Stochastic Auction Research Articles

Model of stochastic auctions using level market index

The following research paper is devoted to the complex topic of modeling stochastic financial markets using the example of auction markets. The presented model for market makers’ behavior on stochastic auction markets contributes practically to the field of studying portfolio optimization, risk management, market participants’ balance processes, and prediction problems via cutting-edge machine learning and statistics approaches. The reliability of the given model is proved practically with the help of modern machine learning methods of validation, namely, combinatorial splits. A client-server model for remote simulation was implemented, as well as interpreted language in C++. XGBoost, Catboost, LSTM, NN Ensemble, and H2O Auto-ML models were considered in the course of building the decision model. Hyperparameters were obtained via Optuna. Besides that, the developed model was backtested on historical data of different financial assets, starting with stocks and ending with commodity prices and foreign exchange rates. Within all models, positive Sharpe ratios have been obtained, which indicates the robustness of the model. The paper offers a valuable framework for market maker decision-making stochastic modeling, examining its pricing mechanisms and financial risk management as crucial for exchanges, funds, and other financial institutions, which makes it relevant in the context of the current dynamics of the development of financial markets and the increase in trading volumes.

Fast and Secure Computational Offloading With Lagrange Coded Mobile Edge Computing

This paper proposes a novel framework based on Lagrange coded computing (LCC) for fast and secure offloading of computing tasks in the mobile edge computing (MEC) network. The network is formed by multiple base stations (BSs) acting as “masters” which offload their computations to edge devices acting as “workers”. The framework aims to ensure efficient allocation of computing loads and bandwidths to workers, and providing them with proper incentives to finish their tasks by the specified deadlines. Thus, each master must decide on the amounts of allocated load and bandwidth, and a service fee paid to each worker given that: i) other masters, i.e., BSs, can be privately-owned or controlled by different operators, i.e., they do not communicate/coordinate their decisions with the master; ii) workers are heterogeneous non-dedicated edge devices with constrained and nondeterministic computing resources. As such, masters compete for the best workers in a stochastic and partially-observable environment. To describe interactions between masters and workers, we formulate a new stochastic auction model with contingent values of bidders, i.e., masters and contingent payments to auctioneers, i.e., workers. To solve the auction, we represent it as a stochastic Bayesian game and develop machine learning algorithms to improve the auction solution.

Stochastic and Strategy-Proof Auctions for Statistical Inferences

We study a stochastic auction in a multi-items and unit-demand setting where bidders have one-dimensional type spaces. In a usual Pareto-efficient auction such as the VCG auction, items are allocated deterministically to bidders depending on their bids. However, even if an auctioneer implements a deterministic auction repeatedly, data that the auctioneer and bidders can get from the auction would be limited because the bidder with the highest bid gets the best item, the bidder with second highest bid gets the second best item, and so on surely. Then, for example, the auctioneer cannot infer what happens if the best item was allocated to the other bidder. We consider a stochastic auction which allocates items stochastically to bidders according to their bids in order to get rich data. We introduce a stochastic auction rule that is strategy-proof and individual rational. Moreover, we show that our stochastic auction can be used to get rich data, and yields relatively high revenue compared to the VCG auction in a computer experiment.

On stochastic auctions in risk-averse electricity markets with uncertain supply

This paper studies risk in a stochastic auction which facilitates the integration of renewable generation in electricity markets. We model market participants who are risk averse and reflect their risk aversion through coherent risk measures. We uncover a closed form characterization of a risk-averse generator’s optimal pre-commitment behaviour for a given real-time policy, both with and without risk trading.

Online Stochastic Buy-Sell Mechanism for VNF Chains in the NFV Market

With the recent advent of network functions virtualization (NFV), enterprises and businesses are looking into network service provisioning through the service chains of virtual network functions (VNFs), instead of relying on dedicated hardware middleboxes. Accompanying this trend, an NFV market is emerging, where NFV service providers create VNF instances, assemble VNF service chains, and sell them for the use of customers, using resources (computing, bandwidth) that they own or rent from other resource suppliers. Efficient service chain provisioning and pricing mechanisms are still missing, to charge assembled service chains according to demand and the supply of resources at any time. We propose an online stochastic auction mechanism for on-demand service chain provisioning and pricing at an NFV provider. Our auction takes in buy bids for service chains from multiple customers and sell bids from various resource suppliers to supplement the NFV provider’s geo-distributed resource pool, with resource occupation/contribution durations. We extend online primal-dual optimization framework for handling both buyers and sellers, with a new competitive analysis. The online mechanism maximizes the expected social welfare of the NFV ecosystem (the NFV provider, customers and resource suppliers) with a good competitive ratio as compared with the expected offline optimal social welfare, while guaranteeing truthfulness in bidding, individual rationality for both buyers and sellers, and polynomial time for computation. We evaluate our mechanism through trace-driven simulation studies, and demonstrate a close-to-offline-optimal performance in expected social welfare under realistic settings.

Centralized and distributed task allocation in multi-robot teams via a stochastic clustering auction

This article considers the problem of optimal task allocation for heterogeneous teams, for example, teams of heterogeneous robots or human-robot teams. It is well-known that this problem is NP-hard and hence computationally feasible approaches must develop an approximate solution. Here, we propose a solution via a Stochastic Clustering Auction (SCA) that uses a Markov chain search process along with simulated annealing. This is the first stochastic auction method used in conjunction with global optimization. It is based on stochastic transfer and swap moves between the clusters of tasks assigned to the various robots and considers not only downhill movements, but also uphill movements, which can avoid local minima. A novel feature of this algorithm is that, by tuning the annealing suite and turning the uphill movements on and off, the global team performance after algorithm convergence can slide in the region between the global optimal performance and the performance associated with a random allocation. Extensive numerical experiments are used to evaluate the performance of SCA in terms of costs and computational and communication requirements. For centralized auctioning, the SCA algorithm is compared to fast greedy auction algorithms. Distributed auctioning is then compared with centralized SCA.

Auctions With A Minimum Requirement Of Bids

We show that in a procurement auction with independent and private costs of production and a positive cost of preparing a bid, the requirement of a minimum number of offers for the good to be bought always yields a unique (perfect) Bayesian equilibrium where no firm enters a bid, whatever its cost of production, the number of potential bidders and the size of the bidding cost.To avoid the no‐bid result, the buyer can commit to subsidise the losing bidders in certain circumstances. Alternatively, it can use a stochastic auction, where the provider of the good is not always the firm that bids the lowest price.