# Hey, what did you expect ? Confirmation bias in credence goods markets: Theoretical and experimental analyses

Maxime Perodaud, Michela Chessa

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Maxime Perodaud, Michela Chessa. Hey, what did you expect ? Confirmation bias in credence goods markets: Theoretical and experimental analyses. Journal of Behavioral and Experimental Economics, 2026, 120, pp.102495. 10.1016/j.socec.2025.102495. hal-05441370

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# Hey, what did you expect ? Confirmation bias in credence goods markets: Theoretical and experimental analyses☆

Maxime Perodaud<sup>a, ID, *</sup>, Michela Chessa<sup>b, c, ID</sup>

## ARTICLE INFO

**JEL classification:**

C79

C91

D82

D83

L15

**Keywords:**

Belief-dependent preferences

Confirmation bias

Credence goods

Disappointment

Experiments

Fraud

Psychological game theory

## ABSTRACT

This study investigates how consumer confirmation bias affects expert decision-making in credence goods markets by combining theoretical modeling with experimental analysis. Using psychological game theory, we develop a dynamic two-player model in which an expert's utility is shaped by both financial incentives and the psychological disutility associated with not meeting consumer expectations. The model predicts that experts with higher psychological sensitivity are more likely to deviate from profit-maximizing strategies to align with consumer beliefs, potentially increasing market inefficiencies. Experimental results show that the extent to which confirmation bias influences expert behavior depends on both the expert's sensitivity and state of the world. Under conditions of high sensitivity and consumer bias, experts adjusted their recommendations to match consumer expectations, even when doing so led to suboptimal outcomes. These findings have policy implications and contribute to the literature on credence goods by highlighting the tension between expert judgment and consumer-driven expectations.

## 1. Introduction

Economic decision-making is particularly susceptible to cognitive biases and judgment errors (Croskerry et al., 2013; Mamede et al., 2008; Nendaz & Perrier, 2012; Tversky & Kahneman, 1974, 1985). These systematic distortions in information processing are often triggered by time constraints, information overload, or contextual cues, leading to deviations from rational choice.

In this paper, we focus on cognitive biases in credence goods markets. Credence goods (Darby & Karni, 1973) are goods whose quality cannot be observed by consumers even after purchase.<fnref n="1" /> This concept applies to various economic situations characterized by significant informational asymmetries, such as car repairs, computer maintenance, financial advice, and certain medical services. These markets require expert diagnosis and recommendation. Since consumers cannot accurately assess whether the service provided was professionally appropriate, they remain vulnerable to fraud and may develop skepticism toward expert judgment.

As a result, even when they lack the expertise to diagnose a condition themselves, consumers often hold preconceived notions about the best treatment or solution. A particularly relevant bias in such scenarios is confirmation bias.<fnref n="2" /> As defined by Nickerson (1998), confirmation bias involves selectively interpreting or seeking evidence that supports preexisting beliefs, thereby distorting how individuals update their views. While much is known about its impact on personal decision-making, the role of confirmation bias in interactions between two parties — such as a consumer and an expert in credence goods markets — remains less explored.

Consider for example a patient presenting with a severe sore throat that could indicate either a viral or bacterial infection. If the patient has a viral infection, antibiotics provide no medical benefit; however, a patient may expect to receive an antibiotic prescription as a standard cure for sore throat. Conversely, a patient with a bacterial infection may harbor strong concerns about antibiotic resistance. Such a patient might expect receiving alternative treatments (e.g., rest, fluids, symptomatic

<footnote><sup>a</sup> Université Côte d'Azur, France</footnote>

<footnote><sup>b</sup> Université Côte d'Azur, CNRS, GREDEG, France</footnote>

<footnote><sup>c</sup> INRIA, Université Côte d'Azur, France</footnote>

<footnote>☆ This article is part of a Special issue entitled: 'PsyEconTheory' published in Journal of Behavioral and Experimental Economics.</footnote>

<footnote>* Correspondence to: Université Côte d'Azur, Petit Valrose, Av. Joseph Vallot, 06100, Nice, France.</footnote>

<footnote>E-mail address: maxime.perodaud@univ-cotedazur.fr (M. Perodaud).</footnote>

<footnote n="1">¹ In contrast, for experience goods, consumers can judge utility after consumption.</footnote>

<footnote n="2">² These beliefs can also be influenced by other heuristics, such as representativeness, which leads individuals to overestimate the likelihood of events that fit a familiar pattern.</footnote>
care) even when antibiotics would be medically indicated and benefi-
cial. In both cases, the patient's confirmation bias regarding treatment
can diverge from the objective medical appropriateness of the interven-
tion. Anticipating the patient's confirmation bias, the expert may have
a tendency to provide unnecessary antibiotics or ineffective alternative
treatments to meet consumers' expectations. Similar considerations can
be made when a gynecologist must choose between natural delivery
and cesarean section, and in many different medical and non-medical
scenarios.

Because consumers' beliefs play such an influential role in how
experts provide credence goods, it is crucial to understand how an
expert's awareness of these beliefs shapes her professional choices. Our
objective is to investigate whether and how consumer confirmation
bias influences expert behavior in credence goods markets. We address
two key questions. First: *How does an expert adjust her recommendations
upon discovering that the consumer suffers from confirmation bias?* Second,
given the inefficiencies commonly observed in these markets, where
experts often fail to provide appropriate treatments: *Might consumer
confirmation bias alleviate or worsen existing market inefficiencies?*

To address these questions, we combine (i) a theoretical analysis
of a two-player psychological Bayesian sequential game with (ii) an
experimental investigation through a controlled laboratory experiment
designed to test our theoretical predictions. Namely, in the theoretical
model, we consider a credence goods game, where the consumer's
confirmation bias causes an expectation about which of two treatments
the expert is likely to provide.<fnref n="3" /> The expert observes the state of the
world, makes a decision about the treatment to provide, and receives
a material payoff as a consequence of her choice. In addition, we
incorporate a psychological component in the theoretical formulation
of the expert's utility function, that we call the expert's *sensitivity toward
expectation dissonance*, allowing the expert to experience disutility if the
consumer receives a treatment different from her expectation.

In our laboratory experiment, 184 participants completed an adap-
tation of the Wason Selection Task (Wason, 1968), enabling us to
measure confirmation bias at the individual level. Next, participants
were then engaged in a fictitious market that reproduced the conditions
for providing credence goods where half of the participants played
the role of consumers, and the other half played the role of experts.
Participants played in couples, an expert with a consumer. Before the
expert made any decision, she learned whether the consumer matched
with her (for a particular round) exhibited confirmation bias or not.
This allowed for a within-subject comparison. Furthermore, we manip-
ulated the probabilities of different states of the world occurring in the
between-subject variation of our design.

Our theoretical framework suggests that experts with stronger psy-
chological sensitivity toward expectation dissonance are more likely to
align with consumer's expectations even when those expectations go
against the true state of the world. The experimental results, however,
show that this effect depends heavily on the specific state of the world.
Under conditions where providing the appropriate treatment yields
minimal payoffs for the experts, we see no notable effect of confir-
mation bias; they tend to offer the correct solution. Yet in scenarios
where providing the appropriate treatment yields maximal payoffs, we
do observe an effect of the consumer's confirmation bias, particularly
when this state is more likely to occur.

Our findings highlight confirmation bias as a pivotal element in
credence goods markets, demonstrating how expert sensitivity to con-
sumer biases can reshape professional decision-making. We show that
expert sensitivity toward consumer confirmation bias can substantially
alter her service recommendations, sometimes compromising consumer
welfare through increased exposure to inappropriate treatments.

The remainder of this paper is organized as follows. Section 2
reviews relevant literature. Section 3 develops our theoretical model.
Section 4 details our experimental design and protocol and defines
our experimental hypotheses. Our experimental findings are reported
in Section 5. Finally, Section 6 offers concluding remarks, addresses
possible limitations, and suggests avenues for future research.

## 2. Related literature

Our paper brings together insights from three important research
streams: credence goods markets, confirmation bias, and psycholog-
ical game theory. We next review each literature and explain our
contributions to these fields.

First, introduced by Darby and Karni (1973), credence goods arise when consumers lack the expertise or information to assess whether a purchased good or service truly meets their needs. Because of the substantial information asymmetry, there are opportunities for fraud, particularly in healthcare (Baker, 2010; Das et al., 2016; Emons, 1997; Fuchs et al., 1973; Gottlieb & Clemens, 2014; Iizuka, 2007), where research shows a clear link between financial incentives and unethical practices. Dulleck and Kerschbamer (2006) demonstrate that when neither liability (ensuring sufficient quality) nor verifiability (preventing overcharging) is guaranteed, credence goods market efficiency remains low.

The experimental literature<fnref n="4" /> typically focuses on (under- and over-) treatment provision (Balafoutas et al., 2013, 2017; Dulleck et al., 2011; Gottschalk et al., 2020; Hennig-Schmidt et al., 2011) and pricing behavior (Kerschbamer et al., 2016, 2019; Mimra et al., 2016). However, more recent work investigates a different inefficiency: mistreatment where services fundamentally mismatch consumer needs (Anagol et al., 2017; Mullainathan et al., 2012; Soraperra et al., 2019).

