Notice bibliographique
Résumé
The recent advancements in information technologies have revolutionized the way firms interact with consumers and collect consumer information on a large scale.Traditional marketing methods have given way to more personalized and targeted approaches, enabled by the wealth of big data available from social media and other platforms. This surge in data collection allows businesses to gain deep insights into consumer behavior, enabling them to tailor their offerings and communication strategies more effectively. However, this data-driven revolution has raised concerns surrounding privacy and the protection of personal information. As a result, this thesis analyzes various perspectives – targeting, advertising and pricing – of firms in such markets and how different entities are affected by information acquisition and privacy regulations. Chapter 1 develops a general equilibrium model of informative advertising to examine the implications of privacy regulations on consumer welfare. Firms reach consumers by placing ads on an advertising platform. Privacy regulations affect ad targetability by either facilitating or hindering the identification of consumers’ preferences. We show that it is possible for some consumers to exhibit a preference for privacy purely for instrumental reasons simply because the presence of consumers with flexible preferences introduces the possibility of greater competition in the product market leading to lower prices and greater consumption for some or all consumers. The platform’s market power in the ad market and the possibility of such cross-selling in the product market—products intended for pickier consumers selling to consumers with flexible preferences under privacy—are critical factors. Chapter 2 studies the dynamic pricing strategies of firms while gradually collecting information about consumers with changing tastes. How should the firm personalize its offers and change them dynamically to learn as well as to adapt to changing tastes when it cannot commit to future behavior? I build a continuous-time bargaining model with one-sided incomplete information where a buyer’s binary type is publicly revealed through Brownian motion and the binary type changes via a Poisson process. Changing tastes benefits both types of consumers at a cost to the firm. If the firm is restricted to constant prices and can use the acquired information to select consumers, it is better off than under dynamic prices. The continuation bargaining process gets resolved slower under constant prices than under flexible prices, which makes consumers more willing to accept a given offer. Chapter 3 investigates the effect of a tailored news report and its targeted release by a politically biased firm on the equilibrium level of media bias with heterogeneous voters. Targeted media strategies include selective information disclosure and audience targeting. When the media firm cannot commit to either strategy, targeted media provides less media bias than traditional media. With full commitment, however, targeted media does not necessarily generate more media bias because selective audience targeting may be more effective in channeling the bias.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,008 |
| Communication savante | 0,007 | 0,006 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».