Third-generation prospect theory
Ulrich Schmidt Department of Economics, Christian-Albrechts-Universität zu Kiel, D-24098 Kiel, and Kiel Institute for the World Economy, 24105 Kiel, Germany. Chris Starmer School of Economics, University of Nottingham, Nottingham, NG7 2RD, UK. Robert Sugden School of Economics, University of East Anglia, Norwich, NR4 7TJ, UK.
We present a new theory of decision under uncertainty: third-generation prospect theory
(PT3). This retains the predictive power of previous versions of prospect theory, but extends
that theory by allowing reference points to be uncertain while decision weights are specified
in a rank-dependent way. We show that PT3 preferences respect a state-conditional form of
stochastic dominance. The theory predicts the observed tendency for willingness-to-accept
valuations of lotteries to be greater than willingness-to-pay valuations. When PT3 is made
operational by using simple functional forms with parameter values derived from existing
experimental evidence, it predicts observed patterns of the preference reversal phenomenon.
Acknowledgements The authors thank Serge Blondel, Louis Lévy-Garboua, William Neilson, Peter Wakker,
Horst Zank and anonymous referees for helpful comments on earlier drafts. We have also
benefitted from discussions with participants at various conferences and seminars where we
have presented this work. Sugden’s work was supported by the Economic and Social
Research Council (award no. RES 051 27 0146).
Keywords: Prospect theory, preference reversal, reference dependence JEL classifications: D81 (Criteria for decision-making under risk and uncertainty)
In this paper we present a new theory of decision under uncertainty: third-generation prospect theory (PT3 for short). The motivation for the theory is empirical: our model is presented as a
descriptive theory intended to outperform the current ‘best buys’ in the literature. PT3 has
three key features: reference dependence, decision weights and uncertain reference points
(i.e. reference points that can be lotteries). The first two features are the common
characteristics of different versions of prospect theory, including the original (or first-
generation)version (Kahneman and Tversky, 1979) and the later cumulative (or second-
generation) versions featuring rank-dependent decision weights (e.g. Starmer and Sugden,
1989; Luce and Fishburn 1991; Tversky and Kahneman, 1992; Wakker and Tversky 1993).
Variants of cumulative prospect theory are increasingly widely applied in both theoretical and
empirical work (recent examples are Davies and Satchell 2004; Trepel, Fox and Poldrack,
2005; Wu, Zhang and Abdellaoui, 2005; Baucells and Heukamp, 2006; Schmidt and Zank
2008) and some have argued that such theories may be serious contenders for replacing
expected utility theory at least for specific purposes (see Camerer, 1989). No doubt this is
partly because there is considerable empirical support for both reference-dependence and
First- and second-generation prospect theory have a common limitation: the reference
points from which prospects are evaluated are assumed to be certainties. If reference points
are interpreted as endowments or status quo positions, these theories cannot be applied to
problems in which a decision maker is endowed with a lottery and has the opportunity to sell
or exchange it. Such problems are common in real economic life – for example, buying
insurance or selling stocks. They also feature in many experimental designs and, in
consequence, a good deal is known about how in fact decision-makers respond to them. This
evidence shows two particularly well-established and robust patterns of deviation from the
predictions of conventional expected utility theory.
The first is that willingness-to-accept (WTA) valuations of lotteries tend to be greater
than willingness-to-pay (WTP) valuations (e.g. Knetsch and Sinden, 1984; Loomes, Starmer
and Sugden, 2003). Intuitively, one might expect that loss aversion, as modelled in prospect
theory, would explain this effect; but because a WTA valuation of a lottery is made from a
reference point at which the decision-maker is endowed with that lottery, that intuition cannot
be expressed in existing versions of prospect theory.
The second deviation is preference reversal (PR). The classic instances of PR involve
decisions relating to pairs of gambles. In the simplest cases, gambles are binary lotteries with
just one positive outcome (the prize); the other outcome is zero. One of the lotteries, usually
called the ‘P bet’, gives the better chance of winning a prize while the other, the ‘$ bet’, has
the larger prize. In a typical experiment, agents’ preference orderings over pairs of such bets
are elicited in two ways: in a pairwise choice task, and by comparing WTA valuations of
lotteries elicited separately for P and $ bets. There is a widely observed tendency for agents
to reveal a preference for the P bet in choice but the $ bet in valuation. We will call this
pattern standard PR. Such inconsistencies between choice and valuation might arise through
chance or error. But the opposite inconsistency, in which the $ bet is chosen but the P bet is
given a higher value (non-standard PR), is much less frequently observed. It is this
asymmetry between the two types of reversal which constitutes the puzzle of PR. Because
existing versions of prospect theory cannot deal with WTA valuations of lotteries, they cannot
We have set ourselves the task of generalisingcumulative prospect theory so that it
can encompass uncertain reference points. Thus, PT3 inherits all the descriptive success (and,
of course, any descriptive failure1) of cumulative prospect theory. As we shall show, it can
also explain both PR and the WTA/WTP disparity for lotteries.
In all versions of prospect theory, the subjective value of a given monetary lottery,
viewed from a given certain reference point, is a weighted average of the subjective values of
the monetary gains or losses associated with the lottery outcomes. Gains and losses are
defined relative to the reference point; an increasing value function (analogous with a utility
function in expected utility theory) is used to transform these into positive or negative indices
of subjective ‘value’. These indices are then aggregated by using decision weights, which are
determined by using a probability weighting function (a strictly increasing mapping from the
interval [0, 1] onto itself) which assigns to each probability p a transformed probability w(p).
