Courses Taught

Academic Year 2026/27

Sciences Po – Campus Paris 

• Digital and AI-related challenges at the crossroads of Culture, Economics and Politics  (24 hours,  145 students, FAC - formation académique commune des Masters, 4 ECTS, in English)
  Links:  Syllabus

Short description
The functioning of contemporary societies is increasingly mediated by digital technologies and sociotechnical ecosystems, including online platforms such as social media, knowledge and data infrastructures such as wikis, and algorithmic systems, like search engines and AI chatbots. These technologies are increasingly central to political, cultural, and economic processes, permeating our daily lives and work activities, as well as shaping related societal issues. 

By reshaping our affordances, attention dynamics, expectations, and behaviours, digital ecosystems and related innovations, such as AI chatbots, can induce profound transformations that are intertwined across four levels:  (i) the technology ecosystem level, which serves as a support and binding force, shaping affordances and constraints; (ii) the societal level, where individuals engage in debates and co-construct arguments and narratives related to digital technologies and their usages; (iii) the market and organizational level, where households, firms, and investors explore emerging opportunities and threats related to digital innovation, evaluate the change it brings and seek to anticipate its impact, adapting their strategies based on expectations; (iv) the geopolitical level, where states, public institutions and international organizations propose, deliberate on, and implement policy and (de)regulation, to address the challenges and opportunities resulting from digital technology and innovation.

This creates an urgent need to develop multi-domain, multi-level strategies to address emerging governance and regulatory challenges. Accordingly, developing the skills to assess, map, and govern these multi-level transformations is indispensable for navigating the opportunities and challenges of living and working in digitally engaged worlds.  Therefore, whether planning a career in public agencies, international organizations, NGOs, media companies, consultancy, technology, finance, or the creative industries, decision makers of the twenty-first century must be equipped to unravel digital transformations and their impact on liberal democracy and its institutions (i.e., markets, states, and media).
The course aims to equip Master’s students at Sciences Po with the critical stance, the background, and the sociotechnical savoir-faire necessary to assess and respond to ongoing multi-level digital transformations and related challenges. Itƒ will empower students to effectively navigate these transformations and their discoursivization, like those related to generative AI, covering technical and societal aspects of cyberspace and digital innovation. 

Throughout the course, students will learn to map and understand how these transformations (re)define affordances, beliefs, behaviors, and exchanges, across multiple domains, ultimately reshaping at the same time our cultural, economic, and political systems, as well as the ways we engage within them. 

Issues discussed during the lectures will include:

• The socio-history and foundations of AI and digital ecosystems | Origins, technological milestones, and developments of the World Wide Web and of Artificial Intelligence (expert systems, ML, etc.); from digital ecosystems to digital communities and cultures and back again.

• Online identities and their mobilization in the Political and Economic domains | Online identity expression and identity-based mobilization; partisan consumerism; digital influencers; big tech ownership and the new faces of digital corporatism; artificial sociality and the mobilizing power of social bots.

• Web economics and the competition for online attention | The attention economy; online consumer behaviour and marketplaces; calculative practices and algorithmic management; the gig economy and its revenue sharing models; search engines, social media and online advertisement; social media and fintech; the future of work at the time of generative AI.

• Web politics and the new (digital) public sphere | Online news production and consumption (heuristics and biases); digitally-mediated collective concerns and risk perceptions; conflicting event narratives; online moderation and fake news; algorithmic biases and polarization; Civic Tech. and AI-supported deliberation.

• Creativity, sharing, collaboration and grassroots movements on the Web | Digital (and AI) tools for co-creation; prosumerism, online sharing and (co)ownership models; grassroots initiatives; citizen science and the web; Internet activism and artivism.

• Accountability, regulation and governance of digital platforms and AI technologies | Regulation of VLOPs, online privacy & digital surveillance, auditing algorithms, platform T&C and users’ rights and freedoms; introduction to EU’s normative framework: the AI Act and Digital Services and Market Acts.

