Essec Business School

As the year draws to a close, it’s time to highlight the ESSEC professors who received major grants in 2025! Five ESSEC faculty members, from four different departments, received funding from CY Initiative and CY Generation this year. CY Initiative is a collaboration between ESSEC Business School and CY Cergy-Paris University, and aims to support researchers from the CY Initiative community and boost scientific excellence. CY Generation aims to support research on sustainability and social change. In 2025, the call for projects supported 26 projects for a total of over 2 million in funding dedicated to research. There are two types of funding: 

Emergence program: For the development of innovative projects by individual researchers 

Horizon program: For large-scale research projects developed by a research center or team, to help these projects grow and support an international outlook 

Read on to see the projects that our professors are working on! 

Diego Delle Donne – Supervised Hyper-rectangular Clustering, CY Générations – EMERGENCE(2025-2027) 

Machine Learning techniques are being widely used to understand large amounts of data. Its main goal is usually to provide a set of rules that can interpret data and serve as predictive models. These rules are not always easily explainable to humans. Hyper-rectangular clustering (HRC), the topic of this project, has been proposed as a model for "explainable clustering", since it is straightforward to describe the clusters by the bounds defining each hyper-rectangle.  In this project we aim at studying HRC problems in order to propose extended formulations (i.e., having an exponential number of variables and branch-and-price procedures for these formulations, with the objective of making progress at solving these problems with optimality. In addition, as it is known that exact approaches usually do not scale up, we aim at developing fast heuristic algorithms which can be able to find good-quality solutions to HRC real-size instances, in reasonable running times. 

About Diego Delle Donne

Diego Delle Donne is Assistant Professor in the department of information systems, data analytics and operations at ESSEC Business School. His main research interests cover topics around combinatorial optimization, applied mathematics, linear integer programming and graph theory. He now works on a project focused on the development of optimization algorithms for a Last Mile Delivery problem which tries to profit from the public transport capacity surplus on non-peak hours for its use in freight delivery within the city. In particular, the project aims to reduce CO2 emissions and traffic in big cities. 

Shanming Liu – Optimizing incentives for Human-AI Creativity, CY Initiative – EMERGENCE(2025-2027)

This project tackles the challenge of motivating employees to collaborate effectively with artificial intelligence (AI) in creative tasks by redesigning incentive systems. While AI excels in handling aspects like feasibility, relevance, usefulness, and presentation quality, generating truly novel ideas remains a human strength. Traditional incentive systems that reward creativity equally across all dimensions do not encourage employees to focus on novelty—the area where their contributions are most valuable.

The proposed solution restructures incentives to emphasize the novelty dimension. By primarily rewarding originality, employees are motivated to invest effort in generating innovative ideas while utilizing AI for other tasks. This addresses two main challenges: over-reliance on AI, where employees let AI handle tasks entirely and neglect their own creativity; and under-reliance on AI, where employees distrust AI and unnecessarily handle tasks better suited for AI. An experimental study will test this approach. The study aims to assess whether emphasizing novelty effectively redirects human effort, enhances creative outputs, and maintains quality in other areas. By treating creativity as a multitask setting and aligning incentives to focus on novelty, the project seeks to optimize human-AI collaboration and enhance overall creative performance. 

About Shanming Liu 

Shanming Liu is Assistant Professor in Accounting and Management Control at ESSEC Business School. His research focuses on management control systems, creative idea generation and selection, and creativity in entrepreneurial teams. He uses laboratory and field experiments as well as qualitative methods to study these topics.

Sofia Ramos – Behavioral Insights in PRIIPs Decision Making, CY Initiative – EMERGENCE(2025-2027)

Investor decision-making often deviates from optimal strategies due to behavioral biases and cognitive limitations, such as the disposition effect, overconfidence, limited attention, and information processing errors. These factors lead to suboptimal financial outcomes, which can significantly erode long-term returns. Regulatory measures like the UCITS KIID and PRIIPs KID aim to enhance transparency and empower investors, yet aligning investor behavior with cost-efficient decision-making remains a challenge. This research examines the impact of the PRIIPs KID’s innovative disclosure features on investor behavior. The study explores how investors allocate cognitive resources, respond to scenarios, and process detailed cost information, while also examining the roles of financial literacy and skewness preferences. Using real-world PRIIPs KIDs in a controlled experimental setting, participants will make investment decisions, enabling the study to analyze behavioral factors driving choices. Testable hypotheses address key challenges, including cognitive overload and limited attention.