Our contribution to this literature is twofold. First, we follow this
recent approach by modeling service appropriateness rather than as-
suming high-quality treatment solves consumer problems. Second, fol-
lowing (Beck et al., 2013), we integrate psychological components
into experts' utility functions, specifically, how expert awareness of
consumer's confirmation bias affects professional decision-making. This
creates a novel trade-off: experts must balance service appropriateness
and material payoffs against consumer psychological satisfaction.

Second, we investigate confirmation bias, a psychological phenomenon extensively studied since Anderson et al. (1980) and Lord et al. (1979), who demonstrated that individuals with opposing views interpret identical evidence to reinforce their existing positions. Economic models explain why such biases persist (Wilson, 2014; Yariv, 2002): Rabin and Schrag (1999) show that people systematically misinterpret signals conflicting with their beliefs, preventing belief correction even with unlimited information. Fryer et al. (2019) confirm that prior opinions powerfully influence evidence processing, while Charity and Dave (2017) demonstrate that strategic thinking can mitigate these effects.

The Wason Selection Task (Wason, 1968) provides the standard
experimental method for detecting confirmation bias. The task presents
subjects with four two-sided cards and asks them to identify the un-
derlying rule that these cards follow by selectively turning over only
those cards necessary to confirm or disconfirm the rule. Wason shows
that people tend to turn over the cards that could confirm the rule and
not the cards that could disconfirm the rule, thereby illustrating the
presence of confirmation bias among subjects.

Studies by Evans et al. (1993) and Kirby (1994) find that task performance varies significantly across domains. Building on this domain-sensitivity finding, research by de Jong et al. (1998), de Jong, Mayer et al. (1997), de Jong and Vroling (2014), de Jong, Weertman et al.

<footnote n="3">³ Importantly, the bias here centers on the expert's supposed choice of treatment, rather than on the true state of the world itself.</footnote>

<footnote n="4">⁴ For broader reviews, see Balafoutas and Kerschbamer (2020) and Kerschbamer and Sutter (2017).</footnote>
(1997) reveals that confirmation bias is more prevalent in contexts involving danger rules rather than safety rules.

Our contribution to this literature addresses a critical gap. While confirmation bias is well-documented in individual decision-making, its strategic implications in (credence goods) markets remain unexplored. Our Wason Selection Task adaptation exploits domain-sensitivity insights by embedding the task within a credence goods context, enabling us to measure how consumer biases influence expert's strategic behavior, a mechanism absent from existing frameworks.

Third, we draw on psychological game theory literature (Attanasi et al., 2025; Battigalli & Dufwenberg, 2009, 2022; Geanakoplos et al., 1989), which accounts for how players' beliefs—about actions or even about other beliefs—shape utility functions. In this vein, Attanasi et al. (2019) study how the disclosure of information on psychological belief-dependent preferences affects incentives to build up a reputation in a trust game. The authors show that it can either increase cooperation when the trustee is guilt-averse, or decrease it when the trustee is selfish.

In a credence goods context, Beck et al. (2013) apply the framework of psychological game theory by incorporating guilt aversion à la Battigalli and Dufwenberg (2007) into an expert's payoff function, leading to multiple possible equilibria. To account for guilt aversion, they extend the expert's utility function by letting the expert feel guilty if she provides less than anticipated by the consumer. The results of their experiment show that non-binding promises can reduce fraudulent behavior and build trust, though other manipulations had less impact. Our study diverges from this approach by examining how consumer's reasoning—rather than manipulation of beliefs—affects expert choices.

Khalmetski et al. (2015) present a theoretical model incorporating belief-dependent preferences, particularly with an extended definition of guilt aversion, in which a player's utility depends on whether they positively or negatively surprise another player's expectations. They assume that some people experience disutility even from positive outcomes when these conflict with their beliefs. Our paper takes a similar direction, by assuming that people may suffer even in the case of positive outcomes simply because the outcomes do not align with their beliefs.

We contribute to the literature by examining an underexplored area of psychological game theory. First, even within the domain of guilt-based or guilt-extended models, previous analyses have primarily focused on the Dictator game and the Trust game (Attanasi et al., 2023). To the best of our knowledge, Beck et al. (2013) is the only study that has investigated guilt in a credence goods context. Second, to account for confirmation bias and to model disutility when the outcomes do not align with pre-existing beliefs, we introduce a new psychological component that we term sensitivity toward expectation dissonance.

### 3. The model

We model the interaction between a consumer and an expert in a credence goods market as a two-player psychological Bayesian sequential game. Both players are assumed to have risk-neutral preferences over payoffs.<fnref n="5" /> Traditional approaches in credence goods markets assume that consumers cannot fully know whether the quality of the provided service addresses their needs, either before or after consumption: a “lower-problem” consumer derives the same utility from receiving the appropriate treatment as from receiving a more intensive one, whereas a “greater-problem” consumer faces reduced utility if

under-treated. Our approach differs slightly in that: we assume consumers can detect whether their needs have been met, with neither under-treatment nor over-treatment occurring. The model adapts Soraperra et al. (2019)'s framework, simplifying the structure of the interaction while introducing belief-dependent motivation, building on Beck et al. (2013) to incorporate the expert's concern for the consumer's beliefs.

We denote the set of players as $N = \{C, E\}$, where C represents the consumer and E the expert. The consumer knows she has a problem drawn by nature as $s \in \{A, B\}$, but does not observe its type. It is common knowledge that the ex-ante objective probability of suffering problem A is $h$, and the probability of problem B is $(1-h)$, where $h \in [0, 1]$. The problem can be solved by visiting an expert, who provides a treatment. The consumer can choose to either enter the market (ENTER, i.e., visit an expert) or not (OUT). If she chooses not to enter, the game ends and both the consumer and the expert get an outside option payoff $o \ge 0$. If the consumer enters the market, she must rely on the expert's treatment recommendation and accept it. Upon the consumer's entry into the market, the expert observes her problem (i.e., the state of the world) and provides a treatment $r \in \{a, b\}$, intended to solve problem A or B, respectively. As is typical in credence goods settings, the expert can offer any treatment regardless of the true nature of the problem.

The consumer's payoff $\pi_C$ is given uniquely by her material payoff,<fnref n="6" /> and it is equal to $v > 0$ when the treatment provided corresponds to her problem, i.e., $s = A$ and $r = a$, or $s = B$ and $r = b$, and 0 otherwise.<fnref n="7" /> The expert's payoff is instead the combination of her material and her psychological payoff:

$$ \pi_E = w_{a,b} - ED(s, r, h, \beta_E | \text{ENTER}) := w_{a,b} - \gamma | \mathbb{E}_{\beta_E}(\pi_C') - \pi_C | \quad (1) $$

We assume that the material payoff is such that $w_b > w_a > 0$. Like in Soraperra et al. (2019), we do not consider the different pricing strategies of the expert, but assume that providing $b$ is more profitable for the expert than providing $a$.

To illustrate how we build the psychological payoff, first be $\alpha_C \in [0, 1]$ the consumer's first-order belief about the expert's behavior in case of entrance. $\alpha_C$ quantifies the consumer's belief that treatment $a$ will be provided by the expert, and it is independent from the objective probability $h$ of being of A type. It represents, in our model, the consumer's confirmation bias toward treatment $a$. If $\alpha_C > \frac{1}{2}$ and $h > \frac{1}{2}$, or if $\alpha_C < \frac{1}{2}$ and $h < \frac{1}{2}$, we say that the confirmation bias of the consumer is in line with the objective probability of having a problem A or a problem B. Intuitively, with consumer's confirmation bias being in line with the objective probability, we mean that a higher probability of being in state A (B respectively) corresponds to a higher expectation that the expert will assign treatment $a$ (b respectively).

Then, $\beta_E$ measures the expert's expectation of the consumer's confirmation bias, conditional on the consumer's entrance. Let $\mathbb{E}_{\beta_E}$ be the conditional expectation operator given the belief $\beta_E$. In other words, $\mathbb{E}_{\beta_E}$ represents the expert's expectation given her feeling $\beta_E$ about the consumer's feeling $\alpha_C$. Then, as in Eq. (1), the expert's psychological payoff is the product of two terms:

<footnote n="6">⁶ This assumption stems from role-dependent sensitivity, reflecting a scenario where only one side — in this case, the expert — exhibits belief-driven emotions (Attanasi et al., 2016). It operates under the premise that sensitivity to any emotion is triggered only when assuming a specific role (in line with evolutionary psychology of emotions, Haselton and Ketelaar (2012)) and the conceptual act theory of emotion Barrett (2006). According to this theory, individuals experience an emotion by categorizing an affective feeling, such as the anticipated disappointment of others. We defer a comprehensive examination of this limitation, along with potential extensions, to our concluding discussion.</footnote>

<footnote n="7">⁷ The assumption is that an incorrect treatment has no value to the consumer. This assumption is reasonable because receiving a more expensive or more elaborate treatment than necessary does not guarantee that the problem is solved and it may even be harmful, especially in the case of credence goods such as healthcare.</footnote>

<footnote n="5">⁵ While the assumption of risk-neutral preferences represents a standard simplification widely adopted in the literature on psychological games (see, e.g., Battigalli et al., 2019), we acknowledge its restrictive nature. We defer a comprehensive examination of this limitation, along with potential extensions incorporating risk aversion or other preference structures, to our concluding discussion.</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. The two-player psychological Bayesian sequential game representing the credence goods market.</figcaption></figure>

$| E_{\beta_E}(\pi_c') - \pi_c |$ is the absolute difference between the expected and the received payoff by the consumer. This gap quantifies the expert's disutility when disappointing a consumer, i.e., when providing her a different treatment than the one she expected. We call this absolute difference *expectation dissonance*;
$\gamma \in [0, +\infty)$ is a psychological parameter that captures the expert's sensitivity toward expectation dissonance.