For each transformation, we will discuss its implications at the four aforementioned levels (i.e., technological, societal, markets and organizations, and geopolitical); highlighting how and to which degree these levels interact in the context of the issue at hand. In addition, the course will highlight how narratives circulating through media mediate these relationships and serve as interfaces of the cultural, economic, and political domains, acting as spillover channels between them. Finally, we will address why the challenges these transformations bring are best analyzed and governed as systemic risks, which are difficult to isolate and manage without a response framework that can integrate various domains, considering the needs of  multiple stakeholder groups.  Through in-class lectures, case studies and group projects, students will decode and (re)code these challenges using theoretical insights and approaches drawn from behavioral sciences, sociology, political science, social psychology, media studies, and computer science. By the end of the course, students will be able to analyze complex digital phenomena and propose informed responses to the emerging challenges faced by digital societies.

This course was designed to contribute to the development of the competencies identified in the common academic framework of the Master’s programmes. In particular, students will be encouraged to adopt a rigorous research-oriented approach by formulating relevant questions, engaging critically with news, literature and empirical evidence, and selecting appropriate analytical methods for their explorations. The course also seeks to emphasize  the importance of combining perspectives from different disciplines and types of evidence in order to assess complex technological and societal phenomena. Throughout the course and projects students will develop their ability to map and compare the perspectives and interests of different actors, critically assess alternative interpretations, and formulate well-supported arguments. Particular attention will also be given to prospective reasoning as students will be asked to consider possible future developments of digital transformations, identify their economic, political, social, and cultural implications, and reflect on potential responses and courses of action.

• AlgoLab (24 hours, 10-14 students, 4 ECTS, in French)

Short description
AlgoLab is a year-long project-based course co-developed within the École d'affaires publiques of Sciences Po, by Soizic Penicaud, Simon Chignard, Léa Douhard and myself,  in which students learn to investigate algorithmic systems as technical, social and institutional objects.

Algorithms increasingly shape how public organizations make decisions, allocate attention, prioritize actions and interact with citizens; and understanding these systems requires more than examining a piece of code of a recommender system or the system prompt of an AI model. It requires looking at the data on which algorithms rely, the choices embedded in their design, the organizations and professionals that use them, and the broader public-policy and societal objectives they serve.
AlgoLab places students directly inside these questions. Working in small groups of 5–7 students, and in close collaboration with a partner organization, students conduct an in-depth investigation of a real algorithmic system. They are supported by Sciences Po faculty and researchers while engaging directly with the teams who design, govern and use the system. The objective is to develop a posture of inquiry and mutual learning rather than to approach the organization with a predefined assessment framework.
The course guides students through three stages of investigation:

1. Understand the system in context | Students begin by immersing themselves in the partner's institutional environment. They identify the purpose of the algorithmic system, the problems it is intended to address, the actors involved in its design and use, and the people or groups affected by it.

Through background research, stakeholder mapping and initial interviews, students formulate questions and hypotheses that will guide their investigation. Rather than starting from the algorithm itself, they first ask: What is this system trying to achieve? Who defines those objectives? How does it fit into existing organizational practices and public or professional responsibilities?

2. Open the algorithmic “black box” | Students then investigate the system from multiple complementary perspectives. Depending on the project, they may examine:

• Code and models: how the system works technically and which design choices have been made;

• Data and indicators: what information enters the system, how it is categorized and measured, and what may be absent;

• Rules and procedures: how algorithmic outputs enter human decision-making processes;

• Uses and organizational practices: how professionals interpret, accept, modify or ignore algorithmic recommendations;

• Outcomes and trade-offs: what the system optimizes for, where tensions arise, and what its limits or unintended consequences may be.

The Radio France project, for example, will allow students to explore tensions between engagement and content diversity in a public-service recommendation system, while the Ministères sociaux project will examine how data-driven indicators can contribute to prioritizing labour inspections.