The research contributes to behavioral finance and regulatory policy by evaluating the effectiveness of enhanced disclosure formats and advancing understanding of how document design influences decision-making. It also sheds light on the effects of document complexity, cognitive limitations, and biases, offering actionable insights for policymakers and financial institutions to optimize disclosures, enhance investor outcomes, and support better decision-making.

About Sofia Ramos 

Sofia Ramos is Associate Professor of Finance at ESSEC Business School. She is co-chair professor of the chair “Shaping the Future of Finance” and academic advisor of the ESSEC Amundi-Chair in Asset & Risk Management.

Angela Sutan – Revise Intergenerational Values for Sustainability, CY Générations – HORIZON (2025-2028)

This project investigates the design and survival of systemic S-frame incentives, to secure intergenerational adoption of sustainable practices by organizations. We implement an interdisciplinary approach to examine the way in which institutions are transformed, while maintaining a sense of intra and intergenerational connection, in which values such as cooperation, altruism, diversity and equity are supported through legitimate incentives, and transform into organizational standards. Protecting natural resources, but also diversity of social capital, as well as equality for future generations (which we all consider as “public goods”) are imperatives of sustainability and rationales for costly societal efforts today. We believe that by using appropriate visualizations, data physicalization (such as materialized numbers and graphs about the quality of the air, the state of inequalities, the diversity standards or a mapping of CSR practices...) and simulations of the many states of the world, we can design controlled experiments in which intra and intergenerational links can be considered together, and perceived globally, which will be one of the major methodological novelty of our project. Exploring the use of better visualizations about norms, regulations, routines, institution design adoption, their diffusion, evolution and, at the end, survival and improvement, will help overcoming memory shortness and incomplete backward and forward perspective and data availability by mapping, in an intuitive way, past behavior and data, and making it salient. This design will allow strategic computations about joint norms adoption and modelization of their interactions. 

About Angela Sutan

Angela Sutan is a professor (HDR) of sustainable development in the Department of Law, Political Science and Society at ESSEC Business School. She serves as the Scientific Director of the ESSEC Experimental Lab and Academic Director of the Executive Master in Strategies for Sustainability (EMS²). Her research primarily uses behavioral and experimental economics to study sustainability, prosocial and antisocial behavior, and level-k reasoning. 

Emiliano Traversi – Learning to Optimize: A Sustainable Approach, CY Initiative – HORIZON(2025-2027)

The project aims to develop a novel framework that integrates machine learning (ML) with classical optimization to solve complex problems in real-time within decentralized environments. It addresses the trade-off between solution exactness and speed, crucial for real-time applications where traditional exact methods are computationally expensive and time-intensive. Using supervised and reinforcement learning, the framework approximates optimal solutions by learning from previously solved problems, enabling efficient problem-solving under tight time constraints. A significant challenge tackled by the project is ensuring privacy in decentralized systems, critical for fields like healthcare, finance, and supply chain management. Sensitive data often cannot be centralized due to privacy concerns. The proposed decentralized approach allows subproblems to be solved locally on different network nodes while ensuring data privacy and enabling scalability. Splitting problems into smaller, parallelizable subproblems significantly reduces overall solution time. The framework leverages decomposition techniques like column generation and Benders decomposition, breaking large problems into a master problem and smaller, manageable subproblems. In a decentralized setup, each node solves subproblems independently, using ML-enhanced optimization. Machine learning models, trained on historical optimization data, predict decision variables or constraints, delivering near-optimal solutions faster than solving subproblems from scratch. By integrating ML with decomposition, the system achieves faster problem-solving and adapts to new instances over time. Continuous learning ensures improved efficiency, eliminating the need for exhaustive search algorithms and making the framework ideal for real-time decision-making. This approach combines speed, scalability, and privacy, providing a robust solution for large-scale optimization challenges.

About Emiliano Traversi 

Emiliano Traversi is an associate professor in the Department of Information Systems, Data Analytics and Operations at ESSEC. His research areas include mathematical optimization, decomposition methods and machine learning.

Projects are listed in alphabetical order by professor surname.