Then, the psychological payoff represents the expert's expectation dissonance aversion, which we denote as ED(s, r, h, β<sub>E</sub>|ENTER).

More formally, our game features players who make decisions and get payoffs according to the sequential game in Fig. 1.<fnref n="8" />

It follows straightforwardly that when the expert does not have any sensitivity toward expectation dissonance, i.e., when $\gamma = 0$, then at subgame perfect equilibrium, the expert will always choose treatment *b* over treatment *a*, regardless of the state of the world that represents the consumer's problem. Anticipating this choice, the consumer will enter the market only if the value *v* of the appropriate treatment is large enough, i.e., it is such that $v > \frac{o}{(1-h)} \ge o$, when $h \in [0, 1)$ (indifference occurs when the equality holds). The consumer will never enter the market when $h = 1$ and $o > 0$, because in such a case it is always preferable a strictly positive utility of *o* instead of a utility of 0, and she will be indifferent when $h = 1$ and $o = 0$.

A positive sensitivity toward expectation dissonance, i.e., when $\gamma > 0$, makes the expert averse toward expectation dissonance, and wanting to meet consumer's expectation. In such case, under some circumstances, the expert can decide to abandon treatment *b* for treatment *a*, to meet the consumer's confirmation bias.

**Definition 1.** When $h \neq \frac{1}{2}$ and $\beta_E \neq \frac{1}{2}$,<fnref n="9" /> we define the threshold $\bar{\gamma}$ for the sensitivity toward expectation dissonance as

$$ \bar{\gamma} = \left| \frac{w_b - w_a}{2v \left(-2h\beta_E + h + \beta_E - \frac{1}{2}\right)} \right| \quad (2) $$

When the state of the world is $s = A$, it holds the following result.

**Proposition 1.** When the consumer enters the market and when the expert observes a state of the world $s = A$:

1. if the confirmation bias of the consumer is in line with the objective probability of having a problem *A* or a problem *B* (i.e., if $\beta_E > \frac{1}{2}$ and $h > \frac{1}{2}$, or if $\beta_E < \frac{1}{2}$ and $h < \frac{1}{2}$), the best strategy for the expert is to provide the correct treatment *r* = *a* whenever $\gamma > \bar{\gamma}$, and the incorrect treatment *r* = *b* otherwise. Indifference holds when $\gamma$ equals the threshold;

2. otherwise, the best strategy for the expert is to provide the incorrect treatment *r* = *b*.

The results in Proposition 1 can be read with the following interpretation. When the expert observes problem *A*, and believes that the consumer has a confirmation bias that is not aligned with the objective probability of having a certain problem, the expert will always provide the incorrect treatment *b*. This is because she guesses that the consumer would expect such a treatment, and thus this choice maximizes both her material and psychological payoff. However, if the expert believes that the consumer's bias is in line with the objective probability of having a certain problem, and if the expert's sensitivity toward expectation dissonance is high enough, the expert will provide the correct treatment *a*, against what her material payoff suggests. She will still provide the incorrect treatment *b* if her sensitivity is low.

In this state of the world, a sensitivity for expectation dissonance helps partially correct the expert's choice toward the appropriate treatment.

When the state of the world is $s = B$, the argument proceeds analogously to that of $s = A$, yielding Proposition 2.

**Proposition 2.** When the consumer enters the market and when the expert observes a state of the world $s = B$:

1. if the confirmation bias of the consumer is not in line with the objective probability of having a problem *A* or a problem *B* (i.e., if $\beta_E > \frac{1}{2}$ and $h < \frac{1}{2}$, or if $\beta_E < \frac{1}{2}$ and $h > \frac{1}{2}$), the best strategy for the expert is to provide the incorrect treatment *r* = *a* whenever $\gamma > \bar{\gamma}$, and the correct treatment *r* = *b* otherwise. Indifference holds when $\gamma$ equals the threshold;

2. otherwise, the best strategy for the expert is always to provide the correct treatment *r* = *b*.

Unlike before, in this state of the world, the psychological component worsens the situation, as it partially corrects the expert's choice toward an inappropriate treatment.

<footnote n="8">⁸ Note that we model nature's realization of the state of the world as occurring after the consumer's choice for simplicity and without loss of generality.</footnote>

<footnote n="9">⁹ These conditions also ensure that the denominator is nonzero, and then the threshold is well-defined.</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 2. Timeline of the experiment.</figcaption></figure>

Our experimental analysis is centered on the expert's best response. For the sake of completeness, we provide the equilibrium analysis in Appendix A.3.

In our proposed psychological formulation, the expert seeks to balance maximizing her own material payoffs with minimizing the consumer's disappointment. Our specification of the expert's psychological payoff differs from the classical psychological payoff model of guilt aversion (Battigalli & Dufwenberg, 2007; Beck et al., 2013). In guilt aversion models, individuals suffer if they inflict harm on others.<fnref n="10" /> Instead, we specify that the expert suffers if she provides something different from what the consumer expects, regardless of whether this results in a higher or lower payoff for the consumer. In other words, as the difference between the expected and received payoffs $|E_{\beta_E}(\pi'_c) - \pi_c|$ increases, the expert's disappointment increases. At the same distance, the expert's disappointment is the same: whether they provide less or more than anticipated by the consumer's beliefs will lead to the same psychological detrimental situation.<fnref n="11" />

# 4. Experimental design

## 4.1. Protocol and treatments

We describe the timeline of the experiment in Fig. 2. Upon arrival, all participants were seated at their randomly assigned computers and listened to the first part of the instructions. They were then asked to complete an adapted version of the Wason selection task, to investigate for possible confirmation bias. We later referenced each participant's results from this task in the main task of the experiment. Afterward, participants heard the remaining instructions for the Credence Goods (CG) game, which was played over 16 rounds in matching groups of 8, 4 players C and 4 players E, together with elicitation of beliefs. Matching groups were fixed within a session. Participants were randomly assigned to different matching groups and they were assigned a role, either C or E. Inside a matching group, pairs between consumers and experts varied during different rounds, such that each player C interacted with the same player E four times. Following the CG game, participants completed two control tasks: a Social Value Orientation (SVO) measure and a HEXACO personality inventory questionnaire. Finally, they answered a brief demographic survey covering gender, age range, field of study, and the highest diploma obtained. Our design simultaneously combines exogenous manipulations of the game parameters with detailed cognitive and personality individual measures, thereby ensuring a direct link between theory and behavior.

We recruited 184 participants among those registered in the ORSEE (Greiner, 2015) database. Females were slightly overrepresented (64%)

in our sample. Sessions took place at the Laboratory for Experimental Economics of Nice (LEEN) in November and December 2022 and our experiment was computerized using the zTree (Fischbacher, 2007) software. We ran nine sessions with 8 to 24 participants each (1 to 3 groups), ensuring no participant attended more than once. Sessions lasted roughly 90 min, including time for instructions, tasks, questionnaires, and payment. During the experiment, we used the fictitious ECU currency that was converted to euros at the rate of 10 ECU = 1 euro. Out of the 16 rounds of the CG game, one was randomly selected and used for payment for this task. Participants also received a maximum of 2 euros for the elicitation of beliefs, a fixed 7-euro payment for completing the HEXACO and demographic questionnaires and a 5-euro show-up fee. They were paid confidentially at the end of the experiment. The average payment was 20.60 euros.

We examined whether a consumer's confirmation bias influenced an expert's provision decisions under the following set of probabilities $h \in \{0.3, 0.5, 0.7\}$, with $h$ reflecting the probability that a given state of the world might occur. These represented the 3 treatments of our experimental design. The information about consumer's confirmation bias was binary: absence or presence. These levels were endogenous to the participants and varied naturally across subjects in the experiment.<fnref n="12" /> Probability $h$ (our treatment manipulation) was assigned between-subjects, whereas consumer confirmation bias varied within-subject. We implemented 3 sessions per treatment, for a total of 9 sessions. Table 1 provides an overview of the experimental design. In total, 32 pairs of participants played the treatment with $h = 0.3$, 32 pairs of participants played the treatment with $h = 0.5$ and 28 pairs of participants played the treatment with $h = 0.7$.<fnref n="13" /> To ensure successful randomization and to justify treating confirmation bias as an individual-level covariate, we fitted a mixed-effects logistic regression with subject-level random intercepts. We compared the odds of exhibiting confirmation bias in the $h = 0.3$ ($\beta_1 = -0.69$, p = 0.708, se = 1.83) and $h = 0.7$ ($\beta_2 = -0.64$, p = 0.735, se = 1.88) conditions against the reference level $h = 0.5$. Neither estimate differed significantly from zero, hence indicating no evidence of imbalance in confirmation bias across treatments.

