scholarly journals Optimal auctions through deep learning

2021 ◽  
Vol 64 (8) ◽  
pp. 109-116
Author(s):  
Paul Dütting ◽  
Zhe Feng ◽  
Harikrishna Narasimhan ◽  
David C. Parkes ◽  
Sai S. Ravindranath
Keyword(s):  
Deep Learning ◽  
Learning Problem ◽  
Auction Design ◽  
Single Item ◽  
Optimal Auctions ◽  
Incentive Compatible ◽  
Optimal Mechanism ◽  
Optimal Auction ◽  
Constrained Learning ◽  
High Degree

Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981. Even after 30--40 years of intense research, the problem remains unsolved for settings with two or more items. We overview recent research results that show how tools from deep learning are shaping up to become a powerful tool for the automated design of near-optimal auctions auctions. In this approach, an auction is modeled as a multilayer neural network, with optimal auction design framed as a constrained learning problem that can be addressed with standard machine learning pipelines. Through this approach, it is possible to recover to a high degree of accuracy essentially all known analytically derived solutions for multi-item settings and obtain novel mechanisms for settings in which the optimal mechanism is unknown.

scholarly journals Countering the winner's curse: Optimal auction design in a common value model

2020 ◽  
Vol 15 (4) ◽  
pp. 1399-1434
Author(s):  
Dirk Bergemann ◽  
Benjamin Brooks ◽  
Stephen Morris
Keyword(s):  
Expected Value ◽  
Winner's Curse ◽  
English Auctions ◽  
Auction Design ◽  
Winner’S Curse ◽  
Common Value ◽  
Optimal Mechanism ◽  
Optimal Auction ◽  
Value Model ◽  
Posted Price

We characterize revenue maximizing mechanisms in a common value environment where the value of the object is equal to the highest of the bidders' independent signals. If the revenue maximizing solution is to sell the object with probability 1, then an optimal mechanism is simply a posted price, namely, the highest price such that every type of every bidder is willing to buy the object. If the object is optimally sold with probability less than 1, then optimal mechanisms skew the allocation toward bidders with lower signals. The resulting allocation induces a “winner's blessing,” whereby the expected value conditional on winning is higher than the unconditional expectation. By contrast, standard auctions that allocate to the bidder with the highest signal (e.g., the first‐price, second‐price, or English auctions) deliver lower revenue because of the winner's curse generated by the allocation. Our qualitative results extend to more general common value environments with a strong winner's curse.

Optimal Auction Design with Deferred Inspection and Reward

2020 ◽  
Author(s):  
Saeed Alaei ◽  
Alexandre Belloni ◽  
Ali Makhdoumi ◽  
Azarakhsh Malekian
Keyword(s):  
Auction Design ◽  
Optimal Auction

scholarly journals A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jared Hamwood ◽  
Beat Schmutz ◽  
Michael J. Collins ◽  
Mark C. Allenby ◽  
David Alonso-Caneiro
Keyword(s):  
Deep Learning ◽  
Human Performance ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Human Observer ◽  
In Series ◽  
Magnetic Resonance Imaging Mri ◽  
High Degree ◽  
Orbital Region ◽  
Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

scholarly journals Implementation of conventional communication system in deep learning

2017 ◽  
Vol 7 (1.1) ◽  
pp. 696
Author(s):  
Satyanarayana P ◽  
Charishma Devi. V ◽  
Sowjanya P ◽  
Satish Babu ◽  
N Syam Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Communication Network ◽  
Deep Learning ◽  
Communication System ◽  
Communication Systems ◽  
Physical Layer ◽  
Learning Capacity ◽  
Constrained Learning ◽  
Complex Channel

Machine learning (ML) has been broadly connected to the upper layers of communication systems for different purposes, for example, arrangement of cognitive radio and communication network. Nevertheless, its application to the physical layer is hindered by complex channel conditions and constrained learning capacity of regular ML algorithms. Deep learning (DL) has been as of late connected for some fields, for example, computer vision and normal dialect preparing, given its expressive limit and advantageous enhancement ability. This paper describes about a novel use of DL for the physical layer. By deciphering a communication system as an auto encoder, we build up an essential better approach to consider communication system outline as a conclusion to-end reproduction undertaking that tries to together enhance transmitter and receiver in a solitary procedure. This DL based technique demonstrates promising execution change than traditional communication system.  

The randomized communication complexity of revenue maximization

2021 ◽  
Vol 19 (2) ◽  
pp. 75-83
Author(s):  
Aviad Rubinstein ◽  
Junyao Zhao
Keyword(s):  
Lower Bounds ◽  
Communication Complexity ◽  
Social Choice Rule ◽  
Valuation Function ◽  
Optimal Auctions ◽  
Incentive Compatible ◽  
First Approximation ◽  
Exponential Separation ◽  
Open Question

We study the communication complexity of incentive compatible auction-protocols between a monopolist seller and a single buyer with a combinatorial valuation function over n items [Rubinstein and Zhao 2021]. Motivated by the fact that revenue-optimal auctions are randomized [Thanassoulis 2004; Manelli and Vincent 2010; Briest et al. 2010; Pavlov 2011; Hart and Reny 2015] (as well as by an open problem of Babaioff, Gonczarowski, and Nisan [Babaioff et al. 2017]), we focus on the randomized communication complexity of this problem (in contrast to most prior work on deterministic communication). We design simple, incentive compatible, and revenue-optimal auction-protocols whose expected communication complexity is much (in fact infinitely) more efficient than their deterministic counterparts. We also give nearly matching lower bounds on the expected communication complexity of approximately-revenue-optimal auctions. These results follow from a simple characterization of incentive compatible auction-protocols that allows us to prove lower bounds against randomized auction-protocols. In particular, our lower bounds give the first approximation-resistant, exponential separation between communication complexity of incentivizing vs implementing a Bayesian incentive compatible social choice rule, settling an open question of Fadel and Segal [Fadel and Segal 2009].

