condition on the DM’s (representation) of preferences that yields animplicit definition of conditional Choquet expectation.

Proposition 3.2.1: Under relation (3.1), for any i ∈ Iτ, the conditional capacity of a set A ∈ Ft,
t > τ, is given by:(i)

If A ⊂ i,ν i (A) =ν (A ∩ i)(Bayes updating rule).
ν (i)

14

(ii)

If AC ⊂ i,

ν i (A) =ν (A ∪ iC ) − ν (iC )(Dempster-Schafer updating rule).
1− ν (iC )Proof: With these notations, relation (3.1), became:∑ [ ∑ X t (s)∆ν i (s)]∆ν (i) = ∑ X t (s)∆ν (s)i ∈Iτ s∈Ss∈S

For the determination of conditional capacities, payoffs shall be defined as characteristic
functions. For A ∈ Ft, the payoffs of Xt at date t are 1A, and then:i M (τ )(3.2)

ν (A) = ∑ν i (A)∆ν (i) .
i =i 1

The conditional capacity νi(A) can only be calculated in two situations:(i) When A ⊂ i, the "comonotonic" case (because 1A and 1i are comonotonic uncertain
variables). In this case, νi(A) ≥ 0 and νj(A) = 0, for j ∈ Iτ, j ≠ i. Relation (3.2) implies:ν(A) = νi(A) ν(i), and then ν i (A) =ν (A) ν (A ∩ i)=
, Bayes formula.
ν (i)
ν (i)

(ii) When AC ⊂ i, the "antimonotonic" case (because 1A and 1i are anticomonotonic, i.e. 1A and–1i are comonotonic uncertain variables). In this case, νi(A) ≤ 1 and νj(A) = 1, for j ∈ Iτ, j ≠ i.
Relation (3.2) implies:ν(A) = νi(A) + [1 – νi(A)] ν(iC), and then ν i (A) =ν (A) − ν (iC ) ν (A ∪ iC ) − ν (iC )=,1− ν (iC )1− ν (iC )Dempster-Shafer formula. QED

3.3. Consequentialism

An other familiar consistency condition known as consequentialism (Hammond (1989)) is
usually imposed on preferences. It is well known, however (see, for instance Sarin andWakker (1998), Machina (1998), Karni and Schmeidler (1991), Ghirardato (2002), Lapied
and Toquebeuf (2007)) that Model consistency, Dynamic consistency and Consequentialism
imply additive (or quasi always additive) models. Ours is not, under the two first assumptions,then it must be that Consequentialism is not satisfied, as we show below.

Definition 3.3.1 (Consequentialism in a dynamic setting):∀ τ = 0,…,T, ∀iτ ∈ Iτ , [∀ t = τ ,…,T, ∀ s ∈ iτ, Xt(s) = X’t (s)] ⇒ X ≈iτ X ' (C)15

Proposition 3.3.1: V = DE does not satisfy (C).
Proof: Let us consider S = {s1,s2,s3,s4}, I1 = {i1,i2}, i1 = {s1,s2}, i2 = {s3,s4}, T = {0,1,2} andtwo risks X, X', with the following payoffs:
X0 = X’0 = 12,
X1({s1}) = X1({s2}) = X’1({s1}) = X’1({s2}) = 10,
X1({s3}) = X1({s4}) = X’1({s3}) = X’1({s4}) = 9,
X2({s1}) = 8, X2({s2}) = 4, X’2({s1}) = 0.2, X’2({s2}) = 0.4,
X2({s3}) = X’2({s3}) = 2, X2({s4}) = X’2({s4}) = 1.

Because X and X’ are S-slice comonotonic cash payoffs, we can apply DE valuation.
2

2

For these risks, consequentialism, implies that V1i ( X ) = V1i ( X ') .
We have:
2

2

2

2

2

V1i ( X ) = 9 + π 1 (2){1 × [1 − ν i ({s1, s2 , s3 })] + 2 × ν i ({s1, s2 , s3 })},
2

V1i ( X ') = 9 + π 1 (2){1 × [1 − ν i ({s3 })] + 2 × ν i ({s3 })}.

From Proposition 3.2.1:- Because {s3} ⊂ i2 its conditional capacity is given by Bayes updating rule:2ν i ({s3 }) =ν ({s3}).
ν (i 2 )- Because {s1,s2,s3}C = {s4} ⊂ i2 its conditional capacity is given by Dempster-Schafer
updating rule:ν ({s1,s2 ,s3 }) − ν (i1 )ν ({s1,s2,s3 }) =.
1− ν (i1 )i2

Let π1(2) = 0.9, and ν be a convex (non linear) capacity with:

ν({s3}) = 0.3, ν(i1) = 0.5, ν(i2) = 0.4, ν({s1,s2,s3}) = 0.9.
2

2

We obtain: V1i (X) = 10.62 > V1i (X') = 10.575 ,which is a contradiction to Consequentialism.

