Discussion Paper No. 558
December 22, 2025
Delegating in the Age of AI: Preferences for Decision Autonomy
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Abstract:
Despite the documented benefits of algorithmic decision-making, individuals often prefer to retain control rather than delegate decisions to AI agents. To what extent are the aversion to and distrust of algorithms rooted in a fundamental discomfort with giving up decision authority? Using two incentivized laboratory experiments across distinct decision domains, hiring (social decision-making) and forecasting (analytical decision-making), and decision architecture (nature and number of decisions), we elicit participants’ willingness to delegate decisions separately to an AI agent and a human agent. This within-subject design enables a direct comparison of delegation preferences across different agent types. We find that participants consistently underutilize both agents, even when informed of the agents’ superior performance. However, participants are more willing to delegate to the AI agent than to the human agent. Our results suggest that algorithm aversion may be driven less by distrust in AI and more by a general preference for decision autonomy. This implies that efforts to increase algorithm adoption should address broader concerns about control, rather than focusing solely on trust-building interventions.
Keywords:
algorithm; delegation; artificial intelligence; trust in ai; experiment; preferences;
JEL-Classification:
C72; C91; D44; D83;
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Discussion Paper No. 556
Visual and Social Anchoring in a Framed Online Rating Experiment
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We conduct an online experiment to assess the effect of the anchoring bias on consumer ratings. We depart from the canonical anchoring literature by implementing non-numerical (visual) anchors in a framed rating task. We compare three anchoring conditions, with either high, low, or socially derived anchors present, against two control conditions – one without anchors and one without framing. Our framing replicates the common observation of overrating. We unveil asymmetric non-numerical anchoring effects that contribute to the explanation of overrating. Both high anchors and socially derived anchors lead to significant overrating compared to the control condition without anchors. The latter finding is driven by instances of high social anchors. The upward rating bias is exacerbated in a social context, where participants exhibit more trust in anchors. In contrast, low anchors and instances of low social anchors have no effect compared to the control condition without anchors. Beyond consumer ratings, our results may have broader implications for online judgment environments, such as surveys, crowdfunding platforms, and other user interfaces that employ visual indicators such as stars, bars, or progress displays.
Keywords:
anchoring bias; consumer judgment; economic experiment; online feedback systems; user interface design;
JEL-Classification:
C91; D80; D91;
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Discussion Paper No. 555
December 1, 2025
Decreasing Returns to Sampling Without Replacement
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Abstract:
We study sampling from a finite population without replacement when seeking an extreme (lowest or highest) value. An example is a buyer searching for the lowest price. It is well known that there are decreasing returns to sampling from continuous populations: the expected minimum is a decreasing and discretely convex function of the sample size. We show that is true for sampling without replacement from a finite population. We also give a simple sufficient condition on population values for the properties to hold for other order statistics.
Keywords:
order statistics; sampling without replacement; decreasing returns; consumer search;
JEL-Classification:
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Discussion Paper No. 546
October 9, 2025
Confidence and Information in Strategy-Proof School Choice
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Abstract:
Contrary to classical theory, we provide experimental evidence that preference reports in a strategy-proof school-choice mechanism systematically depend on beliefs. We employ a "hard-easy gap" to exogenously vary students' beliefs about their priority rank. As predicted, underconfidence induces more manipulation and thus more justified envy than overconfidence. The effect of priority information on justified envy crucially depends on the initial beliefs and the real priority ranks: while top students always gain, non-top students lose from this information. In total, correcting overconfidence/underconfidence increases/decreases justified envy. Finally, we confirm that additionally providing information on school availability through a dynamic implementation of the mechanism reduces justified envy compared to priority information alone.
Keywords:
market design; school choice; overconfidence; strategy-proofness; information;
JEL-Classification:
C92; D47;
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Discussion Paper No. 524
January 29, 2025
Finding a Good Deal: Stable Prices, Costly Search, and the Effect of Entry
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We study markets in which potential buyers engage in costly search to find a good deal. Our novel solution concept for prices builds upon the idea that any movement in a firm's price is followed by an opportunity for its competitors to respond with special offers. This mechanism selects the highest prices such that no firm wishes to undercut a competitor. We identify a distinctive closed-form pattern of disperse prices that uniquely satisfy our pricing solution, and pair that price profile with optimal fixed-sample search. In a stable equilibrium with active search, the intensity of search and consumer surplus are lower and industry profit is higher with more competitors. In a concentrated oligopoly, complete search in equilibrium can eliminate industry profit.
