Francesco Fallucchi

Real-effort tasks, in which participants perform cognitively costly activities whose outcomes depend on actual performance, are widely used in experimental economics. Their validity, however, rests on the assumption that a human performs them. We study whether this assumption still holds in the era of Artificial Intelligence (AI) and Large Language Models (LLMs). Using 8 canonical real-effort tasks and 23 LLMs from three major providers, we show that most tasks can now be solved accurately and at a negligible cost, while only a few resist automation. Performance improves with each model generation, and midtier models are rapidly closing the gap with frontier ones, broadening the set of widely accessible models that can automate these tasks. Additionally, we show that verbally offering monetary incentives has no effect on LLM performance. Our findings establish a boundary condition for the use of real-effort tasks in unsupervised settings: when participants can cheaply outsource task completion to an LLM, observed performance may no longer reflect genuine human effort.

Despite widespread concern about climate change, behavioral engagement and policy support remain limited. We present and reinterpret existing evidence through a collective action framework informed by belief-dependent preferences. Two belief channels—first-order beliefs about others’ behavior (descriptive norms) and second-order beliefs about others’ expectations (social expectations)—are embedded in a behavioral public-goods model. When beliefs are accurate, these channels sustain conditional cooperation and self-fulfilling collective action. Inaction may instead arise when the belief references are biased downward. We distinguish between two empirically grounded sources of distortion: genuine misperceptions, arising from informational limits and bounded rationality, and motivated misperceptions, driven by self-serving and identity-protective reasoning. This distinction guides policy: visibility and feedback correct genuine errors; identity-compatible framing, in-group messages, and narrative persuasion counter motivated bias. We thus connect the behavioral theory of conditional cooperation with empirical evidence on belief distortions and map the different mechanisms to interventions that overcome collective climate inaction.

Across three studies involving more than 5,000 participants, we provide a comprehensive analysis of the effects of incentivizing responses in the Krupka-Weber norm elicitation task. We consider both the potential benefits of incentivization (higher response quality and mitigation of response biases) and its possible drawbacks (distortion of responses in the direction of norm-unrelated focal points and materialistic values). We find no evidence of undesirable effects of incentives. While we report only modest improvements in response quality, we also show that incentives effectively mitigate response biases that arise when participants’ self-serving motivations conflict with accurate responding.

We study the Tullock contest model with loss aversion and endogenously formed reference points. In a contest with n possibly heterogeneous players and convex effort costs, we establish sufficient conditions for a unique Nash equilibrium in pure strategies. Subsequently, we analyze the impact of loss aversion on players' spending behavior, probability of winning, and rent dissipation.

Over the first half of March 2021, the majority of European governments suspended Astrazeneca’s Vaxzevria vaccine as a precaution following media reports of rare blood clots. We analyse the impact of the European Medicines Agency’s (EMA) March 18th statement assuring the public of the safety of Vaxzevria and the immediate reinstatement of the vaccine by most countries on respondents’ intention to get vaccinated. By relying on survey data collected in Luxembourg and neighbouring areas between early March and mid-April, we observe that the willingness to be vaccinated was severely declining in the days preceding the EMA statement. We implement a regression discontinuity design exploiting the time at which respondents completed the survey and find that the vaccine reinstatement substantially restored vaccination intentions.

Competition between groups is ubiquitous in social and economic life, and typically occurs between groups that are not created equal. Here we experimentally investigate the implications of this general observation on the unfolding of symmetric and asymmetric competition between groups that are either homogeneous or heterogeneous in the ability of their members to contribute to the success of the group. Our main finding is that relative to the benchmark case in which two homogeneous compete against each other, heterogeneity within groups per se has no discernable effect on competition, while introducing heterogeneity between groups leads to a significant intensification of conflict as well as increased volatility, thereby reducing earnings of contest participants and increasing inequality. We further find that heterogeneous groups share the labor much more equally than predicted by theory, and that in asymmetric contests group members change the way in which they condition their efforts on those of their peers. Implications for contest designers are discussed.

We design an experiment to study investment behavior in different repeated contest settings, varying the uncertainty of the outcomes and the number of participants in contests. We find decreasing over-expenditures and a higher rate of ‘dropout’ in contests with high uncertainty over outcomes (winner-take-all contests), while we detect a quick convergence toward equilibrium predictions and a near to full participation when this type of uncertainty vanishes (proportional-prize contests). These results are robust to changes in the number of contestants. A learning parameter estimation using the experience-weighted attraction (EWA) model suggests that subjects adopt different learning modes across different contest structures and helps to explain expenditure patterns deviating from theoretical predictions.