1 Introduction

The ability to recover preferences from choice data, and subsequently predict choices from preferences, is fundamental for economic analysis. The revealed preference approach (Samuelson, Reference Samuelson1938, Reference Samuelson1948; Houthakker, Reference Houthakker1950; Arrow, Reference Arrow1959; Richter, Reference Richter1966) essentially views preferences as nothing more than organizing schemes reflecting both observed and predicted choices. More recent accounts have shown that the ‘spirit’ of the revealed preference approach can be conserved under more general conditions. For example, the literature on stochastic choice and random utility models explicitly incorporates variability in choice—a key observation in real-world choice data (Tversky, Reference Tversky1969; Hey and Orme, Reference Hey and Orme1994; Agranov and Ortoleva, Reference Agranov and Ortoleva2017)—by adding random components to true, underlying preferences (McFadden, Reference McFadden and Zarembka1974, Reference McFadden2001). Provided that stable mechanisms govern choice variability, true preferences can still be recovered (e.g., Apesteguía and Ballester, Reference Apesteguía and Ballester2018; Lu and Saito, Reference Lu and Saito2020; Frick et al., Reference Frick, Iijima and Strzalecki2019; Alós-Ferrer et al., Reference Alós-Ferrer, Fehr and Netzer2021). In practice, choice data is universally used to estimate latent and derived concepts ranging from utility functions and risk attitudes to demand functions and social welfare (e.g., Harsanyi, Reference Harsanyi1955; Koopmans, Reference Koopmans1960; Afriat, Reference Afriat1967; Varian, Reference Varian1982; Andreoni and Miller, Reference Andreoni and Miller2002; Cox et al., Reference Cox, Friedman and Sadiraj2008; Deb et al., Reference Deb, Gazzale and Kotchen2014, among many others). The use of choice data, however, entails an implicit but rarely-discussed assumption: stability. Predicting future choices from preferences which themselves are estimated from past choices is only warranted as long as economic agents display well-defined and stable choice patterns (or, at least, stable mechanisms governing choice variability) in the relevant time frame.

Worryingly, the assumption of stable choice patterns (deterministic or stochastic) is at odds with fundamental theories in psychology, which postulate that choices can create and alter preferences (Festinger, Reference Festinger1957; Bem, Reference Bem1967a, Reference Bemb; Slovic, Reference Slovic1995; Simon et al., Reference Simon, Krawezyk and Holyoak2004; Ariely and Norton, Reference Ariely and Norton2008). That is, the mere act of choice, even when no new information is revealed by or after the choice, can lead to fundamental changes in preferences, so that we do not only “choose what we like,” but mechanically also “like what we choose.” Empirical support for such feedback loops between choices and preferences appears to be widespread (Egan et al., Reference Egan, Bloom and Santos2010; Sharot et al., Reference Sharot, Velasquez and Dolan2010; Nakamura and Kawabata, Reference Nakamura and Kawabata2013; Johansson et al., Reference Johansson, Hall, Tärning, Sikström and Chater2014). These alleged preference changes occur within the time span of a few minutes and in the absence of any new, choice-relevant information. They are therefore fundamentally problematic for economics. If such effects extend to economic choices, every choice-based preference elicitation procedure bears the potential to interfere with the very concept it ought to measure. Observed economic choices may then permanently lag behind current preferences, and standard economic applications estimating utilities, demand, and social welfare may be systematically biased.

In light of its potential consequences, it is of paramount importance to investigate the economic validity and significance of this mere choice effect. Evidence from psychology is insufficient to settle the question, due to difficulties with the experimental paradigms applied in that literature (see next section), the hypothetical nature of choices in such studies, and the non-economic nature of the alternatives they study. This paper undertakes the endeavor of establishing the validity of the mere choice effect (preference change due purely to the act of choice) relying on incentivized choices. We develop a parsimonious experimental design that allows researchers to isolate the effect of mere, uninformative choices on future choices in an economically-relevant domain (binary monetary gambles or lotteries). In essence, our experimental design first presents participants with two choice options (lotteries), but, crucially, the experimenter randomly determines whether a certain choice option is transparently inferior or superior (through stochastic dominance). As choices are incentivized, it is in the best interest of participants to choose the objectively superior option and hence follow the pre-determined, randomized choice patterns. Further choices in the experiment then test for preference change in favor or against the previous options. In this way, the design effectively randomizes uninformative (mere) choices. We hereby solve typical issues encountered in the existing literature: unreliable preference measures, hypothetical bias, and deception (we will elaborate on these issues in the next section).