Students therefore combine quantitative and qualitative methods: analysis of data, code and technical documentation alongside interviews, observations and analysis of institutional processes.

3. Make the system intelligible and open to discussion | Finally, students synthesize what they have learned. They document the system's key choices, assumptions, limitations and areas of controversy and develop recommendations where appropriate.

A central challenge is translation: making a complex algorithmic system understandable to audiences beyond the teams that built it. Students may produce reports, visualizations, data analyses, podcasts or other formats aimed at the partner organization, policymakers, researchers, civil society or the wider public. By the end of AlgoLab, students will have learned not simply how an algorithm works, but how to ask the broader and more consequential questions surrounding it: What problem does it define? What choices does it encode? How does it redistribute decisions between humans and machines? How is it used in practice? And how can its workings and consequences be made visible, debatable and accountable?

AlgoLab thus provides students with a practical methodology for critically investigating algorithmic systems in the institutions where they actually operate.

Sciences Po – Campus Menton

• Culture et enjeux du numérique  (32 classroom hours : 2 courses x 16 hours each, 45 students, Collège Universitaire, 3 ECTS, in English)
  Links:  syllabus

Short description
Whether you plan to work in an international organization, a government body, an NGO, a media company, a technology firm, or an academic institution, a deep understanding of digital culture and its implications is important for navigating the contemporary world. This course will teach students to navigate and map the cyberspace and its dynamics, by examining diverse aspects of the digital world through a blend of pop culture, technical foundations, socio-technical controversies, and their socio-political implications. 
The functioning of contemporary societies is increasingly mediated by digital technologies and sociotechnical artifacts, including online platforms, such as social media, and algorithmic systems, such as search engines and AI chatbots. These technologies have become central to cultural and socio-political processes, permeating our everyday lives and practices while also shaping the societal issues and systemic risks that emerge around them.

Particular attention will be devoted to the growing role of generative Artificial Intelligence (AI) in reshaping work, the public sphere, as well as socio-political processes, such as collective mobilization. Topics discussed during the lectures will include:

• The socio-history of AI and digital ecosystems

• Web cultures, cooperation, and mobilization in (hybrid) digital communities

• Politics and the digital public sphere in the age of generative AI

• Systemic risks and public controversies related to digital platforms and AI 

For each of the above topics, we will examine how these transformations unfold jointly at the technological, socio-political and cultural levels, using the evaluation and governance of systemic risks as a cross-cutting perspective. Particular attention will be paid to questions of access and accountability, as well as to controversies surrounding the concentration of power through control over data, proprietary algorithms, computational resources, and digital infrastructures.

The aim is to understand how digital ecosystems, generative AI, and digital cultures increasingly co-evolve, shaping how individuals and groups form expectations, make decisions, coordinate their behavior, and express concerns about socio-technical systems and their consequences for everyday life.

Academic Year 2025/26

Sciences Po – Campus Paris 

• Digital and AI-related challenges at the crossroads of Culture, Economics and Politics  (24 hours,  130 students, FAC - formation académique commune des Masters, 4 ECTS, in English)
  Links:  Syllabus

Short description
The course aims to equip Master’s students at Sciences Po with the critical stance, the background, and the sociotechnical savoir-faire necessary to assess and respond to ongoing multi-level digital transformations and related challenges. It will empower students to effectively navigate these transformations and their discoursivization, like those related to generative AI, covering technical and societal aspects of cyberspace and digital innovation.
Throughout the course, students will learn to map and understand how these transformations (re)define affordances, beliefs, behaviors, and exchanges, across multiple domains, ultimately reshaping at the same time our cultural, economic, and political systems, as well as the ways we engage within them.
Issues discussed during the lectures will include:

• The socio-history and foundations of AI and digital ecosystems
  Origins, technological milestones, and developments of the World Wide Web and of Artificial Intelligence (expert systems, ML, etc.); from digital ecosystems to digital communities and cultures and back again.

