"Co-construction" in deliberative democracy: lessons from the French Citizens' Convention for Climate.

Launched in 2019, the French Citizens' Convention for Climate (CCC) tasked 150 randomly chosen citizens with proposing fair and effective measures to fight climate change. This was to be fulfilled through an "innovative co-construction procedure", involving some unspecified external input alongside that from the citizens. Did inputs from the steering bodies undermine the citizens' accountability for the output? Did co-construction help the output resonate with the general public, as is expected from a citizens' assembly? To answer these questions, we build on our unique experience in observing the CCC proceedings and documenting them with qualitative and quantitative data. We find that the steering bodies' input, albeit significant, did not impair the citizens' agency, creativity, and freedom of choice. While succeeding in creating consensus among the citizens who were involved, this co-constructive approach, however, failed to generate significant support among the broader public. These results call for a strengthening of the commitment structure that determines how follow-up on the proposals from a citizens' assembly should be conducted.

A modelling framework based on MDP to coordinate farmers' disease control decisions at a regional scale.

The effectiveness of infectious disease control depends on the ability of health managers to act in a coordinated way. However, with regards to non-notifiable animal diseases, farmers individually decide whether or not to implement control measures, leading to positive and negative externalities for connected farms and possibly impairing disease control at a regional scale. Our objective was to facilitate the identification of optimal incentive schemes at a collective level, adaptive to the epidemiological situation, and minimizing the economic costs due to a disease and its control. We proposed a modelling framework based on Markov Decision Processes (MDP) to identify effective strategies to control PorcineReproductive andRespiratorySyndrome (PRRS), a worldwide endemicinfectiousdisease thatsignificantly impactspig farmproductivity. Using a stochastic discrete-time compartmental model representing PRRS virus spread and control within a group of pig herds, we defined the associated MDP. Using a decision-tree framework, we translated the optimal policy into a limited number of rules providing actions to be performed per 6-month time-step according to the observed system state. We evaluated the effect of varying costs and transition probabilities on optimal policy and epidemiological results. We finally identifiedan adaptive policy that gave the best net financial benefit. The proposed framework is a tool for decision support as it allows decision-makers to identify the optimal policy and to assess its robustness to variations in the values of parameters representing an impact of incentives on farmers' decisions.

Toward a transnational history of the social sciences.

Historical accounts of the social sciences have too often accepted local or national institutions as a self-evident framework of analysis, instead of considering them as being embedded in transnational relations of various kinds. Evolving patterns of transnational mobility and exchange cut through the neat distinction between the local, the national, and the inter-national, and thus represent an essential component in the dynamics of the social sciences, as well as a fruitful perspective for rethinking their historical development. In this programmatic outline, it is argued that a transnational history of the social sciences may be fruitfully understood on the basis of three general mechanisms, which have structured the transnational flows of people and ideas in decisive ways: (a) the functioning of international scholarly institutions, (b) the transnational mobility of scholars, and (c) the politics of trans-national exchange of nonacademic institutions. The article subsequently examines and illustrates each of these mechanisms.

Automated medical diagnosis with fuzzy stochastic models: monitoring chronic diseases.

As the world population ages, the patients per physician ratio keeps on increasing. This is even more important in the domain of chronic pathologies where people are usually monitored for years and need regular consultations. To address this problem, we propose an automated system to monitor a patient population, detecting anomalies in instantaneous data and in their temporal evolution, so that it could alert physicians. By handling the population of healthy patients autonomously and by drawing the physicians' attention to the patients-at-risk, the system allows physicians to spend comparatively more time with patients who need their services. In such a system, the interaction between the patients, the diagnosis module, and the physicians is very important. We have based this system on a combination of stochastic models, fuzzy filters, and strong medical semantics. We particularly focused on a particular tele-medicine application: the Diatelic Project. Its objective is to monitor chronic kidney-insufficient patients and to detect hydration troubles. During two years, physicians from the ALTIR have conducted a prospective randomized study of the system. This experiment clearly shows that the proposed system is really beneficial to the patients' health.