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        "datetime_modified":"20261002T131533",
        "datetime_start":"20261023T143000",
        "datetime_end":"20261023T163000",
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        "date_start":"2026-10-23",
        "date_end":"2026-10-23",
        "time_start":"14:30:00",
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        "time_zone":"America\/Detroit",
        "event_title":"Online Learning and Decision-Making for Dynamic Pricing and Performative Prediction",
        "occurrence_title":"",
        "combined_title":"Online Learning and Decision-Making for Dynamic Pricing and Performative Prediction: Daniele Bracale",
        "event_subtitle":"Daniele Bracale",
        "event_type":"Lecture \/ Discussion",
        "event_type_id":"13",
        "description":"Many modern decision systems are not passive observers of the world they measure: the decisions they make change the data they subsequently receive. For example, a seller that posts a price influences the decisions of potential buyers, and hence the sales data it goes on to col-\nlect; a lender that deploys a credit-scoring rule changes the nature of the loan applications it receives. In such settings, the classical separation\nbetween estimation and decision-making breaks down, and statistical guarantees must be derived jointly with the policy that generates the\ndata. This dissertation develops estimation theory and online algorithms for two such feedback settings --- dynamic pricing and performative\nprediction --- with a unifying theme: shape-constrained and microfounded structure can replace the tuning parameters, smoothness assump-\ntions, and parametric models on which existing methods rely.\nThe first part concerns dynamic pricing. We first consider a monopolistic setting with censored demand, in which the seller posts a price and\nobserves only whether a sale occurred. In the linear valuation model, the customer\u2019s valuation is linear in the features of the item offered,\nup to market noise with unknown distribution F. Existing methods estimate F by kernel smoothing, bandit techniques, or upper confidence\nbounds; all of them impose Lipschitz or stronger conditions on F, and all of them leave the analyst to choose bandwidths, confidence widths,\nor regularization parameters. We instead exploit the fact that F is monotone and estimate it by isotonic regression. The resulting policy car-\nries no tuning parameters whatsoever, assumes only that F is H\u00f6lder continuous, and attains sublinear regret. It also outperforms existing\nmethods in simulations and on transaction data from Welltower Inc. We then move to a competitive setting in which N sellers post prices\nover T periods, and each seller\u2019s demand follows a single-index model with an unknown monotone, s-concave link. We prove existence and\nuniqueness of the Nash equilibrium, and give a semiparametric least-squares policy under which prices converge to the Nash equilibrium,\nwhile each seller incurs sublinear regret.\nThe second part concerns performative prediction, which involves a learner and a population of agents whose actions follow a distribution\nP. The learner deploys a model M that shifts this distribution from P to P(M), thereby changing the data the learner observes; the map P(M)\nis called the distribution map. Much of the literature assumes that the distribution map is known, whereas we learn it from agent-response\ndata. We study two settings: a discrete-action framework, in which agents choose from a finite set of alternatives, and a continuous-action\nframework, in which an action may be any real number. For discrete actions, we give a nonparametric estimator of the distribution map\ntogether with an exploration--exploitation scheme that we show to be effective in minimizing the performative risk. For continuous actions,\nwe model agents\u2019 responses as a cost-adjusted utility maximization problem and propose an estimator of the cost. Our approach leverages\noptimal transport to align the pre-deployment distribution P with the post-deployment distribution P(M). We provide a rate of convergence\nfor the proposed estimator and assess its quality through empirical demonstrations on a credit-scoring dataset.",
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        "tags":["Dissertation"],
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