• Online identities and their mobilization in the Political and Economic domains
  Online identity expression and identity-based mobilization; partisan consumerism; digital influencers; big tech ownership and the new faces of digital corporatism; artificial sociality and the mobilizing power of social bots.

• Web economics and the competition for online attention
  The attention economy; online consumer behaviour and marketplaces; calculative practices and algorithmic management; the gig economy and its revenue sharing models; search engines, social media and online advertisement; social media and fintech; the future of work at the time of generative AI.

• Web politics and the new (digital) public sphere
  Online news production and consumption (heuristics and biases); digitally-mediated collective concerns and risk perceptions; conflicting event narratives; online moderation and fake news; algorithmic biases and polarization; Civic Tech. and AI-supported deliberation.

• Creativity, sharing, collaboration and grassroots movements on the Web
  Digital (and AI) tools for co-creation; prosumerism, online sharing and (co)ownership models; grassroots initiatives; citizen science and the web; Internet activism and artivism.

• Accountability, regulation and governance of digital platforms and AI technologies
  regulation of VLOPs, online privacy & digital surveillance, auditing algorithms, platform T&C and users’ rights and freedoms; introduction to EU’s normative framework: the AI Act and Digital Services and Market Acts.

For each transformation, we will discuss its implications at the four aforementioned levels (i.e., technological, societal, markets and organizations, and geopolitical); highlighting how and to which degree these levels interact in the context of the issue at hand. In addition, the course will highlight how narratives circulating through media mediate these relationships and serve as interfaces of the cultural, economic, and political domains, acting as spillover channels between them. Finally, we will address why the challenges these transformations bring are best analyzed and governed as systemic risks, which are difficult to isolate and manage without a response framework that can integrate various domains, considering the needs of  multiple stakeholder groups. 

Academic Year 2024/25

Sciences Po – Campus Reims

• Culture et enjeux du numérique  (32 classroom hours: 2 courses x 16 hours each, 50 students, Collège Universitaire, 3 ECTS, in English)
  Links:  Syllabus 1    Syllabus 2 

Sciences Po – Campus Nancy

• Introduction to Digital Culture (15 hours, 25 students, Collège Universitaire, 3 ECTS, in English)
  Links: Syllabus 

Short description

These courses aim to equip students with the skills, methods, and tools necessary to appraise and understand how digital culture and technology (re)define our affordances, interactions, behaviors, and exchanges, reshaping our social, economic and political systems. Whether one plans to work in an international organization, a government body, an NGOs, a media company, a tech corp., or an academic institution, a deep understanding of digital culture and its implications is crucial for navigating the contemporary world.

Designed to provide students with key instruments to explore and map the cyberspace and its dynamics, the course covers diverse aspects of the digital world through a blend of its technical foundations, controversies, pop culture, and socio-economic implications. Topics discussed during the lectures include:

• Internet and the World Wide Web: foundations, design, protocols and architecture;

• The online sharing economy: from prosumers and cocreation to open software, wikis and the semantic web;

• Online social media as privatized agoras: Impacts on democratic processes;

• From the wisdom of the crowd to online herd behaviors and biases;

• Digital mobilization, activism and participatory initiatives;

• Auditing algorithms and machine-mediated interactions in the age of generative AI;

• Privacy, digital surveillance and the "control society";

For each of the above topics, we discuss its impact for public governance and markets, exploring together how digital culture increasingly shapes our consumption and investment decisions, government policies, identities, as well as other major contemporary socio-economic transformations.

Throughout the course, students learn to decode and (re)code digital challenges using interdisciplinary approaches grounded in media studies, sociology, political science, social psychology, behavioral economics, and computer science.