QED

Non consequentialism open the way to non linearity of the valuation.

Proposition 3.3.2: V = DE does not reduce to discounted expected cash flows.

16

Proof: We consider the same example as in proposition 3.3.1.
We have:111V1i (X) = 10 + π 1 (2){4 × [1− ν i ({s1})] + 8 × ν i ({s1})},111V1i (X') = 10 + π 1 (2){0.2 × [1− ν i ({s2 ,s3 ,s4 })] + 0.4 × ν i ({s2 ,s3 ,s4 })}.
From Proposition 3.2.1:- Because {s1} ⊂ i1 its conditional capacity is given by Bayes updating rule:1ν i ({s1}) =ν ({s1}).
ν (i1)- Because {s2,s3,s4}C = {s1} ⊂ i1 its conditional capacity is given by Dempster-Schafer
updating rule:1ν i ({s2 ,s3,s4 }) =ν ({s2,s3 ,s4 }) − ν (i 2 ).
1− ν (i 2 )If we add the following values to the definition of ν:ν({s1}) = 0.3, ν({s2,s3,s4}) = 0.6, we obtain:1

2∑ X 2 (s)∆ν i (s) = 6.4 , ∑ X 2 (s)∆ν i (s) = 1.8, and
s∈S

s∈S
1

0.8

2

∑ X'2 (s)∆ν i (s) = 3 , ∑ X'2 (s)∆ν i (s) = 1.75.

s∈S

s∈S

Relation (3.1) is trivially satisfied for τ = t = 1, we have only to consider the case where τ = 1
and t = 2:

∑ [ ∑ X 2 (s)∆ν i (s)]∆ν (i) = 4.1 = ∑ X 2 (s)∆ν (s) ,i ∈I 1 s∈Ss∈S∑ [ ∑ X'2 (s)∆ν i (s)]∆ν (i) = 0.86 = ∑ X'2 (s)∆ν (s) .

i ∈I 1 s∈Ss∈S

Therefore, relation (3.1) is consistent with the (non linear) capacity ν and with the conditional
capacities defined by proposition 3.2.1.

QED

4. Conditional valuation of T-slice comonotonic cash payoffs

In this section we concentrate on cash payoffs with all their t-sections in the samecomonotonic class in RS, say Ck, hence their uncertain variations can't be hedged. As aconsequence of Ghirardato’s theorem (proposition 2.3.2, part 1), preferences on RS×T are
represented by a V = EkD valuation function, with Ek a Lebesgue integral with respect to a17

probability distribution µk on 2S and D a Choquet integral with respect to a capacity ρ. In thefollowing, we drop the index k.
We know that information only bears on 2S , its only influence on preferences on RT is on thedate at which it is obtained. Otherwise stated: Dit ≡ Dt is a Choquet integral with respect to acapacity ρt that is contingent on date t only, while Eit is the usual conditional expectation andµ it is obtained by the probabilistic Bayes’rule.
We have first to adapt the valuation to this new decomposition between time and uncertainty:
V (X) = ∑ [ ∑ X t (s)∆ρ (t)]µ(s) = ∑ Ds (X)µ(s) ,s∈S t ∈Ts∈S

where: ∀ s ∈ S, Ds (X) = ∑ X t (s)∆ρ(t) .
t ∈TDefine: D (X) 0 … 0  s1
ET(X) =  …
…
… , D (X) 0 … 0  sNwith ρ(0) = 1, we have:
V[ET(X)] = V(X).
Therefore, ET(X) is a temporal equivalent of X.
By the assumption of Model Consistency, we have the same type of value functions for agiven pair time-information.
∀ τ ∈ T, ∀ iτ ∈ Iτ, V iτ (X) = ∑ [ ∑ X t (s)∆ρ τ (t)]µiτ (s) = ∑ Dsτ (X)µiτ (s) ,s∈S t ∈Ts∈S

where: ∀ s ∈ iτ, Dsτ (X) = ∑ X t (s)∆ρ τ (t) ,t ∈Tand define: Dτ (X) 0 … 0  s1τET (X) =  …
…
… , Dτ (X) 0 … 0  sNwith ρτ(τ) = 1, we have: V iτ [ET τ (X)] = V iτ (X ) .
We want to give a null ponderation to the payoffs in the past, when we discount. For this,
suppose that: ∀ F ⊂ T, ρτ(F) = ρτ(F ∩ τ+), where τ+ = {τ,…,T}.

