Modeling Dynamic Procurement Auctions of Standardized Supply Contracts in Electricity Markets including Bidders Adaptation

Descendant Clock Auctions have been increasingly used in power markets. Traditional approaches are focused on discovering the bidders’ best response but neglecting the bidders’ adaptation. This paper presents an algorithm based on decision theory to estimate the bidders’ behavior along the auction. The proposed model uses portfolio concepts and historical data of spot market to estimate a long term contract supply curve. This model was applied to evaluate the Colombia’s Organized Market (MOR). Demand curve parameters and round size were varied to evaluate their impact over auction outputs. Results show that demand curve has a quite small impact over bidders’ decisions and round size management is useful to avoid non-competitive bidders’ behavior. In addition, it is shown that auction’s starting prices strongly influence auction’s clearing prices. These results are extremely helpful to design market structures in power markets

Timber Auction Simulation and Design

RÉSUMÉ : La commercialisation sous forme de vente aux enchères publique du bois provenant de forêts, comme dans la province de Québec, est une tâche difficile. En effet, il est crucial de déterminer les prix représentatifs de la vente aux enchères de bois dans toutes les régions du Québec afin de permettre à plus d'acheteurs potentiels d'accéder au marché. De même, il est également important de concevoir un système d'enchères qui est bénéfique pour les entreprises forestières et les gens de Québec. La vente unitaire, dans lequel les utilisateurs de bois autorisés à soumissionner pour le lot entier, est actuellement appliquée comme une méthode de vente aux enchères. Dans ce système de vente aux enchères du bois, les utilisateurs de bois sont responsables de la récolte de la totalité du lot et pour la revente espèces ligneuses indésirables à d'autres utilisateurs. Dans ce projet, nous analysons d'abord différentes configurations d’enchères à rondes multiples de type premier prix sous pli scellé, tel que proposé par le ministère des Ressources naturelles du Québec, afin de mieux comprendre la dynamique et les facteurs dominants de la réussite de ce type de mécanisme d'allocation de bois. Pour cela, nous utilisons la simulation à base d'agents pour modéliser et simuler des ventes aux enchères, en proposant notamment des comportements de soumissionnaires réalistes, incluant des stratégies d'adaptation et d'apprentissage, qui ont été simulées et comparées dans diverses configurations. Les comparaisons ont été menées en mesurant notamment le taux de succès de gagner l'enchère et le prix unitaire remporté en $/m3. Cette étude suggère également des configurations de paramètres permettant maximiser les recettes pour le commissaire-priseur. À l’étape suivante de la recherche, cette thèse présente la simulation de la vente de plusieurs sortes de bois rond en utilisant une méthode d’enchères combinatoires. Dans ce processus de vente, les soumissionnaires peuvent avoir besoin d’une combinaison des produits. En utilisant l'approche par simulation, les résultats montrent que les revenus générés par enchère combinatoire peuvent être plus élevés que le revenu de l’enchère unitaire. Afin d'effectuer une analyse de sensibilité, les expériences sont répétées et testés avec diverses combinaisons de quatre paramètres de configuration. Les résultats de l'analyse permettent d’évaluer dans quel contexte l’enchère combinatoire peut faire mieux que l’enchère unitaire, et cela dans différents marchés. Enfin, cette thèse présente un système d'enchères combinatoires qui alloue le bois aux soumissionnaires afin d'améliorer la coordination des dépendances entre les soumissionnaires retenus dans les zones forestières mixtes (c’est à dire avec plusieurs types de produits et utilisateurs potentiels). Pour supporter la coordination des opérations et améliorer la fraicheur du bois, nous proposons une vente aux enchères combinatoire, qui permet aux soumissionnaires d’ajuster la valeur des offres en fonction du temps, via une sorte de calendriers. Cette enchère combinatoire permet ainsi au commissaire-priseur de trouver les meilleures combinaisons de soumissions gagnantes maximisant ainsi les préférences temporelles des soumissionnaires. Pour cela, nous définissons un nouveau problème de détermination du vainqueur (WDP) qui utilise ces fonctions de valeur. Afin de comparer l’impact de diverses préférences temporelles, une analyse de sensibilité est menée. Mots-clés: enchères du bois, enchères séquentielles, la stratégie d'apprentissage, systèmes multi-agents, l'affectation enchères combinatoire, la coordination, la fraicheur du bois, et de problèmes de détermination du vainqueur.----------ABSTRACT : The marketing of wood obtained from forests in public auction, such as in the province of Québec, is a challenging task. Indeed, it is crucial to determine representative prices of the wood auction in all regions of Quebec in order to allow more potential buyers to access the market. Similarly, it is also important to design an auction system that is beneficial for forest companies and the people of Québec. Single-unit auction, in which timber users allowed to bid on the entire lot, is currently applied as a method of auction. In this timber auction system, timber users (i.e., winners) are responsible for harvesting the entire lot and for reselling unwanted timber species to other users. In this project, we first analyze various configurations of the multiple-round first-price sealed-bid auction of wood as proposed by the Québec Ministry of Natural Resources to better understand the dynamics and the dominant factors of success of this type of wood allocation mechanism. To do so, we use agent-based simulation to model and simulate auctions with realistic bidders’ behavior. Different bidding patterns including adaptive and learning strategies are then simulated and compared in various setup configurations. The comparisons have been conducted on the success rate of winning the auction and the winning price per m3. This study also suggests parameter configurations to maximize revenue for the auctioneer. In the next step of research, in the last part, this thesis presents the simulation of multiple-round timber combinatorial auction as the bidders may need variety of species and the size of timber companies may be different. Using simulation approach, the results shows the revenue generated by combinatorial auction can be higher than the revenue of a single unit auction. In order to do sensitive analysis of the comparison, the experiments are repeated and tested with different setup configuration of four parameters. The results of analysis help to evaluate how combinatorial auction can perform better than single auction in different markets. Finally, we intend to present an auction system, which allocates wood to bidders in order to improve the coordination of the dependencies between winning bidders in mixed forest areas (i.e., wood lots with multiple users). To achieve the coordination of procurement operations and improve the freshness of the wood, we propose an auction, by allowing the value of bids to be expressed as a function of time, via some sort of timetables, and by using a combinatorial auction that will allow the auctioneer to find the best combinations of winning bids. In order to do that, we define a new winner determination problem (WDP) that use these value functions for coordination procurement and delivery operations and wood freshness. In order to compare the proposed time-based combinatorial auction with combinatorial auction a sensitive analysis is conducted. The comparison is done according to bidders’ and seller’s time flexibility. Keywords: timber auction, sequential auction, learning strategy, multi agent system, allocation combinatorial auction, coordination, wood freshness, and winner determination problem

