Demand Estimation Using Managerial Responses to Automated Price Recommendations

We provide a new framework to identify demand elasticities in markets where managers rely on algorithmic recommendations for price setting and apply it to a data set containing bookings for a sample of midsized hotels in Europe. Using nonbinding algorithmic price recommendations and observed delay in price adjustments by decision makers, we demonstrate that a control-function approach, combined with state-of-the-art model-selection techniques, can be used to isolate exogenous price variation and identify demand elasticities across hotel room types and over time. We confirm these elasticity estimates with a difference-in-differences approach that leverages the same delays in price adjustments by decision makers. However, the difference-in-differences estimates are more noisy and only yield consistent estimates if data are pooled across hotels. We then apply our control-function approach to two classic questions in the dynamic pricing literature: the evolution of price elasticity of demand over and the effects of a transitory price change on future demand due to the presence of strategic buyers. Finally, we discuss how our empirical framework can be applied directly to other decision-making situations in which recommendation systems are used. This paper was accepted by Omar Besbes, revenue management and market analytics.

Improved-Efficacy EM-Based Antenna Miniaturization by Multi-Fidelity Simulations and Objective Function Adaptation

The growing demand for the integration of surface mount design (SMD) antennas into miniaturized electronic devices has imposed increasing limitations on the structure dimensions. Examples include embedded antennas in applications such as on-board devices, picosatellites, 5G communications, or implantable and wearable devices. The demands for size reduction while ensuring a satisfactory level of electrical and field performance can be managed through constrained numerical optimization. The reliability of optimization-based size reduction requires utilization of full-wave electromagnetic (EM) analysis, which entails significant computational costs. This can be alleviated by incorporating surrogate modeling techniques, adjoint sensitivities, or the employment of sparse sensitivity updates. An alternative is the incorporation of multi-fidelity simulation models, normally limited to two levels, low and high resolution. This paper proposes a novel algorithm for accelerated antenna miniaturization, featuring a continuous adjustment of the simulation model fidelity in the course of the optimization process. The model resolution is determined by factors related to violation of the design constraints as well as the convergence status of the algorithm. The algorithm utilizes the lowest-fidelity model for the early stages of the optimization process; it is gradually refined towards the highest-fidelity model upon approaching convergence, and the constraint violations improve towards the preset tolerance threshold. At the same time, a penalty function approach with adaptively adjusted coefficients is applied to enable the precise control of constraints, and to increase the achievable miniaturization rates. The presented procedure has been validated using five microstrip antennas, including three broadband, and two circularly polarized structures. The obtained results corroborate the relevance of the implemented mechanisms from the point of view of improving the average computational efficiency of the optimization process by 43% as compared to the single-fidelity adaptive penalty function approach. Furthermore, the presented methodology demonstrates a performance that is equivalent or even superior to its single-fidelity counterpart in terms of an average constraint violation of 0.01 dB (compared to 0.03 dB for the reference), and an average size reduction of 25% as compared to 25.6%.

Hierarchical integration of mitochondrial and nuclear positioning pathways by the Num1 EF hand

Positioning organelles at the right place and time is critical for their function and inheritance. In budding yeast, mitochondrial and nuclear positioning require the anchoring of mitochondria and dynein to the cell cortex by clusters of Num1. We have previously shown that mitochondria drive the assembly of cortical Num1 clusters, which then serve as anchoring sites for mitochondria and dynein. When mitochondrial inheritance is inhibited, mitochondrial-driven assembly of Num1 in buds is disrupted and defects in dynein-mediated spindle positioning are observed. Using a structure-function approach to dissect the mechanism of mitochondria-dependent dynein anchoring, we found the EF hand-like motif (EFLM) of Num1 and its ability to bind calcium are required to bias dynein anchoring on mitochondria-associated Num1 clusters. Consistently, when the EFLM is disrupted, we no longer observe defects in dynein activity following inhibition of mitochondrial inheritance. Thus, the Num1 EFLM functions to bias dynein anchoring and activity in nuclear inheritance subsequent to mitochondrial inheritance. We hypothesize that this hierarchical integration of organelle positioning pathways by the Num1 EFLM contributes to the regulated order of organelle inheritance during the cell cycle.

Labels distance in bucket recursive trees with variable capacities of buckets

The bucket recursive tree is a natural multivariate structure. In this paper, we apply a trivariate generating function approach for studying of the depth and distance quantities in this tree model with variable bucket capacities and give a closed formula for the probability distribution, the expectation and the variance. We show as j → ∞, lim-iting distributions are Gaussian. The results are obtained by presenting partial differential equations for moment generating functions and solving them.