Our general purpose is to analyze decisions related to mode choice and residential location, with a special emphasis on within-family decision process and on policy implications. Our approach extends the current literature by explicitly recognizing that the decisions of different family members are interrelated and we describe them as the outcome of a within-family bargaining process. On the one hand, this project will provide a new application field to family economics, and on the other hand, urban and transportation economics will benefit from economic literature on bargaining and collective decisions, largely ignored till now, with a few exceptions by some consortium members. In Part 1, we model spouses’ joint mode choice for commuting trips. The interaction is due to the fact that spouses may share a single car, or may carpool. As such, the choice of the car (or of who is driving it if it is shared) is described as the outcome of a bargaining process. In Part 2, we study residential location choices, which are the outcome of a subtle compromise when the two spouses work at different places, extending up to a three-stage nested Logit model. More precisely, we will model tenure choice, residential location choice and workplace choices, in the context of hierarchical nested models, including the (Pareto) weights of each spouse. The research combines stated preferences (using survey and experimental economics data), and revealed preferences (using census data for the Paris-Ile-de-France Region). In Part 3, we investigate the effect of public urban policies on household residential location choices by extending Part 2 models to investigate the effects of (1) capacity constraint (when prices do not clear the market and supply is lower than demand in some places); and (2) credit constraints on household tenure and location choices. Credit constraints either refrain households from buying some apartment or house, or induce them to move far enough from the city center (and often far from their job location) in order to find an affordable housing. These financial constraints are based on household income and other characteristics, and affect household joint residential decisions. The project is coordinated by University of Cergy-Pontoise (UCP), which has established a tradition in transportation and urban economics, and associates ENS-Cachan, with researchers specialized in discrete choice models and public policies, and Ecole Polytechnique, who plays a major role in the management of large data sources, and in data collection. This mix of resources and knowledge is crucial to attain the purpose of this interdisciplinary project. This collaborative project involving scholars in France, Europe, USA and Australia builds on the results of two previous projects coordinated scientifically by ENS-Cachan, for the first one and by UCP, for the second one. (1) In SustainCity, a collaborative PF7 European project (with 12 partners), a European Land Use and Transport Integrated (LUTI) model was developed and applied by UCP to Paris Region. (2) In the French Predit project MobMen, an interactive survey (MIMéTTIC, Mobilité Individuelle, mobilité des Ménages, Tarification des Transports Individuels et Collectifs) was administered. UCP elaborated an innovative protocol and collected individual data (4,000 respondents, including 1,000 couples). This unique dataset provides the information required to build mobility family decision models. Given the innovative dimension of this project, such data based was required for the development of realistic couple decision models. Other data will be used: The French General Population Census, and the data collected with on-line questionnaires and in experimental economics laboratories. The medium and long-term social and economic implications of several policies will be analyzed, such as: tolling, zoning, regulation, provision of infrastructure or provision of social housing.

StratiGraph (Knowledge Graphs for Stratigraphy) aims to transform archaeological and paleontological documentation by developing innovative open collaborative tools that integrate advanced knowledge graphs, artificial intelligence, and 3D field documentation techniques. The project's "Data-Ready" approach enables clean, structured data collection during fieldwork for seamless integration into knowledge graphs without additional curation burden. Key innovations include tools that function effectively in all conditions - a "works-everywhere" solution allowing technology use even where power and connectivity pose challenges. The project will validate these tools through diverse case studies including the Basilica Iulia in Rome, Sarmizegetusa Regia in Romania, St. John Lampadistis Monastery in Cyprus, the Colonia Iulia Ilici Augusta in Spain, the Kraków Spadzista Palaeolithic site, the Santa Olalla site in Spain, and the dataset from Cesarea in Israel. StratiGraph will deliver: -Knowledge graph infrastructure to organize stratigraphic and spatial data -AI-powered tools for creating and enriching annotated bibliographies -Advanced 3D documentation capabilities for recording stratigraphy -Variable connectivity protocols for real-time and seamless offline/online work -Comprehensive training modules and technical support resources The project emphasizes open science principles, with all tools being open-source and data FAIR-compliant. Through integration with the ECCCH, StratiGraph will establish new standards for digital documentation while fostering a skilled, collaborative research community across Europe. This will significantly reduce the time from discovery to publication while improving documentation accuracy and interpretation of our shared cultural heritage. By maintaining exclusive data control and ownership within EU-based institutions, the project supports Europe's strategic autonomy and long-term data governance objectives.

