Мультиагентне моделювання послідовних багатоелементних японських аукціонів

Досліджуються особливості моделювання аукціонів з точки зору імітаційного (мультиагентного) моделювання. Наводиться характеристика аукціонів як об’єкта моделювання. Виконується постановка задачі та пропонується метод побудови механізму проведення послідовних багатоелементних японських аукціонів, який забезпечує використання агентами домінуючих стратегій та дозволяє побудувати оптимальний аукціон. Експериментально підтверджується ефективність запропонованого метода.Исследуются особенности моделирования аукционов с точки зрения имитационного (мультиагентного) моделирования. Приводится характеристика аукционов как объекта моделирования. Выполняется постановка задачи и предлагается метод построения механизма проведения последовательных многоэлементных японских аукционов, который обеспечивает использование агентами доминирующих стратегий и позволяет построить оптимальный аукцион. Экспериментально подтверждается эффективность предложенного метода.Features of modeling of auctions are investigated from the point of view of simulation (multiagent) modeling. The characteristic of auctions as object of modeling is resulted. Statement of a task carries out and the method of construction of the mechanism of carrying out of sequential multiunit japanese auctions, which provides use by agents of dominant strategies is offered and allows to construct optimal auction. Efficiency of the suggested method experimentally proves to be true

Designing Intelligent Software Agents for B2B Sequential Dutch Auctions: A Structural Econometric Approach

We study multi-unit sequential Dutch auctions in a complex B2B context. Using a large real-world dataset, we apply structural econometric analysis to recover the parameters governing the distribution of bidders’ valuations. The identification of these parameters allows us to simulate auction results under different designs and perform policy counterfactuals. We also develop a dynamic optimization approach to guide the setting of key auction parameters. Given the bounded rationality of human decision makers, we propose to augment auctioneers’ capabilities with high performance decision support tools in the form of software agents. Our paper contributes to both theory and practice of auction design. From the theoretical perspective, this is the first study that explicitly models the sequential aspects of Dutch auctions using structural econometric analysis. From the managerial perspective, this paper offers useful implications to business practitioners for complex decision making in B2B auctions

Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges

We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.dynamic pricing;machine learning;market forecasting;Trading agents

Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime†models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches.dynamic pricing;trading agent competition;agent-mediated electronic commerce;dynamic markets;economic regimes;enabling technologies;price forecasting;supply-chain

Exploring auction based energy trade with the support of MAS and blockchain technology

This document describes a simulation of the local energy market with support of multi-agent approach and blockchain technology. The investigated points include blockchain technology and its applications, Ethereum platform and smart contracts as a tool for storing data of operations and creating assets, multi-agent approach to model the local energy market. The document explores building a solution for proposed problem with blockchain technology, agent interactions on the simulated market and auction models, that provide sustainability and profit for the local energy market overall

An Empirical Analysis of User Participation on Crowdsourcing Platform: A Two-sided Network Market Perspective

Crowdsourcing has recently emerged as a new platform for matching the demand and supply between professionals and businesses who seek external expertise for business task execution. Driven by the unique features of the two-sided crowdsourcing markets (such as auction-style competition on quality by professionals), this study seeks to examine how the dynamics of the two-sided crowdsourcing platform affect customers and professionals’ strategic behaviors and market outcomes. Using longitudinal transaction data from a crowdsourcing websites, we plan to empirically examine how the participation of professionals and customers, task reward and task completion rate are affected by the characteristics of the professionals such as distribution of the winning professionals and their reputation. The results of our study are expected to contribute to the growing literature on crowdsourcing and provide important insights on the design and assessment of the sustainability and profitability of the crowdsourcing business model

A Hybrid SDN-based Architecture for Wireless Networks

With new possibilities brought by the Internet of Things (IoT) and edge computing, the traffic demand of wireless networks increases dramatically. A more sophisticated network management framework is required to handle the flow routing and resource allocation for different users and services. By separating the network control and data planes, Software-defined Networking (SDN) brings flexible and programmable network control, which is considered as an appropriate solution in this scenario.Although SDN has been applied in traditional networks such as data centers with great successes, several unique challenges exist in the wireless environment. Compared with wired networks, wireless links have limited capacity. The high mobility of IoT and edge devices also leads to network topology changes and unstable link qualities. Such factors restrain the scalability and robustness of an SDN control plane. In addition, the coexistence of heterogeneous wireless and IoT protocols with distinct representations of network resources making it difficult to process traffic with state-of-the-art SDN standards such as OpenFlow. In this dissertation, we design a novel architecture for the wireless network management. We propose multiple techniques to better adopt SDN to relevant scenarios. First, while maintaining the centralized control plane logically, we deploy multiple SDN controller instances to ensure their scalability and robustness. We propose algorithms to determine the controllers\u27 locations and synchronization rates that minimize the communication costs. Then, we consider handling heterogeneous protocols in Radio Access Networks (RANs). We design a network slicing orchestrator enabling allocating resources across different RANs controlled by SDN, including LTE and Wi-Fi. Finally, we combine the centralized controller with local intelligence, including deploying another SDN control plane in edge devices locally, and offloading network functions to a programmable data plane. In all these approaches, we evaluate our solutions with both large-scale emulations and prototypes implemented in real devices, demonstrating the improvements in multiple performance metrics compared with state-of-the-art methods