Decentralized Resource Scheduling in Grid/Cloud Computing

In the Grid/Cloud environment, applications or services and resources belong to different organizations with different objectives. Entities in the Grid/Cloud are autonomous and self-interested; however, they are willing to share their resources and services to achieve their individual and collective goals. In such open environment, the scheduling decision is a challenge given the decentralized nature of the environment. Each entity has specific requirements and objectives that need to achieve. In this thesis, we review the Grid/Cloud computing technologies, environment characteristics and structure and indicate the challenges within the resource scheduling. We capture the Grid/Cloud scheduling model based on the complete requirement of the environment. We further create a mapping between the Grid/Cloud scheduling problem and the combinatorial allocation problem and propose an adequate economic-based optimization model based on the characteristic and the structure nature of the Grid/Cloud. By adequacy, we mean that a comprehensive view of required properties of the Grid/Cloud is captured. We utilize the captured properties and propose a bidding language that is expressive where entities have the ability to specify any set of preferences in the Grid/Cloud and simple as entities have the ability to express structured preferences directly. We propose a winner determination model and mechanism that utilizes the proposed bidding language and finds a scheduling solution. Our proposed approach integrates concepts and principles of mechanism design and classical scheduling theory. Furthermore, we argue that in such open environment privacy concerns by nature is part of the requirement in the Grid/Cloud. Hence, any scheduling decision within the Grid/Cloud computing environment is to incorporate the feasibility of privacy protection of an entity. Each entity has specific requirements in terms of scheduling and privacy preferences. We analyze the privacy problem in the Grid/Cloud computing environment and propose an economic based model and solution architecture that provides a scheduling solution given privacy concerns in the Grid/Cloud. Finally, as a demonstration of the applicability of the approach, we apply our solution by integrating with Globus toolkit (a well adopted tool to enable Grid/Cloud computing environment). We also, created simulation experimental results to capture the economic and time efficiency of the proposed solution

