MonProgramme2022.org is a digital participation platform that allows users to create their own government programs. This platform allows citizens to select and rank more than 100 proposals taken from the government programs of the main candidates for the 2022 presidential election.

Collective Intelligence

MyProgram2022.org is a simple collaborative exercise that allows users to not only build their own government programs, but also identify areas of agreement and disagreement with other participants. The platform presents users with proposals extracted from the candidates' government programs. It asks them to select the proposals they agree with, and then to rank these agreed upon proposals in order of preference. Each participation, even partial, is taken into account in the creation of the collective program and in identifying the divisive proposals.

Our goal

The team's goal is not to influence the 2022 election, but to obtain new knowledge about online participation systems and to better know the empirical limits of social choice theory.

Why we don't recommend which candidate to vote for

The implementation of a voter recommendation system is based on a number of assumptions specific to the election in question and can easily result in biases for or against certain candidates. It is difficult to guarantee that the recommendations provided to the user are impartial. In our research labs, we take the issue of algorithmic bias very seriously, and one of the goals of the MyProgramme2022 platform is to generate data that can help us think about how to build better recommender systems.

About us

The platform was built by a team from the Center for Collective Learning, the flagship interdisciplinary research laboratory of the 3IA Institute of the University of Toulouse (ANITI). The team is led by Cesar A. Hidalgo, a Chilean-American researcher who moved to France in 2020, after leading a research laboratory at the Massachusetts Institute of Technology (MIT) from 2010 to 2019. The platform has was built by Carlos Navarrete, a doctoral candidate under the direction of César Hidalgo, in collaboration with lawyers, computer scientists and mathematicians, including experts in social choice theory from the Institut de Recherche en Informatique de Toulouse (IRIT), the CNRS, the Paris 3IA PRAIRIE institute and the Paris-Dauphine University. Hidalgo and Navarrete have previously worked together on similar platforms, including Chilecracia, a digital participation platform that garnered over 7 million citizen preferences during the 2019 Chilean revolution.

For any question please contact: hello@centerforcollectivelearning.org

Our Team

Junior Researchers

Carlos Navarrete
is a Ph.D. student at ANITI’s Center for Collective Learning and is the main responsible for the development and implementation of the monprogramme2022 software. He has led the development of multiple platforms for digital participation, such as chilecracia.org, lebanocracia.org, colombiacracia.org, georgiacracia.org, asuprioriza.org, and constitutin.cl. He is currently pursuing a Ph.D. on digital democracy at the Center for Collective Learning in Toulouse.

Jingling Zhang
has an undergraduate degree in public administration from the Southwest University of Political Science and Law in Chongqing, China; a Master in European Business Law, from the University of Aix-Marseille in France, and a Master in International Business Management at the Jean Jaurès University in Toulouse.

Nicole Ferrada
is a lawyer and a digital democracy fellow at the Center for Collective Learning. She has worked on several digital democracy projects including chilecracia.org and constitutin.cl. Nicole led the selection and curation of the proposals in monprogramme.

Mariana Macedo
is a postdoctoral at the Center for Collective Learning. Mariana is brazilian and has a PhD in Computer Science at the University of Exeter (UK). Mariana is currently working on data augmentation of civic participation in digital platforms, gender gaps in mobility and science and explainable AI (swarm intelligence).

Rachael Colley
is a PhD student at the University of Toulouse 1 Capitole and a member of the Institut de Recherche en Informatique de Toulouse (IRIT). She is working under the supervision of Umberto Grandi, researching topics such as computational social choice, judgment aggregation and liquid democracy.

Principal Investigators

César A. Hidalgo
leads the Center for Collective Learning and has academic appointments at the Universities of Toulouse, Manchester, and Harvard, and at the Toulouse School of Economics (TSE), the Institute of Advanced Studies of Toulouse (IAST), and IRIT. Prior to moving to Toulouse, he led MIT's Collective Learning group and worked as a research fellow at Harvard's Kennedy School of Government. Hidalgo has led the development of multiple public data distribution and civic participation platforms including the Observatory of Economic Complexity (oec.world), Data USA (datausa.io), Data Mexico (datamexico.org), and Chilecracia (chilecracia.org), among many others. Hidalgo holds a PhD in physics from the University of Notre Dame. He is the author of dozens of research papers and three books. His latest book is How Humans Judge Machines (MIT Press, 2021).

Jérôme Lang
is a CNRS senior scientist and a member of LAMSADE (University Paris-Dauphine, PSL). He holds a PhD in computer science from the University of Toulouse. He works in the field of computational social choice, where he designs and studies mechanisms and algorithms for collective decision making. He is the co-editor of the Handbook of Computational Social Choice and the author or coauthor of around 200 research articles.

Umberto Grandi
is associate professor in computer science at the University of Toulouse 1 Capitole since 2014. He is a member of the Institut de Recherche en Informatique de Toulouse (IRIT), where he co-leads the logic, interaction, language and computation research group. Umberto holds a PhD from the University of Amsterdam. He is a recognised young scholar in the field of computational social choice, regularly publishing in top AI conferences on topics such as judgment aggregation, social choice and social networks, and knowledge representation for collective decisions.


Center for Collective LearningIRITANITICNRSUniversité Paris DauphinePR[AI]RIEUniversité de Toulouse Capitole (UT1)