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Risky choice: Probability weighting explains independence axiom violations in monkeys

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Unlabelled: Expected Utility Theory (EUT) provides axioms for maximizing utility in risky choice. The Independence Axiom (IA) is its most demanding axiom: preferences between two options should not change when altering both options equally by mixing them with a common gamble. We tested common consequence (CC) and common ratio (CR) violations of the IA over several months in thousands of stochastic choices using a large variety of binary option sets. Three monkeys showed consistently few outright Preference Reversals (8%) but substantial graded Preference Changes (46%) between the initial preferred gamble and the corresponding altered gamble. Linear Discriminant Analysis (LDA) indicated that gamble probabilities predicted most Preference Changes in CC (72%) and CR (88%) tests. The Akaike Information Criterion indicated that probability weighting within Cumulative Prospect Theory (CPT) explained choices better than models using Expected Value (EV) or EUT. Fitting by utility and probability weighting functions of CPT resulted in nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina triangle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT models predicted Preference Changes better than EV and EUT models. Indifference points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT ICs. Finally, while the few outright Preference Reversals may reflect the long experience of our monkeys, their more graded Preference Changes corresponded to those reported for humans. In benefitting from the wide testing possibilities in monkeys, our stringent axiomatic tests contribute critical information about risky decision-making and serves as basis for investigating neuronal decision mechanisms. Supplementary information: The online version contains supplementary material available at 10.1007/s11166-022-09388-7.
Experimental design for testing the independence axiom (IA). (A) Visual stimulus predicting a three-outcome gamble. The vertical position of each horizontal bar represents reward amount (m1, m2, m3; ml of juice); the length of each bar represents reward probability (p1, p2, p3) of the respective amounts m1, m2, m3. (B) Principle of testing the IA with two option sets {A,B} and {C,D}. Options C and D are obtained by adding the same gamble G to both options A and B, weighted by probability p. (C) Common consequence test and its representation in the Marschak-Machina triangle. The x-and y-axes represent the probabilities of the low outcome (p1) and high outcome (p3), respectively (probability of middle outcome: p2 = 1-p1-p3). Blue dots represent the original option set {A,B}, and green dots represent its modified set {C,D} for testing the IA. The yellow arrow indicates how option set {A,B} becomes option set {C,D} by adding the same probability k to the probability p1 of the low outcome m1 in both gambles A and B. Grey lines connect gambles with same expected value and highlight the linear and parallel nature of indifference curves tested by the IA. IA compliance requires same preferences: if (A ≻ B) then (C ≻ D), or if (A ≺ B) then (C ≺ D) and same choice probabilities. (D) Common ratio test. Multiplication of option set {A,B} by the same ratio r results in test option set {C,D}. Option set {A,B} becomes option set {C,D} by multiplying a common ratio r with the probability of the middle outcome (p2) of gamble A and with the probability of the high outcome (p3) of gamble B. * and ** and lengths of red lines indicate different distances of gambles from expected values (grey lines), which may result in potential preference changes between the two option sets without necessarily indicating IA violation
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Journal of Risk and Uncertainty (2022) 65:319–351
https://doi.org/10.1007/s11166-022-09388-7
1 3
Risky choice: Probability weighting explains independence
axiom violations inmonkeys
SimoneFerrari‑Toniolo1 · LeoChiU.Seak1 · WolframSchultz1
Accepted: 2 June 2022 / Published online: 22 July 2022
© The Author(s) 2022
Abstract
Expected Utility Theory (EUT) provides axioms for maximizing utility in risky
choice. The Independence Axiom (IA) is its most demanding axiom: preferences
between two options should not change when altering both options equally by mix-
ing them with a common gamble. We tested common consequence (CC) and com-
mon ratio (CR) violations of the IA over several months in thousands of stochastic
choices using a large variety of binary option sets. Three monkeys showed con-
sistently few outright Preference Reversals (8%) but substantial graded Preference
Changes (46%) between the initial preferred gamble and the corresponding altered
gamble. Linear Discriminant Analysis (LDA) indicated that gamble probabilities
predicted most Preference Changes in CC (72%) and CR (88%) tests. The Akaike
Information Criterion indicated that probability weighting within Cumulative Pros-
pect Theory (CPT) explained choices better than models using Expected Value (EV)
or EUT. Fitting by utility and probability weighting functions of CPT resulted in
nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina trian-
gle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT
models predicted Preference Changes better than EV and EUT models. Indifference
points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT
ICs. Finally, while the few outright Preference Reversals may reflect the long expe-
rience of our monkeys, their more graded Preference Changes corresponded to those
reported for humans. In benefitting from the wide testing possibilities in monkeys,
our stringent axiomatic tests contribute critical information about risky decision-
making and serves as basis for investigating neuronal decision mechanisms.
Keywords Choice· Probability· Gamble· Preference reversal
JEL D01· D81· C5
Simone Ferrari-Toniolo and Leo Chi U. Seak contributed equally to this study.
* Simone Ferrari-Toniolo
Simone.ferraritoniolo@gmail.com
Extended author information available on the last page of the article
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Journal of Risk and Uncertainty (2022) 65:319–351
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1 Introduction
Most decisions we face include some degree of uncertainty. Economic decision
theories that quantify the uncertainty associated with the choice options propose
rigorous mathematical foundations for choice under risk. Expected Utility Theory
(EUT), large parts of which were formalized by von Neumann and Morgenstern
(1944), defines a mathematical framework based on four simple axioms, com-
pleteness, transitivity, continuity and independence. These axioms constitute the
necessary and sufficient conditions for maximizing a specific subjective quantity,
Expected Utility (EU): we simply choose the option with the highest EU. Utility
(U) is the subjectively assigned value to a reward magnitude m (
U=u(m)
), and
the subjective value of a probabilistic reward corresponds to the expected value
of the utility distribution, called Expected Utility (
EU =u(mi)
pi
).
The independence axiom (IA) constitutes the fourth EUT axiom and is cen-
tral to defining EU as subjective value. Together with the continuity axiom, the
IA defines how magnitude and probability are combined to compute the global,
subjective value of a risky choice option. The IA had been implicitly assumed
by von Neumann and Morgenstern in their description of EUT tests and formu-
lated, discussed and empirically tested by Marschak (1950); Allais (1953); and
Savage (1954). The IA states that our preferences should not change when mix-
ing all choice options with a common gamble. However, experiments have shown
for decades that humans fail to comply with the IA (Allais, 1953; Kahneman &
Tversky, 1979; Loomes & Sugden, 1987; Moscati, 2016; Starmer, 2000), which
motivated additions to the existing utility theories, including prominently Pros-
pect Theory (Kahneman & Tversky, 1979; Tversky & Kahneman, 1992).
Several sources of IA violations have been proposed, including subjective proba-
bility weighting, the certainty effect, the fanning-out hypothesis and heuristic schemes
(Camerer, 1989; Kahneman & Tversky, 1979; Katsikopoulos etal., 2008; Machina,
1982; Savage, 1954). Past studies have also suggested that violations may not be as
systematic as initially thought, reporting a significant proportion of EUT-compliant
subjects (Harless & Camerer, 1994; Hey & Orme, 1994). Moreover, among the stud-
ies showing significant failures of the IA, high variability and conflicting types of
violations have been reported (Battalio etal., 1990; Blavatskyy etal., 2022; Conlisk,
1989; List & Haigh, 2005; Ruggeri etal., 2020; Wu & Gonzalez, 1998). The type
and strength of violations also differed among distinct populations of subjects (Huck
& Müller, 2012). Finally, human choices were usually tested with a small choice set
and not repeated, missing effects of choice variability within each subject. Altogether,
these results leave a fragmented picture on the extent, types and causes of IA viola-
tions. Clarifying these aspects would crucially contribute to understanding the mech-
anism underlying economic decisions.
The IA has not been tested in non-human primates, leaving an open question
about the limits of compliance with EUT of our closest, experimentally viable, evo-
lutionary relative. Monkeys can choose between actual outcomes that are tangibly
delivered after every choice (as opposed to hypothetical outcomes) and can perform
hundreds of daily choices. Monkeys allow systematic and incentive-compatible tests
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Journal of Risk and Uncertainty (2022) 65:319–351
of EUT axioms in the same subject across a wide range of tests. Their reliable and
stable performance minimizes errors and rules out insufficient learning, as noted for
rodent tests of the IA axiom (Camerer, 1989; Kagel etal., 1990). Monkeys’ choices
satisfy first-, second- and third-order stochastic dominance, allow comparisons
between risky and riskless utility functions, reveal nonlinear probability weighting,
comply with the EUT continuity axiom, and can respect the Independence of Irrel-
evant Alternatives of two-component bundles (Bujold etal., 2021; Ferrari-Toniolo
etal., 2019, 2021; Genest etal., 2016; Pastor-Bernier etal., 2017; Pelé etal., 2014;
Stauffer etal., 2014, 2015). However, without testing the IA, these choice data do
not yet allow us to identify specific forms of subjective value computation. Work on
monkeys is particularly suitable for achieving this goal, as the typical collection of
large data sets facilitates thorough comparisons of economic models. As ultimate
goal, well-defined behavioral assessments of EUT axioms, and in particular of the
IA, would allow stringent, concept-based brain investigations of economic choice
mechanisms with the high precision of primate single-cell neurophysiology. Given
the evolutionary relationship between humans and monkeys, evidence of similar IA
violations in the two species would help further our understanding of human deci-
sion making, both from an economic perspective and from a neurophysiological one.
Here, we used the IA to test the conditions and limits of utility-maximizing sto-
chastic choices in three rhesus monkeys. The animals performed thousands of choices
between gambles to identify specific forms of value computation, notably utility and
probability-weighting. We systematically varied the gambles’ probabilities in com-
mon consequence and common ratio tests across the whole Marschak-Machina tri-
angle to gain a comprehensive and detailed view of axiom compliance and violation.