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Discussion Paper No. 523
January 23, 2025
Stable Price Dispersion
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Abstract:
We study the pricing of homogeneous products sold to customers who consider different sets of suppliers. We seek prices that are stable in the sense that no firm wishes to undercut any rival or to raise its price when rivals have a subsequent opportunity to undercut it. We identify stable and dispersed prices that emerge from both collective choice and non-cooperative pricing games, and derive predictions for prices across several price-consideration specifications. We show how the implications for firms and customers compare to those generated by conventional approaches.
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Discussion Paper No. 519
December 30, 2024
Do Women Comply More Than Men? Experimental Evidence from a General Population Sample
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Women are often perceived as more compliant than men; however, the literature provides inconclusive evidence. Using a novel experimental design comprising two complementary experiments, we test this claim in online samples representative of the German adult population. The first experiment (N=1600) features a probabilistic social dilemma game (PDG) in which participants can increase their individual payoff at the expense of exposing themselves and their group to probabilistic losses. In two treatment conditions, they receive either a recommendation on socially optimal behavior or a recommendation and information on weakly non-compliant peer behavior. We find that the recommendation strongly affects behavior but more so for women than for men. However, information on the non-compliant behavior of others does not induce significantly different responses in men and women. In the second experiment (N=522), we elicit empirical and normative expectations about behavior in the PDG with a recommendation to study the role of norms in following it. While men and women are expected to hold similar normative beliefs, men are expected to follow the recommendation less often, suggesting that compliance is a female social norm.
Keywords:
gender; compliance; public good; social dilemma; risk-taking; social norms;
JEL-Classification:
J16; I12; D81; H41;
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Discussion Paper No. 508
July 31, 2024
Measuring Preferences for Algorithms
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Abstract:
We suggest a simple method to elicit individual preferences for algorithms. By altering the monetary incentives for ceding control to the algorithm, the menu-based approach allows for measuring, in particular, the degree of algorithm aversion. Using an experiment, we elicit preferences for algorithms in an environment with measurable performance accuracy under two conditions|the absence and the presence of information about the algorithm's performance. Providing such information raises subjects' willingness to rely on algorithms when ceding control to the algorithm is more costly than trusting their own assessment. However, algorithms are still underutilized.
Keywords:
algorithm aversion; delegation; experiment; preferences;
JEL-Classification:
C91; D83; D91;
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Discussion Paper No. 498
February 23, 2024
The Strategic Value of Data Sharing in Interdependent Markets
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Abstract:
Large, generalist, technology firms—so-called “big-tech” firms—powerful in their primary market, routinely enter secondary markets consisting of specialist firms. Naturally, one might expect a specialist firm to be fiercely protective of its data as a way to maintain its market position in the secondary market. Counter to this intuition, we demonstrate that a specialist firm willingly shares its market data with an intruding tech generalist. We do so by developing a model of crossmarket competition in which data collected via consumer usage in each market is a factor of product quality in both markets. We show that a specialist firm shares its data to strategically create co-dependence between the two firms, thereby softening competition and transforming the generalist firm from a traditional competitor into a co-opetitor. For the generalist intruder, data from the specialist firm substitute for its own investments in product quality in the secondary market. As such, the act of sharing data makes the intruder a stakeholder in the valuable data collected by the specialist, and consequently in the specialist’s continued success. Moreover, while the firms benefit from data sharing, consumers can be worse off from the weaker price competition and lower investments in innovation. Our results have managerial and policy implications, notably on account of backlash against data collection and the market power of big tech firms.
Keywords:
data-driven quality improvements; externalities; co-opetition; data sharing;
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Discussion Paper No. 476
December 13, 2023
Overcoming Time Inconsistency with a Matched Bet: Theory and Evidence from Exercising
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This paper introduces the matched-bet mechanism. The matched bet is an easily applicable and strictly budget-balanced mechanism that aims to help people overcome time-inconsistent behavior. I show theoretically that offering a matched bet helps both sophisticated and naive procrastinators to reduce time-inconsistent behavior. A field experiment on exercising confirms the theoretical predictions: offering a matched bet has a significant positive effect on gym attendance. Self-reported procrastinators are significantly more likely to take up the matched bet. Overall, the matched bet proves a promising device to help people not to procrastinate.
Keywords:
monetary incentives; market design; field experiment; health behavior;
JEL-Classification:
C93; D47; D90; I12;