Étienne Borocco (Commission de Régulation de l'Énergie) Titre: Etude sur la sensibilité du prix de l’électricité aux publications d’indisponibilités des moyens de production en France Résumé:  La présente étude porte sur la sensibilité du prix des marchés de court terme de l’électricité français aux publications des informations relatives aux indisponibilités des moyens de production en France effectuées en application du règlement REMIT. La méthodologie d’analyse retenue dans cette étude repose sur des méthodes statistiques correspondant à différentes régressions linéaires estimant l’effet d’une publication, relative à l’indisponibilité fortuite ou planifiée d’une centrale de production en France, sur le prix de gros de l’électricité français, entre la clôture de l’enchère sur le marché day-ahead et le moment de la dernière transaction effectuée sur le marché infra-journalier français pour le produit horaire concerné. Les résultats de l’étude montrent que l’effet d’1 GW supplémentaire d’indisponibilité annoncée sur l’écart entre le prix infra-journalier et le prix day-ahead est estimé entre 0 et 2,3 euros/MWh, ce qui pourrait être considéré comme peu « sensible » au sens du règlement REMIT. https://www.cre.fr/Actualites/la-cre-publie-une-etude-sur-la-sensibilite-du-prix-du-marche-de-gros-de-l-electricite-aux-publications-d-informations-relatives-aux-indisponibilite

Pierre Lavigne (Institut Louis Bachelier) Titre: Gradient conditionnel généralisé pour les jeux à champ moyen potentiels    (A joint work with Laurent Pfeiffer) Résumé:  Nous considérons des jeux à champ moyen potentiels dans un cadre EDP et nous présentons une méthode numérique pour les résoudre appelée "gradient conditionnel généralisé".Cette présentation est construite en trois parties :1) Dans une première partie nous introduisons et présentons les jeux à champ moyen avec interaction par les distributions et les contrôles.2) Dans une seconde partie nous présentons un algorithme appelé "gradient conditionnel généralisé" dans un cadre abstrait que nous appliquons pour résoudre ces jeux à champ moyen potentiels. Nous établissons un lien avec l'algorithme de "fictitious play" et nous présentons des garanties théoriques de convergence pour les variables du problème, le coût potentiel et l'exploitabilité.3) Une dernière partie est consacrée à la présentation de deux illustrations numériques : un model de congestion et un model de compétition à la Cournot.Nous présentons 4 règles d'apprentissage et comparons leurs performances empiriques. Download slides

Annika Kemper (Bielefeld University) Titre: Linear Quadratic Principal Multi-Agent Incentive Problems with Applications to Development of Renewable Energy    (A joint work with R. Aïd, H. Farhat, and N. Touzi)   Abstract:In this work we provide a class of tractable Principal-Agent problems. We follow the resolution methodology of Cvitanić et al. (2018) in a Linear Quadratic setting with exponential utilities. We tackle then the incentives for development of renewable energy both in a monopolistic and competitive setting as examples of applications. A numerical study highlights how to avoid carbon emissions and how competition affects incentives. Download slides  

Huyên Pham (LPSM, Université de Paris) Titre: Optimal bidding strategies  for digital advertising with social interactions   A joint work with Médéric Motte (LPSM).   Abstract: With the emergence of new online channels and information technology, digital advertising  tends to substitute more and more to traditional advertising by offering the opportunity to companies to target  the consumers/users that  are potentially interested by their products or services. We introduce a continuous time model  for the study of optimal bidding strategies associated to different types of advertising, namely, commercial advertising for triggering purchases or subscriptions, and social marketing for alerting population  about unhealthy behaviours (anti-drug, vaccination, road-safety campaigns). Our framework encodes users online behaviours via their web-browsing at random times, social interactions in a large population of users, and the targeted advertising auction mechanism widely used on Internet. We address the attribution problem of how to efficiently diffuse advertising information by means of digital channels in order to generate conversion. Our main results are to provide semi-explicit formulas for the optimal value and bidding policy.  We show sensitivity properties of the solution with respect to model parameters, and analyse how the different sources  of digital information  accessible to users including  the social interactions affect the optimal bid  for advertising auctions. We also study how to efficiently combine targeted advertising and non-targeted advertising mechanisms.   Download slides

Maria Flora (CREST, ENSAE) & Peter Tankov (CREST, ENSAE) Titre: Green investment and asset stranding under transition scenario uncertainty Abstract: We develop a real-options approach to evaluate energy assets and potential investment projects under transition scenario uncertainty. Dynamic scenario uncertainty is modelled by assuming that the economic agent acquires the information about the scenario progressively by observing a signal. The problem of valuing an investment is formulated as an American option pricing problem, where the optimal exercise time corresponds to the time of entering into a potential investment project or the time of selling a potentially stranded asset. To illustrate our approach, we apply representative scenarios from different integrated assessment models to the examples of a coal-fired power plant without Carbon Capture and Storage (CCS) and potential investment into a biomass power plant with CCS.   Download slides    