This project tackles the issue of the diversity of opinions in a society. It is grounded in Economics but is at the intersection with several other fields: Psychology, Statistics, Decision Theory, Analytical Philosophy, Social Choice, and Political Science. It goes beyond equating individuals' opinions with their probabilistic beliefs, a standard practice in Economics, and aims at encompassing notions like unawareness, ambiguity, and competence to analyze opinion heterogeneity more broadly. The general questions we will explore are the following ones: - How does diversity of opinions come about and how can it survive? - What is the impact of this diversity on economic arrangements? - How do individuals react when confronted with other's opinions? - How can one aggregate the various opinions to make the best decisions? These questions deserve to be treated both normatively and positively, calling for various analytical approaches (e.g. axiomatic treatment, modelling of markets, etc.) and experimental approaches (from psychophysics and social psychology). From a positive side, we will provide models explaining how individuals exposed to the same information can disagree. This requires to think outside of the usual Bayesian, common prior framework. Getting outside of this framework is also required to provide an understanding of how disagreements can survive in the long run, as we aim to show, contrary to a long-standing view in economics that "irrational" opinions get wiped out of the market. Experimentally, we will focus on how individuals integrate heterogeneous opinions when making decisions and, in particular, how social pressure affects individuals' opinions. We will finally analyze how opinion heterogeneity affects economic arrangements such as bargaining, risk sharing and financial markets, incentive provision, etc. From a normative side, we will concentrate on the many ways different opinions can be aggregated to form a social or group opinion. A particular attention will be given to procedures that allow one to extract a measure of competence or expertise from opinions expressed by members of a society or group. These degrees of competence will then be used to weight the various opinions in order to make the best informed decision for the group. We will provide an experimental assessment of these rules, compared with other rules studied in the literature. We will also go beyond taking opinions as exogenous and will see how correlations (and, possibly, failure to recognize these) among the sources of information accessible to individuals changes the way aggregating procedures like voting work. This study will fit into a more general research question which is how to aggregate "ill-defined" or "biased" opinions. The project's aim is mainly to advance scientific knowledge on these issues. It could lead to define practical ways of aggregating opinions or rankings that could be of interest for the private and public sectors. The team of researchers assembled for this project have expertise in the various fields the project is touching. They have already worked, produced scientific papers published in the best international outlets and organized events together.

Concrete is the world’s second most-used material in construction, after water. It comprises three main ingredients: cement, water and aggregates. The latter constitute 60-70% of concrete’s total volume, representing a heavy environmental burden. Presently, the use of recycled aggregates (RA) in concrete production (reusing their embodied energy and decreasing waste dumping) is still marginal, even though its feasibility has been demonstrated at the research level. The overarching goal of the CIRCLE project is to form an international network of 11 organisations working to tackle the challenges in upscaling recycled aggregate concrete (RAC). The CIRCLE team includes interdisciplinary researchers renowned in this area, which will integrate their expertise towards the project’s overarching goal, following a carefully planned flowchart of activities: enhancing existing knowledge on bio-based self-healing and carbonation of RAC; fostering high-performance RAC; optimizing the quality of RA and RAC, and evaluating the potential of RAC through multi-criteria life cycle assessment; and assessing RAC in full-scale real applications. The intercontinental 342 PM planned in secondments within CIRCLE will be the vehicle for joint research, training and networking, which will benefit the institutions’ staff at all levels of seniority, as well as PhD students. CIRCLE spans European and Asian countries with a massive need for concrete infrastructure development. Given the overwhelming dominance of concrete worldwide as a structural material, the potential environmental, economic and social positive impacts of upscaling RAC are priceless. This has been perceived by authorities and stakeholders and there are ongoing initial efforts in some of the consortium countries to regulate this possibility, which makes CIRCLE very timely in terms of guidelines contribution. Promoting RAC through CIRCLE will foster sustainability and a circular economy, aligned with European and UN policies.

As Internet of Things (IoT) and IoT-Edge-Cloud continuum technologies advance, physical environments are becoming increasingly equipped with sensors, fuelling the development of smart space ecosystems. Massive quantities of data produced by IoT devices revolutionize the way such ecosystems operate via the exploitation of AI models/services. This has led to the emergence of the so-called Artificial Intelligence of Things (AIoT) systems. In general, designing techniques to promote robustness, efficiency and continual operation of AIoT systems requires realistic and trustworthy data at scale. However, such data is not always easy to obtain due to the cost of smart space construction, the inconvenience of long-term device tracking, the sensor/knowledge data gaps in diverse scenarios of a smart space, and the restrictions imposed on sensitive data sharing. Furthermore, an efficient AIoT system operation requires trustworthy AI services, as well as novel approaches for speeding up their inference across the IoT-Edge/Cloud continuum. PANDORA aims to devise and implement a comprehensive framework enabling the delivery of trustworthy datasets of smart space ecosystems, as well as the deployment and green operation of AIoT systems in such spaces. PANDORA spans two phases: (1) prior to AIoT system deployment; (2) post AIoT system deployment and operation. Phase 1 proposes and combines a series of novel techniques such as synthetic data generation, quantification of uncertainties, and data summarization for the delivery of trustworthy datasets, as well as explainable AI and domain-informed model training/testing in smart space ecosystems. Phase 2 defines novel AIaaS and CaaS techniques for the robust, explainable, green and continual operation of AIoT systems deployed in such spaces. The trustworthiness and applicability of the PANDORA framework will be tested through five pilot cases hosting AIoT applications in smart buildings, factories and critical infrastructures.