4.1 Dynamic consistency

18

Dynamic consistency has the same definition than in section 2.

Proposition 4.1.1: (DC) implies :∀ τ = 1,…,T–1, ∀ s ∈ S,

∑ X tτ (s)∆ρ(t) = ∑ X t (s)∆ρ(t)
t ∈τ

−

(4.1)

t ∈T

where: X tτ (s) = X t (s), if t ∈ τ − − {τ }, X tτ (s) = Dsτ (X), if t = τ ,τ– = {0,…,τ}, and∀ s ∈ S, Dsτ (X) = ∑ X t (s)∆ρ τ (t) .
t ∈TProof: ∀ τ = 1,…,T–1, define: X … X (s ) Dτ (X) 0 … 0 τ −1 1s1
 0

Zτ =  …
…
…
… .
 X … X (s ) Dτ (X) 0 … 0 τ −1 NsN 0

∀ τ = 1,…,T–1, ∀ t = 0,…,τ–1, ∀ s ∈ S, Xt(s) = Zτt(s).
For any s ∈ S, consider a permutation of the t = 0,…,τ – 1 such that:0 ≤ X (0) (s) ≤ … ≤ X (k ) (s) ≤ Dsτ (X) ≤ X (k +1) (s) ≤ … ≤ X (τ −1) (s) .
(k )∑ Z tτ ∆ρτ (t) = ∑ X (t ) (s){ρτ [(t),…,(τ −1), τ ] − ρτ [(t + 1),…,(τ −1), τ ]}t ∈T(t)=(0)+Dsτ (X){ρ τ [(k + 1),…,(τ −1), τ ] − ρ τ [(k + 1),…,(τ −1)]}+(τ −1)∑ X (t ) (s){ρτ [(t),…,(τ −1)] − ρτ [(t + 1),…,(τ −1)]}(t )=(k +1)= Dsτ (X) ρ τ (τ ) = Dsτ (X).
∀ iτ ∈ Iτ, V iτ (Z τ ) = ∑ [ ∑ Z tτ ∆ρ τ (t)]µiτ (s) = ∑ Dsτ (X)µiτ (s) = V iτ (X).
s∈S t ∈Ts∈S

Therefore, with (DC), we have: V (Z τ ) = V (X) , which implies:∑ [ ∑ X tτ (s)∆ρ(t)]µ(s) = ∑ [ ∑ X t (s)∆ρ(t)]µ(s) ,s∈S t ∈τ −s∈S t ∈T

where: X tτ (s) = X t (s), if t ∈ τ − − {τ }, X tτ (s) = Dsτ (X), if t = τ .
This equality is satisfied for any X, and then it should be true for each state s:∀ τ = 1,…,T–1, ∀ s ∈ S,

19

∑ X tτ (s)∆ρ(t) = ∑ X t (s)∆ρ(t)
t ∈τ

−

(4.1)

t ∈T

QED

4.2 "Upstating" capacities on Time

If updating means that we modify the measure of uncertainty according to information atsome date, then we dubb "upstating" the fact that we modify the measure of time according to
information (on the set of states) at the date at which it is obtained. We still assume that theDM’s preferences satisfy the axiom of Dynamic Consistency, i.e. relation (4.1) so that we
have:

Proposition 4.2.1: Under relation (4.1), for F ⊂ T, with τ– = {0,…,τ}, and τ+ = {τ,…,T},
the “upstated” discount factors are given by:(i)

If ρ(F) ≥ ρ[(F ∩ τ – ) ∪ {τ }]:ρ (F) – ρ[(F ∩ τ – ) ∪ {τ }] + ρ({τ })ρ (F ∩ τ ) =.
ρ ({τ })
τ

(ii)

+

If ρ(F) ≤ ρ[(F ∩ τ – ) ∪ {τ }]:ρ τ (F ∩ τ + ) =ρ(F) – ρ(F ∩ τ – ).
ρ[(F ∩ τ – ) ∪ {τ }] – ρ(F ∩ τ – )Proof: We drop the reference to the state s in relation (4.1) wlog:(4.2)∑ X tτ ∆ρ(t) = ∑ X t ∆ρ(t) .

t ∈τ −t ∈T

For F ⊂ T and X = 1F, we have:∑ X t ∆ρ(t) = ρ(F), and Dτ (X) = ∑ X t ∆ρτ (t) = ρτ (F ∩ τ + ) .
t ∈τ +t ∈T

