Programme

Joel Lamb (University of Exeter) “Strategy of the Commons: Causal Evidence from a Lottery in Parliament” 

How do politicians secure top government positions? We argue that politicians make strategic decisions about the legislation they propose to advance their careers. We introduce a model of political capital that yields predictions about the legislation proposed by politicians. We exploit a natural experiment in the UK House of Commons, where politicians enter a lottery to win the opportunity to introduce legislation of their choosing. First, we verify that winning this lottery improves career outcomes, leading to a 71% increase in ministerial appointments five years after treatment. Next, we use Natural Language Processing techniques to analyse the content of the bills presented by ballot winners. We provide evidence that politicians who strategically use this opportunity to push party objectives see a larger boost to their careers.

Giacomo Manferdini (Bocconi University) “Blameocracy: Causal Attribution in Political Communication” 

Gonzalo Arrieta (University of Zurich) “Procedural Decision-Making In The Face Of Complexity” 

A large body of work documents that complexity affects individuals’ choices, but the literature has remained mostly agnostic about why. We provide direct evidence that individuals use different choice processes for complex and simple decisions. We hypothesize that individuals resort to “procedures”—cognitively simpler choice processes that we characterize as being easier to describe to another person—as the complexity of the decision environment increases. We test our hypothesis using two experiments, one with choices over lotteries and one with choices over charities. We exogenously vary the complexity of the decision environment and measure the describability of choice processes by how well another individual can replicate the decision-maker’s choices given the decision-maker’s description of how they chose. We find strong support for our hypothesis: Both of our experiments show that individuals’ choice processes are more describable in complex choice environments, which we interpret as evidence that decision-making becomes more procedural as complexity increases. We show that procedural decision-makers choose more consistently and exhibit fewer dominance violations, though we remain agnostic about the causal effect of procedures on decision quality. Additional secondary evidence suggests that procedural decision-making is a choice simplification that reduces the cognitive costs of decision-making.

Caterina Giannetti (University of Pisa) “Teaming up with artificial players”

Lara Berger (University of Cologne) “How digital media markets amplify news sentiment”

This paper investigates whether digitization, by increasing incentives to capture reader attention, leads to more emotional news headlines. Comparing online and offline editions of major newspapers, I find that online headlines are significantly more emotional—particularly negative—than their print counterparts. Two experiments with professional journalists further suggest that these differences arise from stronger economic incentives: when journalists are compensated based on click or subscription rates, they systematically choose more emotional headlines. A connected reader experiment reveals that such sensational framing increases reading times but reduces factual retention, highlighting potential social costs of an intensified emotional slant in digital news environments.

Prashant Garg (Imperial College London) ”Political Expression of Academics on Social Media”

Magdalena Wasilewska (University of Amsterdam) ”Explanations of Inequality”

Economic inequality has become an important element of political debates, capturing significant attention in media coverage, public discourse, as well as academic research. In a survey experiment on a representative sample of the Dutch population (N = 4501), we examine how individuals explain economic inequality. We employ responses from an open-ended survey question about sources of inequality and link them on an individual level to administrative data on respondents’ income and wealth. We find that respondents predominantly attribute inequality to structural factors rather than personal responsibility or luck. Explanations are somewhat insensitive to information about inequality in the Netherlands, or about one’s standing in the income or wealth distributions. We observe significant differences in the content of the explanations based on respondents’ characteristics, such as wealth or political affiliation, and inequality-related attitudes. In a subsequent data collection, we aim to estimate the causal impact of explanations of inequality on redistributive preferences.

J. Jobu Babin (DePaul University) “Text Analysis of Subjects’ Perceptions of One-shot Trust Games”

The Trust Game (TG) is a widely used experimental paradigm to study trust and prosocial behavior, yet its interpretation remains debated. This study leverages natural language processing and language AI to analyze how subjects conceptualize TG interactions and predict behavior. Using a novel dataset and an experimental setup across three binary variants, we examine how participants explicitly frame their perception of the interaction and subsequent decisions. BERTopic modeling and ensemble machine learning models reveal that themes involving "trust" and "working together" strongly predict passing behavior, while risk-related and game-theoretical concepts play a lesser role. These findings both validate and challenge conventional assumptions, highlighting the importance of social context in economic decision-making.

Can Celebi (VCEE, University of Vienna) “Using Large Language Models for Text Classification in the Social Sciences”