This paper reports the results of a large-scale, preregistered online experiment (the paper was evaluated at the journal previous to data collection) relying on the basic design described above. The mere choice effect was assessed by measuring whether merely-chosen options were subsequently chosen more often than merely-rejected ones. The results hence allow us to establish whether or not the mere choice effect is relevant for economics and whether or not it is warranted to maintain a unidirectional link between choices and preferences in the domain we study. Hereby, we contribute to a stream of literature that discusses the possibility of past experiences shaping future preferential choices. For example, the literature on preference discovery postulates that decision makers do not know their true tastes until they (incompletely) discover them through consumption experience (Plott, Reference Plott, Arrow, Colombatto, Perlman and Schmidt1996; Braga and Starmer, Reference Braga and Starmer2005; Delaney et al., Reference Delaney, Jacobson and Moenig2020). Naturally, past choices are a vital input source for the discovery process. Frick et al. (Reference Frick, Iijima and Strzalecki2019), on a related note, discuss in their concluding remarks that their dynamic random utility framework could accommodate endogenously evolving preferences (as a function of the agent’s past consumption level). This could take the form of habit formation or simply reflect the fact that past consumption provides payoff-relevant information. In contrast, in this paper we aimed to establish whether past choices affect future choices even when there is nothing to be learned from them. A possible mechanism would be that decision makers, to some extent, are used (or hard-wired) to learn from past choices and they mistake uninformative choices for informative ones. This could result in “decision inertia” as studied by Alós-Ferrer et al. (Reference Alós-Ferrer, Hügelschäfer and Li2016b) even in cases where objectively superior options are available (see also Jung et al., Reference Jung, Erdfelder, Broeder and Dorner2019). For example, Cerigioni (Reference Cerigioni2017) studies how past exposure to a choice option creates inertia or stickiness towards the exposed option. This stickiness, known as the mere exposure effect (Zajonc, Reference Zajonc1968,, Reference Zajonc2001), may explain behavioral regularities like the status-quo bias, and could at least partially drive the mere choice effect (we will discuss this latter possibility below).

We committed to our conditional conclusions prior to data collection. In case supporting evidence for the mere choice effect would have been found, the intention of our work was to contribute to the development of better and more predictively-accurate preference elicitation methods. For example, if preference change followed regular patterns, standard elicitation procedures could be corrected by taking into account quantitative predictions about the expected magnitude of preference change. However, our experiment found no supporting evidence for the mere choice effect. Merely-chosen and merely-rejected lotteries were subsequently chosen with almost identical frequencies, which in turn were almost identical in magnitude to a baseline measurement. Since our study had sufficient power, the conclusion is that the effects reported in psychology are likely to be too small for economic choices to merit sparking a major reevaluation of economic methods.

The remainder of the paper is structured as follows. Sect. 2 reviews the existing literature on choice-induced preference change and briefly discusses the main theory underlying the effect. Sect. 3 presents our experimental design, including the derivation of our main hypothesis and the power analysis. Sect. 4 presents the statistical analyses (as planned before data collection and actually carried out) and discusses the interpretation of the results and Sect. 5 concludes. Additional results and supplementary experimental materials (experimental instructions and screenshots) are presented in the online appendix.