The animals consistently showed relatively few outright Preference Reversals, possi-
bly due to their extended experience with the gambles, but substantial graded Prefer-
ence Changes. Comparisons between economic model fits to the measured choices
demonstrated that the probability weighting of Cumulative Prospect Theory (CPT)
explained the choices better than models using Expected Value (EV) or EUT. The
graded Preference Changes in our monkeys compared in frequency and strength to
those reported for humans. These axiom-driven experiments identified the critical
decision variables for utility-maximizing choices according to the IA and provide a
basis for investigating the underlying neuronal signals in primates.
2 Methods
2.1 Animals
Three adult male rhesus macaques (Macaca mulatta) were used in this experiment:
Monkey A (13kg), Monkey B (11.5 kg) and Monkey C (11kg). The animals were
born in captivity at the Medical Research Council’s Centre for Macaques (CFM) in
the UK. Monkey A (’Tigger’) and Monkey B (’Ugo’) had been surgically implanted
with a headpost and a recording chamber for neurophysiological recording; they were
headposted for 2—3h on each test day of the current experiment, which was intermin-
gled with neuronal recordings on separate days. Both animals had previous experience
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with the visual stimuli and experimental setup (Ferrari-Toniolo etal., 2019). Monkey
C (’Aragorn’) had no implant, no head posting and no previous task experience.
All experimental procedures had been ethically reviewed and approved and were
regulated and continuously supervised by the following institutions and individuals
in the UK and at the University of Cambridge (UCam): the Minister of State at the
UK Home Office, the Animals in Science Regulation Unit (ASRU) of the UK Home
Office implementing the Animals (Scientific Procedures) Act 1986 with Amend-
ment Regulations 2012, the UK Animals in Science Committee (ASC), the local
UK Home Office Inspector, the UK National Centre for Replacement, Refinement
and Reduction of Animal Experiments (NC3Rs), the UCam Animal Welfare and
Ethical Review Body (AWERB), the UCam Governance and Strategy Committee,
the Home Office Establishment License Holder of the UCam Biomedical Service
(UBS), the UBS Director for Governance and Welfare, the UBS Named Information
and Compliance Support Officer, the UBS Named Veterinary Surgeon (NVS), and
the UBS Named Animal Care and Welfare Officer (NACWO).
2.2 Task design
Each animal was seated in custom-made a primate chair (Crist instruments) in which
he chose on each trial between two discrete and distinct options that were simultane-
ously presented at the right and left on a computer monitor at a distance of 50cm in
front of it. The animal indicated its choice by moving a joystick (Biotronix Work-
shop, University of Cambridge) either to the right or the left by an equal distance.
The position of the joystick was monitored via custom code using Psychtoolbox3 in
Matlab (The MathWorks). The animals were first trained in > 10,000 trials to learn
the independently set reward magnitudes (m) and probabilities (p) that were indicated
by a specifically set visual stimulus. Reward magnitude was signaled by the vertical
position of a horizonal line; the probability of receiving that reward magnitude was
proportional to the length of the horizonal line away from stimulus center (Fig.1A).
A stimulus with a full-length, single horizontal line corresponded to a sure reward
Fig. 1 Experimental design for testing the independence axiom (IA). (A) Visual stimulus predicting a
three-outcome gamble. The vertical position of each horizontal bar represents reward amount (m1, m2,
m3; ml of juice); the length of each bar represents reward probability (p1, p2, p3) of the respective
amounts m1, m2, m3. (B) Principle of testing the IA with two option sets {A,B} and {C,D}. Options
C and D are obtained by adding the same gamble G to both options A and B, weighted by probability
p. (C) Common consequence test and its representation in the Marschak-Machina triangle. The x-and
y-axes represent the probabilities of the low outcome (p1) and high outcome (p3), respectively (probabil-
ity of middle outcome: p2 = 1-p1-p3). Blue dots represent the original option set {A,B}, and green dots
represent its modified set {C,D} for testing the IA. The yellow arrow indicates how option set {A,B}
becomes option set {C,D} by adding the same probability k to the probability p1 of the low outcome m1
in both gambles A and B. Grey lines connect gambles with same expected value and highlight the linear
and parallel nature of indifference curves tested by the IA. IA compliance requires same preferences: if
(A ≻ B) then (C ≻ D), or if (A ≺ B) then (C ≺ D) and same choice probabilities. (D) Common ratio test.
Multiplication of option set {A,B} by the same ratio r results in test option set {C,D}. Option set {A,B}
becomes option set {C,D} by multiplying a common ratio r with the probability of the middle outcome
(p2) of gamble A and with the probability of the high outcome (p3) of gamble B. * and ** and lengths
of red lines indicate different distances of gambles from expected values (grey lines), which may result
in potential preference changes between the two option sets without necessarily indicating IA violation
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Journal of Risk and Uncertainty (2022) 65:319–351
(i.e. a degenerate gamble, p = 1), whereas multiple horizontal lines with less than full
length indicated multiple possible gamble outcomes. At the end of each trial, the cho-
sen option, and no other option, was paid out. From that paid-out option, one, and
only one, of the outcomes was delivered to the animal. Thus, both the options and
the outcomes of each option were mutually exclusive and collectively exhaustive. We
used three fixed reward magnitudes: 0ml (low; m1), 0.25ml (middle; m2) and 0.5ml
(high; m3) of the same fruit juice or water; reward probabilities of the three reward
magnitudes (p1, p2, p3) varied between 0 and 1, with a minimum step of 0.02. All
option sets alternated pseudo-randomly. More details can be found in our previous
study employing the same presentation design (Ferrari-Toniolo etal., 2021).
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Journal of Risk and Uncertainty (2022) 65:319–351
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2.3 Revealed preference andchoice indifference
Each animal’s preferences were considered to be revealed from its choices and used
as a basis for quantification in later analyses. Preference was defined as the probabil-
ity of choosing one gamble over the alternative gamble in the same binary option
set; a gamble was considered to be revealed preferred to another gamble if the first
gamble was chosen with P > 0.5. Thus, for a binary option set {A,B}, a stochastic
preference relation was defined as P(A|{A,B}) = NA/NAB, where NA was the num-
ber of trials in which the monkey chose A over B, and NAB was the total number
of trials with the {A,B} gamble set. When P(A|{A,B}) > 0.5 (i.e. when A was cho-
sen in more than 50% of the trials), the monkey stochastically revealed preferred
A to B. We used the binomial test (P < 0.05; 1-tailed) to assess the statistical sig-
nificance of such preference relation in a specific direction (either P{A,B} > 0.5 or
P{A,B} < 0.5) against choice indifference (probability of choosing each option with
P = 0.5). When P(A|{A,B}) = 0.5, the animal was indifferent between two options A
and B (i.e. gamble A was as much revealed preferred as gamble B).
2.4 Defining theIA
The IA states that for any gamble A that is preferred to a gamble B, the combina-
tion of gamble A with gamble G should be preferred to the combination of gamble
B with gamble G; the combined options are themselves gambles and are called C
and D. Compliance with IA requires that the commonly added gamble G does not
change the preference for the options. Thus, individuals who prefer gambles A to B
should also prefer gambles C to D (Fig.1B). Any preference change constitutes an
IA violation. These notions are formally stated as follows:
with A, B and G as gambles and p as probability. Gamble A was always a degener-
ate gamble with only a safe middle reward (m2, p2 = 1), as used in Allais’ original
test (Allais, 1953).
2.5 Testing theIA
We assessed IA compliance in two commonly used tests: the common consequence
(CC) test and the common ratio (CR) test.
The CC test consisted of adding (or subtracting) the same specific probability
of an outcome (’common consequence’) commonly to both options. Gamble A had
a single outcome (m2 = 0.25 ml, p2 = 1), gamble B had three outcomes with fixed
magnitudes m1, m2, m3 and varying probabilities p1, p2, p3 (Fig.1C). For the CC
test, we added a common probability k to the probability of the lower outcome (p1)
to gambles A and B, which defined two new gambles C and D; probability k equals
p2 of option B. Adding probability k to the p1 of gambles A and B consisted of
reducing the original p2 by k in both gambles (and thus reducing p2 in gamble B to
0) to maintain the sum = 1.0 of all probabilities in each gamble.
(1)
∀A B
pA + (1−p
)GpB+(1−p
)G;∀G,p∈[0, 1]
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Journal of Risk and Uncertainty (2022) 65:319–351
The CR test consisted of multiplying the same ’common ratio’ with the prob-
abilities of all non-zero outcomes commonly for both gambles A and B. Gamble A
had the same single outcome as in the CC test (m2 = 0.25ml, p2 = 1), but gamble B
had two outcomes with fixed magnitudes m1, m3 and varying probabilities p1, p3
(Fig.1D). For the CR test, we multiplied a common ratio r with the probability of
the middle outcome (p2) of gamble A and with the probability of the high outcome
(p3) of gamble B and thus defined two new gambles C and D (thus, p2 and p3 of the
new gambles C and D equalled p2 and p3 of gambles A and B commonly multiplied
by r; p1’s of gambles C and D were adjusted to maintain the sum = 1.0 of all prob-
abilities in each gamble).