## 4.2. Tasks

**Task 1: Adapted Wason Selection Task (WST).** Confirmation bias was measured at the individual level using an adapted version of the Wason Selection Task (WST) (de Jong et al., 1998; de Jong, Mayer et al., 1997; Smeets et al., 2000; Wason, 1968) before role assignment in the credence goods game. The WST is a deductive reasoning problem in which participants seek information to evaluate the logical correctness of a conditional rule. Each consumer's result — indicating the presence or absence of confirmation bias — was subsequently revealed to the

<footnote n="10">¹⁰ For instance, Beck et al. (2013) explore guilt in experts under a setup where high-level treatment can solve both a “low” and “high” problem, whereas low-level treatment only solves the low problem. In that setting, guilt arises if the expert provides less than what the consumer expects. However, in our model and because we assume no hierarchy between the problems and the treatments, we do not rely on this restrictive definition of guilt.</footnote>

<footnote n="11">¹¹ Psychologically, this might be interpreted as discomfort stemming from cognitive dissonance (Festinger, 1962) on the part of the consumer, which the expert wishes to avoid. While this could be nuanced by the possibility that an unexpected outcome sometimes brings positive surprises, we focus on scenarios where any mismatch is viewed as detrimental.</footnote>

<footnote n="12">¹² In our experiment, the presence or absence of confirmation bias was inferred from behavior in the adapted Wason Selection Task, thereby reflecting actual cognitive behavior rather than exogenous assignment. Consequently, the sample sizes differed: 64 consumers exhibited having a confirmation bias, while 28 indicated no confirmation bias. We provide more details on these results in Section 5.2.</footnote>

<footnote n="13">¹³ Despite of our efforts in the recruitment process, due to no-show, we could not achieve the complete equal balance in the number of subjects and matching groups between the three treatments.</footnote>
<table><caption>**Table 1**
Experimental design: treatments at the pair level.</caption><thead><tr><th></th><th>h = 0.3</th><th>h = 0.5</th><th>h = 0.7</th><th>Total</th></tr></thead><tbody><tr><td>Confirmation bias</td><td>288</td><td>416</td><td>320</td><td>1024</td></tr><tr><td>No confirmation bias</td><td>224</td><td>96</td><td>128</td><td>448</td></tr><tr><td># Observations</td><td>512</td><td>512</td><td>448</td><td>1472</td></tr><tr><td># Sessions</td><td>3</td><td>3</td><td>3</td><td>9</td></tr><tr><td># Matching groups</td><td>8</td><td>8</td><td>7</td><td>23</td></tr><tr><td># Pairs of subjects</td><td>32</td><td>32</td><td>28</td><td>92</td></tr></tbody></table>

Numbers represent pairs in each treatment. Confirmation bias information is recorded at the pair level from the consumer's perspective.

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Expert's view in step 3 of the game.</figcaption></figure>

matched expert before her decision in the main task. Section 4.3 provides detailed illustrations of both the original task and our adaptation. Readers uninterested in these technical details may skip that section, while those interested in our WST adaptation will find a comprehensive description there.

**Task 2: The credence goods game with elicitation of beliefs (main task).** In our main task, half of the participants played the role of consumers, players C, and the other half the role of experts, players E. Once a pair was formed at the beginning of a round, each player was asked to make two decisions:

**Decision 1:** the elicitation of beliefs;

**Decision 2:** the decision in the Bayesian sequential game presented in Section 3.

For simplicity of exposition, we present the two decisions in reverse order. Decision 2 was implemented in three steps:

**Step 1 - Player C:** Player C was provided with an opaque cup under which a ball was hidden. It was common knowledge that the color *s* of the ball under the cup was *s* ∈ {White, Black}, and that this color was randomly chosen by nature with respective probabilities *h* and (1-*h*). Player C did not observe the color of the ball. The consumer decided whether to enter the market (ENTER) or not (OUT). If she chose not to enter, the game ended and both the consumer and the expert got an outside option payoff *o* = 16. Otherwise, the game continued;

**Step 2 - Nature:** The nature randomly drew the color *s* ∈ {White, Black} of the ball hiding under the cup according to the probabilities *h* and (1-*h*), with *h* ∈ [0, 1];

**Step 3 - Player E:** Player E observed that the consumer had entered the market and the color of the ball under the cup. Fig. 3 shows a screenshot of what Player E saw in both states of nature. Then, she decided to provide a color *r* ∈ {White, Black}.

If the color provided by the expert was the same as the color hiding under the cup of the consumer (*s* = White and *r* = White, or *s* = Black and *r* = Black) then the consumer received 100 ECU, otherwise 0 ECU.<fnref n="14" /> Independently from the state of the world, the payoff of the experts depended only on the color provided: she received 100 ECU for providing the Black color and 80 ECU for providing the White color.<fnref n="15" />

**Consumer, 1st order beliefs:** First, we elicited the consumers' first-order beliefs about the expert's behavior in the CG game. At the beginning of each round, the consumers had to report her beliefs $\alpha_C$ about how likely (between 0 and 1, to the hundredth) she thought the expert<fnref n="16" /> would provide her with the color White. In order to incentivize<fnref n="17" /> belief elicitation, conditional on the consumer's decision to interact with the expert, we gave the consumers the following payoff function (in ECU):

$$ \Pi_C: \text{Elicitation} = \begin{cases} 20 - 20 \times |\alpha_C - \mathbb{1}_{\{r=\text{White}\}}| & \text{if ENTER} \\ 0 & \text{if OUT.} \end{cases} \quad (3) $$

where $\mathbb{1}_{\{r=\text{White}\}}$ takes the value 1 if the color provided by the expert is White, and 0 otherwise.

**Expert, 2nd order beliefs:** Second, we elicited the experts' second-order beliefs about the consumers' first-order beliefs. At each round, after receiving information about the consumer's confirmation bias, the experts had to guess the consumers' first-order beliefs regarding the expert's provision of the white color (again between 0 and 1, to the hundredth), $\beta_E$. We incentivized belief elicitation and gave the experts the following payoff function (in ECU)<fnref n="18" />:

$$ \Pi_E: \text{Elicitation} = \begin{cases} 20 - 20 \times |\beta_E - \alpha_C| & \text{if ENTER} \\ 0 & \text{if OUT.} \end{cases} \quad (4) $$

For both the consumer and the expert, an accurate belief thus resulted in a maximum payment of 2 euros.

**Tasks 3 & 4: SVO and HEXACO personality inventory questionnaire.** Because our theoretical framework suggests that individual differences among experts can affect how they provide credence goods, we gathered information on participants' social preferences and psychological traits.

**Social Value Orientation (SVO)** We measured each participant's social preferences using the SVO Slider Measure (Murphy et al., 2011) under

<footnote n="14">¹⁴ Our material-payoff framework indicates that if $v > \frac{o}{1-h}$, consumers will opt to enter. While an alternative approach involving psychological payoffs suggests $v > \frac{o}{h}$ when the consumer anticipates treatment *b*, our analysis confirms that no equilibrium exists under an expectation of treatment *a*. Consequently, basing *v* on the material-payoff condition ensures entry in both potential states, allowing us to derive the expert's best-reply strategies more effectively.</footnote>

<footnote n="15">¹⁵ We chose these parameter values based on two primary considerations. First, for the consumer's entry condition, we set $v = 100$ to ensure $v > \frac{o}{1-h}$. Second, we set $\Delta\pi_E = 20$ to achieve a balanced incentive for the expert. By selecting 20, we maintain a reasonable interplay between the expert's potential strategies and the level of concern needed to deviate from profit-maximizing behavior.</footnote>

<footnote n="16">¹⁶ The use of this probabilistic method to assess participants' beliefs implies that our observations may be polarized. This is because the matched expert, unlike the average participant, cannot embody multiple behavioral types. This limitation could significantly impact the results.</footnote>

<footnote n="17">¹⁷ We acknowledge that paying participants both for accurate predictions and for strategic decisions may induce risk-averse participants to align beliefs with actions, as noted by Blanco et al. (2010). However, the potential payoff from the main task typically exceeded that from belief elicitation, which likely mitigated this effect.</footnote>

<footnote n="18">¹⁸ The experts were informed about the consumers' choice to interact before having to report their beliefs.</footnote>
a random matching protocol.<fnref n="19" /> In this task, participants were presented with various distributions of money for themselves and an anonymous partner, and asked to choose their preferred option. Responses produce an SVO angle, allowing classification into altruistic, prosocial, individualistic, or competitive profiles. In our analysis, we used the SVO angle variable as a proxy for sensitivity (γ), hypothesizing that more prosocial or altruistic experts may be likelier to accommodate a consumer's expectation.<fnref n="20" />

**HEXACO Personality Inventory** We also collected self-reported personality data using items from the 100-item HEXACO questionnaire (Ashton & Lee, 2008). In particular, we focused on four facet-level variables — fairness, anxiety, forgiveness, and sentimentality<fnref n="21" /> — given their relevance to strategic behavior and interactions. Each facet was measured through four statements rated on a 1-to-5 scale, with some negatively worded for which we reversed the scoring. Scores thus ranged from 1 (absent) to 5 (strongly present).