2 Literature review: choice-induced preference change

Psychological theories that explain how choices can create preferences often draw an analogy between how we make inferences about others’ preferences and how we make inferences about our own preferences (Bem, Reference Bem1967a, Reference Bemb; Ariely and Norton, Reference Ariely and Norton2008). As we cannot fathom what others feel and think, we infer their preferences and beliefs by what we can observe: their behavior. If we observe a stranger on the street giving money to a homeless person, we infer that the stranger is altruistic. Analogously, if our own preferences are vague, imprecisely formulated, or incomplete, we cannot fathom what we ourselves feel and think. Thus, we infer our own preferences from what we can observe: our own past behavior. Imagine a consumer standing in front of a drug-store shelf filled with many shampoo brands. One particular brand catches her eye. She is not quite certain of whether she likes the brand or not, but remembers buying it in the past. She deduces that there must have been a good reason for that decision. Being a rational consumer, the shampoo must have fulfilled her needs. She infers that she likes the shampoo and buys it again. This line of reasoning can lead us astray because memory often inaccurately captures hedonic experiences. For example, it is well understood that unrelated situational factors can impact behavior and that we are not always aware of their influence (Slovic, Reference Slovic1995; Ariely et al., Reference Ariely, Loewenstein and Prelec2003; Ariely and Norton, Reference Ariely and Norton2008; see, however, Fudenberg et al., Reference Fudenberg, Levine and Maniadis2012 and Maniadis et al., Reference Maniadis, Tufano and List2014). Maybe the consumer correctly remembers buying the shampoo, but forgets having been in a rush that day, or that the shampoo was part of a promotional deal. In that case, her self-inference process was based on an inaccurate recollection of a past event. This is the logic behind the mere choice phenomenon, with the only caveat that, in psychology, processes of preference change are assumed to happen subconsciously. Uninformative (mere) choices can serve as input factors for the self-inference process, which itself may then lead to wrongly imputed preferences.Footnote ¹

Most of the relevant evidence on preference change in psychology has been collected using the following three-stage setup. In stage 1, participants rate or rank certain objects, like artistic paintings, on their desirability. In stage 2, they are asked to make a choice between two previously-rated objects. Participants are led to believe that they have made a free choice, but, in reality, researchers use some form of deceptive technique to manipulate choice and randomly determine what was chosen and rejected, e.g. alleged subliminal choice (Sharot et al., Reference Sharot, Velasquez and Dolan2010). In the third and final stage, objects are rated or ranked again. Preference change is measured by comparing how much chosen objects have increased in self-reported desirability relative to rejected objects. The typical finding is that chosen objects are reevaluated upwards and non-chosen ones are reevaluated downwards, even if choices were randomly assigned. If preferences are stable, one should have observed no changes in desirability.

In spite of an apparently-overwhelming body of evidence, economists should be skeptical about the relevance of the mere choice phenomenon as currently established. First, the extant literature typically studies the effect of past choice on future desirability measures, e.g., liking ratings or rankings (Sharot et al., Reference Sharot, Velasquez and Dolan2010; Nakamura and Kawabata, Reference Nakamura and Kawabata2013). In economics, the most relevant data source is actual choices, and preferences are just binary relations organizing those choices, which decision makers might or might not have conscious access to. Whether (typically unincentivized) desirability measures proxy choice data sufficiently well is not self-evident (Cason and Plott, Reference Cason and Plott2014). Hence, it is important to establish the mere choice effect on actual, subsequent choices and not only self-reported desirability scales. Second, the available experimental evidence exclusively investigates preferences in hypothetical choice scenarios over ill-defined options, which do not reference all preference-relevant option dimensions (Egan et al., Reference Egan, Bloom and Santos2010). Examples include hypothetical holiday destinations described by their destination names only, or the attractiveness of human faces (Sharot et al., Reference Sharot, Velasquez and Dolan2010; Johansson et al., Reference Johansson, Hall, Tärning, Sikström and Chater2014). In such cases, behavior might be extremely noisy and easily swayed by irrelevant factors (Murphy et al., Reference Murphy, Allen, Stevens and Weatherhead2005; Fudenberg et al., Reference Fudenberg, Levine and Maniadis2012). The hypothetical bias identified in related domains casts doubts on whether the behavior observed in such paradigms is informative enough to study preference change (Hertwig and Ortmann, Reference Hertwig and Ortmann2001; Murphy et al., Reference Murphy, Allen, Stevens and Weatherhead2005; Harrison and Rutström, Reference Harrison, Rutström, Plott and Smith2008). Third, the existing literature has adopted research designs that deceive participants to achieve experimental control. For example, experimenters give wrong feedback about past choice using card tricks (swapping choices) or present cover stories about subliminal decision making and have a computer prompt a random choice (Sharot et al., Reference Sharot, Velasquez and Dolan2010; Nakamura and Kawabata, Reference Nakamura and Kawabata2013; Johansson et al., Reference Johansson, Hall, Tärning, Sikström and Chater2014). Deception is obviously inappropriate in experimental economics and, through lab reputation, would render any incentivized design ineffective. In summary, it remains unresolved whether actual choices, in contrast to perceived and make-believe choices, lead to preference change.