scholarly journals Computational Logistics for Container Terminal Handling Systems with Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Bin Li ◽  
Yuqing He
Keyword(s):  
Deep Learning ◽  
Container Terminal ◽  
Container Terminals ◽  
Hardware Platform ◽  
Logistics Network ◽  
Carbon Efficiency ◽  
The Past ◽  
Global Logistics ◽  
High Degree ◽  
Deep Learning Model

Container terminals are playing an increasingly important role in the global logistics network; however, the programming, planning, scheduling, and decision of the container terminal handling system (CTHS) all are provided with a high degree of nonlinearity, coupling, and complexity. Given that, a combination of computational logistics and deep learning, which is just about container terminal-oriented neural-physical fusion computation (CTO-NPFC), is proposed to discuss and explore the pattern recognition and regression analysis of CTHS. Because the liner berthing time (LBT) is the central index of terminal logistics service and carbon efficiency conditions and it is also the important foundation and guidance to task scheduling and resource allocation in CTHS, a deep learning model core computing architecture (DLM-CCA) for LBT prediction is presented to practice CTO-NPFC. Based on the quayside running data for the past five years at a typical container terminal in China, the deep neural networks model of the DLM-CCA is designed, implemented, executed, and evaluated with TensorFlow 2.3 and the specific feature extraction package of tsfresh. The DLM-CCA shows agile, efficient, flexible, and excellent forecasting performances for LBT with the low consuming costs on a common hardware platform. It interprets and demonstrates the feasibility and credibility of the philosophy, paradigm, architecture, and algorithm of CTO-NPFC preliminarily.

scholarly journals Informationally Robust Optimal Auction Design

2016 ◽  
Author(s):  
Dirk Bergemann ◽  
Benjamin A. Brooks ◽  
Stephen Morris
Keyword(s):  
Auction Design ◽  
Optimal Auction

Analyzing the Dual Channels for Online Retailer

2011 ◽  
Vol 48-49 ◽  
pp. 232-235
Author(s):  
Sheng Li Chen
Keyword(s):  
Decision Model ◽  
Design Principle ◽  
Optimal Decision ◽  
Threshold Policy ◽  
Auction Design ◽  
Decision Variables ◽  
Optimal Auction ◽  
Dual Channels ◽  
High Valuation ◽  
Dual Mechanism

By constructing the exponential delay cost function, we formulate the consumer decision model based on the threshold strategies in dual-mechanism, and prove that there exists a unique symmetric Nash equilibrium in which the high-valuation consumers use a threshold policy to choose between the two selling channels. On the basis of the consumer’s threshold strategies, taking the auction length, the auctioned quantity in each period, and the posted price as the decision variables, we develop the seller’ optimal decision model in dual-mechanism, and show the optimal auction design principle and strategy by numerical analysis.

scholarly journals CoronaNet: A Novel Deep Learning Model for COVID-19 Detection in CT Scans

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Rohan Bhansali ◽  
Rahul Kumar ◽  
Duke Writer
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Biomedical Applications ◽  
Feature Detection ◽  
Ct Scans ◽  
Affine Transformations ◽  
Global Pandemic ◽  
Deep Cnn ◽  
High Degree ◽  
Ct Scan Analysis

Coronavirus disease (COVID-19) is currently the cause of a global pandemic that is affecting millions of people around the world. Inadequate testing resources have resulted in several people going undiagnosed and consequently untreated; however, using computerized tomography (CT) scans for diagnosis is an alternative to bypass this limitation. Unfortunately, CT scan analysis is time-consuming and labor intensive and rendering is generally infeasible in most diagnosis situations. In order to alleviate this problem, previous studies have utilized multiple deep learning techniques to analyze biomedical images such as CT scans. Specifically, convolutional neural networks (CNNs) have been shown to provide medical diagnosis with a high degree of accuracy. A common issue in the training of CNNs for biomedical applications is the requirement of large datasets. In this paper, we propose the use of affine transformations to artificially magnify the size of our dataset. Additionally, we propose the use of the Laplace filter to increase feature detection in CT scan analysis. We then feed the preprocessed images to a novel deep CNN structure: CoronaNet. We find that the use of the Laplace filter significantly increases the performance of CoronaNet across all metrics. Additionally, we find that affine transformations successfully magnify the dataset without resulting in high degrees of overfitting. Specifically, we achieved an accuracy of 92% and an F1 of 0.8735. Our novel research describes the potential of the Laplace filter to significantly increase deep CNN performance in biomedical applications such as COVID-19 diagnosis.

Credible Auctions: A Trilemma

Econometrica ◽  
2020 ◽  
Vol 88 (2) ◽  
pp. 425-467 ◽  
Author(s):  
Mohammad Akbarpour ◽  
Shengwu Li
Keyword(s):  
Extensive Form ◽  
Optimal Auctions ◽  
Incentive Compatible ◽  
Private Values ◽  
Price Auction ◽  
Strategy Proof ◽  
Form Mechanism ◽  
Static Mechanism ◽  
Independent Private Values

Consider an extensive‐form mechanism, run by an auctioneer who communicates sequentially and privately with bidders. Suppose the auctioneer can deviate from the rules provided that no single bidder detects the deviation. A mechanism is credible if it is incentive‐compatible for the auctioneer to follow the rules. We study the optimal auctions in which only winners pay, under symmetric independent private values. The first‐price auction is the unique credible static mechanism. The ascending auction is the unique credible strategy‐proof mechanism.

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