Essays on Public Good Game Experiments

Cooperation, i.e., paying a cost to benefit others, is a recurring phenomenon in human interactions and a fundamental prin- ciple of our societies. Hence, it is of great interest to under- stand under what conditions this behavior can be promoted. In the context of public good games and multilevel public goods games, I behaviorally and experimentally investigate if and how cooperation varies along with or as a response to other factors, namely norms, social efficiency, group identity, and risk. First, I find that personal norms, i.e., what one un- conditionally believes to be the right thing to do, have major explanatory power over cooperation than social norms, i.e., what one believes others will do and think is the right thing to do. Moreover, I find that individuals positively react to social efficiency increases related to an upper-level (global) public good. The documented increase in contributions to- ward the global good comes at the expense of the contribu- tions to a lower-level (local) public good, with the total contri- bution remaining unvaried. Furthermore, I obtain evidence that this result is robustly replicated in the context of groups primed with a strong sense of national identity and facing a task framed to recall real-world institutions (national and European Union public budgets). Lastly, I document that the presence of a probability of facing significant losses - whether independent or correlated among group members - does not impact contributing behavior in the public good compared to deterministic scenarios. These results, while building on re- cent cutting-edge experimental literature, suggest interesting avenues for new research

Predict-AI-bility of how humans balance self-interest with the interest of others

Generative artificial intelligence holds enormous potential to revolutionize decision-making processes, from everyday to high-stake scenarios. However, as many decisions carry social implications, for AI to be a reliable assistant for decision-making it is crucial that it is able to capture the balance between self-interest and the interest of others. We investigate the ability of three of the most advanced chatbots to predict dictator game decisions across 78 experiments with human participants from 12 countries. We find that only GPT-4 (not Bard nor Bing) correctly captures qualitative behavioral patterns, identifying three major classes of behavior: self-interested, inequity-averse, and fully altruistic. Nonetheless, GPT-4 consistently overestimates other-regarding behavior, inflating the proportion of inequity-averse and fully altruistic participants. This bias has significant implications for AI developers and users

Political Ideology and Generosity Around the Globe

In a world severely put under stress by COVID-19, generosity becomes increasingly essential both when able to transcend local boundaries, building upon universalistic values, and when directed toward more local contexts, such as the native country. This study aims to investigate an underresearched determinant of generosity at these two levels, a factor that captures one’s beliefs, values, and opinions about society: political ideology. We study the donation decisions of more than 46,000 participants from 68 countries in a task with the possibility of donating to a national charity and an international one. We test whether more left-leaning individuals display higher generosity in general (H1) and toward international charities (H2).Wealso examine the association between political ideology and national generosity without hypothesizing any direction. We find that more left-leaning individuals are more likely to donate in general and more likely to be generous internationally. We also observe that more rightleaning individuals are more likely to donate nationally. These results are robust to the inclusion of several controls. In addition, we address a relevant source of cross-country variation, the quality of governance, which is found to have significant informative power in explaining the relationship between political ideology and the different types of generosity. Potential mechanisms underlying the resulting behaviors are discussed

Assessing Large Language Models’ ability to predict how humans balance self-interest and the interest of others

Generative artificial intelligence (AI) holds enormous potential to revolutionize decision-making processes, from everyday to high-stake scenarios. By leveraging generative AI, humans can benefit from data-driven insights and predictions, enhancing their ability to make informed decisions that consider a wide array of factors and potential outcomes. However, as many decisions carry social implications, for AI to be a reliable assistant for decision-making it is crucial that it is able to capture the balance between self-interest and the interest of others. We investigate the ability of three of the most advanced chatbots to predict dictator game decisions across 108 experiments with human participants from 12 countries. We find that only GPT-4 (not Bard nor Bing) correctly captures qualitative behavioral patterns, identifying three major classes of behavior: self-interested, inequity-averse, and fully altruistic. Nonetheless, GPT-4 consistently underestimates self-interest and inequity-aversion, while overestimating altruistic behavior. This bias has significant implications for AI developers and users, as overly optimistic expectations about human altruism may lead to disappointment, frustration, suboptimal decisions in public policy or business contexts, and even social conflict

Language-based game theory in the age of artificial intelligence

This file contains data and analyses for the project with Matjaž Perc under the same title. Abstract: Understanding human behaviour in decision problems and strategic interactions has wide-ranging applications in economics, psychology, and artificial intelli- gence. Game theory offers a robust foundation for this understanding, based on the idea that individuals aim to maximize a utility function. However, the exact factors influencing strategy choices remain elusive. While traditional mod- els try to explain human behaviour as a function of the outcomes of available actions, recent experimental research reveals that linguistic content significantly impacts decision-making, thus prompting a paradigm shift from outcome-based to language-based utility functions. This shift is more urgent than ever, given the advancement of generative AI, which has the potential to support humans in mak- ing critical decisions through language-based interactions. We propose sentiment analysis as a fundamental tool for this shift and take an initial step by analyzing 61 experimental instructions from the dictator game, an economic game captur- ing the balance between self-interest and the interest of others, which is at the core of many social interactions. Our meta-analysis shows that sentiment analy- sis can explain human behaviour beyond economic outcomes. We discuss future research directions. We hope this work sets the stage for a novel game theoretical approach that emphasizes the importance of language in human decisions

Personal norms in the online public good game

This paper shows that personal norms have a prominent role in explaining pro-social contribution in an online public good game. This finding suggests that the role of social norms might be loosened when subjects are distanced and interaction occurs online and in complete anonymity. Moreover, we found no statistically significant difference between the elicited norms and the norms that were elicited in a group of subjects not facing the contribution task, thus ruling out a potential self-justification bias