It needs to be pointed out that a large part of the literature on choice-induced preference change in psychology has studied a related but different question, namely whether and how choices involving some sort of tradeoff change preferences (Brehm, Reference Brehm1956; Harmon-Jones and Mills, Reference Harmon-Jones and Mills1999; Shultz et al., Reference Shultz, Léveillé and Lepper1999; Jarcho et al., Reference Jarcho, Berkman and Lieberman2011; Alós-Ferrer et al., Reference Alós-Ferrer, Granić, Shi and Wagner2012; Izuma and Murayama, Reference Izuma and Murayama2013). The dominant theory behind such effects is cognitive dissonance (Festinger, Reference Festinger1957; Akerlof and Dickens, Reference Akerlof and Dickens1982). In a nutshell, the underlying hypothesis is that a choice involving tradeoffs creates dissonance (psychological discomfort). Dissonance arises, because the chosen option has some negative characteristics and the rejected option has some positive ones (i.e., tradeoffs). The decision makers unconsciously reduce this dissonance by adjusting their preferences. Hereby, they reevaluate the chosen options up and rejected ones down. However, it has been recently shown that the experimental paradigm which has guided the development of this literature for over 50 years is regrettably flawed. It contains a statistical bias that can result in apparent preference change even if participants have stable preferences (Chen and Risen, Reference Chen and Risen2010; Izuma and Murayama, Reference Izuma and Murayama2013; Alós-Ferrer and Shi, Reference Alós-Ferrer and Shi2015). Although some improved designs have been proposed (e.g., Alós-Ferrer et al., Reference Alós-Ferrer, Granić, Shi and Wagner2012), how the effect of tradeoff choices in economically-relevant domains could be studied remains an unresolved issue at the time of writing. Although beyond the scope of the current paper, it would of course also be valuable for economics to understand if and when tradeoff choices change preferences. This work, however, concentrates on the mere choice effect, which more clearly isolates the possible effects of the act of choice on preferences.

Finally, it should be pointed out that the mere choice effect may be related to a general tendency in decision makers to develop a ‘preference’ for alternatives merely because they have been exposed to them. This tendency, know as the mere exposure effect, is robust and has been replicated across many domains (Zajonc, Reference Zajonc1968; Bornstein, Reference Bornstein1989; Monahan et al., Reference Monahan, Murphy and Zajonc2000; Zajonc, Reference Zajonc2001). It may be the case that exposure is asymmetric, stronger for chosen options than for non-chosen ones (see, e.g., Alós-Ferrer et al., Reference Alós-Ferrer, Hügelschäfer and Li2016b; Cerigioni, Reference Cerigioni2017). In this view, preference changes are then not caused by the act of choosing, but by exposure alone. Returning to the example at the beginning of this section, imagine that the consumer remembers her reasoning process, but forgets what choice it led her to. Asymmetric mere exposure would nevertheless predict an increase in ‘preference’ for the chosen shampoo, at least against options which were not visible or available at the time. We will provide a critical discussion of how the mere exposure effect might affect our experiment and the results we find in Sect. 4.