### 4.3. Methodological adaptation of the Wason selection task

In its original version, the task consists of four cards and a conditional rule in the form of “if *p*, then *q*”. Each card contains two pieces of information: one on the face and one on the back. On the face, the card displays whether the antecedent is true or false (i.e., *p* or *¬p*). On the back, the card displays whether the consequent is true or false (i.e., *q* or *¬q*). Subjects only observe one side of each card: one card made *p* visible, one *¬p*, one *q* and one *¬q*. The task requires participants to choose a combination of cards (from 1 to 4 cards) to turn over to determine the correctness of the rule “if *p*, then *q*”. For this purpose, one must first verify that the antecedent is true by turning over the *p* card (also known as *modus ponens*). However, one must also deny the consequent by turning over the *¬q* card (also known as *modus tollens*) to check that *¬q* implies *¬p*.

As discussed in Section 2, previous studies have shown that this task may be strongly context-dependent (de Jong, Mayer et al., 1997; Evans et al., 1993; Kirby, 1994). Given this context sensitivity, we employed a modified version of the WST that was specifically adapted to our credence goods game scenario. In particular, we adapted the conditional rule to align with the instructions of the CG game (i.e., the main experimental task). It could be argued that our adaptation adds complexity to the original task, as participants may find it easier to reason about the rule when dealing with cards rather than a credence goods market. However, this adaptation offers several important advantages. First, it addresses the context-dependency that we believe to be crucial in our scenario. Second, it prevents cognitive overload by reducing the burden of learning multiple, distinct sets of instructions: participants could familiarize themselves with the WST while simultaneously learning about the main experimental task.

Table 2, provides a comprehensive overview of our adaptation. Subjects were presented with the following affirmation: “If the color provided by Player E to Player C is the same as the color of the ball under the cup of the player C, then player C receives 100 ECU”. Instead of using cards, this affirmation was accompanied by four fictitious hypothetical former players C who could be consulted to obtain information related to this affirmation. The four players were associated with four different events. Players C-1 and C-2 (respectively representing *p* and *¬p*) have experience with the antecedent part and can be queried about whether the consequent is true or false. On the contrary, players C-3 and C-4

(respectively representing *q* and *¬q*) have experience with the consequence part and can be asked about the antecedent. Each participant had the opportunity (if they wished to do so) to ask a fixed question to each of these fictitious players C.<fnref n="22" />

In our study, participants were presented with the following scenario: Player C-1 was given the same color as the color under her cup (*p*), and the fixed question was “Did you receive 100 ECU?” (*q* or *¬q*). Player C-2 did not receive the same color as the color under her cup (*¬p*), and the fixed question was again “Did you receive 100 ECU?” (*q* or *¬q*). Player C-3 received 100 ECU (*q*), and the fixed question was “Was the color given by Player E the same as the color of the ball under your cup?” (*p* or *¬p*). Finally, Player C-4 did not receive 100 ECU (*¬q*), and again the fixed question was “Was the color given by Player E the same as the color of the ball under your cup?” (*p* or *¬p*). Participants were instructed to indicate which player(s) C (by pressing the button under each player) they needed to ask a question to check the correctness of the affirmation. They were advised to ask only the required and adequate questions. If a participant selected Player C-1, this indicated reliance on a verification strategy (i.e., if *p*, then *q*). However, selecting Player C-4 indicated that the participant’s reasoning was based on a falsification strategy (i.e., *¬q* then *¬p*).

We focused on the consumer’s falsification strategy, as it is one of the key variables in our design. By the rules of propositional logic, the correct test of the conditional “if *p* then *q*” is to examine both *p* and *¬q*. Any selection involving *¬q* is classified as a falsification strategy. Participants who are less susceptible to confirmation bias are more inclined to adopt this strategy, whereas those who exhibit confirmation bias tend to avoid it. Because adopting a falsification strategy does not demonstrate the absence of confirmation bias (nor does its omission indicate its presence), we opted to inform the expert, for each consumer, of the participant’s observed falsification strategy and its association with confirmation bias.<fnref n="23" />

### 4.4. Experimental hypotheses

Fig. 4 provides two heat maps derived from our simulation-based analysis, each focusing on a different state of the world: *White*, in the left figure, and *Black*, in the right one. On the horizontal axis we vary the objective probability *h* of the ball to be white, and on the vertical axis the expert’s second-order belief $\beta_E$. In the figures, gray tiles represent regions (pairs of parameters) where the expert invariably provides *Black* (treatment *b* in Fig. 1) at best response of the psychological game, which coincides with the material payoff maximizer strategy. Blue tiles represent instead regions in which the expert switches strategy and provide *White* (treatment *a*) at best response of the psychological game. As predicted in Propositions 1 and 2, this happens when the two parameters ($h$ and $\beta_E$) are in line and when the state of the world is *White* (left figure), or when they are not and the state of the world is *Black* (right figure). The darkest the blue in the heat maps, the highest the required sensibility for the expert to deviate from the material payoff maximizer strategy (*Black*) in favor of the choice of providing *White*.

Building upon these simulations, we formulated our (behavioral) hypotheses.

When the two states of the world are equally probable ($h = 0.5$), we expect the expert to behave as a self-interested material payoff player and always supply her more profitable option *Black*. As shown in Fig. 4, both heat maps remain gray at $h = 0.5$, suggesting no apparent

<footnote n="19">¹⁹ Detailed instructions are in Appendix F.2.</footnote>

<footnote n="20">²⁰ We acknowledge that, despite the link between SVO and psychological sensitivity, using this measure may oversimplify the multidimensional nature of the expert's moral motivation. In particular, psychological sensitivity to others' expectations, may also be shaped by broader personality traits.</footnote>

<footnote n="21">²¹ Definitions for these facets follow Ashton and Lee (2008) and appear in Table C.9.</footnote>

<footnote n="22">²² The participants were aware of the existence of two types of players: C and E. However, at this stage of the experiment, they did not yet know which role they would be assigned to. Each participant completed this task and was then randomly assigned to one of the two player roles.</footnote>

<footnote n="23">²³ We explain and define this variable more explicitly in Table C.9 in Section 5.1.</footnote>
<table><caption>**Table 2**

Adapted Wason Selection Task.</caption><thead><tr><th colspan="4">Hypothetical players:</th></tr><tr><th>C-1 (p)</th><th>C-2 (~p)</th><th>C-3 (q)</th><th>C-4 (~q)</th></tr></thead><tbody><tr><td>The color provided by the player E is the same as the color of the ball under the cup of the player C-1.</td><td>The color provided by the player E is not the same as the color of the ball under the cup of the player C-2.</td><td>Earned 100 ECU.</td><td>Did not earned 100 ECU.</td></tr><tr><th colspan="4">Fixed questions:</th></tr><tr><th>(q or ~q)</th><th>(q or ~q)</th><th>(p or ~p)</th><th>(p or ~p)</th></tr><tr><td>Did your earn 100 ECU ?</td><td>Did your earn 100 ECU ?</td><td>Was the color provided by the player E to you the same as the color of the ball under your cup ?</td><td>Was the color provided by the player E to you the same as the color of the ball under your cup ?</td></tr><tr><th colspan="4">Logical reasoning:</th></tr><tr><td>Verification strategy: Modus ponens.</td><td>Denying the antecedent.</td><td>Affirming the consequent.</td><td>Falsification strategy: Modus tollens.<br>Employing a falsification strategy indicates lower confirmation bias</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** The expert's best reply depending on $\gamma$ levels.</figcaption></figure>

incentive to deviate from material payoffs. This is because in our model, the equal probability of occurrence for the two states does not allow for the formation of oriented beliefs. Moreover, if the consumer lacks confirmation bias, the expert consistently maximizes material payoff by delivering *Black*. Furthermore, we formulate the first two behavioral hypotheses as follows:

**H1:** When $h = 0.5$, experts systematically provide the more profitable color, *Black*.

**H2:** In the absence of consumer confirmation bias, experts always provide the more profitable color, *Black*.

However, once the consumer does exhibit confirmation bias, and when $h \neq 0.5$, the expert's best strategy varies across different states, as shown in Fig. 4, and when $\gamma$ is above a certain threshold. Tables B.7 and B.8 in Appendix B detail how confirmation bias, second-order belief direction, and $\gamma$ jointly determine the expert's best strategy.

Specifically, in state $s = White$, when $\beta_E \le 0.3$ at $h = 0.3$ or $\beta_E \ge 0.7$ at $h = 0.7$, the expert will provide $r = White$ (i.e., the matching color) only if $\gamma$ exceeds 0.5, which is represented by the blue tiles in the lower left and upper right corners of the left part of Fig. 4. Otherwise, the expert defaults to *Black* (i.e., the inappropriate color). Similarly, in state $s = Black$, when $\beta_E \ge 0.7$ at $h = 0.3$ or $\beta_E \le 0.3$ at $h = 0.7$,

the expert will provide $r = White$ (i.e., the inappropriate color) only if $\gamma$ exceeds 0.5. Otherwise, the expert will provide *Black* (i.e., the appropriate color). Furthermore, we formulate additional behavioral hypotheses as follows:

**H3-a:** In state *White*, when $|\beta_E - h|$ is small (meaning the expert's second-order beliefs are close to the objective probability), highly sensitive experts are more likely to provide *White* to consumers having a confirmation bias compared to consumers not having a confirmation bias.

