Dishonesty is a pervasive phenomenon of our society. It occurs when an agent violates certain moral or legal rules to gain a personal advantage. Dishonest practices have extremely detrimental consequences for society, negatively impacting growth, democracy and living conditions. They increase transaction costs, reduce trust, impede the functioning of the political system, and undermines security. Identifying the factors that influence the decision to behave dishonestly is of fundamental importance in order to understand many economic behaviors and promote a virtuous society. DECISION develops a pioneer analysis of the mechanisms, internal and external to the individual, that are involved in the decision to act dishonestly. To achieve this, it adopts a distinctive three-fold strategy. First, it looks at the individual and its internal psychological, cognitive and affective states. After that, it considers the wider context of an organization and the workplace where multiple individuals interact together. Then, it further enlarges the focus by studying how dishonesty varies and spreads in society where the interactions between individuals are regulated and shaped by formal and informal institutions. More specifically, DECISION studies: (i) The emotional and cognitive determinants of dishonest behavior (Task 1); (ii) Which features of an organization contribute to individual and joint dishonesty (Task 2); (iii) How deterrence institutions and social norms impact on intrinsic honesty (Task 3). DECISION is based on a four-year interdisciplinary program which incorporates knowledge from different disciplines, including economics, psychology, political science, and neurosciences. It also embraces a multi-methodological approach involving theory, experiments (in the laboratory, in the field and on-line), the study of psychometric data, and the analysis of real world data. Over the course of these four years, DECISION is expected to generate substantial research advances to the understanding of dishonest behavior and provide important policy recommendations on how to reduce dishonest behavior and minimize its social impact.

Social media raises the possibility of profound social, political, and economic changes, mobilizing large communities to push for disruptive moves. By allowing mass information diffusion at lower cost, social media offers an unprecedented platform to a broad number of agents in the society: from resource-poor grassroots organizations and marginalized politicians to private interest groups and powerful mainstream political parties. In this context, the critical question of who, in the society, is gaining from this major structural transformation in the market for information and influence, calls for deeper empirical investigations. In light of recent public debates about the role of social media in today’s society, DISRUPT proposes to carefully document the use of these platforms by politicians, non-profits, firms, workers and labor unions in advanced and developing economies, and address the multifaceted effects of social media on a rich set of political and economic outcomes (including regulation, employment and regional economic growth). To this aim, DISRUPT mobilizes innovative data collection technics with frontline econometric methodologies to provide a comprehensive empirical analysis addressing the following questions: How does social media affect influence across interest groups with unequal financial capacity? With which effects on the economy? Which politicians use social media in advanced and developing democracies? With which effects on politics, policy and regional economic development?

The aim of this project is to explore the impact of artisanal mines (legal or illegal) on the economic development of Sub-Saharan African countries from 2000 to 2021. This project proposes to collect exhaustive information for all Sub-Saharan African countries on the opening/closing of artisanal mines with GPS coordinates. I make use of satellite image time series and machine learning tool, namely convolutional neural network, to identify precisely the location of artisanal mines. Equipped with this original dataset, I will address the first order question of the local impact of artisanal mining, at a very large scale. Notably, I will study the local impact of artisanal mines on individual income, health, migration and conflict.