3 Experimental design and procedures

3.2 Procedures

Table 1 Sample demographics. N and $N\%$ represent absolute and relative frequencies, respectively. Percentages in $N\%$ columns were calculated excluding “Prefer not to disclose” (PNTD) answers. Percentages in UK% columns represent the most recent UK adult population figures taken from the UK Office for National Statistics and the OECD

	N	$N\%$	UK%
Household income
Less than 19,000	199	25	17
19,000 to 31,999	197	25	28
32,000 to 47,999	157	20	24
48,000 to 63,999	125	16	14
64,000 or more	105	14	17
Prefer not to disclose (PNTD)	57	–	–
Total	840	100.0	100.0
Highest education level
No academic or professional qualifications	7	1	7.9
Level-1	35	4	9.9
Level-2	68	8	15.7
Trade Apprenticeship	10	1	2.9
Level-3	231	28	16.8
Level-4+	474	58	40.2
Other	0	0	6.6
PNTD	15	–	–
Total	840	100.0	100.0
Student status
Yes	227	27	5.7
No	599	73	94.3
PNTD	14	–	–
Total	840	100.0	100.0
Gender
Female	553	66	50.6
Male	276	33	49.4
Other	8	1	–
PNTD	3	–	–
Total	840	100.0	100.0
Employment status
Full-time	373	46	NA
Part-time	177	22	NA
Not in paid work	262	32	NA
PNTD	28	–	–
Total	840	100.0	NA

We conducted an online experiment to investigate the economic validity of the mere choice effect and test our main research hypothesis (H1). Participants were recruited via the research platform Prolific and sampled from a U.K. general population.Footnote ² Table 1 presents the descriptive statistics on the sample demographics. The sample shows the typical characteristics of an online panel (mean age was 33.1 years, SD $=$ 11.8).

Each participant made 16 choices in total, each between two lotteries with two monetary outcomes and two probabilities. Eight choices were of the type (a vs. c), the remaining eight ones of the type (a vs. b). We implemented a standard between-participants design and randomized whether target lotteries a were inferior or superior to constructed lotteries c at the participant level. Lotteries were presented as icon arrays and we used a colored-balls-in-a-box framing (Garcia-Retamero and Galesic, Reference Garcia-Retamero and Galesic2010; Dambacher et al., Reference Dambacher, Haffke, Groß and Hübner2016). All relevant design aspects of the presentation format were counterbalanced, e.g. colors, the position of the lotteries on screen, or the order of presentation within stages. Figure 1 summarizes the experimental design using sample screenshots and Table 2 shows the lotteries used in the experiment, each row representing one (a, b) lottery pair.

Fig. 1 Schematic overview experimental design, including screenshots from actual decision screens. Lottery labels a, b, and c are for illustrative purposes only, and were not shown to participants

All participants first went through a standard attention screening, a typical procedure to reduce noise in online experiments (Oppenheimer et al., Reference Oppenheimer, Meyvis and Davidenko2009).Footnote ³ After passing the attention check, participants received detailed instructions on our lottery presentation format. They were then required to answer a small control quiz ensuring that they understood the lottery presentation format. After passing the quiz, each participant faced two decision stages, a mere-choice task in stage 1 and a preference-choice task in stage 2. In both choice tasks, participants were presented with pairs of lotteries, one pair at a time. They were instructed to choose the lottery they preferred in each pair.

The mere-choice task in stage 1 consisted of eight pairs of lotteries. Each mere-choice pair displayed one target lottery of type a (see Table 2) and a new lottery c constructed on the spot. Lotteries c were constructed to induce predetermined choice patterns and did not replicate any of the lotteries from Table 2. For the construction of c, we relied on transparent first-order stochastic dominance (FOSD). A lottery a first-order stochastically dominates another lottery c if for any monetary outcome x, a gives at least as high a probability of receiving at least x as does c, with strictly higher probability for some x. If a lottery first-order stochastically dominates another lottery, the former is objectively superior, independently of underlying risk preferences, as long as participants prefer larger amounts of money over smaller ones (the same remains true if decision makers are described correctly by cumulative prospect theory or rank-dependent utility instead of expected utility theory).

In the experiment, participants were randomly assigned to one of two possible treatments. In the CHOOSE treatment, all lotteries of type a dominated the corresponding c-type lotteries. Participants who obeyed FOSD thus ‘merely-chose’ a. In the REJECT treatment, the FOSD relationship was reversed so that participants obeying FOSD ‘merely-rejected’ a. To obtain transparent FOSD relationships, we changed one lottery attribute keeping the other one constant. For robustness reasons, we split the treatments into sub-treatments at the participant level, randomly determining whether probabilities were changed or whether monetary outcomes were changed (more details are provided below).