In cumulative prospect theory, this function is used in a rank-dependent way, which we
describe in more detail in Section 2. A zero gain is defined to have zero value, and so has no
impact on the overall value of the lottery. Decision weights for gains are assigned
cumulatively, beginning with the largest gain and working downwards. A mirror-image
method is used to assign weights to losses. As an illustration of this operation applied to
gains, consider a lottery which gives monetary gains of zero, £5 and £10 with respective
probabilities 0.5, 0.3 and 0.2. The weight for the largest positive gain, £10, is determined by a
direct transformation of the relevant probability, giving the weight w(0.2). The decision
weight for the £5 gain is then defined as w(0.2 + 0.3) – w(0.2). Notice that the sum of the
decision weights for the £5 and £10 gains is w(0.5), i.e. the transformation of the sum of the
corresponding probabilities. This construction ensures that, for any two lotteries A, B, if A
stochastically dominates B, then A is preferred to B, viewed from any certain reference point.
A generalisation of this theory to uncertain reference points requires two components.
The first is a definition of ‘gain’ and ‘loss’ relative to such reference points. Our approach, in
the tradition of Savage (1954), is to define preferences over acts, where an act is an
assignment of consequences to states of the world. A stochastic reference point is modelled
as a particular act, the reference act. Gains and losses are defined separately for each state of
the world. For example, consider a lottery with tickets numbered 1, ., 100; one ticket will be
drawn at random and its owner will win £100. Consider an agent who is endowed with ticket
1 and who treats this as her reference act. She is offered the opportunity to exchange ticket 1
for ticket 2. If she consents to this exchange, there is a 0.01 probability that ticket 1 will be
drawn, in which case she will be £100 worse off than if she had kept her initial endowment.
There is also a 0.01 probability that ticket 2 will be drawn, in which case she will be £100
better off. If any other ticket is drawn, she is neither better off nor worse off. Thus, the
option of taking ticket 2 in exchange for ticket 1, viewed from the reference act, gives £100
gain with probability 0.01, £100 loss with probability 0.01, and no change in wealth with
The second component for a generalisation is a method for determining decision
weights when reference points are uncertain. Given our Savage-style framework, we need a
rank-dependent method of assigning decision weights to any act f, viewed relative to any
reference act h. For any state of the world s, we use f(s) and h(s) to denote the outcomes of f
and h in that state. Our approach is to rank states in terms of the ex post net gain from
choosing f rather than h – that is, by the value of f(s) – h(s); separate rankings are constructed
for ‘gain’ states (for which f(s) – g(h) is positive) and ‘loss’ states (for which it is negative).
These rankings are then used to determine decision weights, just as rankings of outcomes are
used in second-generation prospect theory. This approach ensures that preferences respect a
state-conditional form of stochastic dominance, even when the reference point is uncertain.
By using these two components, any parameterised form of second-generation
prospect theory can be generalised to the case of uncertain reference points. No additional
parameters are required. This is significant, because the empirical literature provides a lot of
information about the parameterisations of prospect theory that are most successful in
organising experimental data, and about the values that the various parameters typically take.
Thus, we can investigate whether PT3 explains observations of behaviour in decision
problems with uncertain reference points, using parameterisations whose empirical validity
We show that PT3 predicts the observed WTA/WTP disparity for lotteries, given only
the standard assumption of loss aversion in the value function. Perhaps more surprisingly, we
show that, when PT3 is configured with parameterisations typical of those already established
in the empirical literature, it predicts standard patterns of PR. Consequently we suggest that
PT3 is a best buy theory: it offers the predictive power of previous variants of prospect theory
and adds to that an explanation of PR. The latter comes ‘free of charge’ since it involves no
extra parameters and no re-parameterisation.
2. PT3 in its Most General Form
In this section we introduce PT3. In this theory, preferences are defined over Savage acts.
Consider a finite state space S, consisting of the states si, i = 1, …, n, and a set of consequences X given by an interval of the real line. In interpreting the theory, we treat
consequences as levels of wealth. As in the case of second-generation prospect theory, PT3
can be formulated either for risk or for uncertainty. In this paper, we restrict attention to risk.
The extension to uncertainty is straightforward and is exactly as in second generation prospect
theory. For decision making under risk each state si has an objective probability πi ≥ 0, with ∑i πi = 1. F is the set of all acts. A particular act f ∈ F is a function from S to X, i.e. an act f
specifies for each state si the resulting consequence f(si) ∈ X.
As in other versions of prospect theory, preferences over acts are reference-dependent.
We formalise this following the approach of reference-dependent subjective expected utility
theory (RDSEU), as proposed by Sugden (2003). For any three acts f, g, h, f f h g denotes
that f is weakly preferred to g viewed from h, the reference act. (The corresponding relations
of strict preference and indifference are written as f φh g and f ~h g.) For present purposes the
reference act can be interpreted as the status quo position. We depart from earlier versions of
prospect theory by not requiring h to be a constant act (i.e. an act which gives the same
consequence in every state). Instead, we adopt a key innovation of RDSEU.
Sugden’s axiom system implies maximisation of the function:
In this expression, v(f[si], h[si]) is a relative value function. It can be interpreted as the desirability of the consequence of act f in state si relative to the consequence of a reference act h in the same state. This function is strictly increasing in its first argument, with v(f[si], h[si]) = 0 when f(si) = h(si). The function V(f, h) is the expectation of relative value. It assigns a
real value to any act f ∈ F viewed from any reference act h ∈ F (i.e. V: F × F→ R ). It is a
preference representation in the sense that, for all f, h, g in F, f f h g ⇔ V(f, h) ≥ V(g, h).
In opting for the RDSEU approach, we have made a significant modelling decision.
This approach is based on a state-contingent conception of reference-dependence. That is,
gain/loss comparisons are made separately for each state of the world; thus, the pattern of
gains and losses associated with any act f, viewed from any reference act h, depends on the
state-contingent juxtaposition of consequences in f and h. An alternative approach, proposed
by Kőszegi and Rabin (2006, 2007), takes no account of state-contingency. Instead,
reference-dependent preferences are defined over prospects (i.e. probability distributions over
consequences). Notice that two acts can have different assignments of consequences to states
of the world, and thus be distinct acts, while inducing the same probability distribution over
consequences and so being represented by a single prospect. Implicitly, the Kőszegi–Rabin
(KR) approach treats such acts as equivalent to one another. It specifies the expected relative
value of any prospect A, viewed from any (certain or uncertain) reference prospect R, in such
a way as to coincide with the implications of the RDSEU specification (1) in the special case
in which A and R are stochastically independent.2 Since this condition holds trivially when R
is a degenerate prospect, the KR and RDSEU approaches coincide when reference points are
However, the KR approach has paradoxical implications. Consider any two non-
degenerate acts f and h that induce the same probability distribution of consequences A.
