arXiv subjects

Emerson Melo

Publications and source records attributed to Emerson Melo.

14 recordsLinked to original sources

arXiv2026

One-Shot Pricing for Hands-Off-the-Wheel Advertising Markets

Per-impression auctions have long served as the allocation mechanism in online advertising. We argue that in ``hands-off-the-wheel'' (HOTW) markets, where advertisers declare budgets and ``return-on-investment'' (ROI) targets and the exchange's ML models predict click values, auctions are no longer necessary: all information required for optimal pricing is already known to the exchange, which occupies the position of a monopolist pricing against a downward-sloping demand curve. A HOTW market is a Fisher market, whose competitive equilibrium can be computed via the convex program of Eisenberg and Gale, yielding market-clearing prices and allocations satisfying all budget and ROI constraints simultaneously. This competitive-equilibrium price is revenue-optimal for the exchange among all uniform-price mechanisms: avoiding the demand reduction problem in typical uniform-price multi-unit auctions. The resulting competitive equilibrium is moreover outcome-equivalent to sequential first-price auctions with pacing, with pacing multipliers computable ex-ante by the exchange. This one-shot approach replaces millions of individual auctions with one convex program, which is not only operationally simpler than dynamically evolving bidding strategies, but revenue-optimal for the exchange, while delivering the same equilibrium outcome.

econ.TH↗

arXiv2025

Welfare at Risk: Distributional impact of policy interventions

This paper proposes a framewrok for analyzing how the welfare effects of policy interventions are distributed across individuals when those effects are unobserved. Rather than focusing solely on average outcomes, the approach uses readily available information on average welfare responses to uncover meaningful patterns in how gains and losses are distributed across different populations. The framework is built around the concept of superquantile and applies to a broad class of models with unobserved individual heterogeneity. It enables policymakers to identify which groups are most adversely affected by a policy and to evaluate trade-offs between efficiency and equity. We illustrate the approach in three widely studied economic settings: price changes and compensated variation, treatment allocation with self-selection, and the cost-benefit analysis of social programs. In this latter application, we show how standard tools from the marginal treatment effect and generalized Roy model literature are useful for implementing our bounds for both the overall population and for individuals who participate in the program.

econ.EM↗

arXiv2025

Beyond Softmax: A New Perspective on Gradient Bandits

We establish a link between a class of discrete choice models and the theory of online learning and multi-armed bandits. Our contributions are: (i) sublinear regret bounds for a broad algorithmic family, encompassing Exp3 as a special case; (ii) a new class of adversarial bandit algorithms derived from generalized nested logit models \citep{wen:2001}; and (iii) \textcolor{black}{we introduce a novel class of generalized gradient bandit algorithms that extends beyond the widely used softmax formulation. By relaxing the restrictive independence assumptions inherent in softmax, our framework accommodates correlated learning dynamics across actions, thereby broadening the applicability of gradient bandit methods.} Overall, the proposed algorithms combine flexible model specification with computational efficiency via closed-form sampling probabilities. Numerical experiments in stochastic bandit settings demonstrate their practical effectiveness.

cs.LG↗

arXiv2025

Multimode Nanobeam Photonic Crystal Cavities for Purcell Enhanced Quantum Dot Emission

Epitaxial III-V semiconductor quantum dots in nanopthonic structures are promising candidates for implementing on-demand indistinguishable single-photon emission in integrated quantum photonic circuits. Quantum dot proximity to the etched sidewalls of hosting nanophotonic structures, however, has been shown to induce linewidth broadening of excitonic transitions, which limits emitted single-photon indistinguishability. Here, we design and demonstrate GaAs photonic crystal nanobeam cavities that maximize quantum dot distances to etched sidewalls beyond an empirically determined minimum that curtails spectral broadening. Although such geometric constraint necessarily leads to multimode propagation in nanobeams, which significantly complicates high quality factor cavity design, we achieve resonances with quality factors $Q\approx10^3$, which offer the potential for achieving Purcell radiative rate enhancements $F_p\approx100$.

physics.optics↗

arXiv2025

Learning in Random Utility Models Via Online Decision Problems

This paper examines the Random Utility Model (RUM) in repeated stochastic choice settings where decision-makers lack full information about payoffs. We propose a gradient-based learning algorithm that embeds RUM into an online decision-making framework. Our analysis establishes Hannan consistency for a broad class of RUMs, meaning the average regret relative to the best fixed action in hindsight vanishes over time. We also show that our algorithm is equivalent to the Follow-The-Regularized-Leader (FTRL) method, offering an economically grounded approach to online optimization. Applications include modeling recency bias and characterizing coarse correlated equilibria in normal-form games

econ.TH↗

arXiv2025

Censored Beliefs and Wishful Thinking

We present a model elucidating wishful thinking, which comprehensively incorporates both the costs and benefits associated with biased beliefs. Our findings reveal that wishful thinking behavior can be characterized as equivalent to superquantile-utility maximization within the domain of threshold beliefs distortion cost functions. By leveraging this equivalence, we establish WT as driving decision-makers to exhibit a preference for choices characterized by skewness and increased risk. Furthermore, we discuss how our framework facilitates the study of optimistic stochastic choice and optimistic risk aversion.

econ.TH↗

arXiv2024

Wishful Thinking is Risky Thinking

We develop a model of wishful thinking that incorporates the costs and benefits of biased beliefs. We establish the connection between distorted beliefs and risk, revealing how wishful thinking can be understood in terms of risk measures. Our model accommodates extreme beliefs, allowing wishful-thinking decision-makers to assign zero probability to undesirable states and positive probability to otherwise impossible states.