Our experimental set-up shares some elements with the standard asymmetric dominance / decoy effect design (Huber et al., Reference Huber, Payne and Puto1982; Herne, Reference Herne1999; Sürücü et al., Reference Sürücü, Djawadi and Recker2019). That is, lottery c in the CHOOSE treatment is dominated by a, but not by b, making a more attractive than b as a result of the decoy effect. However, we remark that in our design the three lotteries are never presented on the same screen, but rather sequentially. Nevertheless, the decoy effect could still be present in our sequential presentation format, confounding the mere choice effect. We therefore implemented an additional sub-treatment in the CHOOSE condition to control for the decoy effect. When constructing c, we made lottery c so inferior monetary-wise that it was dominated by both a and b. We call such c-lotteries junk lotteries. With junk lotteries there is no asymmetric dominance present, and the decoy effect is shut down. To preserve symmetry between conditions, we used an analogous design for the REJECT condition. That is, star c-lotteries were made so attractive monetary-wise that they dominated both a and b.

The sub-treatments discussed above were counterbalanced between participants, yielding a 2 $\times$ 3 between-participants design with a total of six experimental conditions: CHOOSE with FOSD manipulation by probability, CHOOSE with FOSD manipulation by outcome, CHOOSE with junk-FOSD manipulation by outcome, REJECT with FOSD manipulation by probability, REJECT with FOSD manipulation by outcome, and REJECT with star-FOSD manipulation by outcome.Footnote ⁴ Figure 1 includes a schematic overview of our FOSD construction for the probability domain. Randomization into treatments occurred after passing the control quiz.

The preference-choice task in stage 2 followed a setup analogous to the mere-choice task. It consisted of eight pairs of lotteries. Each preference pair presented one target lottery a and the corresponding lottery b given in the same row in Table 2. We thus had eight fixed preference pairs of the form (a, b) as given in Table 2.

Table 2 Lotteries used in experiment. Lotteries pay amount Outcome 1 with probability p, and Outcome 2 with probability $1-p$ . All outcomes are in British Pounds (£), as Prolific is UK-based and compensates participants in £

Lotteries a						Lotteries b
ID	p	Outcome 1	Outcome 2	EV		ID	p	Outcome 1	Outcome 2	EV
1	0.32	4.70	1.50	2.52		9	0.38	3.70	1.70	2.46
2	0.40	4.20	1.40	2.52		10	0.46	3.50	1.60	2.47
3	0.56	3.60	1.20	2.54		11	0.62	3.20	1.40	2.52
4	0.70	3.00	1.00	2.40		12	0.78	2.70	1.20	2.37
5	0.24	4.90	1.50	2.32		13	0.14	3.60	1.70	1.97
6	0.38	3.80	1.30	2.25		14	0.31	2.80	1.50	1.90
7	0.54	3.00	1.10	2.13		15	0.47	2.20	1.30	1.72
8	0.72	2.50	1.00	2.08		16	0.63	1.90	1.10	1.60

To incentivize decisions, we implemented a random lottery incentive system (Cubitt et al., Reference Cubitt, Starmer and Sugden1998). A participant’s payment for the experiment was derived by selecting one of the sixteen lottery pairs from stage 1 and stage 2 at random. The participant then received the lottery she had chosen and that lottery was played out. This was done after all decision-relevant data was collected. On the basis of past experience with comparable experiments, the experiment was expected to last about 7 minutes and yield an average remuneration of £3.04.Footnote ⁵ Actual average duration was 6 minutes and 34 seconds, and actual average remuneration was £3.09.

The lotteries in Table 2 were designed such that no FOSD relation obtains among any preference pair (a, b); lotteries of type c do not duplicate any of the existing lotteries a or b; all lotteries are non-degenerate, i.e., no certainty is involved; and the expected average payment of the experiment meets the current standards in experimental economics. The first four (a, b)-lottery pairs from Table 2 are hard / difficult decisions, because they involve a clear tradeoff, while the last four pairs are comparatively easier (see Sect. 4 for more details). The online appendix contains screenshots of all phases of the experiment.