What is the preference ranking of f and h, viewed from h? The KR approach treats this
problem as if f and h were stochastically independent. If those acts were in fact independent,
moving from h to f would induce gains in some states and losses in others. These gains and
losses exactly offset one another in money terms, but if preferences are loss-averse (as is
assumed in most versions of prospect theory), the subjective impact of the losses outweighs
that of the gains. Expressed in the language of RDSEU, we have h φh f, which makes
intuitive sense if the acts really are perceived as stochastically independent. In fact, there is
experimental evidence of such preferences: subjects who have been endowed with numbered
lottery tickets are typically unwilling to exchange these for equivalent but differently
numbered tickets, even when offered small payments for doing so (Bar-Hillel and Neter,
1996).3 But in the KR framework, this result has to be expressed in terms of reference-
dependent preferences over prospects. If A is the prospect that represents the acts h and f, we
reach the conclusion that A is strictly preferred to A, viewed from A, with the apparent
implication that the agent has a strict preference for ‘keeping’ some lottery rather than
‘exchanging’ it for exactly the same lottery. One way to avoid such paradoxical conclusions
would be to interpret the KR theory as assuming that each lottery under evaluation is
perceived by the agent as stochastically independent of the reference point. On this
interpretation, the KR theory is less general than RDSEU, but yields the same conclusions in
The preference representation in (1), like that proposed by KR, is linear in
probabilities. PT3 relaxes this restriction of RDSEU by generalising (1) to:
V(f, h) = Σi v(f[si], h[si]) W(si ; f, h)
where W(si; f, h) is the decision weight assigned tostate si when f is being evaluated from h. In principle, decision weights could be determined by a simple transformation of state
probabilities (i.e. W(si; f, h) = w(πi)) as in Handa (1977). However, in the contemporary
literature on prospect theory it has become conventional to construct decision weights
cumulatively using a rank-dependent transformation (Quiggin, 1982; Starmer and Sugden,
1989; Tversky and Kahneman, 1992). One of the key theoretical rationales for the cumulative
construction is that, unlike first-generation prospect theory (which included some elements of
Handa’s approach), it results in monotonic preferences. That is, if one prospect stochastically
dominates another, the first is preferred to the second when viewed from any certain reference
point. In PT3 we retain the rank-dependent approach, but reconfigure it to work with state-
Given that we have chosen to follow the RDSEU approach to the definition of
reference-dependence, there is little remaining freedom of manoeuvre for choosing how to
specify rank-dependent decision weights. In order to construct cumulative weights for a
given f, h pair, states must be ordered according to the ‘attractiveness’ of f’s consequences in
each state. In a cumulative construction, the weight attached to a given state depends not only
on the probability of that state but also on the position of its consequence in the ranking of all
consequences associated with f. To ensure that monotonicity is preserved when probabilities
are transformed, it is essential that the ‘attractiveness’ ranking is determined by the function
whose decision-weighted expected value represents preferences. In RDSEU, this is the
relative value function. Thus, the position of each state si in the ranking must be determined by the ranking of v(f[si], h[si]) values. This implies that the ordering of states must be constructed separately for each f, h pair. Further, if we are to generalise cumulative prospect
theory, we must have separate rank-dependent transformations of probability for gains and
losses, and these transformations must be mirror-images of one another.
Consider any f, h pair. Relative to that pair, there is a weak gain in a state si if f(si) ≥
h(si), and a strict loss if f(si) < h(si). Let m+ be the number of states in which there are weak gains and let m– = n – m+ be the number of states in which there are strict losses. We re-
assign subscripts so that, for all subscripts i, j, we have i > j if and only if v(f[si], h[si])≥
v(f[sj], h[sj]), and so that the states with weak gains are indexed m+, ., 1 and the states with strict losses are indexed –1, ., – m–.
Cumulative decision weights are then defined as follows:
w+( ∑(j ≥ i) πj) – w+( ∑(j > i) πj)
w–( ∑(j ≤ i) πj) – w–( ∑(j < i) πj)
where w+ and w– are, respectively, probability weighting functions for the gain and loss
domains (i.e. w+, w– are strictly increasing mappings from [0, 1] onto [0, 1]). This
specification has two important implications. First, if h is a constant act, (3) implies the same
assignment of decision weights as in cumulative prospect theory. Thus, the monotonicity
property of cumulative prospect theory carries over to PT3. That is, for all acts f, g, and for all
constant acts h, if the probability distribution of consequences induced by f stochastically
dominates that induced by g, then f is strictly preferred to g, viewed from h. Second, a state-
contingent form of monotonicity holds for all reference acts. For any acts f, g, we will say
that f statewise dominates g if f(si) ≥ g(si) for all states si, with a strict inequality for at least
one state with non-zero probability. It follows from (2) and (3) that if f statewise dominates
g, then for all reference acts h, f φh g.
PT3, as specified by (2) and (3), straightforwardly captures several models as special
cases. RDSEU is the special case in which decision weights are untransformed state
probabilities (i.e. w+(πi) = w–(πi) = πi for all i). Cumulative prospect theory is the special case
in which the relative value function takes the form v(f[si], h[si]) = u(f[si] – h[si]), where u(.) is a ‘value’ function,4 and in which reference acts are constrained to be certainties (i.e. h(si) = h(sj) for all i, j). Expected utility theory is the special case in which decision weights are untransformed state probabilities, as in RDSEU, and relative value is independent of the
reference outcome (i.e. v(f[si], h[si]) = u(f[si]) where u(.) is a von Neumann-Morgenstern utility function).