We have to consider two cases:(i)ρ τ (F ∩ τ + ) ≥ 1, then:∑ Xτt ∆ρ(t) = ρ[(F ∩ τ – ) ∪ {τ }] + ρ({τ })[ρτ (F ∩ τ + ) – 1] = ρ(F) ,t ∈τ −(ii)ρ τ (F ∩ τ + ) ≤ 1, then:∑ Xτt ∆ρ(t) = ρ[(F ∩ τ – ) ∪ {τ }]ρτ (F ∩ τ + ) + ρ(F ∩ τ – )[1 – ρτ (F ∩ τ + )] = ρ(F) .

t ∈τ −

20

These relations yield the “upstating” formulas under the equivalent conditions given in the
proposition. QEDIn the more familiar case where F = {0,…,T} we have the following:

Corollary 4.2.1: Under relation (4.1), for F = {0,…,T}, the “upstated” discount factors are
given by:ρ τ ({τ ,…,T}) =ρ({0,…,T}) – ρ ({0,…, τ }) + ρ({τ }).
ρ ({τ })The interpretations of these “upstating” formula are not straightforward. An important point tonote, is that in contrast with the additive case, i.e. the usual compound discount factors
formula, which, in the corollary, would yield: π τ ({τ ,…,T}) =π ({τ ,…,T}), the value of theπ ({τ })

past does count. We shall come back to this in section 4.3 where we’ll see that it is what
violates consequentialism. Here, in contrast with the case of conditioning uncertainty,
comonotonicity doesn’t plays a direct role. In fact it does through the ranking of values
obtained before and after information is revealed at date τ.

4.3. Consequentialism

With the definition of consequentialism in section 3, we have:

Proposition 4.3.1: V = ED does not satisfy (C).

Proof: Let us consider the following certain payoffs X and X':
X0 = 1, X1 = 0, X2 = 0, X3 = 1, X4 = 1,

X'0 = 0, X'1 = 1, X'2 = 0, X'3 = 1, X'4 = 1,

or:

X’ = 1H, H = {1,3,4}.

X = 1F, F = {0,3,4},

Let ρ be a capacity such that: ρ(F) = ρ(0,3,4) > ρ(0,2) = ρ[(F ∩ τ–) ∪ {τ}], andρ(H) = ρ(1,3,4) < ρ(1,2) = ρ[(F ∩ τ–) ∪ {τ}].
From Proposition 4.2.1,

ρ 2 (F ∩ τ + ) = ρ 2 (3,4) =ρ (0,3,4) − ρ(0,2) + ρ(2)
>1
ρ(2)

21

case (i)ρ 2 (H ∩ τ + ) = ρ 2 (3,4) =ρ (1,3,4) − ρ(1)<1ρ(1,2) − ρ(1)case (ii)

For these payoffs, consequentialism, implies that V2 ( X ) = V2 ( X ') . We have:4

V2 ( X ) = ∑ X t ∆ρ 2 ( t ) = D2 ( X ) = ρ 2 ( F )t=0= ρ 2 (F ∩ τ + ) = ρ 2 (3,4) =ρ (0,3,4) − ρ(0,2) + ρ(2)
> 1,
ρ(2)

4

V2 ( X ') = ∑ X ' t ∆ρ 2 ( t ) = D2 ( X ') = ρ 2 ( H )t=0= ρ 2 (H ∩ τ + ) = ρ 2 (3,4) =ρ (1,3,4) − ρ(1)< 1,
ρ(1,2) − ρ(1)We obtain: V2 (X) > V2 (X ') , which is a contradiction to consequentialism.

QED

Proposition 4.3.2: V = ED does not reduce to linear discounted cash flows.

Proof: We consider the same example as in proposition 4.3.1.
We have:
2

4

t=0

t=0

2

4

t=0

t=0

∑ X t2∆ρ(t ) = 1 × [ρ(0,2) − ρ(2)] + D2 ( X ) × ρ(2) = ρ(0,3,4) = ∑ X t ∆ρ(t ) ,∑ X '2t ∆ρ( t ) = D2 ( X ') × [ρ(1,2) − ρ(1)] + 1 × ρ(1) = ρ(1,3,4) = ∑ X 't ∆ρ(t ) ,therefore relation (4.1) is satisfied for τ = 2.
The same results holds for τ = 1 and τ = 3.
Relation (4.1) is then consistent with a (non linear) capacity ρ and with the conditional
capacities defined by proposition 4.2.1.

QED

5. Conclusions

The conclusion are left to the reader…

22