Welcome

• What Drives Inaction on Climate Change? A Review of the Literature. With F. Fallucchi and E. Manzoni, Working Paper n. 35, University of Bergamo, DSE. [Revise and Resubmit to the Journal of Economic Behavior and Organization, 2026]

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.

• Making Information Stick: Goal Setting and Attention. With A. Guido, R. Romaniuc, K. Boun My,  E. Dimant, D. Dubois, M. Chessa, X. Gassmann, A. Martinangeli, E. Spiegelman, and A. Sutan. [WP, September 2025]

Information campaigns often fail to induce behavior change as individuals attribute low instrumental value to the information provided. However, pursuing a goal may increase the perceived value of information and enhance attention. We test this idea in a field experiment on mobile data consumption and carbon awareness. While information provision alone does not produce any significant change in consumption behavior, the addition of goal setting increases attention in the short run and recall in the long run. These effects together reduce mobile data consumption even after intervention removal. Evidence from a follow-up survey indicates that this change is not due to alternative mechanisms, such as automated data management technologies, thereby lending support to theories of rational attention. 

• Boozing and Schmoozing: Do they Improve Negotiations? with A. Kupfer Schneider, M. Moffitt, A. Borbély, M. Bonescus & A. Sutan [WP, Manuscript available upon demand]

• Using Experiments to study Mental Models, with A. Guido, [Book Chapter]

Decision-makers in organizations rely on causal representations of how their actions translate into outcomes — mental models — to navigate complex policy environments. Understanding these models is central to organizational cognition, yet observational methods can document what models people hold without establishing how they form, which model is selected when several are consistent with the same evidence, or what shifts them. Experiments fill this gap: by controlling the information environment and varying causal structures, they allow researchers to identify which models are activated, why some are adopted over others, and how they respond to persuasion. This chapter reviews the experimental evidence along these three dimensions. On measurement, we distinguish decontextualized designs — which benchmark model formation against a known data-generating process and reveal systematic failures and narrative-induced distortions — from contextualized approaches that elicit full subjective causal maps through open-ended questions. On selection, the evidence shows that when multiple interpretations are consistent with the available data, individuals typically adopt a single model, and the choice is shaped by optimism, cost-aversion, and cognitive simplicity rather than statistical fit. On persuasion, causal narratives shift models by reorganizing how shared facts are causally connected, without conveying new information. Together, these findings suggest that organizational interventions targeting behavior directly may fail if they leave the underlying causal representation unchanged.

• Eyes on Me or Counting on Me? A Field Experiment on Goal Setting and Environmental Behavior (with A. Guido and collaborators from Orange Innovation Research) [Collecting data]

Goal setting is a widely used strategy to promote desirable behaviors in domains ranging from education to workplace productivity and environmental conservation. While numerous interventions target individuals with personalized goals, many organizations and institutions rely on group-based and shared objectives. Yet, whether pursuing goals individually or collectively yields more effective outcomes remains an open question. In collaboration with a large telecommunication company in France, we propose a field experimental design to answer this question in the context of pro-environmental behavior: reducing carbon emissions linked to mobile data usage by encouraging responsible digital practices. We focus on two psychological mechanisms that can make group goal setting more effective: social image concerns, the desire to be seen favorably by others, and guilt aversion, the aversion to letting others down. Our design allows us to disentangle the relative importance of these two factors and analyze their effect on behavior and the welfare consequences of imposing group vs. individual goals. 

• Climate Change Narratives Around the Globe (with A. Guido, R. Romaniuc, B.Sapone, and Many Lab Collaborators) [Collecting data]

We study how individuals make sense of climate change through narratives, defined as subjective causal models linking perceived causes to proposed solutions. Using open-ended survey responses from 11 countries, we elicit what respondents believe causes climate change and what governments should do in response. We then translate these answers into taxonomy-based Directed Acyclic Graphs, which allow us to recover the structure of climate narratives across countries on an Individualism-Collectivism scale, identify their dominant causal pathways, and relate them to policy preferences, climate expectations, and willingness to share one's view. Preliminary evidence points to substantial heterogeneity in how respondents explain climate change, with a broad distinction between natural-cause narratives and human-cause narratives. Within anthropogenic answers, structural and systemic drivers appear more salient than individual behaviors. We also find that proposed policy responses vary systematically with the underlying causal narratives. Methodologically, the project contributes a scalable framework for measuring subjective causal reasoning from open-ended text using a combination of human coding and LLM-assisted annotation. More broadly, this mapping exercise provides the basis for future experimental work on how alternative climate narratives shape beliefs, policy support, and diffusion. 

• Prosociality and Sustainable Behavior: A Day-Reconstruction Method Approach (with F. Fallucchi, A. Marietta Leina, and S. Quercia) [Data analysis]

We study whether laboratory measures of prosociality predict sustainable behavior in the field. We measure prosociality among a representative sample of the Italian adult population using two games: (i) a public goods game, which captures individuals’ willingness to cooperate in a setting with positive externalities for others, and (ii) an externality game, which measures aversion to generating negative externalities for personal gain. The same participants subsequently complete a 14-day evening diary based on the Day Reconstruction Method (DRM; Kahneman et al., 2004; Navarro Martínez et al., 2023), providing detailed information on their daily behaviors. These behaviors are categorized into three key domains of sustainable action: mobility, household energy use, and food consumption. For mobility, we record whether participants travel to work or study and the modes of transportation used. Household energy use is assessed through reported heating, showering, washing machine use, and other energy-related practices. For food consumption, we collect data on meal composition and whether meals are predominantly plant-based.  This study aims to link experimentally measured social preferences with real-world sustainable behaviors, thereby contributing to a better understanding of the behavioral determinants of environmental sustainability and informing future research and policy design in this area. 

• Guilt, Beliefs, and Selective Attention (single-authored) [Winner of the 15th ASFEE conference's Poster Competition, 1500€ award] [Theory in progress]

We develop a psychological game-theoretic model of conditional cooperation in public good games driven by guilt and second-order beliefs about others' expectations. When beliefs are correct, guilt implies equilibrium cooperation that is conditional on perceived expectations. We then introduce motivated beliefs formation: agents can reduce the expectations they feel accountable to by endogenously selecting (i) which social identity defines the relevant reference group and (ii) which social information within that group is attended to. These choices allow defection with minimal guilt and provide a microfoundation for persistent norm misperceptions. The model yields testable predictions on when cooperation breaks down; e.g., when identity switching is easy, information is selectively sampled, and accountability is weak, and clarifies how similarity governs whose expectations predict behavior. 

• Climate Narratives on Reddit (with H.Fares) [Collecting data]

This study examines how climate change narratives evolved on Reddit’s r/worldnews before, during, and after the 2024/2025 U.S. presidential election, focusing on their framing, sentiment, and potential amplification by bots. Using BERTopic, we identify dominant narratives. Sentiment analysis (VADER) assesses emotional tone, while a Random Forest classifier detects bot activity. We hypothesize that pro-climate narratives will become more urgent, while contrarian narratives will increase in volume and extremity due to political influence. Additionally, bots are expected to amplify polarizing content, particularly misinformation, with activity peaking during the election. Results will clarify whether shifts in climate discourse are organic or artificially driven, offering insights for policymakers and fact-checkers on combating misinformation and understanding digital climate debates.