The Marschak-Machina triangle constitutes an elegant scheme for graphically
representing choice options and highlighting the predictions and consequences of
IA compliance and violation (Machina, 1982; Marschak, 1950). In this abstract
space (Fig. 1C, D, bottom), the x-axis represents the probability of obtaining the
low outcome (p1) and the y-axis represents the probability of obtaining the high
outcome (p3). The probability of the middle outcome (p2) derives from all prob-
abilities summing to 1.0: p2 = 1—p1—p3. Each point inside the triangle represents
a gamble. An IA test with two option sets is represented by two parallel lines that
connect the original gambles A and B (blue), and the compounded gambles C and D
(green) (note that these lines simply connect the gambles and do not indicate choice
indifference; see below). In CC tests, the probability of the highest magnitude (p3)
remains unchanged between gambles B and D (Fig. 1C bottom); thus, gamble D
has the same vertical position as gamble B (y-axis). Thus, the change from option
set {A,B} to option set {C,D} is graphically represented by a horizontal shift of the
option set by probability k. In CR tests, probability p3 changes between gambles B
and D; these gambles have different vertical positions along the hypotenuse (Fig.1D
bottom). The change from option set {A,B} to option set {C,D} is graphically rep-
resented by a horizontal shift of gamble A to become gamble C, and by a downward
shift along the hypotenuse from gamble B to gamble D.
In the Marschak-Machina triangle, equally revealed preferred gambles are con-
nected by indifference curves (IC), whereas unequally preferred gambles are posi-
tioned on different ICs (see Fig.5). While ICs should be linear and parallel with
physical Expected Values (EV) and with utilities estimated according to EUT, they
become non-parallel or curved with violations of the IA axiom.
2.6 Definitions ofIA violations
We used two measures for IA violation, Preference Reversal and Preference Change,
both of which were based on the revealed preference of gamble A to gamble B indi-
cated by the probability of choice of the initial gambles P(A|{A,B}). Thus, we used
stochastic choices and correspondingly stochastic models to test the IA. While both
outright Preference Reversal and graded significant Preference Change can detect
IA violations, they cannot assess significant axiom compliance, as this corresponds
to the null hypothesis of our statistical test. Nonlinear and non-parallel indifference
curves in the Marschak-Machina triangle demonstrate IA violations, as proposed
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Journal of Risk and Uncertainty (2022) 65:319–351
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by several non-EU theories (Bhatia & Loomes, 2017; Bordalo, 2012; Kahneman &
Tversky, 1979; Machina, 1982; Savage, 1954).
Our first, binary measure for IA compliance was Preference Reversal. When
the fixed gamble A was stochastically revealed preferred to gamble B, Preference
Reversal was manifested as stochastically preferring gamble D to gamble C:
This reversal, in a non-stochastic setting, was originally observed in humans (Allais-
type reversal; Allais, 1953; Kahneman & Tversky, 1979). To adapt this measure to sto-
chastic choices, we assessed the significance of the initial preference P(A|{A,B}) > 0.5
in comparison with choice indifference (P(A|{A,B}) = 0.5) using the binomial test
(statistical P < 0.05; 1-tailed). Then Preference Reversal was evidenced as significant
stochastic preference in the opposite direction (P(C|{C,D}) < 0.5) (binomial test).
To the opposite, when gamble B was stochastically revealed preferred to gam-
ble A, we defined Preference Reversal as stochastically preferring gamble C to
gamble D:
We assessed the significance of this reverse Allais-type Preference Reversal (Blavatskyy,
2013b; Conlisk, 1989) in analogy to the (regular) Allais-type reversal.
Our second, more graded measure for IA violation was Preference Change.
We used the metric S introduced by Conlisk (1989) who had assessed IA viola-
tions non-stochastically from single choices of multiple human participants. We
adapted the Conlisk’S assessment to repeated, stochastic choices of individual
animals and quantified Preference Change stochastically as ratio of probabilities
of Allais-type and reverse Allais-type reversals:
P(AD) indicates the probability of Allais-type reversals: P(A|{A,B}) > 0.5 and P
(D|{C,D}) > 0.5. P(BC) indicates the probability of reverse Allais-type reversals: P
(B|{A,B}) > 0.5 and P(C|{C,D}) > 0.5. We set Conlisk’S = 0 when P(AD) + P(BC) = 0.
Assuming that choices in different trials were independent, we computed
P(AD) = P(A|{A,B}) P(D|{C,D}), obtaining:
The Conlisk’S measure is a real number that varies between -0.5 and 0.5. We defined
the significance of Conlisk’S as difference from zero (P < 0.05 on pooled sessions
from a given monkey; one-sample t-test). To avoid unreasonably large violation
measures from infrequent violations, we weighted the Conlisk’S with respect to the
total proportion of violations and obtained the Preference Change S:
(2)
P(A|{A,B}) >0.5 & P(C|{C,D}) <0.5
(3)
P(A|{A,B}) <0.5 & P(C|{C,D}) >0.5
(4)
Conlisk
S=
P(AD)
P(AD)+P
(BC)
0.5
(5)
Conlisk
S=
P(A|{AB})
P(D|{CD})
P(A|{AB})
P(D|{CD}) + P(B|{AB})
P(C|{CD})
0.5
(6)
S = ConliskS
[P(AD) + P(BC)]
;
with P((AD) + P(BC))
1.0
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Journal of Risk and Uncertainty (2022) 65:319–351
In this way, S is a real number that still varies between -0.5 and 0.5 and indicates
the same direction of systematic IA violation as Conlisk’S but corresponds better to
the fraction of trials producing the violation. The S is a measure of how much the
preferences vary between option sets {A,B} and {C,D} (i.e. the non-vertical nega-
tive and positive slopes, indicating S > 0 and S < 0, respectively, in our preference
comparisons in Figs.2 and 3, red). All subsequent analyses used this S as measure
of Preference Change.
With repeated choices, IA violations may seem to occur simply because of
some random variability in preferences, but the measure of S cancels out random
violations in opposite directions and thus results in a robust measure of Preference
Change. Compliance with the IA is manifested as Preference Changes resulting in
S = 0, which reflects either an absence of IA violations or a balanced number of ran-
dom IA violations in the two directions. Preference Changes are manifested by posi-
tive or negative S values that differ significantly from zero (P < 0.05; one-sample
t-test).
In our stochastic version of EUT, Preference Changes (significant S 0) repre-
sent IA violations in CC tests in which gambles B and D have equal distance from
the respective parallel choice indifference lines representing equal expected utility
in the Marschak-Machina triangle (Fig.1C); the equal utility difference should pre-
serve their preference within their respective option sets and thus maintain linear
and parallel ICs. By contrast, Preference Changes in CR tests are necessary but not
sufficient for defining IA violations. The CR test places gambles B and D at different
distances from the respective parallel indifference lines (Fig.1D) that reflect differ-
ent expected utility differences for the two option sets that may result in preference
Fig. 2 Preference Changes S during IA common consequence tests. (A) Significant violations with posi-
tive Preference Change measure S (P < 0.05 on pooled sessions from a given monkey; one-sample t-test
against zero). Each panel shows at the left the small Marschak-Machina triangle for the tested options.
The center plot shows the probability of choosing one option over the other option (Options A and C
on the left; Options B and D on the right). Each dot represents the probability of choosing A over B or
C over D in one session; red vertical bars represent averages of probability of choices (A—B or C—D)
across sessions; red intervals show the 95% confidence interval; blue intervals show Standard Deviations
(SD); the black line links preferences in one example session (red dotted line: average across sessions).
Small histograms (right) show the distribution of S’s that quantifies the IA violation, across sessions. (B)
Significant negative Preference Changes S. (C) Insignificant Preference Changes S
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changes (but not outright preference reversals) without indicating IA violation. In
other words, when using a stochastic model, linear and parallel ICs can produce
non-zero S values in the CR test. Thus, non-zero S values do not necessarily indicate
IA violations in CR tests.
2.7 Classification analysis
To check whether the Preference Changes in the IA tests depended on reward proba-
bility, we performed classification analyses. We used a Linear Discriminant Analysis
(LDA) classifier (fitcdiscr function in Matlab) to predict the sign of the Preference
Change measure S. We characterized the changes with a leave-one-out procedure
using Linear Discriminant Analysis (LDA). We trained the LDA with all data except
for those from the predicted leave-out choice tests to build 36 models (18 CC tests
and 18 CR tests) for each animal (we discarded one option set in CR tests with Mon-
key C in which S was zero). As each of the 36 models was used to predict the left-
out data, we obtained 36 predictions for each animal. We compared these predic-
tions to the measured directions of Preference Change to check the accuracy of the
prediction. To illustrate the test sensitivity (true positive rate / ability to predict one
class) and specificity (false positive rate / ability to predict the other class), we drew
a confusion matrix for the CC and CR tests, separately for each animal (see Fig.4).
2.8 Economic modeling ofchoice behavior
We defined a standard discrete choice softmax function (McFadden, 2001) to describe
stochastic preferences. The probability P of choosing a generic option A over another
option B was defined as:
Fig. 3 Preference Changes (S) during IA common ratio tests. (A) Significant positive Preference Change
measure S (all P < 0.05; one-sample t-test). For conventions, see Fig.2. (B) Significant negative Prefer-
ence Changes S. (C) Insignificant Preference Changes S
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329
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Fig. 4 Probability dependency of IA Preference Changes. (A) Common consequence test. The x- and
y-axes show the probabilities of low and high magnitudes of option B, respectively. Purple and cyan dots
represent positive and negative Preference Change measures S, respectively. Black circles around dots
indicate significance (P < 0.05; one-sample t-test). Insets show confusion matrices from classifications
using Linear Discriminant Analysis (LDA). (B) Common ratio test. The x- and y-axes show the ration
and the probability of high magnitudes of option B, respectively. One option set with S = 0 not shown in
Monkey C
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where
λ
represents the noise parameter, defining the steepness of the preference
function (steeper for higher
values). Based on EV theory, EUT and Cumulative
Prospect Theory (CPT), we used three models to define the value (V) of gambles.
These models returned different estimates of choice probability according to Eq.7.