3. A Parameterised Form of PT3
One way of evaluating PT3 as a descriptive theory is to test its novel predictions – that is, the
predictions it makes about behaviour in decision problems with uncertain reference points –
using parameterisations whose validity has been established for earlier versions of prospect
theory. This approach requires that we select specific functional forms for our general model.
In doing this we are guided by three criteria. First, we seek a model flexible enough to allow
us to investigate how decision-making behaviour varies with three key aspects of the agent’s
preferences: attitudes to consequences, attitudes to probability, and attitudes to gain and loss.
Second, subject to that constraint, we seek to use the simplest model possible – that is, a
model with just one parameter for each of the three attitudes we consider. Third, for
comparability with existing evidence, we use wherever possible the functional forms that are
most common in previously published research.
We begin by imposing the restriction that the relative value function takes the form
v(f[si], h[si]) = u(z), where z = f(si) – h(si). When h is a constant act, this special case of state-contingent reference-dependence is equivalent to that built into earlier generations of prospect
theory; u(.) is the counterpart of the value function in those theories.
Next, we specify a functional form for u(.). We adopt the power function which has
been widely used in recent empirical literature (see Starmer 2000). Specifically,
The parameters α and λ are required to be strictly positive. The first of these parameters
controls the curvature of the value function. If α < 1, this function is concave in the domain
of gains and convex in the domain of losses (the property of diminishing sensitivity).
Diminishing sensitivity imparts a tendency for risk aversion with respect to gains and risk-
loving with respect to losses. While the empirical literature has suggested some differences in
the exponents of the value function between the domains of gains and losses, in the interests
of parsimony we will apply the same exponent in both domains. The parameter λ controls
attitudes to gain and loss. With λ = 1 there is loss neutrality. For λ values above unity, there
is loss aversion: losses are weighted more heavily than gains. For values below unity, the
We model decision weights by means of a single-parameter probability weighting
function. Again, for reasons of parsimony we impose the restriction of identical weighting
functions for gains and losses (i.e. w+(π) = w–(π)). Hence for the purpose of parameterisation
the probability weighting function is denoted simply by w(π); it takes the form
with β > 0. This type of weighting function has been discussed by Tversky and Kahneman
(1992) and Prelec (1998); variants of it have been widely used in the empirical literature.
With β = 1, decision weights are linear (i.e. w(π) = π) but for values of β less than 1 and
going down to around 0.4, the function generates an inverse-S pattern of weights with over-
weighting (under-weighting) of probabilities below (above) some critical probability π*.
Inverse-S weighting has been reported across a wide range of empirical studies (Wu and
Gonzalez, 1996, 1999; Abdellaoui, 2000; Bleichrodt and Pinto, 2000; Abdellaoui, Vossmann
That completes the specification of the generic model to be used in the calibrations.
We will refer to this specification as parameterised PT3.
Notice that, when applied to cases in which reference acts are certainties,
parameterised PT3 can also be interpreted as a parameterisation of cumulative prospect theory.
In fact, models of this kind have already been estimated using experimental data (e.g. Tversky
and Kahaneman, 1992; Loomes, Moffatt and Sugden, 2002). Thus, parameter values from
these estimations are applicable to our model.
We take the following to be relatively well-established stylised facts concerning the
median values of the three parameters for experimental subjects. First, many studies suggest
the existence of loss aversion, while its opposite is almost unknown; values of the loss
aversion parameter in the range 1 ≤ λ ≤ 2.5 would capture a reasonably wide range of
evidence. Studies fitting variants of prospect theory with power utility almost invariably find
diminishing sensitivity. Although some studies have found values of α as low as 0.22
(Loomes, Moffatt and Sugden, 2002, note 17), values in the range 0.5 ≤ α ≤ 1 are typical.
Inverse-S probability weighting, while not universal, is a very common finding; it would be
reasonable to expect values of β in the range 0.5 ≤ β ≤ 1. These ranges of values will be the
focus for evaluating the predictions of our model.
4. Explaining WTA/WTP Disparities for Lotteries
We now show that parameterised PT3 implies that WTA valuations of lotteries exceed WTP
valuations whenever λ > 1. This is not a surprising prediction: intuitively, one might expect
the WTA/WTP disparity to be explained by loss aversion, which is represented in our model
by the parameter restriction λ > 1. Nevertheless, as we pointed out in Section 1, previous
versions of prospect theory have not been able to make this prediction. Further, it is
significant that our model makes a clear-cut prediction about WTA/WTP disparities despite
the complications introduced by decision weights.
The hypothesis that individuals tend to prefer to retain status quo positions rather than
to move away from them can be expressed in terms of the non-reversibility of reference-
dependent preferences.5 We will say that an agent’s preferences are non-reversible if, for all
acts f and g, f •g g ⇒ f •f g; they are strictly non-reversible if f •g g ⇒ f φf g. Intuitively,
suppose the agent is endowed with g and is willing to exchange this for f. Non-reversibility
implies that if she makes this exchange, and if her reference point adjusts to f, she does not
then have a strict preference for reversing the exchange. Given the assumptions built into
parameterised PT3, strict non-reversibility implies the WTA/WTP disparity. To see why, let g
be any act, let h be a constant act which gives consequence z in every state, and suppose that
h ~g g. Then z is the WTA valuation of g. Let h′ be a constant act, giving z′ in every state,
such that h′ ~h′ g. If income effects are zero, as implied by the assumptions of parameterised
PT3, h′ is the WTP valuation of g. If preferences are strictly non-reversible, the supposition h
~g g implies h φh g: the agent would not be willing to pay z to get g. Thus, by virtue of the
monotonicity properties of PT3, the WTP valuation of g is less than the WTA valuation.