This project belongs to the priority “Ethics -Human and Social Sciences” and the topic “Representations, perceptions, attitudes, behavior relative to the epidemic”. Context. To stop the current coronavirus pandemic, the governments of most developed countries are implementing drastic measures to contain populations, in order to create social distancing and limit contagion. Given the unprecedented nature of the current health crisis situation and the scale of political interventions to fight infection, our knowledge of the impact of such social distancing rules on the social preferences of individuals is almost null. Yet, it is crucially important to know whether the pro-sociality of individuals, their willingness to trust others as well as their social norms will be affected by the pandemic and containment measures and if so, in which direction (reinforcement of pro-sociality vs. selfishness). This likely modification of such preferences that are fundamental for life in society could have spillover effects in other areas of economic and social decision-making in the future, beyond the end of the confinement rules and of the pandemic. The potential change in social preferences may affect the way people will respond to future interventions adopted to contain the spread of the virus. Objective. Our objective is to study whether the ongoing coronavirus pandemic (COVID-19) and the massive and drastic public policies of social distancing implemented to curb the spread of the virus (in particular, confinement from March 16, 2020 and possibly curfew in the future if the situation worsens) affect the social preferences of individuals (specifically, their pro-sociality, trust in others, and perception of the social norm regarding the violation of the social distancing rule). The research question is whether the withdrawal generated by confinement, social isolation and fear of contagion will lead individuals to develop more selfish preferences and less trust in others, and weaken the perception of the social norm regarding the violation of the rule. On the other hand, individuals may increase their use of social networks to stay in contact with others, and they may develop compassion for the victims; in this case, their pro-sociality and trust in others may increase and the norm against violators of the rule may become stricter. The net result of social distancing on social preferences is unknown. The research program will be able to determine which changes in social preferences and trust will result from this major and unprecedented health crisis. The results will be also an important test for behavioral economics that admits the malleability of preferences contrary to the standard approach in economics that assumes the stability of preferences. Method. The approach is rooted in behavioral and experimental economics. It consists of eliciting individuals’ social preferences and normative views through incentivized decision making in various games. Precisely, we plan to conduct an online experiment with approximately 350 participants who will have to log in each week from March 18 to June 24 on our website (15 sessions). The composition of each session will be similar. It will include a social value orientation test (SVO test of Murphy et al., 2011), a trust game and a questionnaire which will mainly measure the degree of social distancing with family and friends, and the social norm regarding the violation of the social distancing rule. These various measures seek to identify the degree of pro-sociality of the individuals, their propensity to trust strangers and to reciprocate others’ trust, and their perception of the social norm regarding the violation of the social distancing rule. The repetition of the measurements every week for three months will allow us to measure if social preferences and normative views evolve over time according to the possible hardening and subsequent lifting of the confinement measures.

The neural mechanisms underlying the influences of emotions and motivation on decision making remain poorly understood. New bridges have recently been made between monkey neurophysiology, human neuroimaging, behavior and theoretical models that provide a formal and quantitative account of emotions and decision-making. Yet, there is a need for building an integrative account of emotional and decisional processes, bridging the gap between fundamental computational principles and the brain system level. The current project seeks a better understanding of the psychological and neurobiological basis of the influences of emotions on decision-making to determine the validity of current models of economic choice behavior. It adopts a combination of interdisciplinary and multidisciplinary perspective, combining psychophysics, neuroeconomics, cognitive neuroscience, neurology and multimodal neuroimaging (intracranial recordings in humans and model-based fMRI) to investigate the neural mechanisms of the interactions between emotions and decision making processes, both in single individuals and in social context. This project also seeks to provide a framework that brings together computational and neurobiological findings by highlighting the connections between neuroscience, psychology and economics. Ultimately, the goal is to integrate computational principles underlying choice behavior with those defining brain functions. Such integration will be necessary if we are to ultimately model how emotions influence decision making in health and disease. The specific aims of this project are to characterize: 1) basic neural coding of positive and negative emotions induced by different rewards and punishments and how they are influenced by probability and uncertainty; 2) the influences of positive/negative emotions induced by rewards/punishment on learning to associate neutral cues with rewarding/aversive outcomes; 3) the neural computations underlying cognitive regulation during value-based decision making; 4) the neural mechanisms by which emotion and cognitive control interact. We will investigate the temporal dynamics of the influence of negative emotions on cognitive control to understand how negative emotions reduce cognitive flexibility; 5) the relationship between emotions and cognition by focusing on the role of self-image in prosocial decision-making; 6) differences between choices made by groups and by individuals in isolation, focusing on the specific roles of emotions such as envy, compassion and the motivation to avoid guilt and blame when making decisions that affect others’ welfare and the social pressure to conform to certain norms when one is in a group setting. To address these questions, we will combine four complementary approaches: 1) computational modeling using concepts from neuroeconomics, which is important to specify the relationships between the neural mechanisms, behaviour and the patterns of brain activation observed with model-based neuroimaging; 2) human intra-cranial recordings and fMRI, which is key for a comprehensive understanding of different descriptions levels (local field potentials and BOLD signal); 3) investigation of the interactions between emotions and decision making using a computational model-based fMRI approach; 4) behavioral economics laboratory experiments, some of them including physiological measures (SCR). This combination of approaches will contribute to a fundamental understanding of the neural mechanisms underlying the influences of emotions on decision making in humans. The ground-breaking nature and potential impact of this research is to establish a neurobiological foundation for understanding disorders affecting motivational and decision making processes and for better understanding how the influences of emotions may explain deviations in actual decision-making from equilibrium predictions in economic models.