In the EV model, each option’s value was its objective Expected Value:
For a generic three outcome gamble in our task, it corresponded to (
m1
was zero in
our task and therefore
p1
m1=0)
:
In the EUT model, each option’s subjective value was defined via the utility function
(u) as its Expected Utility:
In our gambles’ space this mapped to:
In the CPT model, each option’s subjective value was called a Prospect Value and
defined by a utility function (u) together with a probability weighting function (w),
combined in a cumulative form (Tversky & Kahneman, 1992):
where
𝜋i=w(pi+... +pn)−w(pi+1+... +pn)
, with n indicating the number of out-
comes, and index i corresponding to the outcomes ordered from worst to best (
m1
and
m3
respectively, in our task). For a generic three-outcome gamble (with prob-
abilities
p1,p2,p3
), Eq.12 becomes:
which, with our set of magnitudes and normalized utility, corresponds to
In these three value-estimating equations, each
pi
represents the probability of get-
ting the respective reward magnitude (
mi
):
p1
and
m1
represent the probability and
magnitude of the lowest outcome (0ml);
p2
and
m2
the probability and magnitude
of the middle outcome (0.25 ml);
p3
and
m3
are relative to the highest outcome
(0.5ml). In the EUT and CPT models, the utility function was defined as a power
function (free parameter
ρ
), normalized to the highest magnitude level:
(7)
P(A|AB)=
1
∕(
1
+e
−λ
(
VAVB
))
(8)
EV = i
pim
i
(9)
EV(
p
1
,p
2
,p
3)
=p
2
m
2
+p
3
m
3
(10)
EU = i
piu(mi
)
(11)
EU(
p1,p2,p3
)
=p2u
(
m2
)
+p3u
(
m3
)
(12)
Prospect Value
=
i
𝜋iu(mi
)
(13)
Prospect Value
=u
(
m
1)
(
1w
(
p
2
+p
3))
+u
(
m
2)
(
w
(
p
2
+p
3)
w
(
p
3))
+u
(
m
3)
w
(
p
3)
(14)
Prospect Value
=u
(
m
2)
(
w
(
p
2
+p
3)
w
(
p
3))
+w
(
p
3)
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Journal of Risk and Uncertainty (2022) 65:319–351
The
ρ
parameter defines a convex (
ρ>1
) or concave (
ρ<1
) utility function, with
ρ=1
corresponding to linear utility. Note that having only three magnitude levels in
the current experiment implied that the only meaningful utility value was that of the
middle outcome magnitude (m2) in relation to the other two outcomes (m1 and m3).
Thus, although a larger set of magnitudes may result in more complex utility func-
tions, a power function would be sufficient to account for the difference in subjective
evaluation of the three reward magnitudes used in our study.
In the CPT model, cumulative probability weighting was defined as a two-parameter
Prelec function (Prelec, 1998; Stott, 2006) as in our earlier study (Ferrari-Toniolo etal.,
2019):
where
𝛼
allows the function to vary from inverse-S-shaped (
𝛼<1
) to S-shaped
(
𝛼>1
), while
β
shifts the function vertically.
We estimated the functions’ parameters (
𝜃
) with the maximum likelihood esti-
mation (MLE) method, by maximizing the log-likelihood function defined (for a
choice between generic options A and B; using fminsearch in Matlab) as:
The experimental choice outcome was defined for each trial
i
by the binary variable
yi
(1 when A chosen, 0 when B chosen) and
y
i
(1 when B chosen).
To validate our economic models, we used an out-of-sample dataset that con-
sisted of a set of gambles that differed from the gamble set used for the IA tests.
We presented monkeys with choices between one fixed option (J) on the x axis
(p1 between 0 and 0.8 in 0.2 increments) or on the y axis (p3 between 0.2 and
0.8 in 0.2 increments) and another option (K) with variable p1 (and p3) and fixed
p2 (with p2 between 0.2 and 1, in 0.2 increments). For each option J, by vary-
ing the probability p1 in option K, we identified an indifference point (IP) as the
point within the triangle where a fitted softmax preference function would take
the value of 0.5. All choice trials in the out-of-sample test were pseudo-randomly
intermingled. IPs were estimated separately in each weekdaily session.
2.9 Comparison withhuman choices
We tested whether the observed IA violations in the 18 CC tests in monkeys cor-
responded to the violations reported in 39 human studies (Blavatskyy etal., 2015),
using data pooled from all three monkeys. We used two different comparison meth-
ods, a confusion matrix using binary classes of Preference Change (either S > 0 or
S < 0), and a Pearson correlation using real-number Preference Changes (S varying
between -0.5 and + 0.5). However, the gambles used in our monkeys differed from
the gambles used in the human studies (see Fig.7A). Therefore, for more accurate
(15)
u
(mi)=
(
mi
m
3)𝜌
(16)
w(p)=e
𝛽
(−
ln
(
p
))𝛼
(17)
LL
(𝜃
|
y)=
n
i=1
yi𝑙𝑜𝑔(P(A
|
AB))+
n
i=1
yi𝑙𝑜𝑔(P(B
|
AB)
)
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332
Journal of Risk and Uncertainty (2022) 65:319–351
1 3
comparisons, we first needed to predict the Preference Changes S that would have
occurred in our monkeys had we used the exact same gambles as in humans; to con-
trol for directionality of testing, we also needed to predict, in the reverse direction,
the Preference Changes S that would have occurred in the human studies had they
used the same gambles as we did in our monkeys.
For the confusion matrix, we predicted the S’s for the unused gambles with an
LDA classifier trained on the S’s of the actually used gambles. For predicting the
monkey S’s for the gambles used in the human studies, we trained the LDA with the
binary monkey S’s (S > 0, S < 0), and the probabilities for the low and the high mag-
nitudes of the monkey gambles (p1, p3). In the reverse direction, for predicting the
human S’s for the gambles used in our monkeys, we trained the LDA with the binary
human S’s, the probabilities for the low and the high magnitudes of the human gam-
bles (p1, p3), and the ratio of the middle and high magnitudes of the human gambles
(m2 / m3). Then we used the confusion matrix to compare measured human S’s with
predicted monkey S’s (see Fig.7C left) and, vice versa, measured monkey S’s with
predicted human S’s (see Fig.7D left). The accuracy of the comparison was defined
in percent from the ratio: (total number of successful comparisons) / (total number
of comparisons). For example, in the confusion matrix shown in Fig.7C, the total
number of successful comparisons is (6 + 26) / (6 + 7 + 0 + 26) = 0.82, which equals
82%.
For the Pearson correlation, we predicted the S’s for the unused gambles with two
different multiple linear regression systems depending on the direction of compari-
son. The regression for the comparison of measured human Preference Changes S
with predicted monkey S’s first estimated the beta parameters for the S’s measured
in monkeys as follows:
with p1-monkey and p3-monkey as probabilities of lowest and highest magnitude of
the gambles used in option B in monkeys. Then we applied the estimated betas from
Eq.18 to all gambles used in the 39 human studies to predict the numeric Prefer-
ence Change S for these gambles in monkeys:
with p1-human and p3-human as probabilities of lowest and highest magnitude of
the gambles used in option B in humans. Then we compared the measured human
S’s with the predicted monkey S’s using a Pearson correlation (see Fig.7C right).
In the reverse direction, comparing measured monkey S’s with predicted human
S’s, the regression first estimated the beta parameters for the Preference Change S
measured in humans with the modified regression model:
with p1-human and p3-human as probabilities of lowest and highest magnitudes of
gamble B, and m2-human and m3-human as middle and highest magnitude used
in humans (magnitudes varied across the human studies but were constant in all
(18)
Measured
S
monkey
=b0+b1p1
−monkey
+b2p3
−monkey
(19)
Predicted
S
monkey
=b0+b1p1
−human
+b2p3
−human
(20)
Measured
S
human
=b
0
+b
1
p
1−human
+b
2
p
3−human
+b
3
(m
2−human
m
3−human)
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Journal of Risk and Uncertainty (2022) 65:319–351
monkey gambles). Then we applied the estimated betas from Eq.20 to all gambles
used in our monkeys to predict the numeric Preference Change S for these gambles
in humans:
with p1-monkey and p3-monkey as probabilities of lowest and highest magnitudes
of gamble B, and m2-monkey and m3-monkey as middle and highest magnitude
used in monkeys. Then we compared the measured monkey S’s with the predicted
human S’s using Pearson correlation (see Fig.7D right).
3 Results
3.1 Experimental design
We used stochastic choices to test compliance with the independence axiom (IA)
in three monkeys. Two visual bar stimuli indicated two respective choice options
on a computer monitor in front of the animal. The animal chose by moving a joy-
stick towards one of the two options and 1.0s later received the reward of the cho-
sen option. Each option was a gamble defined by three reward magnitudes (m1, m2,
m3; ml of fruit juice) occurring with specific probabilities (p1, p2, p3; sum = 1.0)
(Fig.1A). Reward magnitude was indicated by bar height (higher was more), and
the probability of delivering each magnitude was indicated by bar length away from
stimulus center (longer was higher).
Testing the IA began with two gambles A and B that formed option set {A,B}.
Gamble A was a degenerate gamble with safe and fixed middle reward magnitude
(m2 = 0.25ml; p2 = 1.0), whereas gambles B, C and D were two- or three-outcome
gambles. The test gambles C and D derived from the common addition of gamble G
and constituted option set {C,D} (Eq.1). Stochastic compliance with the IA requires
that preferences do not change significantly between option sets {A,B} and {C,D}
(Fig.1B). We assessed the IA in the common consequence (CC) test and in the com-
mon ratio (CR) test (see Sect.2for definitions; Fig.1C, D). When representing the
gambles in the Marschak-Machina triangle, an IA test was plotted as a parallel shift
of the line connecting the two gambles of each option set ({A,B} and {C,D}), with
an additional line length change for a CR test.