We now show that parameterised PT3 satisfies strict (weak) non-reversibility if λ > 1
(if λ ≥ 1). Consider any two acts f and g. Let F be the set of states si such that f(si) > g(si) and
let G be the set of states sj such that g(sj) > f(sj). Combing (2) with the parameterisation of the relative value function:
V(f, g) = Σi∈F (f[si] – g[si])α W(si ; f, g) – Σj∈G λ(g[sj] – f[sj])α W(sj ; f, g)
V(g, f) = Σj∈G (g[sj] – f[sj])α W(sj ; g, f) – Σi∈F λ(f[sj] – g[sj])α W(si ; g, f).
The specification of decision weights in (3) implies W(si ; f, g) = W(si ; g, f) for all i ∈ F and
W(sj ; f, g) = W(sj ; g, f) for all j ∈ G. Hence:6
V(f, g) + V(g, f) = (1 – λ) (Σi∈F [f(si) – g(si)]α W[si ; f, g] +
Σj∈G [g(sj) – f(sj)]α W[sj ; g, f]).
The right-hand side of (8) is positive, zero, or negative according to whether λ is less than,
equal to, or greater than unity. Thus, the parameter restriction λ > 1 implies V(f, g) + V(g, f)
< 0 or, equivalently, f •g g ⇒ f φf g, i.e. strict non-reversibility. Similarly, λ ≥ 1 implies weak
5. Explaining Preference Reversal
In this section, we apply parameterised PT3 to preference reversal experiments.7 For
simplicity, we restrict attention to P and $ bets that give either a positive payoff or zero. This
case has been widely studied in the empirical literature. Consider two acts with this structure.
Specifically, let fP represent an act giving an increment of wealth x with probability p and a
zero increment otherwise, and let f$ be an act giving an increment of wealth y with probability
q and a zero increment otherwise, with y > x > 0 and 1 > p > q > 0. As a normalisation, we
define consequences as increments or decrements of wealth relative to the agent’s wealth
(treated as a certainty) prior to the PR experiment. This pre-experimental endowment is
denoted by the constant act h, where h(si) = 0 for all i.
A feature of the power utility function used in parameterised PT3 is that model
predictions are unchanged if all outcomes are multiplied by any positive constant. Exploiting
this property, we may normalise the expected value of the P bet to unity by setting its payoff x
= 1/p. Given this normalisation, we can characterise any pair of P and $ bets by a three-
parameter vector (p, q, r), where p is the probability of winning the prize in the P bet, q is the
corresponding probability for the $ bet, and r is the expected value of the $ bet as a ratio of
the expected value of the P bet (implying that the positive payoff of the $ bet is y = r/q).
Notice that the condition y > x (i.e. the $ bet has the higher prize) implies rp > q.
Consider an agent choosing between the two bets in the PR experiment. Her reference
point is the constant act h. Using (2), (3) and (4), the agent’s choice between the two bets is
(9) fP f h f$ ⇔ w(p) (1/p)α ≥ w(q) (r/q)α;
the values of w(p) and w(q) are given by (5).
Now consider the agent’s WTA valuations of the bets. Take the case of the P bet.
The agent is endowed with this bet and is asked to consider selling it. Her situation is
depicted by the following matrix (in which the columns are states, with probabilities shown at
the top, the rows are acts, and the entries in the cells are consequences):
Her reference act, denoted hP, is the P bet. Her WTA valuation of this bet, denoted zP, is the increment of wealth such that she is indifferent between retaining hP or giving up hP in
exchange for the certainty of that increment. Hence, we define zP as the sure payoff of some constant act gP defined such that V(gP, hP) = 0. With z$ defined in an analogous way, the values of zP and z$ are then determined, respectively, by the solutions to equations
Substituting in the parameterisation of w(.), the solutions of these equations are:
(12) zP = (1/p) / ([(1 – p)/ p]β/α (1/ λ)1/α + 1) and
(13) z$ = (r/q) / ([(1 – q)/ q]β/α (1/ λ)1/α + 1].
It is easy to see that the preference ranking of the bets, as given by (9), need not be the
same as the ranking of their WTA valuations, as given by (12) and (13). A sufficient
condition for the two rankings to be the same, and hence for PR not to occur, is the
combination of parameter values λ = 1, α = 1 and β = 1, implying that both choice and
valuation are determined by the maximisation of expected monetary value. In general,
however, differences between the two rankings can be induced by asymmetric attitudes to
gain and loss (i.e. λ ≠ 1), non-linearity of the value function (i.e. α ≠ 1), or non-linearity of
the probability weighting function (i.e. β ≠ 1).
To see that attitudes to gain and loss are relevant, it is sufficient to notice that λ
appears in both (12) and (13) but not in (9). To see that the shape of the probability weighting
function is relevant, it is sufficient to notice that the terms w(1 – p) and w(1 – q) appear in
(10) and (11) respectively, but not in (9). To see that the curvature of the value function is
relevant, notice that the choice and valuation expressions use different points on this function.
For example, in the case of the P bet (for which the prize is 1/p), the choice expression
includes the term (1/p)α, whereas the valuation expression includes the terms z α
Given all this, it would not be surprising to find (as is in fact the case) that some
combinations of parameter values induce standard PR, that is, the conjunction of fP φ f$ and zP
< z$. But our objective is to do much more than this: it is to explore whether our model provides an empirically convincing account of observed instances of PR. We therefore need
to ask whether standard PR is predicted when the model is calibrated using empirically-
It is convenient to explore the implications of the model graphically in (α, λ) space.
This space is divided into quadrants by the lines α = 1 and λ = 1. The stylised facts presented
in Section 3 suggest that we should focus on the north-west quadrant, in which the value
function is either linear or exhibits diminishing sensitivity (i.e. α ≤ 1) and in which there is
either loss neutrality or loss aversion (i.e. λ ≥ 1). We call this the empirically plausible
For any given pair of bets and any given value of the decision weight parameter β, (α,
λ) space can be divided into four regions by identifying two boundaries. One boundary – thechoice boundary – identifies the locus of (α, λ) pairs along which the P and $ bets are
indifferent in choice (i.e. fP ~h f$). A second boundary – thevaluation boundary – is the locus
of (α, λ) pairs along which the P and $ bets have equal WTA valuations.