Speaker: Ahmed Kebaier (LaMME, Université d'Evry) Titre: Quantifying uncertainty with a derivative tracking SDE model and application to wind power forecast data Abstract: We develop a data-driven methodology based on parametric Itô’s Stochastic Differential Equations (SDEs) to capture the real asymmetric dynamics of forecast errors, including the uncertainty of the forecast at time zero. Our SDE framework features time-derivative tracking of the forecast, time-varying mean-reversion parameter, and an improved state-dependent diffusion term. Proofs of the existence, strong uniqueness, and boundedness of the SDE solutions are shown by imposing conditions on the time-varying mean-reversion parameter. We develop the structure of the drift term based on sound mathematical theory. A truncation procedure regularizes the prediction function to ensure that the trajectories do not reach the boundaries almost surely in a finite time. Inference based on approximate likelihood, constructed through the moment-matching technique both in the original forecast error space and in the Lamperti space, is performed through numerical optimization procedures. We propose a fixed-point likelihood optimization approach in the Lamperti space. Another novel contribution is the characterization of the uncertainty of the forecast at time zero, which turns out to be crucial in practice. We extend the model specification by considering the length of the unknown time interval preceding the first time a forecast is provided through an additional parameter in the density of the initial transition. All the procedures are agnostic of the forecasting technology, and they enable comparisons between different forecast providers. We apply our SDE framework to model historical Uruguayan normalized wind power production and forecast data between April and December 2019. Sharp empirical confidence bands of wind power production forecast error are obtained for the best-selected model.   A joint work with Renzo Caballero, Marco Scavino and Raúl Tempone. Download slides

Mohamed Balhali (Climate Economics Chair & Université Paris-Dauphine). Titre: A mean-field model for the spatial distribution of labour, housing and urban air pollution Abstract:  There exists a relationship between urban air pollution and economic activity: economic activity generates pollution, for instance through heating and transportation ; in turn, pollution spreads around and generates economic disutility. We develop a mean-field model of city coupling a labour market, a housing market, and pollution resulting from automobile commuting. Pollution is modelled through an advection-diffusion equation aiming at representing its physical dispersion.  Agents choose where to work and live in order to maximize their utility, by consuming goods, housing surface and valuing air quality. We prove existence of equilibria, and explore uniqueness when the number of job locations is finite. We provide numerical simulations and we obtain analytical results in the case of a linear monocentric city. A joint work with Quentin Petit (CEREMADE, Université Paris-Dauphine). Download slides

Title: Product Recommendations Systems and Price Competition Abstract: We study competing retailers’ choice of product recommender systems and the impact of these choices on price competition. We start by comparing two approaches to product recommendation: (i) broadcast product recommendations, a one-size-fits-all, and (ii) personalized product recommendations. We examine the role of consumer reactance in moderating firms’ approach to product recommendations. We then propose a competition-based rationale for the prevalent collaborative filtering approach to product recommendation. Download slides

Ankur A. Kulkarni (Indian Institute of Technology, Bombay)   Title: Extracting Information from a Strategic Sender   Abstract:  The COVID-19 pandemic has brought to the fore the need for segregating travellers, visitors and the general population based on answers given to standardized questionnaires. However, not all such answers can be relied upon to be truthful and therefore the design of such questionnaires for extracting true information becomes a strategic question. We introduce a setting where a receiver aims to perfectly recover a source known privately to a strategic sender by means of such questionnaires. The sender is endowed with a utility function and sends signals to the receiver with the aim of maximizing this utility. Due to the strategic nature of the sender not all the transmitted information is truthful, and signals sent by the sender are not codewords. This leads to the question: how much true information can be extracted by the receiver from such a sender? And how does it extract this information? This talk will study this question in an information-theoretic setting. We show that, in spite of the sender being strategic and the presence of noise in the channel, there is a strategy for the receiver by which it can perfectly recover an exponentially large number of source sequences. Our analysis leads to the notion of the information extraction capacity of the sender. Operationally, this capacity can be thought of as the (exponent of the) optimal length of a questionnaire to be provided to a strategic sender. We show that the information extraction capacity generalizes the Shannon capacity of a graph, and establish bounds on this capacity. We also identify cases where this capacity is equal to its theoretical maximum, and also when it is strictly less than maximum. In the latter case, we show that the capacity is sandwiched between the independence number and the Shannon capacity of a suitably defined graph. These results lead to an exact characterization of the information extraction capacity in a large number of cases. We show that in the presence of a noisy channel, the rate of information extraction achieved by the receiver is the minimum of the zero-error capacity of the channel and the information extraction capacity of the sender. Our analysis leads to insights into a novel regime of communication involving strategic agents. Time permitting, I will consider a dual model in which the receiver plays a passive role by letting the sender commit to a strategy first. Remarkably, we find that the receiver could even benefit from letting the sender take a lead.   Bio:Ankur A. Kulkarni is an Associate Professor with the Systems and Control Engineering group and the Centre for Machine Intelligence and Data Science at the Indian Institute of Technology Bombay (IITB). His research interests include information theory, game theory, stochastic control, combinatorial coding theory problems, optimization, and operations research. He received his B.Tech. from IITB in 2006, followed by M.S. in 2008 and Ph.D. in 2010, both from the University of Illinois at Urbana-Champaign (UIUC). From 2010-2012 he was a post-doctoral researcher at the Coordinated Science Laboratory at UIUC. He was an Associate (from 2015--2018) ...