**H3-b:** In state *Black*, when $|\beta_E - h|$ is large (meaning the expert's second-order beliefs deviate substantially from the objective probability), highly sensitive experts are more likely to provide *White* to consumers having a confirmation bias compared to consumers not having a confirmation bias.

We stress an important conceptual nuance in the formulation of H3-a and H3-b. The two hypotheses may be understood as symmetrical. However, this is not the case, because deviation in H3-a is in favor of a correct treatment, while H3-b is in favor of an incorrect one. H3-b captures a kind of “over-accomodation” to the consumer’s expectation, leading to suboptimal outcomes.
Finally, we predict<fnref n="24" /> that expert's intrinsic traits, may influence her likelihood of providing a correct or incorrect treatment. We rely on three HEXACO-based personality scores–Fairness, Forgiveness, and Anxiety (Ashton & Lee, 2008) –to capture relevant traits. Then, we propose the following hypotheses:

**H4:** *Experts scoring high in Fairness are likelier to provide the appropriate color.*

**H5:** *Consumers with high Forgiveness and/or low Anxiety scores are more likely to enter the market.*

# 5. Results

## 5.1. Variables and methodology

This section presents the statistical procedures used to investigate the effect of a consumer's confirmation bias on the expert's provision decision. Table C.9, in Appendix C, defines and provides an overview of the variables used in the analysis, including their definitions and construction methods.

To estimate how different treatments influenced experts' provision decisions, we compare expert's color choices under the different experimental conditions. We test our hypotheses using regression models with interaction terms. The consumer confirmation bias enters as a binary variable; the SVO angle enters as a continuous proxy for $\gamma$ (higher values indicate greater prosociality); and belief alignment $|\beta_E - h|$ enters as a discrete regressor via *High Distance*, a data-driven median split (*High Distance* = 1 if $|\beta_E - h|$ exceeds the sample median). These variables are interacted with treatment indicators, and all specifications include controls. Throughout our regressions, consumer-expert pairs that did not show confirmation bias under T5 serve as the baseline category. For each estimation, we report two goodness-of-fit measures: the McFadden R-squared value<fnref n="25" /> and the log-likelihood ratio (LLR) value.<fnref n="26" />

When estimating the expert's color provision choice, since this decision was endogenous and influenced by beliefs, we use a two-stage probit least squares (2SPLS) estimator to control (and eliminate) for potential covariates at the subject level:

- First stage: we use our treatment variable and a vector of individuals' characteristics to predict $|\beta_E - h|$.<fnref n="27" />

- Second stage: we estimate the likelihood of occurrence of the following dependent variables: *Efficient provision* and *White*. *Efficient provision* means the color given matches the real state of the world ($s = r$). By contrast, *White* explores all scenarios in which the expert opts for *White*, whether or not it is correct in both states of the world.

To better capture the key components of each decision, the analysis is also conducted separately for each state of nature (i.e., *White* or *Black*), as outlined in Table C.10 in Appendix C, that summarizes the methodology for analyzing the data.

<table><caption>**Table 3**
Participant characteristics.</caption><thead><tr><th></th><th>Mean</th><th>S.d.</th><th>Min</th><th>Max</th><th># Independent obs.</th></tr></thead><tbody><tr><td>Female</td><td>0.64</td><td>0.48</td><td>0</td><td>1</td><td>184</td></tr><tr><td>Altruist</td><td>0</td><td>0</td><td>0</td><td>0</td><td>184</td></tr><tr><td>Prosocial</td><td>0.51</td><td>0.50</td><td>0</td><td>1</td><td>184</td></tr><tr><td>Individualist</td><td>0.49</td><td>0.50</td><td>0</td><td>1</td><td>184</td></tr><tr><td>Competitor</td><td>0.01</td><td>0.07</td><td>0</td><td>1</td><td>184</td></tr><tr><td>Fairness</td><td>3.41</td><td>1</td><td>1</td><td>5</td><td>184</td></tr><tr><td>Anxiety</td><td>3.75</td><td>0.94</td><td>1.25</td><td>5</td><td>184</td></tr><tr><td>Sentimentality</td><td>3.75</td><td>0.76</td><td>1.25</td><td>5</td><td>184</td></tr><tr><td>Forgiveness</td><td>2.27</td><td>0.81</td><td>1</td><td>4.25</td><td>184</td></tr></tbody></table>

## 5.2. Data and sample characteristics

We collected data from 184 individuals, forming 92 consumer-expert pairs who interacted over 16 rounds (with pairs between consumers and experts who varied during different rounds), yielding a total of 1472 observations (see Table 1 for distribution across treatments). Our panel is unbalanced because some consumers opted not to ENTER the market in certain periods, which meant no expert decision could be recorded in those instances.

Table 3 summarizes the key characteristics of our sample. Notably, women ($Female = 1$) comprised 64% of participants. Our participants were evenly split between *Prosocial* (51%) and *Individualist* (49%) SVO types. Figure D.5 (Appendix D), illustrates the distribution of Social Value Orientation (SVO) scores.<fnref n="28" /> On average, participants reported moderate-to-high levels of *Fairness*, *Anxiety*, and *Sentimentality* (above 3.5 on a 5-point scale), but relatively low *Forgiveness*. High standard deviations on these measures indicate considerable individual variation.

Table 4 reports participant choices in the adapted Wason Selection Task (WST). Panel (a) shows that 66% adopted a verification strategy ($p$), while 37% chose a falsification strategy ($\neg q$). Only 7% used both (which was the correct logical reasoning for this task). The most common combination, $pq$ (verification strategy + affirming the consequent), constituted one-quarter of observations. We explore correlations between falsification-based strategies and other variables in our experiment (Appendix D, Figure D.6) but find no relevant correlations.

Table 5 provides further descriptive statistics, grouped by treatment condition (T3, T5, T7), while figures D.7 and D.8 in Appendix D visualize how these variables evolved over time. Several patterns emerge. The overall frequency of consumer who chose ENTER in the first step of the game (*Interaction* = 1) is 0.87, with a slight downward trend as $h$ increased.<fnref n="29" /> Experts provided the correct color 73% of the time in T3 (when the objective probability of being in the state *White* was low) but only 53% in T7 (when the probability was high), suggesting experts were more likely to do so when it aligned closely with their own material incentives.³⁰ The fraction of *White* provision was 0.24 in T3, 0.25 in T5, and 0.34 in T7 (thus, slightly increasing with the objective

<footnote n="28">²⁸ *Individualist* and *Prosocial* categories capture 99% of our observations, with a roughly equal split between the two. Two participants were classified as *Competitor* and none as *Altruist*. To gauge the behavioral realism of the model's threshold, we map it to observable proxies. In our data, SVO angles range from -12° to 57° with substantial dispersion, and the histogram D.5 in Appendix D shows that the region where deviations are predicted to occur, from 22° to 57° (i.e., more prosocial profiles), is populated. Accordingly, the threshold values required by the simulations are empirically plausible within our sample.</footnote>

<footnote n="29">²⁹ However, the difference in willingness to enter across the three treatments was relatively small ($\Delta = 0.09$ at most). The small difference between the treatments, combined with the high interaction frequencies (above 0.8), seems to support our discussion in Footnote 15 regarding the choice of the parameter value $v$ for allowing consumers to enter the market at the first node of the game.</footnote>

<footnote n="24">²⁴ Unlike H1-H3, which follow directly from our psychological game, H4 and H5 are exploratory extensions motivated by prior findings linking these traits to economic behavior.</footnote>

<footnote n="25">²⁵ Computed as $\frac{\text{Log-likelihood value intercept-only model} - \text{Log-likelihood value model considered}}{\text{Log-likelihood value intercept-only model}}$.</footnote>

<footnote n="26">²⁶ Computed as $2(\text{Log-likelihood unconstrained model} - \text{Log-likelihood constrained model})$. We used the LLR value for joint hypothesis testing.</footnote>