econ.TH↗

arXiv2023

Discrete Choice Multi-Armed Bandits

This paper establishes a connection between a category of discrete choice models and the realms of online learning and multiarmed bandit algorithms. Our contributions can be summarized in two key aspects. Firstly, we furnish sublinear regret bounds for a comprehensive family of algorithms, encompassing the Exp3 algorithm as a particular case. Secondly, we introduce a novel family of adversarial multiarmed bandit algorithms, drawing inspiration from the generalized nested logit models initially introduced by \citet{wen:2001}. These algorithms offer users the flexibility to fine-tune the model extensively, as they can be implemented efficiently due to their closed-form sampling distribution probabilities. To demonstrate the practical implementation of our algorithms, we present numerical experiments, focusing on the stochastic bandit case.

stat.ML↗

arXiv2023

On the Distributional Robustness of Finite Rational Inattention Models

In this paper we study a rational inattention model in environments where the decision maker faces uncertainty about the true prior distribution over states. The decision maker seeks to select a stochastic choice rule over a finite set of alternatives that is robust to prior ambiguity. We fully characterize the distributional robustness of the rational inattention model in terms of a tractable concave program. We establish necessary and sufficient conditions to construct robust consideration sets. Finally, we quantify the impact of prior uncertainty, by introducing the notion of \emph{Worst-Case Sensitivity}.

econ.TH↗

arXiv2023

A Distributionally Robust Random Utility Model

This paper introduces the distributionally robust random utility model (DRO-RUM), which allows the preference shock (unobserved heterogeneity) distribution to be misspecified or unknown. We make three contributions using tools from the literature on robust optimization. First, by exploiting the notion of distributionally robust social surplus function, we show that the DRO-RUM endogenously generates a shock distributionthat incorporates a correlation between the utilities of the different alternatives. Second, we show that the gradient of the distributionally robust social surplus yields the choice probability vector. This result generalizes the celebrated William-Daly-Zachary theorem to environments where the shock distribution is unknown. Third, we show how the DRO-RUM allows us to nonparametrically identify the mean utility vector associated with choice market data. This result extends the demand inversion approach to environments where the shock distribution is unknown or misspecified. We carry out several numerical experiments comparing the performance of the DRO-RUM with the traditional multinomial logit and probit models.

econ.TH↗

arXiv2022

Learning in Random Utility Models Via Online Decision Problems

This paper studies the Random Utility Model (RUM) in a repeated stochastic choice situation, in which the decision maker is imperfectly informed about the payoffs of each available alternative. We develop a gradient-based learning algorithm by embedding the RUM into an online decision problem. We show that a large class of RUMs are Hannan consistent (\citet{Hahn1957}); that is, the average difference between the expected payoffs generated by a RUM and that of the best-fixed policy in hindsight goes to zero as the number of periods increase. In addition, we show that our gradient-based algorithm is equivalent to the Follow the Regularized Leader (FTRL) algorithm, which is widely used in the machine learning literature to model learning in repeated stochastic choice problems. Thus, we provide an economically grounded optimization framework to the FTRL algorithm. Finally, we apply our framework to study recency bias, no-regret learning in normal form games, and prediction markets.

econ.TH↗

arXiv2021

A Recursive Logit Model with Choice Aversion and Its Application to Transportation Networks

We propose a recursive logit model which captures the notion of choice aversion by imposing a penalty term that accounts for the dimension of the choice set at each node of the transportation network. We make three contributions. First, we show that our model overcomes the correlation problem between routes, a common pitfall of traditional logit models, and that the choice aversion model can be seen as an alternative to these models. Second, we show how our model can generate violations of regularity in the path choice probabilities. In particular, we show that removing edges in the network may decrease the probability for existing paths. Finally, we show that under the presence of choice aversion, adding edges to the network can make users worse off. In other words, a type of Braess's paradox can emerge outside of congestion and can be characterized in terms of a parameter that measures users' degree of choice aversion. We validate these contributions by estimating this parameter over GPS traffic data captured on a real-world transportation network.

econ.EM↗

arXiv2017

Discrete Choice and Rational Inattention: a General Equivalence Result

This paper establishes a general equivalence between discrete choice and rational inattention models. Matejka and McKay (2015, AER) showed that when information costs are modelled using the Shannon entropy function, the resulting choice probabilities in the rational inattention model take the multinomial logit form. By exploiting convex-analytic properties of the discrete choice model, we show that when information costs are modelled using a class of generalized entropy functions, the choice probabilities in any rational inattention model are observationally equivalent to some additive random utility discrete choice model and vice versa. Thus any additive random utility model can be given an interpretation in terms of boundedly rational behavior. This includes empirically relevant specifications such as the probit and nested logit models.

econ.EM↗

arXiv2016

Testing the Quantal Response Hypothesis

This paper develops a non-parametric test for consistency of players' behavior in a series of games with the Quantal Response Equilibrium (QRE). The test exploits a characterization of the equilibrium choice probabilities in any structural QRE as the gradient of a convex function, which thus satisfies the cyclic monotonicity inequalities. Our testing procedure utilizes recent econometric results for moment inequality models. We assess our test using lab experimental data from a series of generalized matching pennies games. We reject the QRE hypothesis in the pooled data, but it cannot be rejected in the individual data for over half of the subjects.

stat.AP↗