Venice School of Management
Academic Year 2023/24

• Research Methods (30 hours, Master in International Management, 70 students, 6 ECTS, in English)
  Links: Syllabus

•  Research Methods (30 hours, Master in Innovation and Marketing, 80 students, 6 ECTS, in English)
  Links: Syllabus

Academic Year 2022/23

• Research Methods (30 hours, Master in Management, 30 students, 6 ECTS, in English)
  Links: Syllabus

Short description

This course introduces Master's students to the foundations and practices of research in the socio-economic sciences. It aims to develop students’ abilities in research conceptualization, academic writing, and analytical understanding, while enriching their critical thinking and methodological skills.

Students are introduced to research design, data collection and curation techniques, as well as qualitative and quantitative analysis and modeling methods. The course offers an applied understanding of what it means to conduct research in the fields of management and socio-economic sciences. Through case studies, interactive hands-on sessions, and group projects, students face real-world challenges and decisions encountered in the research process. Key topics include:

• The research process in socio-economic sciences: what, how, when, who, where, and why;

• Paradigms and theoretical approaches to studying markets and socio-economic transformations;

• Research philosophy and approaches to theory development;

• Literature exploration, critical reading, and academic review;

• Research design and project planning;

• Data sources, data collection strategies, and curation techniques;

• API querying, web data crawling and scraping;

• Data exploration, visualization, and descriptive statistics;

• Research methodologies: from conceptual design to practical implementation;

• Qualitative methods and modeling techniques;

• Quantitative methods and statistical modeling;

• Inference-making and hypothesis testing;

• Evaluating reliability, validity, and generalizability of findings;

• Academic writing and dissertation presentation;

• Ethics, privacy, intellectual property, and the societal impact of research;

Designed as a comprehensive primer, this course equips students with the conceptual frameworks and practical tools needed to formulate, conduct, and communicate original research. It is particularly suited for those preparing thesis work or aiming for careers in research-intensive roles across academia, industry, or policy sectors. The course is tailored to the specific focus of each Master's curriculum, particularly in the selection of case studies and the emphasis placed on qualitatice and quantitiative methodological approaches most relevant to the program's disciplinary orientation.

Department of Economics

Academic Year 2022/23

• Behavioural Economics (30 hours, 25 students, Master in Economics, Finance and Sustainability, 6 ECTS, in English)
  Links: Syllabus

From Academic Year 2018/19 to Academic Year 2022/23

• Behavioural Economics (150 classroom hours: 5 courses x 30 hours each, 125 students, Master in QEM and Finance, 6 ECTS, in English)
  Links: Syllabus

Short description

This course introduces Master's students to the core conceptual, modeling, experimental, and empirical aspects of contemporary research in behavioural economics. It is designed to complement students’ previous knowledge of neoclassical microeconomics by highlighting key theoretical and methodological differences, particularly in terms of assumptions, modeling strategies, and behavioural predictions.

Throughout the course, behavioural theories will be systematically compared with their neoclassical counterparts, focusing on how they address empirically and experimentally observed violations of expected utility theory. Emphasis is placed on the contributions of behavioural economics to understanding decision-making under risk, strategic interaction, and bounded rationality.

Students will explore how behavioural models draw on insights from psychology, neuroscience, and other social and cognitive sciences to offer more realistic accounts of human behaviour. The course covers the following core topics:

• Behavioural models of decision-making under risk

• Human sociality and other-regarding preferences

• Behavioural time discounting theories

• Levels of thinking and strategic behaviour

• Reinforcement learning and belief-based learning

• Emotions, cognition, and economic behaviour

• Bounded rationality in financial markets

• Introduction to neuroeconomics

In addition to these foundational topics, the course explores two advanced themes:

• Risk and uncertainty perception, including the phenomenon of social amplification

• Causal and associative reasoning in mental models and expectation formation

These themes are discussed both during lectures and in dedicated weekly atelier sessions, where students engage with the instructor and research group to deepen their understanding through applied discussion and collaborative analysis.