3.2 IA violations
We performed 18 different CC tests and 18 different CR tests in each of the three
monkeys; each test was repeated on average 7.5 times per daily session for Monkey
A, 17.7 times per session for Monkey B and 6.1 times per session for Monkey C. We
systematically varied the reward probabilities and thereby tested the IA across the
whole range represented by the Marschak-Machina triangle. We tested two violation
(21)
Predicted
S
human
=b
0
+b
1
p
1−monkey
+b
2
p
3−monkey
+b
3
(m
2−monkey
m
3−monkey)
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334
Journal of Risk and Uncertainty (2022) 65:319–351
1 3
directions: either gamble A was stochastically revealed preferred to gamble B (prob-
ability of choice P(A|{A,B}) > 0.5) and gamble D was stochastically preferred to
gamble C (P(C|{C,D}) < 0.5) (Allais-type violation; Allais, 1953), or gamble B was
revealed preferred to gamble A (P(A|{A,B}) < 0.5) and gamble C was stochastically
preferred to gamble D (P(C|{C,D}) > 0.5) (reverse Allais-type violation; Blavatskyy,
2013b). We considered two IA violation types, the more substantial binary Prefer-
ence Reversals and the more subtle graded Preference Changes.
Preference Reversals across option sets {A,B} and {C,D} were defined by Eqs.2
and 3 for AD and BC preference directions and tested for significance using the
1-tailed binomial test applied separately to Allais-type and reverse Allais-type rever-
sals (P < 0.05; see Sect.2). IA violations indicated by significant Preference Rever-
sals occurred only in a few of the 36 tests (N = 8 for Monkey A, N = 1 for Monkey
B, N = 0 for Monkey C; total of 8% (CC: 11%, CR: 6%); Table1). Note that all
animals were highly familiar with gamble variations from tens of thousands of trials
performed during several months of weekdaily experimentation.
Preference Changes were defined by Eqs.4 - 6 that computed the variable S
derived from Conlisk’S and tested for significance using a one-sample t-test against
S = 0 in pooled sessions from a given monkey (P < 0.05). In contrast to the few out-
right Preference Reversals, significant Preference Changes using the metric S were
rather frequent in all animals (N = 21 for Monkey A, N = 12 for Monkey B, N = 17
for Monkey C; total of 46% (CC: 41%, CR: 52%); Table2).
Preference Changes are sufficient for defining IA violations in CC tests; here,
gambles B and D have equal distance from the respective parallel choice indifference
Table 1 Preference Reversals
while testing the independence
axiom
x ≻ y indicates ’x preferred to y’, x ≺ y indicates ’y preferred to x’.
A ≻ B leading to C ≺ D constitutes a stochastic Allais-type Pref-
erence Reversal, as indicated by significant probability of choice:
P(C|{C,D}) < 0.5 (statistical P < 0.05; 1-tailed; binomial test) (see
Eq.2). By contrast, A ≺ B leading to C ≻ D constitutes a stochastic
reverse Allais-type Preference Reversal: (P(D|{C,D}) < 0.5 (statisti-
cal P < 0.05) (see Eq.3)
CC common consequence test, CR common ratio test
Test Preference Reversal
Monkey A CC A ≻ B ⇒ C ≺ D 0/18
A ≺ B ⇒ C ≻ D 5/18
CR A ≻ B ⇒ C ≺ D 0/18
A ≺ B ⇒ C ≻ D 3/18
Monkey B CC A ≻ B ⇒ C ≺ D 1/18
A ≺ B ⇒ C ≻ D 0/18
CR A ≻ B ⇒ C ≺ D 0/18
A ≺ B ⇒ C ≻ D 0/18
Monkey C CC A ≻ B ⇒ C ≺ D 0/18
A ≺ B ⇒ C ≻ D 0/18
CR A ≻ B ⇒ C ≺ D 0/18
A ≺ B ⇒ C ≻ D 0/18
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Journal of Risk and Uncertainty (2022) 65:319–351
lines representing equal expected utility in the Marschak-Machina triangle (Fig.1C);
the equal utility difference should preserve their preference within their respective
option sets and thus produce no violation. Preference Changes S can be conveniently
graphed as slopes between option sets {A,B} and {C,D}; negative and positive slopes
indicate S > 0 and S < 0, respectively (Fig. 2). The strongest positive Preference
Changes S were significant across sessions in all animals: S = 0.026 ± 0.013 (Monkey
A), S = 0.095 ± 0.015 (Monkey B), S = 0.052 ± 0.010 (Monkey C) (mean ± Standard
Error of the Mean, SEM; all P < 0.05; one-sample t-test) (Fig. 2A). The strongest
negative S’s were also significant across sessions in all animals: S = -0.254 ± 0.020
(Monkey A), S = -0.167 ± 0.014 (Monkey B), S = -0.123 ± 0.014 (Monkey C)
(Fig.2B). The weakest absolute S’s differed only insignificantly from zero and thus
failed to demonstrate IA violation (Fig.2C). Fig.S1 shows the full pattern of Prefer-
ence Changes in all CC tests.
In CR tests, Preference Changes are only necessary and not sufficient for IA viola-
tions; the test places gambles B and D at different distances from the respective parallel
indifference lines (Fig.1D), reflecting different expected utility differences for the two
option sets that may result in graded Preference Changes and thus non-zero S values
(but not outright Preference Reversals) but do not indicate IA violations. The strong-
est positive Preference Changes S were significant across sessions: S = 0.095 ± 0.0143
(Monkey A), S = 0.014 ± 0.015 (Monkey B), S = 0.078 ± 0.009 (Monkey C); all
P < 0.05) (Fig.3A), as were the strongest negative S’s: S = -0.183 ± 0.014 (Monkey A),
S = -0.227 ± 0.019 (Monkey B), S = -0.086 ± 0.012 (Monkey C) (Fig.3B). The small-
est measured absolute S’s were insignificant (Fig.3C). Fig.S2 shows the full pattern
of Preference Changes in all CR tests.
Table 2 Preference Changes
while testing the independence
axiom
Preference Changes were measured as real-number S based on a
modification of Conlisk’S (see Eqs.4 - 6) and tested for significance
at P < 0.05 (one-sample t-test against zero). Preference Changes rep-
resent IA violations for CC tests but are only necessary and not suf-
ficient for claiming IA violations in CR tests
CC common consequence test, CR common ratio test
Test Preference Change
Monkey A CC S > 0 1/18
S < 0 8/18
CR S > 0 1/18
S < 0 11/18
Monkey B CC S > 0 2/18
S < 0 3/18
CR S > 0 2/18
S < 0 5/18
Monkey C CC S > 0 2/18
S < 0 6/18
CR S > 0 1/18
S < 0 8/18
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Journal of Risk and Uncertainty (2022) 65:319–351
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To summarize, all monkeys showed significant Preference Changes in both CC
and CR tests. Below we describe these results in more detail to identify possible fac-
tors contributing to the observed patterns of Preference Changes.
3.3 Probability dependency ofpreference changes
Whereas previous human studies tested specific gambles, behavioral studies with
monkeys can last several months during which large numbers of behavioral tests can
be carried out. We have therefore been able to study choices of gambles over larger
ranges of probabilities that fill wider areas of the Marschak-Machina triangle. This
possibility allowed us to test whether the Preference Changes might depend on the
probabilities of gamble outcomes, irrespective of particular preferences between the
initial gambles A and B.
For the CC test, we varied the probability of the low outcome of gamble
B (Bp1; i.e. the probability of receiving 0 ml in option B) and the probability of
the high outcome (Bp3; i.e. the probability of receiving 0.5 ml in option B; thus,
Bp2 = 1—Bp3Bp1). In accordance with the definition of the CC test, we defined
gambles C and D by adding a common probability k to options A and B (Fig.1C).
This corresponded to adding probability Bp2 to the probabilities p1 of gambles A
and B (and thus reducing the original p2 of gambles A and B). Therefore, we fully
identified each CC test by the set of probabilities for gamble B (Bp1, Bp3), without
the need to explicitly introduce the probability k. Significant IA Preference Changes
occurred in both directions in different parts of the Marschak-Machina triangle
(Fig.4A; S > 0, purple dots; S < 0, cyan dots; black circles indicate P < 0.05, one-
sample t-test against zero). As shown in the confusion matrices, LDA classifications
correctly predicted 14 out of 18 tests (78%) in each of Monkey A and Monkey B,
and 11 out of 18 tests (61%) in Monkey C, which exceeded random prediction (50%)
and was no less than prediction with majority class (i.e. the majority type of the
direction of Preference Change for each monkey; 72% for Monkey A, 50% for Mon-
key B and 61% for Monkey C) (Fig.4A insets). These results suggested a systematic
relationship between Preference Changes and reward probabilities in the CC test.
For the CR test, we varied the ratio r and the high-outcome probability in gamble
B (Bp3; i.e. the probability of receiving 0.5ml in option B; thus, Bp1 = 1—Bp3). We
defined gambles C and D by multiplying the common ratio r with the probabilities
of all non-zero outcomes of gambles A and B. Therefore, the two variables Bp3 and
r defined fully a particular CR test. Significant IA Preference Changes occurred in
both directions in different parts of the parameter space (Fig.4B; S > 0, purple dots;
S < 0, cyan dots; P < 0.05). The confusion matrices showed that LDA classifications
correctly predicted 17 out of 18 tests (94%) in Monkey A, 15 out of 18 tests (83%) in
Monkey B, and 15 out of 17 tests (88%) in Monkey C, all of which exceeded random
prediction and was no less than prediction with majority class (94% for Monkey A,
67% for Monkey B, 82% for Monkey C) (Fig.4B insets). Hence, similar to the CC
test, the Preference Changes in the CR test depended on gamble probabilities.