The following property of the choice boundary is an immediate implication of (9):
Property 1: The choice between P and $ is independent of the value of λ. For any
given value of β, there is a critical value of α at which the two bets are indifferent. At
lower values of α, P is chosen; at higher values, $ is chosen.
This property reflects the fact that, in the choice task, all consequences are positive or zero.
Thus, loss aversion has no role in determining choice. Because the negative domain of the
value function is not relevant for this task, diminishing sensitivity (i.e. α < 1) plays essentially
the same role in PT3 as diminishing marginal utility does in expected utility theory: the lower
the value of α, the greater the attractiveness of the safer P bet relative to the riskier $ bet.
An examination of the expression for zP/z$, derived from (12) and (13), yields the
Property 2: As λ increases, the value of zP/z$ falls; in the limit, as λ → ∞, this value
In other words, increases in the loss aversion parameter λ increase z$ relative to zP; at
sufficiently high values of λ, we have z$ > zP. Intuitively, this is because the act of selling a
bet carries the risk of losing the prize of that bet in the state in which the bet wins; since the $
bet has the higher prize, the potential for loss in selling it is greater. Thus, loss aversion
induces a particular reluctance to sell the $ bet.
Properties 1 and 2 are enough to give a preliminary sense of some of the combinations
of parameter values that will induce PR. In order for the P bet to be selected in the choice
task, α must be lower than some critical value. Given any such value of α, the $ bet will have
the higher WTA valuation if the value of λ is sufficiently high. Thus, standard PR is induced
by the combination of sufficiently low α and sufficiently high λ.
Figure 1 plots the choice and valuation boundaries for a typical pair of bets, defined by
(p, q, r) = (0.8, 0.2, 1), with β = 1. This particular combination of parameters will be called
the benchmark case. Standard PR occurs in the region above the valuation boundary and to
the left of the choice boundary; non-standard PR occurs in the region below the valuation
boundary and to the right of the choice boundary. The two boundaries intersect at (1, 1).
(This reflects the fact that, when α = 1, λ = 1 and β = 1, our model reduces to the
maximisation of expected value; since the two bets have equal expected value, they are
equally preferred and have equal valuations.) In this benchmark case, the empirically
plausible quadrant is made up of two sub-regions, separated by the valuation boundary.
Above this boundary there is standard PR. Below it, the P bet is both preferred in the straight
choice and valued more highly. Thus, for the benchmark case, our model predicts the classic
asymmetry between standard and non-standard reversals: the former occur at parameter
values within the empirically plausible quadrant, while the latter do not.
It is easy to see that this qualitative conclusion is unaffected by changes in the values
of the probability parameters p and q (given the defining condition p > q). Whenever r = 1
and β = 1, the choice and valuation boundaries intersect at (1, 1). Thus, P is chosen
everywhere in the empirically plausible quadrant, ruling out the possibility of non-standard
PR. There is always a non-empty region of this quadrant, to the left of the choice boundary
and above the valuation boundary, in which standard PR occurs.
We now consider the effect of changes in the value of r (the expected value of the $
bet as a ratio of that of the P bet). Figure 2 plots the choice and valuation boundaries for r =
0.8, r = 1.2 and r = 1.4 when the other parameters take their benchmark values (i.e. p = 0.8, q
= 0.2, β = 1). As r increases, both boundaries shift to the left, expanding both the region in
which the $ bet is chosen and the region in which it has the higher valuation. The intuition for
this is straightforward: an increase in r increases the $-bet prize relative to the P-bet prize, and
so makes the $ bet relatively more attractive. Notice that, at all values of r, standard PR
occurs at some points in the empirically plausible quadrant, while non-standard PR occurs
only outside that quadrant. If r > 1, the empirically plausible quadrant is made up of three
sub-regions. Below the valuation boundary, the P bet is favoured in both choice and
valuation. To the right of the choice boundary, the $ bet is favoured in both choice and
valuation. Above the valuation boundary and to the left of the choice boundary, there is
In the cases we have considered so far, our model has consistently predicted the
classic asymmetry between standard and non-standard PR in the empirically plausible
quadrant. However, it has failed to predict another stylised fact about PR experiments: that,
even when the two bets have equal expected value, a significant proportion of subjects not
only value the $ bet more highly but also choose it in preference to the P bet. Because of the
role of diminishing sensitivity in choice, our model predicts that P will be chosen whenever r
= 1, α < 1, and β = 1. To show that this is not a problem for our approach, we note that the
benchmark assumption β = 1 is an extreme case – the case in which the probability weighting
function is linear. We now consider the implications of assuming lower values of β, that is,
Figure 3 plots the choice and valuation boundaries for three empirically plausible
values of β, namely 0.9, 0.75 and 0.6. The other parameters take their benchmark values (i.e.
p = 0.8, q = 0.2, r = 1). Essentially, the effect of reducing the value of β is to shift both
boundaries to the left, expanding the regions in which the $ bet is favoured. The intuition for
this is that, as the value of β falls, small probabilities (such as 0.2, the probability that the $
bet wins) are increasingly overweighted while large probabilities (such as 0.8, the probability
that the P bet wins) are increasingly underweighted. The resulting configurations of choice
and valuation boundaries are similar to those generated by setting r > 1. Again, the
empirically plausible quadrant of (α, λ) space is made up of three sub-regions. In one, the P
bet is favoured in both choice and valuation; in another, the $ bet is favoured in both choice
and valuation; in the third, there is standard PR.10
All the diagrams we have presented so far have the common feature that standard PR
occurs only when λ > 1, and non-standard PR occurs only when λ < 1. The reader should not
infer from this that loss aversion is essential if PT3 is to predict standard PR. To the contrary,
both standard and non-standard PR are compatible with λ = 1 for some pairs of bets. Figures
4 and 5 illustrate these possibilities for, respectively, the pairs of bets (0.8, 0.4, 1) and (0.6,
0.2, 1) with β = 0.7.11 Conversely, PT3 can predict standard PR in cases in which the model
differs from expected utility theory only in respect of loss aversion. (Consider the case in
which p = 0.8, q = 0.2, r = 0.8 and β = 1, shown in Figure 2. Notice that standard PR occurs
at some points at which α = 1 and λ > 1.) But these cases depend on special assumptions
about the characteristics of the two bets. In contrast, the classic PR phenomenon occurs
across a wide range of values of p, q and r. Because our model includes the effects of loss
aversion, diminishing sensitivity and inverse-S probability weighting, it is able to explain PR
across the range in which it has been observed.