<footnote n="27">²⁷ The results of this first-stage estimations are reported in Section Appendix E, Table E.13, columns $|\beta_E - h|$.</footnote>
<table><caption>**Table 4**
Participants' choices in the Wason task.</caption><thead><tr><th colspan="5">Panel (a): Frequency of choices</th></tr><tr><th></th><th><i>p</i></th><th><i>~p</i></th><th><i>q</i></th><th><i>~q</i></th></tr></thead><tbody><tr><th scope="row">N</th><td>121</td><td>64</td><td>99</td><td>68</td></tr><tr><th scope="row">Freq</th><td>0.66</td><td>0.35</td><td>0.54</td><td>0.37</td></tr></tbody><thead><tr><th colspan="5">Panel (b): Selection patterns</th></tr><tr><th></th><th><i>p</i></th><th><i>q</i></th><th><i>~p</i></th><th><i>~q</i></th><th><i>pq</i></th><th><i>p~p</i></th><th><i>p~q</i></th><th><i>q~p</i></th><th><i>q~q</i></th><th><i>~p~q</i></th></tr></thead><tbody><tr><th scope="row">N</th><td>13</td><td>5</td><td>6</td><td>4</td><td>46</td><td>13</td><td>13</td><td>9</td><td>9</td><td>10</td></tr><tr><th scope="row">Frequency</th><td>0.07</td><td>0.03</td><td>0.03</td><td>0.02</td><td>0.25</td><td>0.07</td><td>0.07</td><td>0.05</td><td>0.05</td><td>0.05</td></tr></tbody><thead><tr><th></th><th><i>p~pq</i></th><th><i>p~p~q</i></th><th><i>pq~q</i></th><th><i>All</i></th><th><i>None</i></th></tr></thead><tbody><tr><th scope="row">N</th><td>4</td><td>6</td><td>10</td><td>16</td><td>20</td></tr><tr><th scope="row">Frequency</th><td>0.02</td><td>0.03</td><td>0.05</td><td>0.09</td><td>0.11</td></tr></tbody></table>

<table><caption>**Table 5**
Summary statistics (mean per period).</caption><thead><tr><th></th><th><i>All</i></th><th><i>T3</i></th><th><i>T5</i></th><th><i>T7</i></th></tr></thead><tbody><tr><th scope="row">Interaction</th><td>0.87</td><td>0.92</td><td>0.84</td><td>0.83</td></tr><tr><th scope="row">Efficient provision</th><td>0.63</td><td>0.74</td><td>0.61</td><td>0.53</td></tr><tr><th scope="row">White</th><td>0.27</td><td>0.24</td><td>0.25</td><td>0.34</td></tr><tr><th scope="row">Beliefs experts</th><td>0.47</td><td>0.40</td><td>0.48</td><td>0.52</td></tr><tr><th scope="row">Beliefs consumers</th><td>0.45</td><td>0.43</td><td>0.43</td><td>0.51</td></tr><tr><th scope="row">Profit experts (ECU)</th><td>94.53</td><td>100.53</td><td>92.21</td><td>90.33</td></tr><tr><th scope="row">Profits consumers (ECU)</th><td>67.64</td><td>79.72</td><td>65.15</td><td>56.69</td></tr><tr><th scope="row"># Observations</th><td>1472</td><td>512</td><td>512</td><td>448</td></tr></tbody></table>

a priori probability of being in the corresponding state of the world). Despite occasional peaks, the rate declined over rounds, possibly reflecting end-of-game effects (Andreoni, 1988). Experts and consumers held similarly moderate beliefs (0.45–0.47), except in T5, where their views diverged slightly. T5’s equal probability of White or Black may have decreased participants’ confidence about which outcome was more likely, leading to more dispersed expectations.<fnref n="31" /> Several additional panel-data random-effects linear specifications, which examine the determinants of expert beliefs ($\beta_E$) and consumer beliefs ($\alpha_C$), are presented in Appendix D.

### 5.3. Findings

Table 6 presents a summary of the empirical results testing our study’s hypotheses. For each hypothesis, the table reports the statistical test used, estimated coefficients from the regression models (when applicable), p-values, and an interpretation of results in relation to theoretical predictions. The empirical findings reveal mixed support for our theoretical framework: while some hypotheses are not confirmed by the data, Hypotheses 3b and 5 align with theoretical predictions, while Hypotheses 1, 2 and 4 are partially in line. We discuss these results in greater detail below.

First, we observe that experts did not systematically choose the more profitable color (Black), even under conditions where theory predicts they should. In treatment T5 (when equal a priori probability of being in the state White or Black), experts provided the color White in 25% of cases and Black in 75%, leading us to reject Hypothesis 1. Similarly, in treatments T3 and T7 (both when the probability of being in the state White was small or large), still absent any consumer confirmation bias, experts provided White 26% and 35% of the time, respectively, contrary to the prediction of Hypothesis 2. In both instances,

expert behavior deviated significantly from pure profit-maximization. However, even if the median frequency of providing White significantly differs from zero, we can observe a clear tendency to assign the profit maximizer treatment (Black) most of the time.

Hypotheses 3a and 3b specifically address how consumer confirmation bias, i.e., consumers’ strong prior belief about which color the expert was likely to provide, should affect expert decision-making. Our data reveal that confirmation bias did not affect expert’s behavior the same on the two states of the world. In Hypothesis 3a, we consider the case in which consumers entered the market with beliefs which were in line with the objective probability of being in a particular state of the world. Empirically, we observe that experts’ choices remained unaffected by consumers’ confirmation bias. Under these circumstances, in fact, experts faced no conflict when providing White was at the same time the most efficient strategy (where, we recall, efficiency in our analysis coincides with providing the correct treatment) and the choice that was likely to meet consumer’s expectations. This result does not support Hypothesis 3a and indicates that when consumers’ beliefs reinforce the objectively efficient option (here, the costless option), confirmation bias has no effect on expert’s decision-making. Hypothesis 3b, on the contrary, address the effect of confirmation in the opposite scenario. The true state was Black, and in this case consumers who entered the market with beliefs not in line with the objective probability of being in a given state of the world were expected to be provided color White. In this scenario, our data reveal that expert behavior was affected by consumer confirmation bias and experts accepted to deviate from the payoff maximizing strategy when the probability of the White state was low (in treatment T3 where $h = 0.3$). Indeed, we observe that the coefficient associated with the interaction term $T3 \times CCB \times Distance$ High \times SVO is positive and statistically significant (where Distance High reflects the non alignment of beliefs). This finding provides support for Hypothesis 3b and illustrates how strong but erroneous consumer priors beliefs can lead to the provision of inefficient goods and services, even against the observed state of the world and against the payoff maximizing choice for the expert (White was provided, even if the true state was Black).

Our analysis of Hypothesis 4 indicates that experts with higher Fairness scores were indeed more likely to provide the efficient treatment, but this effect was observed only in the White state of nature. In the Black state, Fairness did not significantly influence expert’s decisions. Turning to Hypothesis 5, which investigates the relationship between consumers’ personal characteristics and their willingness to enter the market, our results also provided support for the hypothesis. Specifically, we found that consumers with higher levels of Anxiety were less likely to enter, while those with higher Forgiveness scores were more likely to enter.

## 6. Discussion and conclusion

Credence goods markets are well-known for the significant information asymmetries they entail, which can expose consumers to fraudulent behavior and markets to inefficiencies. Although many studies

<footnote n="30">³⁰ The graphs in Appendix D corroborate this pattern in T3 and T7 (with T3>T7) throughout the 16 periods, although they also display wide variations in the trends for T5.</footnote>

<footnote n="31">³¹ This difference is noticeable in the graph (Figure D.7 in Appendix D, panel (c)), where the line with the triangle markers is consistently higher than the other two throughout the 16 rounds of the game.</footnote>
<table><caption>Table 6</caption><caption>Results.</caption><thead><tr><th>Hypothesis</th><th>Statistical procedure</th><th>Coefficient</th><th>P-value</th><th>Result</th><th>Interpretation</th><th>In line with hypothesis ?</th></tr></thead><tbody><tr><td>1</td><td>A two-sided Wilcoxon signed-rank test.</td><td>Not applicable</td><td>p &lt; 0.001</td><td>The median frequency of providing White significantly differs from zero.</td><td>In treatment T5, the experts did not systematically provide the color Black.</td><td>Not in line but clear tendency.</td></tr><tr><td>2</td><td>Two two-sided Wilcoxon signed-rank test (one for each treatment, T3 and T7).</td><td>Not applicable</td><td>p &lt; 0.001 each</td><td>The median frequency of providing White significantly differs from zero in each test.</td><td>In treatments T3 and T7, the experts did not systematically provide the color Black, despite the absence of confirmation bias among consumers.</td><td>Not in line but clear tendency.</td></tr><tr><td rowspan="2">3a</td><td rowspan="2">Panel probit regression (Table E.13, Appendix E.1), comparing the coefficients of (i) the probability of receiving the color White without confirmation bias, i.e. a benchmark effect vs. (ii) the additional effect of confirmation bias on the probability of receiving White.</td><td>T3 × SVO : -1.13<br>T3 × CCB × SVO : -2.32</td><td>Not significant</td><td>The coefficient T3 × CCB × SVO is not significant in the regression : no difference between T3 × SVO and T3 × CCB × SVO.</td><td>No additional effect of the confirmation bias in T3 in state White.</td><td>Not in line.</td></tr><tr><td>T7 × SVO : -1.67<br>T7 × CCB × SVO : -1.67</td><td>Not significant</td><td>The coefficient T7 × CCB × SVO is not significant in the regression : no difference between T7 × SVO and T7 × CCB × SVO.</td><td>No additional effect of the confirmation bias in T7 in state White.</td><td>Not in line.</td></tr><tr><td rowspan="2">3b</td><td rowspan="2">Panel probit regression (Table E.13, Appendix E.1), comparing the coefficients of (i) the probability of receiving the color White without confirmation bias, i.e. a benchmark effect vs. (ii) the additional effect of confirmation bias on the probability of receiving White when Distance High apply.</td><td>T3 × SVO × Distance High : -3.11<br>T3 × CCB × Distance High × SVO : -1.15</td><td>p &lt; 0.05</td><td>The benchmark value is -3.11, whereas the coefficient for the interaction term -1.15 and this difference is statistically significant (and robust to joint hypothesis test).</td><td>In the state Black under T3, consumers with a confirmation bias therefore have a higher probability of receiving the color White.</td><td>In line.</td></tr><tr><td>T7 × SVO × Distance High : -6.04<br>T7 × CCB × Distance High × SVO : -2.71</td><td>Not significant</td><td>The coefficient T7 × CCB × Distance High × SVO is not significant in the regression : no difference between T7 × Distance High × SVO and T7 × CCB × Distance High × SVO.</td><td>No additional effect of the confirmation in T7 in state Black.</td><td>Not in line.</td></tr><tr><td>4</td><td>Panel probit regression (Table E.14, Appendix E.1).</td><td>Positive effect in the White state (0.26).</td><td>Significant in White state (p &lt; 0.05), not in Black</td><td>Fairness affects the provision of the color only in the White state.</td><td>Fair experts are more likely to provide the appropriate color only in the White state of nature.</td><td>Partially in line.</td></tr><tr><td>5</td><td>Panel probit regression (Table E.16, Appendix E.3).</td><td>Negative effect of Anxiety, Positive effect of Forgiveness.</td><td>p &lt; 0.05 and p &lt; 0.1 respectively</td><td>Anxiety significantly reduces entry; Forgiveness significantly increases entry.</td><td>Consumers with higher Forgiveness and/or lower Anxiety scores are more likely to enter the market.</td><td>In line.</td></tr></tbody></table>