The systematic nature of the observed Preference Changes in both the CC tests
and the CR test encouraged us to model the observed changes mathematically using
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337
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Journal of Risk and Uncertainty (2022) 65:319–351
economic theory. Therefore, we next fitted our data using different economic choice
models and tested whether these models might explain the observed violations.
3.4 Economic models characterizing preference changes
We compared different economic choice models in their ability to explain the
observed pattern of Preference Changes. We fitted choice data to stochastic imple-
mentations of basic constructs of three economic theories: objective Expected Value
(EV), Expected Utility Theory (EUT) and Cumulative Prospect Theory (CPT).
We defined a standard discrete choice softmax function (McFadden, 2001) to
describe stochastic preferences as the probability of choosing one option over another,
in repeated trials (Eq.7). This function calculates the probability of choosing the first
of two options from the value difference between the two options and includes a noise
term that accounts for variability in choices. The difference between the choice mod-
els consisted of different value computations: in the EV model, each option’s value
corresponded to its objective Expected Value (EV; Eqs.8 and 9); in the EUT model,
value was defined as Expected Utility using a utility function (EU; Eqs.10 and 11);
in the CPT model, value was defined as Prospect Value and resulted from a utility
function (u) and a probability weighting function (w), combined in a cumulative form
(Eqs.1214). The utility function was a power function (one free parameter), normal-
ized to the highest magnitude (Eq.15). The probability weighting function was a two-
parameter Prelec function (Eq.16). These parametric functions have been shown to
maximize the information extraction from participant data (Stott, 2006). Finally, we
used a maximum likelihood estimation procedure to identify the model parameters
that best represented the behaviorally measured probability of choice: we estimated
the parameters that maximized the standard log-likelihood function (Eq.17).
We used the Akaike Information Criterion (AIC) for an initial comparison of the
accuracy of each model, based on the maximum likelihood function (lower AIC val-
ues indicate better fit). The AICs of the EV model were 204.4 ± 11.4 for Monkey
A, 144.8 ± 4.8 for Monkey B, and 216.8 ± 8.0 for Monkey C (mean ± standard error
of the mean, SEM). The AICs of the EUT model across sessions were 149.5 ± 10.8
for Monkey A, 118.7 ± 4.2 for Monkey B, and 115.6 ± 4.2 for Monkey C. The AICs
of the CPT model were 140.5 ± 10.1 for Monkey A, 114.4 ± 4.1 for Monkey B, and
110.4 ± 4.3 for Monkey C. The differences between the three AIC values were sig-
nificant in each animal (P = 6.08∙10–05 for Monkey A, P = 1.50∙10–06 for Monkey B,
and P = 4.67∙10–33 for Monkey C; one-way ANOVA). Pairwise post-hoc compari-
son showed significant differences between the EUT and EV models (P = 2.98∙10–16
for Monkey A, P = 8.47∙10–20 for Monkey B, and P = 5.55∙10–30 for Monkey C;
paired t-test) and between the CPT and EUT models (P = 8.41∙10–10 for Monkey
A, P = 2.02∙10–10 for Monkey B, and P = 8.58∙10–11 for Monkey C). Thus, the CPT
model showed the lowest AIC values in all three monkeys and thus explained our
choice data best. We therefore used the CPT model for our further analyses.
According to the CPT model, Monkey A had basically a linear utility function
(U(m); estimated parameter:
ρ
= 1.01) and a probability weighting function with an
S shape (W(p); estimated parameters: α= 1.86; β = 0.42), whereas Monkeys B and C
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had convex utility functions (ρ = 1.43 and ρ = 1.63, respectively) and S-shaped prob-
ability weighting functions (α = 1.31; β = 1.13 and α = 1.37; β = 0.767, respectively)
(Fig.5A, insets).
To better understand and visualize how the CPT model might explain the IA
violations, we computed the indifference curves (ICs) in the Marschak-Machina tri-
angle (Fig.5A left), based on the utility and probability weighting functions esti-
mated from the best-fitting CPT model (Fig.5A, insets). According to the EV and
EUT models, the ICs in the Marschak-Machina triangle should be linear and paral-
lel to each other, while CPT produces non-linear and non-parallel ICs. The indif-
ference map (i.e. the full set of ICs) computed from the best fitting CPT model in
each animal showed monkey-specific patterns of non-linear ICs, which reflected the
subjective value of choice options (Fig.5A, colored lines). We considered the “fan-
ning” direction of the ICs to further characterizes IA violations (Machina, 1982);
“fanning-out” (higher ICs more horizontal than lower ones) characterizes Allais-
type violations, and “fanning-in” characterizes reverse Allais-type violations. We
observed a predominantly fanning-in pattern, although areas of fanning-out existed
within the triangle. This pattern reflected the mostly negative values of the measured
Preference Changes, supporting the idea that IA violations reflected a non-linear
distribution of subjective values within the Marschak-Machina triangle, which is
incompatible with EUT.
To examine how well CPT explained the observed Preference Changes, we cal-
culated the S values that were predicted by the model for each CC and each CR test.
On a session-by-session basis, we estimated the choice probability from outcome
probability and magnitude according to Eq.7 together with Eqs.12 - 14. The esti-
mated choice probability was then used to calculate each S using Eq.6. When com-
paring the measured and predicted S values for each session, we found significant
Pearson correlation coefficients in all monkeys in the CC test (Monkey A: ρ = 0.46,
P = 2.1∙10–53; Monkey B: ρ = 0.21, P = 8.9∙10–9; Monkey C: ρ = 0.29, P = 4.4∙10–49)
as well as in the CR test (Monkey A: ρ = 0.74, P = 1.1∙10–188; Monkey B: ρ = 0.60,
P = 2.4∙10–72; Monkey C: ρ = 0.64, P = 2.6∙10–287) (Fig.5B). Thus, the CPT model
was compatible with the observed pattern of violations.
We tested the robustness of the CPT model’s IC estimation with out-of-sample tests.
The animal chose between a fixed option, plotted on one of the axes of the Marschak-
Machina triangle, and a varying two- or three-outcome gamble (see Sect.2). In each
session, indifferent points (IPs) were estimated by fitting a softmax function to the
measured animal’s choices. If the modeled indifference map reflected the true subjec-
tive evaluation pattern, the modeled IPs should be close to the measured ICs. A graphi-
cal comparison between the IPs and the ICs in Fig.6A predicted by the CPT model
demonstrated good correspondence between the out-of-sample IPs (colored points) and
the modeled ICs (lines with same color as IPs). When quantifying the distance between
the measured out-of-sample IPs and the ICs predicted by the EV, EUT or CPT models,
we found significant residuals in all three models (P < 0.01 against the ICs; one-sample
t-test). The residuals were significantly different across the models in all three monkeys,
as revealed by one-way ANOVA tests (P = 3.10∙10–86 for Monkey A, P = 2.66∙10–33
for Monkey B, and P = 5∙10–324 for Monkey C); post-hoc paired t-tests demonstrated
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Fig. 5 Cumulative prospect theory (CPT) modeling can explain the measured Preference Changes. (A)
Choice indifference curves (ICs) in the Marschak-Machina triangle. Each line represents one IC, and all
gambles on a given line are equally preferred to each other. Insets show estimated utility and probabil-
ity weighting functions, using the CPT model, from all trials (common ratio and common consequence
tests). The three monkeys had individually differing, mostly non-linear utility and probability weighting
functions. Note that utility functions were only estimated for three points (black dots), which were the
only three magnitudes used in the experiment (m1, m2, m3); thus, the dashed lines do not represent the
full shape of the utility function. (B) Pearson correlations between measured S’s and S’s predicted by
the CPT-modeled utility and probability weighting functions for the common consequence and common
ratio tests
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smaller residuals for the CPT model compared to the EV and EUT models (Fig.6B),
except for data from Monkey B resulting in a non-significant difference between EUT
and CPT model residuals (EUT vs EV: P = 5.24∙10–26 for Monkey A, P = 2.84∙10–32
for Monkey B, and P = 1.35∙10–134 for Monkey C; CPT vs EUT: P = 1.28∙10–08 for
Monkey A, P = 0.734 for Monkey B, and P = 9.26∙10–22 for Monkey C). Thus, the CPT
model captured the out-of-sample IPs more accurately than the other models.
Although in their original deterministic formulation the EV and EUT models
would not theoretically produce all-or-none Preference Reversals, their stochastic
versions using the softmax choice function could in principle result in graded Pref-
erence Changes, in particular in the CR test (see Sect.2below Eq.6). We thus inves-
tigated in more detail how much the EUT and EV models would explain our data
(Figs.S3,S4). Our analysis showed that EV and EUT models failed to explain viola-
tions in the CC test, always predicting null Preference Changes (S = 0) (Fig. S3B,
inset). On the other hand, both models predicted the violation pattern in the CR test
to some degree (Figs.S3B, S4B), consistent with previous studies employing sto-
chastic versions of the EUT model (Blavatskyy, 2007). However, the Pearson cor-
relation coefficients of the EV and EUT models had worse prediction power (smaller
correlation coefficients) compared to the CPT model (EV: ρ = 0.26, P = 3.1∙10–18 for
Monkey A, ρ = 0.51, P = 9.3∙10–50 for Monkey B, and ρ = 0.22, and P = 1.7∙10–28 for
Monkey C; and EUT: ρ = 0.55, P = 4.4∙10–86 for Monkey A, ρ = 0.59, P = 2.1∙10–69
for Monkey B, and ρ = 0.52, P = 8.6∙10–172 for Monkey C) (Figs.S3 and S4).