Bidder Behavior in Complex Trading Environments: Modeling, Simulations, and Agent-Enabled Experiments

University of Minnesota Ph.D. dissertation. January 2018. Major: Business Administration. Advisor: Gediminas Adomavicius. 1 computer file (PDF); vi, 90 pages.Combinatorial auctions represent sophisticated market mechanisms that are becoming increasingly important in various business applications due to their ability to improve economic efficiency and auction revenue, especially in settings where participants tend to exhibit more complex user preferences and valuations. While recent studies on such auctions have found heterogeneity in bidder behavior and its varying effect on auction outcomes, the area of bidder behavior and its impact on economic outcomes in combinatorial auctions is still largely underexplored. One of the main reasons is that it is nearly impossible to control for the type of bidder behavior in real world or experimental auction setups. In my dissertation I propose two data-driven approaches (heuristic-based in the first part and machine-learning-based in the second part) to design and develop software agents that replicate several canonical types of human behavior observed in this complex trading mechanism. Leveraging these agents in an agent-based simulation framework, I examine the effect of different bidder compositions (i.e., competing against bidders with different bidding strategies) on auction outcomes and bidder behavior. I use the case of continuous combinatorial auctions to demonstrate both approaches and provide insights that facilitate the implementation of this combinatorial design for online marketplaces. In the third part of my thesis, I conduct human vs. machine style experiments by integrating the bidding agents into an experimental combinatorial auction platform, where participants play against (human-like) agents with certain pre-determined bidding strategies. This part investigates the impact of different competitive environments on bidder behavior and auction outcomes, the underlying reasons for different behaviors, and how bidders learn under different competitive environments

Automated Auction Mechanism Design with Competing Markets

Resource allocation is a major issue in multiple areas of computer science. Despite the wide range of resource types across these areas, for example real commodities in e-commerce and computing resources in distributed computing, auctions are commonly used in solving the optimization problems involved in these areas, since well designed auctions achieve desirable economic outcomes. Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. Following this line of work, we present what we call a grey-box approach to automated auction mechanism design using reinforcement learning and evolutionary computation methods. We first describe a new strategic game, called \cat, which were designed to run multiple markets that compete to attract traders and make profit. The CAT game enables us to address the imbalance between prior work in this field that studied auctions in an isolated environment and the actual competitive situation that markets face. We then define a novel, parameterized framework for auction mechanisms, and present a classification of auction rules with each as a building block fitting into the framework. Finally we evaluate the viability of building blocks, and acquire auction mechanisms by combining viable blocks through iterations of CAT games. We carried out experiments to examine the effectiveness of the grey-box approach. The best mechanisms we learnt were able to outperform the standard mechanisms against which learning took place and carefully hand-coded mechanisms which won tournaments based on the CAT game. These best mechanisms were also able to outperform mechanisms from the literature even when the evaluation did not take place in the context of CAT games. These results suggest that the grey-box approach can generate robust double auction mechanisms and, as a consequence, is an effective approach to automated mechanism design. The contributions of this work are two-fold. First, the grey-box approach helps to design better auction mechanisms which can play a central role in solutions to resource allocation problems in various application domains of computer science. Second, the parameterized view and the reinforcement learning-based search method can be used in other strategic, competitive situations where decision making processes are complex and difficult to design and evaluate manually