A further feature of the PR phenomenon has been identified by Tversky, Slovic and
Kahneman (1990). Consider any pair of P and $ bets faced by any given agent. Suppose that
standard PR occurs: the P bet is strictly preferred in the choice task, but the $ bet has the
higher WTA valuation. Let gP and g$ be the constant acts defined so that, for all states si, gP(si) = zP and g$(si) = z$ (where, as before, zP and z$ are the WTA valuations of the respective bets). Now consider the ranking of the four acts fP, f$, gP, g$, viewed from the constant
reference act h, where h(si) = 0 for all si. The occurrence of standard PR implies fP φh f$ and
(on the assumption that preferences are monotonic) g$ φh gP. How can these apparently
conflicting preferences be reconciled? One possibility is that fP φh gP. That is, given a
pairwise choice between the P bet and a certainty equal to its WTA valuation, the agent would
strictly prefer the bet. If this is the case, the agent is said to have underpriced the P bet. A
second possibility (not incompatible with the first) is that g$ φh f$. That is, in a pairwise
choice between the $ bet and a certainty equal to its WTA valuation, the agent would strictly
prefer the certainty. If this is so, the $ bet has been overpriced. If neither of these
possibilities is the case, we have fPφh gP, gP •h fP, fP φh f$, and f$ •h g$, which is a violation of
transitivity. Tversky et al. report an experiment designed to discriminate between these
possibilities; they find that standard PR is most commonly associated with overpricing of the
This observation is consistent with PT3. At the parameter values that induce standard
PR, our model predicts overpricing of both the $ and P bets. We show this for the $ bet; the
analysis for the P bet is essentially the same. The $ bet is overpriced if it is strictly preferred
$, viewed from h. This is the case if and only if z$ > w(q) (r/q)α, which
Combining this inequality with (10), the equation which defines z$, the condition for overpricing is:
$ – w(q)([r/q]α – z$ ) > w(1 – q)z$ – w(q)λ([r/q] – z$)α .
As one would expect, the LHS and RHS of (15) are equal (and equal to zero) if λ = 1, α = 1
and β = 1 (implying w(q) = q and w(1 – q) = 1 – q). In this case, z$ = r, i.e. the WTA
valuation of the bet is its expected monetary value, and there is neither underpricing nor
Now consider the implications of variations in the agent’s attitude to gain and loss. It
is easy to see that, if α and β are held constant at the values α = 1 and β = 1, the inequality
(15) is satisfied if and only if λ > 1. In other words, loss aversion induces overpricing.
Alternatively, consider the implication of variations in the agent’s attitude to consequences.
If λ and β are held constant at λ = 1 and β = 1, (15) is satisfied if and only if α < 1.12 Thus,
diminishing sensitivity induces overpricing. Finally, consider variations in the agent’s
attitude to probability. If λ and α are held constant at λ = 1 and α = 1, (15) is satisfied if and
only if β < 1.13 Thus, inverse-S probability weighting induces overpricing.
Bringing these results together, PT3 explains PR as a result of the interaction of
empirically plausible degrees of loss aversion, diminishing sensitivity and probability
weighting. The same attitudes induce the ‘overpricing’ effect observed by Tversky et al.
We have presented a new theory of choice under uncertainty: third generation prospect theory
(PT3). PT3 retains the empirically grounded features of previous variants of prospect theory
(loss aversion, diminishing sensitivity and non-linear probability weighting), but extends that
theory by allowing reference points to be uncertain. The resulting theory retains all the
predictive power of those previous variants, but in addition provides a framework for
determining the money valuation that an agent places on a lottery. We have shown that PT3
predicts the observed tendency for willingness-to-accept (WTA) valuations of lotteries to be
greater than willingness-to-pay (WTP) valuations. More surprisingly, when PT3 is made
operational by using simple functional forms with parameter values derived from existing
experimental evidence, it predicts observed patterns of preference reversal (PR) across a wide
range of specifications of P and $ bets, consistent with the range in which that phenomenon
PR is one of the most notorious anomalies in individual decision making, but despite
the large volume of literature it has generated, no satisfactory preference-based account of it
has thus far been produced. In the psychology literature it has been common to interpret PR
as evidence that preferences do not satisfy procedural invariance but, instead, depend upon the
method used to elicit them. On this view, if preferences are to be invoked at all in explaining
PR, those preferences must be context-sensitive: that is, they must allow different preferences
to govern decisions in choice and valuation tasks. In the economics literature, various models
of context-free preferences have been proposed as possible accounts of PR. One approach is
to relax the independence and/or reduction axioms of expected utility theory (Holt, 1986;
Karni and Safra, 1987; Segal, 1988). Subsequent studies, however, have generated strong PR
in experimental designs implementing controls for the explanations postulated in these
theories (Tversky, Slovic and Kahneman, 1990; Cubitt, Munro and Starmer, 2004). Another
possible explanation is that PR arises as a consequence of context-free, but non-transitive
preferences. Persistent non-transitive cycles of choice analogous to PR have been observed in
experimental studies (Loomes, Starmer and Sugden, 1989, 1991; Humphrey 2001), but the
only preference theory that has been put forward to explain such behaviour is regret theory
(Bell, 1982; Loomes and Sugden, 1983), which has failed other tests (Starmer and Sugden
1998). A third approach is to explain PR by supplementing a conventional theory of
preferences with some mechanism of stochastic error; but this has been shown to be
empirically unconvincing (Schmidt and Hey, 2004). Against this unpromising background, it
is remarkable to find that observed patterns of PR are predicted by a simple extension of an
existing and empirically well-supported preference-based theory.