have shown that individuals tend to disregard challenging information due to confirmation bias, most work has focused on individual decision-making contexts. To the best of our knowledge, this paper is the first to examine whether confirmation bias operates similarly in strategic-decision-making, particularly in expert-consumer interactions. Beck et al. (2013) pioneered the integration of belief-dependent motivations into a credence goods setting, building a theoretical framework centered on belief-based guilt aversion. In this paper, we add to the literature by looking at how a consumer's confirmation bias might affect an expert's decisions. In that sense, our paper extends psychological game theory beyond guilt-based models and helps rationalize the deviations we observe in the real world.

To explore this idea, we investigated how a consumer's predisposition to expect a certain state of the world can lead to the consumer forming specific beliefs about the correct treatment. Through psychological game theory, we modeled how the expert can feel disappointment upon failing to meet the consumer's expectations. We refer to this mechanism as *expectational dissonance aversion* (i.e., sensitivity to

expectation-outcome mismatch), a behavioral and psychological motive distinct from guilt aversion because it can arise independently of harm to the consumer. We model the expert's utility function with a psychological component capturing this disappointment from the consumer's disappointment. Our model shows that an expert's sensitivity to such disappointment crucially influences provision behavior: experts with high sensitivity levels are more likely to satisfy the consumer's belief-based expectations, even if these expectations are biased. Subsequently, we tested these theoretical insights in a controlled laboratory experiment with 184 subjects. The experiment design aimed at identifying how consumer confirmation bias shapes the expert's decision to provide services or goods with credence characteristics.

The experimental results revealed some intriguing patterns. Our data show that the psychological effect arises precisely when there is a tension between the payoff-maximizing action and the consumer's expectation. When expectations were aligned with the objective state, so that providing White was both efficient and expected, experts did not respond to consumer confirmation bias: with no tension, experts'
behavior remained unchanged. By contrast, when the true state was *Black* but consumers expected *White*, experts' behavior was affected by confirmation bias. In the treatment with a low probability of *White* (*h* = 0.3), experts were more likely to provide *White* for consumers exhibiting confirmation bias and large belief distance. This pattern indicates accommodation of biased expectations, hence inefficient provision, contrary to both the efficient and the payoff-maximizing action. These findings are consistent with our framework based on expectational dissonance aversion.

Overall, our results highlight that the consumer's confirmation bias can indeed distort outcomes in credence goods transactions, increasing the risk of market failure. These results are consistent with our framework based on expectational dissonance aversion. In our experiment, the consumer's confirmation bias proved to be problematic in payoff-maximizing scenarios (ie. where the expert would have earned a higher payoff by providing the appropriate treatment). Rather than simply following the profit-maximizing strategy, experts in these conditions sometimes chose to align with the consumer's (potentially incorrect) expectations to avoid seeming opportunistic or disappointing the consumer. Paradoxically, this leads to a scenario where empathy results in the consumer receiving the wrong treatment and incurring (monetary) losses. This finding underscores how a strong desire to meet client beliefs can override purely economic considerations, ultimately leading to suboptimal results for both parties.

Such observations have substantial real-world implications. In many industries characterized by incomplete or asymmetric information (e.g., automotive repairs, financial advising, or certain specialized consultancy services), clients often enter with preconceived notions of what they need or expect. Experts sensitive to maintaining positive relationships may be tempted to follow these expectations. Over time, these patterns could erode the integrity of professional services, induce welfare losses for consumers, and diminish the efficiency of the entire market. Looking ahead, it seems promising to extend psychological game-theoretic models to incorporate reputational concerns more explicitly. Existing literature has discussed how reputation can generate more consumer-friendly outcomes (Ely et al., 2009; Ely & Välimäki, 2003; Kreps & Wilson, 1982; Schneider, 2012), yet few studies have integrated the presence of belief-dependent preferences into finite or repeated settings. The work of Attanasi et al. (2019) provides an interesting basis for future research examining these reputational factors in combination with experts' sensitivity to consumer expectations. As social media and online platforms make it easier for consumers to share reviews and experiences, experts may face heightened social image concerns. Indeed, studies like Soraperra et al. (2019) suggest that social-image pressures can alter experts' willingness to deliver high-quality services. Since many credence goods environments include consumer expectations at their core, understanding how these expectations interact with experts' strategies represents a fruitful avenue for further investigation. This underscores the importance of advancing theoretical and empirical works to capture the multifaceted nature of expert–consumer interactions in credence goods markets.

Despite the contributions of this paper, three principal limitations stand out. First, we derived predictions from theoretical psychological game-theoretic model, which assumes alignment between first-order and second-order beliefs; yet, it may not be the case. In practice, experts may lack complete information about their clients' beliefs, making this assumption restrictive. Second, although we elicited confirmation bias separately from the core credence game, people might display such bias in some contexts but not others. It is possible that such biases manifest context-specifically in real-world scenarios. Examining domain-specific forms of confirmation bias directly within the structure of the credence good setting could yield more precise insights. Moreover, by communicating the strategy used by the consumer to the expert, we assume that the expert is explicitly informed about whether the matched consumer exhibits confirmation bias, as determined by their performance in the adapted Wason Selection Task. While this

assumption is valuable for isolating the causal effect of confirmation bias, we acknowledge that such clear observability may seem artificial from a real-world perspective, where cognitive traits are typically difficult to detect. Future research could explore more realistic settings in which the expert's information about the consumer's bias is noisy, probabilistic, or inferred through repeated interactions. Finally, we were unable to explore the potential interaction effect of both expert and consumer biases due to our sample size constraints. Extending the design in this direction would be a natural next step. We also encourage future research to design and test targeted interventions that mitigate psychological distortions in provision decisions. A promising example is *BalancedView*, a digital tool proposed by Thornhill et al. (2019) to reduce online misinformation and help users form more balanced judgments. *BalancedView* could lessen inefficiencies arising from expectation-outcome misalignment by recalibrating consumers' beliefs ex ante with the presentation of source-documented reasons for and against each option. On the expert side, the tool can lower the psychological incentive to accommodate biased expectations and provide credible corroboration when objective evidence conflicts with priors.

As policy goals aim to improve public services, our study represents a crucial first step in better understanding the importance of paying attention to patients' or clients' belief systems. By situating confirmation bias within a framework that blends psychological game theory and empirical investigation, our study reveals important insights into how biases interact with altruistic and monetary motivations in service contexts. In healthcare, for example, patients often arrive with assumptions about their illnesses. A physician who is overly attentive to these expectations, especially when diagnosing ambiguous conditions, may be unwittingly guiding patients toward suboptimal therapies simply to validate preconceived notions. Understanding and mitigating the role of confirmation bias in these sectors could thus be a critical step toward improving public service delivery and overall consumer welfare.

## CRediT authorship contribution statement

Maxime Perodaud: Writing – original draft, Visualization, Software, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Michela Chessa: Writing – original draft, Visualization, Supervision, Project administration, Methodology, Investigation, Formal analysis, Conceptualization.

## Funding

This work has been supported by the French government through the UCA JEDI Investments in the Future project, managed by the National Research Agency (ANR) under reference number ANR-15-IDEX-01.

## Acknowledgments

We are grateful to Giuseppe Attanasi, Nobuyuki Hanaki, Angela Sutan, Mira Toumi, Radu Vranceanu, and the participants of the LEEN lab seminars for their contributions, insightful discussions, and support throughout this research. Special thanks are due to Mira Toumi for assisting in the conduct of experimental sessions.

## Appendix A. Supplementary material

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.socec.2025.102495.

## Data availability

Data will be made available on request.
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