As a further control, we explicitly tested the hypothesis of ICs being linear and
parallel, as implied by EUT (Fig. S5). Because previous human studies usually
performed tests on only a few gambles, as plotted in the Marschak-Machina trian-
gle (Fig.7A; Blavatskyy etal., 2015), this method has never been used to investi-
gate EUT. In the current study, we tested separately the linearity and parallelism
of the ICs. To test for parallelism, we assumed linear ICs and used a least-squares
model to estimate the slopes of the ICs and compare them (Kruskal–Wallis one-way
ANOVA; Fig. S5A, B). The linearity of the ICs was tested through the residuals
of indifferent points in each IC that were estimated with linear least squares using
out-of-sample IPs (one-sample t-test against 0; Fig.S5C). We found significant non-
linearity (p < 0.001) and non-parallelism (p < 0.05) for some ICs, suggesting that
EUT was not able to capture the subjective values for varying probabilities. This
result demonstrates systematic violations in EUT, which was consistent with our
AIC and residual analyses. The pattern of generally increasing ICs slopes (Fig.S5B)
also served as confirmation for a mostly fanning-in direction of the indifference
map, which explained the observed pattern of IA violations with mostly negative
Preference Change values.
Taken together, these results showed that the CPT-based economic choice model
predicted the IA violations in both CC and CR tests, outperforming both the EV and
the EUT models. These findings suggest that the observed violation pattern might
arise from the subjective, non-linear weighting of reward probabilities, in line with
the explanation provided by CPT.
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Fig. 6 Out-of-sample tests on indifference curves (ICs) modeled by Cumulative Prospect Theory (CPT).
(A) Close relationship between measured out-of-sample indifference points (IP, colored dots) and ICs
modeled by CPT (colored lines) in common ratio and common consequence tests. Colored dots show
mean IPs across all sessions, corresponding to the same-colored ICs; lines show Standard Deviations
(SD) of IPs across all sessions. (B) Left: bar charts of means and Standard Errors of the Mean (SEM) of
model residuals (distances between model ICs and the measured out-of-sample IPs). Asterisks: P < 0.05
in post-hoc paired t-test (n.s.: not significant). Right: residual differences from different models (means
from individual sessions; top: Expected Value (EV) minus Expected Utility Theory (EUT); bottom: EUT
minus CPT. Dotted line: mean value; P: post-hoc paired t-test p-value. Smaller residuals for CPT than
EUT or EV indicate that CPT better captured the pattern of the measured out-of-sample IPs
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3.5 Comparison ofpreference changes withhumans
To explore the possibility of common economic decision mechanisms between evo-
lutionary close species, we compared the observed IA violations in monkeys with
those found in humans. We considered results from 39 human studies investigating
the CC effect (Blavatskyy etal., 2015). Many of these studies repeated the Allais
test; others defined different tests which, when represented in the Marschak-Machina
triangle (as p1 and p3 of gamble B), were mostly concentrated in the lower left area
(Fig.7A). The human studies reported significant Preference Changes characterized
by S > 0 or S < 0, as well as insignificant changes (S ~ 0) (Fig.7B).
We used two methods to compare our monkey data with the published human
data, a confusion matrix and a Pearson correlation. The gambles used in the human
studies differed from each other and from those used in monkeys. To nevertheless
allow accurate comparisons, we predicted the Preference Changes S for the untested
Fig. 7 Correspondence of Preference Changes S between monkeys and humans. (A) Gamble positions
in the Marschak-Machina triangle of 39 independent human common consequence studies (Blavatskyy
etal., 2015). The x-axis represents the probability of getting the low outcome in option B and the y-axis
represents the probability of getting the high outcome in option B (see option B in Fig.1D). The diagram
illustrates the location of the reward probability tested in the human studies (black dots). Gray circles cor-
respond to the CC tests we performed on monkeys in the current study. (B) Results from the 39 human
studies. Blue and green bars refer to option sets 1 {A, B} and 2 {C, D}, respectively (see Fig.1B). The
y-axis indicates the probability of choosing option 1 (A or C). (C) Correspondence between measured
human S’s (39 studies; B) and predicted monkey S’s. Left: confusion matrix of classes of human S’s and
classes of monkey S’s predicted from actually used monkey gambles by Linear Discriminant Analysis
(LDA). The LDA prediction of monkey S’s allowed comparison of same gambles between the two spe-
cies. Right: Pearson correlation between measured human S’s and monkey S’s predicted by regression
(Eqs.18, 19), using the same gambles. (D) Correspondence between measured monkey S’s and predicted
human S’s. This inverse control test relative to the test shown in C was based on the actual gambles used
in monkeys and employed predictions of human S’s for these gambles via different LDA and regression
(Eqs.20, 21; see Sect.2)
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gambles with an LDA classifier for the confusion matrix, and with two multiple lin-
ear regressions for the Pearson correlation (see Sect.2; Eqs.18 - 21).
For the missing gambles, we first used the LDA and the regressions (Eqs.18 and
19) to predict the S’s in our monkeys for the gambles that had been used in humans.
Then we compared the actually measured human S’s with the predicted monkey S’s.
The confusion matrix showed that the actual measured S’s in humans corresponded
successfully to the LDA-predicted S’s in monkeys with 82% accuracy (Fig.7C left),
which exceeded random (50%) and majority class (i.e. the majority type of the direc-
tion of S’s across all human studies, 67%). The Pearson correlation between human
S’s that had been measured and monkey S’s that had been predicted by regression
(Eqs.18 and 19) was significant (ρ = 0.56, P = 2.44∙10–4) (Fig.7C right).
To test the robustness of these comparisons, we reversed the direction of pre-
dicting S’s for untested gambles: using LDA and regressions (Eqs.20 and 21), we
predicted the S’s in humans for the gambles we had used in monkeys. The confusion
matrix showed that the measured monkey S’s corresponded to the LDA-predicted
human S’s with 70% accuracy, which exceeded random (50%) and majority class
(61%) (Fig. 7D left). The Pearson correlation between the measured monkey S’s
and the regression-predicted human S’s (Eqs.20 and 21) was significant (ρ = 0.45,
P = 5.61∙10–4; Fig.7D right).
Thus, while we saw less Preference Reversals than are generally reported in
humans, the Preference Changes in our monkeys corresponded well to those in
humans. The result suggests shared decision mechanisms across primate species and
encourages neurophysiological investigations in monkeys of neuronal signals and
circuits that may underlie these common choice mechanisms.
4 Discussion
We studied stochastic choices in rhesus monkeys in the two most widely used tests
of the IA, common consequence and common ratio, which provide stringent assess-
ment for utility maximization. All three tested monkeys showed consistently few
outright Preference Reversals between the initial and the altered option sets, pos-
sibly due to the animals’ extended laboratory experience with weekdaily tests; how-
ever, the animals showed substantial graded Preference Changes (Figs. 2 and 3;
Tables1 and 2) that depended on gamble probabilities and were largely explained
by nonlinear probability weighting (Figs.4 and 5). According to AIC and out-of-
sample analyses, a CPT model with probability weighting explained the choices bet-
ter than EUT and EV models without probability weighting (Fig.6). Classification
and regression analyses demonstrated similarities between our monkeys’ choices
and reported human choices (Blavatskyy etal., 2015, 2022) (Fig.7). Together, these
results indicate systematic Preference Changes in IA tests in monkeys that can be
explained by probability weighting of CPT.
While human studies played a crucial role in identifying and explaining IA viola-
tions, in particular non-linear probability weighting (Blavatskyy, 2007; Blavatskyy
etal., 2022; Camerer & Ho, 1994; Conlisk, 1989; Harman & Gonzalez, 2015; Quiggin,
1982; Ruggeri etal., 2020; Schneider & Day, 2016; Tversky & Kahneman, 1992), the
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studies were restricted by a number of species-specific factors, including limited trial
numbers, limited test variations, limited test repetitions, insufficient learning, behavio-
ral errors and, of course, language and cultural influences. To compensate for limits of
trial numbers, some human studies combined data from multiple participants; however,
the validity of such tests depends on the subjectivity of individual utility functions and
on cultural differences (Loubergé & Outreville, 2011; Ruggeri etal., 2020). Thus, more
comprehensive assessments of IA compliance would benefit from wider test variations
with more trials than are feasible in humans. This is where monkeys come in. Working
with monkeys not only avoids cultural biases but also allows large variations of test
conditions during thousands and tens of thousands of trials during weekdaily tests over
weeks and months. With such large trial numbers, errors are minimized and learning
would be completed and thus play no uncontrolled role. The resulting consistent per-
formance allowed us to investigate the robustness of economic models that confirm the
dominant role of probability weighting in common consequence and common ratio IA
tests.
This study found fewer outright Preference Reversals (8%) than those seen in
human studies; we saw primarily graded Preference Changes. The limited violations
in the IA tests resemble the compliance of the Independence of Irrelevant Alterna-
tives of two-component bundles (Pastor-Bernier etal., 2017). Good compliance in
the two tests may be due to the high task familiarity of the animals tested in thou-
sands of trials. To assure well-controlled test conditions, our monkeys performed
in our specific primate testing laboratory away from their living area in the animal
house. For ethical reasons, such laboratory tests are limited to a few animals. How-
ever, in this highly standardized test situation the different animals performed very
consistently and similarly to each other. In support of this notion, further four mon-
keys in two separate studies in our laboratory showed consistent risk attitude that
was compatible with S-shaped, convex-linear-concave utility functions with increas-
ing juice volumes (Genest etal., 2016; Stauffer et al., 2015). Thus, while ethical
considerations, general welfare and individual comfort are essential for obtaining
reliable results from cooperative animals, the presented research on monkeys adds
important data to the notion of assessing utility maximization with the IA axiom
that had so far been tested in humans and, in select cases, in rodents (Battalio etal.,
1985; Kagel etal., 1990).