We do not claim that PT3 provides a complete explanation of PR. We recognise that
psychologists have proposed credible non-preference mechanisms of context-sensitive choice
and valuation behaviour that are consistent with observations of PR. Predictions based on
those mechanisms have been tested and confirmed in experimental tasks other than PR and, in
some cases, outside the domain of theories of choice under uncertainty (Slovic, Griffin and
Tversky, 1990). This evidence clearly suggests that non-preference mechanisms contribute to
PR. We assert only that PT3 has a similar claim to be a model of mechanisms which
contribute to that phenomenon. It too is based on psychologically credible hypotheses – loss
aversion, diminishing sensitivity, the overweighting of small probabilities and the
underweighting of large ones. It too is consistent with observations of PR. It too has been
tested and confirmed in experimental tasks other than PR – namely, pairwise choices between
lotteries involving gains and losses. If one accepts prospect theory as an explanation of
observed regularities in choice among lotteries, it seems reasonable to infer that the
mechanisms modelled by PT3 play a significant role in the explanation of PR.
We should also acknowledge that we have treated the use of WTA valuations as one
of the defining characteristics of a PR experiment. Insofar as it relies on the assumption of
loss aversion, the explanation of PR provided by PT3 is specific to such valuations. In fact,
there have been surprisingly few experiments in which WTP valuations of P and $ bets have
been used. Such experiments have produced mixed results, but asymmetric PR is generally
less pronounced than in WTA experiments, and sometimes is not present at all. It seems that
WTP treatments tend to reduce the frequency of standard reversals and to increase the
frequency of non-standard ones (Lichtenstein and Slovic, 1971; Knez and Smith, 1987;
Casey, 1991). These findings are compatible with the hypothesis that PR is the product of
several causal mechanisms, at least one of which is in some way linked to WTA valuations.
We suggest that PT3 captures a mechanism of the latter kind.
More generally, we offer PT3 as a natural extension of prospect theory – a theory that is
already widely accepted as empirically successful. By allowing reference points to be
uncertain, we have filled a major gap in that theory’s previous domain of application. The
result is a flexible and parsimonious model of choice under uncertainty which organises a
large body of experimental evidence. We hope that it will find fruitful applications in future
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1 Some descriptive limitations of prospect theory are discussed by Birnbaum and Bahara (2007). 2 Compare (1) above with equation (2) in Kőszegi and Rabin (2007). 3 In Bar-Hillel and Neter’s ‘Experiment 2’, subjects are unwilling to exchange lottery tickets even if they know that the number of the winning ticket will not be announced. This suggests that subjects use a state-contingent conceptualisation of gains and losses even if they will never know which state is realised. 4 The restriction v(f[si], h[si]) = u(f[si] - h[si]) prevents cumulative prospect theory from
taking account of income effects. Kahneman and Tversky (1979, pp. 277-278) comment that ‘strictly speaking’, value should be defined as a function in two arguments – changes in wealth relative to a current asset position, and that position itself. The simpler function they use is presented as ‘a satisfactory approximation’. 5 This idea was introduced by Tversky and Kahmenan (1991), and developed by Munro and Sugden (2003), to characterise reference-dependence in preferences over multi-dimensional consumption bundles. 6 Notice that the derivation of this result uses two symmetries between the treatments of gains and losses that are specific to our parameterisation: the weighting function is the same for gains and losses, and the exponent of u(.) is the same for gains and losses. 7 Our analysis of PR does not depend on the cumulative transformation of probabilities. The acts that we analyse have no more than one strictly positive consequence and no more than
one strictly negative one. For such acts, the cumulative transformation is observationally equivalent to Handa’s simple transformation. 8 In addition, fP ~h f$ ⇔ w(p)/ w(q) = (pr/ q)α. From now on, to avoid cluttering the
exposition, we will not state conditions for indifference explicitly. In all cases, the condition for indifference can be constructed from the condition for weak preference by substituting an equality for a weak inequality. 9 The reader may wonder why, in all the cases represented in Figure 2, the valuation boundary intersects the choice boundary at λ = 1. It can be shown that this property is induced by a special feature of the benchmark case, namely that the P and $ bets are symmetrical in the sense that q = 1 – p. If the bets are symmetrical and if β = 1, the valuation boundary passes through the point (ln[p/q] / ln[rp/q], 1). Whether the bets are symmetrical or not, this point lies on the choice boundary. (Since this result has little substantive significance, we leave the proof to sufficiently curious readers.) If the assumption of symmetry is relaxed, the two boundaries may intersect at positive or negative values of λ. If r > 1 and if the intersection is at a positive value of λ, there is a (typically small) region of the empirically plausible quadrant at which non-standard PR occurs. 10 The reader may have noticed that, in all three cases, the choice and valuation boundaries intersect at (β, 1). On the assumption that r = 1, it can be shown that, for all admissible values of p and q, the valuation boundary passes through (β, 1). The choice boundary passes through the same point if and only if q = 1 – p (compare note 9). Figures 4 and 5, discussed in the next paragraph, illustrate some cases in which this symmetry condition does not hold. 11 In each case, the valuation boundary passes through (β, 1), illustrating the general result stated in note 10. In general, if β < 1 and r = 1, the choice boundary lies to the right of (respectively passes through, lies to the left of) the point (β, 1) if p + q is greater than (equal to, less than) 1. The proof of this result is omitted for brevity. 12 If λ = 1 and β = 1, (11) implies 0 < z$ < r/q, while (15) reduces to ([r/q] – z$)α > (r/q)α – z α
13 If λ = 1 and α = 1, (15) reduces to 1 – w[q] > w(1 – q). This inequality holds if and only if β < 1.
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