Probability weighting is a particularly interesting and important explanation for
IA violations. Although reward probability can influence the type and level of IA
violations, most human IA violation tests used only limited levels of probability. We
tested many probability levels across the Marschak-Machina triangle and substanti-
ated probability weighting as major mechanism underlying Preference Changes in
both the common consequence and the common ratio tests. We did not make any
hypothesis about the existence of probability weighting but instead demonstrated
empirically that probability weighting explains Preference Changes. Specifically,
our leave-one-out classification with LDA demonstrate probability as key factor
underlying IA Preference Changes in both common consequence and common ratio
tests (Fig. 4), and the CPT probability weighting function fitted to the measured
behavioral choices successfully predicted Preference Changes (Fig. 5). Thus, our
study provides a detailed and robust account of the role of probability weighting in
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IA tests. In addition to probability weighting, it has been proposed that the salience
of the visual cues for reward probability information (i.e., the length of the stimulus
bar in our study) could contribute to choice biases (Spitmaan etal., 2019), suggest-
ing a future direction for investigating its role in IA violations.
Past studies have reported different shapes of the probability weighting function.
Humans show anti-S and S-shape probability weighting with instructed and expe-
rienced probabilities, respectively (Cavagnaro et al., 2013; Farashahi et al., 2018;
Gonzalez & Wu, 1999). Monkeys show anti-S probability-weighting with pseudor-
andomly varying probabilities (Stauffer etal., 2015) and S-shape weighting in trial
blocks (Ferrari-Toniolo et al., 2019). When presenting a larger set of magnitudes
and probabilities, and allowing for more complex shapes of the utility function, our
monkeys’ choices were best explained by a mostly concave probability weighting
function (Ferrari-Toniolo etal., 2021). Humans show a similar concave probability
weighting function (Blavatskyy, 2013a). Our current results confirm concave prob-
ability weighting with a larger set of finely varying probabilities in three-outcome
gambles that allowed us to uniformly sample the whole probability space (Fig.5A).
Our results highlight a series of possible factors contributing to the estimated shape
of the probability weighting function: the choice of the functional form for utility
and probability weighting, the range and resolution of the tested magnitudes and
probabilities, and the complexity and representation of choice options (especially
two- and three-outcome gambles). Further investigations are required to better iso-
late the factors influencing the shape of the probability weighting function, includ-
ing task particulars and elicitation procedures.
Past studies graphed IA violations via the fanning-in and fanning-out directions of
ICs in the Marschak-Machina triangle (Machina, 1982). Non-linear ICs, compatible
with nonlinear probability weighting, produce different local fanning directions in
different areas of the triangle (Fig.5A) (Camerer & Ho, 1994; Wu & Gonzalez, 1998;
Kontek etal., 2018). Furthermore, different stochastic versions of EUT (Blavatskyy,
2007) and, more in general, different contributions of noise to the value computation
mechanism (Bhatia & Loomes, 2017; Hey & Orme, 1994; Woodford, 2012) might
explain IA violations without nonlinear probability weighting. These considerations
highlight the complexity in the relation between the shape of the probability weight-
ing function, the pattern of indifference curves and the experimentally revealed types
of IA violations. Further theoretical work and model simulations, which are outside
of the scope of the current work, should help to elucidate these relations.
Human tests of the IA describe Preference Changes characterized by S > 0 or
S < 0 (Allais, 1953; Blavatskyy etal., 2022; Starmer, 2000). Because of these viola-
tions, many economic theories have been developed to explain economic choices
under risk, including Rank-Dependent Utility (Quiggin, 1982), Cumulative Prospect
Theory (Tversky & Kahneman, 1992), and Target-Adjusted Utility (Schneider &
Day, 2016). Consistent with human choices, we found that our monkeys’ choices
show both types of violations in the two most studied tests (common consequence
and common ratio). Interestingly, although humans and monkeys may show differ-
ent probability weighting functions, the Preference Changes S seen in the repeated,
stochastic choices of our monkeys correspond with 70%—82% accuracy to the S’s
computed from choices averaged across human participants (Fig.7C, D). This result
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is not only interesting for general inter-species comparisons but indicates that viola-
tions in primates are similar and robust despite methodological differences, such as
trial numbers and averaging within subjects as opposed to across subjects.
The IA is arguably the most constrained and direct test that defines Expected
Utility, and its maximization, on a numeric, cardinal scale. With these properties,
the IA provides for a stringent test framework for investigating brain mechanisms
of economic choice. So far, human fMRI studies demonstrate subjective value cod-
ing in reward-related brain regions, including the ventral striatum, midbrain, amyg-
dala, and orbitofrontal and ventromedial prefrontal cortex (Gelskov etal., 2015; Hsu
etal., 2009; Seak etal., 2021; Wu etal., 2011). Neurophysiological studies in mon-
keys demonstrate the coding of subjective value in midbrain dopamine neurons and
orbitofrontal cortex (Kobayashi & Schultz, 2008; Lak etal., 2014; Padoa-Schioppa
& Assad, 2006; Stauffer etal., 2014; Tremblay & Schultz, 1999) and formal util-
ity coding in dopamine neurons (Stauffer etal., 2014). Further, neurons in monkey
orbitofrontal cortex carry single-dimensional utility signals for two-dimensional
choice options designed according to Revealed Preference Theory (Pastor-Bernier
etal., 2019). However, despite attempts of economic decision theories to explain IA
violations (such as prospect theory), the neuronal mechanisms underlying IA viola-
tions are unknown. To address the issue, an animal model would be desirable that
demonstrates IA violations similar to those seen and analyzed in humans. Our own
studies showed that monkeys’ choices follow indifference curves of Revealed Prefer-
ence Theory, satisfy first-, second- and third-order stochastic dominance, demon-
strate probability weighting, and comply with the first three EUT axioms (complete-
ness, transitivity, continuity) (Ferrari-Toniolo etal., 2019, 2021; Genest etal., 2016;
Pastor-Bernier etal., 2017; Stauffer etal., 2015), all of which suggests compliance
with fundamental concepts of economic choice. The current study describes compli-
ance and violation of the fourth EUT axiom, IA, which is the most demanding and
investigated EUT axiom in humans. Tests in rodents have revealed globally similar
IA violations as in humans (Battalio etal., 1985; Kagel etal., 1990), but the results
have so far not been used for neurophysiological investigations in this species. As
the performance of our monkeys in IA tests is also consistent with that in humans,
researchers may want to use neurophysiology in animals to understand neuronal
choice mechanisms in humans.
Although our study provides systematic and stochastic data on IA violations,
there are a few incompletely addressed directions that can be investigated in the
future. For example, further research may test whether reward magnitude can
influence IA violations, as it does in humans (Blavatskyy etal., 2022). Further,
in the absence of own human data, we can only relate our results to those from
human experiments that did not necessarily have the exact same design. Some of
the observed differences between human’s and monkey’s IA violations could be
due to the unequal sampling of the probability space across species, with human
studies usually focusing on a specific region of the Marschak-Machina triangle.
Our analyses revealed a difference in the magnitude of the S values between spe-
cies, together with a minority of incompatible predictions for the Preference
Change direction (Fig. 7C, D; confusion matrix and correlation plots). These
differences might reflect the fact that our evaluations depended on the indirect
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comparison between measured and predicted Colinsk’ S values across species. To
more directly compare IA violations between humans and monkeys (in which neu-
rophysiological studies are more feasible), future studies might adapt our experi-
mental design to that used in humans. We also observed some differences in effect
size between positive and negative S values (Fig.2), which suggest future inves-
tigations of distinct behavioral strategies leading to violations in different direc-
tions. Additionally, future experiments could investigate different classes of eco-
nomic models that might capture more reliably the pattern of IA violations when
allowing for the stochasticity of choice (Blavatskyy & Pogrebna, 2010; Loomes &
Pogrebna, 2014). Our tests might support further development of decision theory
and computer algorithms, for example by using our data for advancing model-
free and model-based reinforcement learning theory into the domain of economic
choice research (Daw etal., 2011; Miranda et al., 2020). It would be interesting
to see how subjective values are updated after win or loss trials in IA violated
gambles (model-free: based only on stimuli; model-based: update the whole prob-
ability and utility model; or a combination of both). Neurophysiology research on
value updating by reinforcement could benefit from the developed experimental
designs. Thus, because of its multidisciplinary nature, our current behavioral study
may provide the basis for further investigations of behavioral and neuronal mecha-
nism of economic decision-making under risk.
Supplementary information The online version contains supplementary material available at https:// doi.
org/ 10. 1007/ s11166- 022- 09388-7.
Acknowledgements We thank Ms. Christina Thompson, Mr. Aled David and Dr. Henri Bertrand for
animal and technical support. The Wellcome Trust (WT 095495, WT 204811), the European Research
Council (ERC; 293549) and the US National Institutes of Mental Health Conte Center at Caltech (NIMH;
P50MH094258) supported this work.
Author contributions SF-T and WS designed the experiment, SF-T and LCUS conducted the experi-
ments, SF-T and LCUS analyzed the data, SF-T, LCUS and WS wrote the paper.
Data availability An earlier version has been uploaded to bioRxiv and SSRN in November 2021
(CC-BY).
The data from this study will be made available upon reasonable request.
This article will be distributed under the Creative Commons Attribution License 4.0 (CC-BY).
Declarations
Competing interest The authors declare no competing interests.
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Authors and Affiliations
SimoneFerrari‑Toniolo1 · LeoChiU.Seak1 · WolframSchultz1
Leo Chi U. Seak
Chiuseak@gmail.com
Wolfram Schultz
Wolfram.Schultz@Protonmail.com
1 Department ofPhysiology, Development andNeuroscience, University ofCambridge,
Cambridge, UK
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2.
3.
4.
5.
6.
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