4,316 research outputs found
An Investigation Report on Auction Mechanism Design
Auctions are markets with strict regulations governing the information
available to traders in the market and the possible actions they can take.
Since well designed auctions achieve desirable economic outcomes, they have
been widely used in solving real-world optimization problems, and in
structuring stock or futures exchanges. Auctions also provide a very valuable
testing-ground for economic theory, and they play an important role in
computer-based control systems.
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.
This report aims to survey the theoretical and empirical approaches to
designing auction mechanisms and trading strategies with more weights on
empirical ones, and build the foundation for further research in the field
Evolutionary Optimization of ZIP60: A Controlled Explosion in Hyperspace
The âZIPâ adaptive trading algorithm has been demonstrated to out-perform human traders in experimental studies of continuous double auction (CDA) markets. The original ZIP algorithm requires the values of eight control parameters to be set correctly. A new extension of the ZIP algorithm, called ZIP60, requires the values of 60 parameters to be set correctly. ZIP60 is shown here to produce significantly better results than the original ZIP (called âZIP8â hereafter), for negligable additional computational costs. A genetic algorithm (GA) is used to search the 60-dimensional ZIP60 parameter space, and it finds parameter vectors that yield ZIP60 traders with mean scores significantly better than those of ZIP8s. This paper shows that the optimizing evolutionary search works best when the GA itself controls the dimensionality of the search-space, so that the search commences in an 8-d space and thereafter the dimensionality of the search-space is gradually increased by the GA until it is exploring a 60-d space. Furthermore, the results from ZIP60 cast some doubt on prior ZIP8 results concerning the evolution of new âhybridâ auction mechanisms that appeared to be better than the CDA
Agent-Based Computational Economics
Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.Agent-based computational economics; Autonomous agents; Interaction networks; Learning; Evolution; Mechanism design; Computational economics; Object-oriented programming.
Chain: A Dynamic Double Auction Framework for Matching Patient Agents
In this paper we present and evaluate a general framework for the design of
truthful auctions for matching agents in a dynamic, two-sided market. A single
commodity, such as a resource or a task, is bought and sold by multiple buyers
and sellers that arrive and depart over time. Our algorithm, Chain, provides
the first framework that allows a truthful dynamic double auction (DA) to be
constructed from a truthful, single-period (i.e. static) double-auction rule.
The pricing and matching method of the Chain construction is unique amongst
dynamic-auction rules that adopt the same building block. We examine
experimentally the allocative efficiency of Chain when instantiated on various
single-period rules, including the canonical McAfee double-auction rule. For a
baseline we also consider non-truthful double auctions populated with
zero-intelligence plus"-style learning agents. Chain-based auctions perform
well in comparison with other schemes, especially as arrival intensity falls
and agent valuations become more volatile
Explorations in Evolutionary Design of Online Auction Market Mechanisms
This paper describes the use of a genetic algorithm (GA) to find optimal parameter-values for trading agents that operate in virtual online auction âe-marketplacesâ, where the rules of those marketplaces are also under simultaneous control of the GA. The aim is to use the GA to automatically design new mechanisms for agent-based e-marketplaces that are more efficient than online markets designed by (or populated by) humans. The space of possible auction-types explored by the GA includes the Continuous Double Auction (CDA) mechanism (as used in most of the worldâs financial exchanges), and also two purely one-sided mechanisms. Surprisingly, the GA did not always settle on the CDA as an optimum. Instead, novel hybrid auction mechanisms were evolved, which are unlike any existing market mechanisms. In this paper we show that, when the market supply and demand schedules undergo sudden âshockâ changes partway through the evaluation process, two-sided hybrid market mechanisms can evolve which may be unlike any human-designed auction and yet may also be significantly more efficient than any human designed market mechanism
A theoretical and computational basis for CATNETS
The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing
A Grey-Box Approach to Automated Mechanism Design
Auctions play an important role in electronic commerce, and have been used to
solve problems in distributed computing. Automated approaches to designing
effective auction mechanisms are helpful in reducing the burden of traditional
game theoretic, analytic approaches and in searching through the large space of
possible auction mechanisms. This paper presents an approach to automated
mechanism design (AMD) in the domain of double auctions. We describe a novel
parametrized space of double auctions, and then introduce an evolutionary
search method that searches this space of parameters. The approach evaluates
auction mechanisms using the framework of the TAC Market Design Game and
relates the performance of the markets in that game to their constituent parts
using reinforcement learning. Experiments show that the strongest mechanisms we
found using this approach not only win the Market Design Game against known,
strong opponents, but also exhibit desirable economic properties when they run
in isolation.Comment: 18 pages, 2 figures, 2 tables, and 1 algorithm. Extended abstract to
appear in the proceedings of AAMAS'201
Theoretical and Computational Basis for Economical Ressource Allocation in Application Layer Networks - Annual Report Year 1
This paper identifies and defines suitable market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. --Grid Computing
Agent-Based Models and Human Subject Experiments
This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms
Three Minimal Market Institutions with Human and Algorithmic Agents: Theory and Experimental Evidence
We define and examine three minimal market games (sell-all, buy-sell, and double auction) in the laboratory relative to the predictions of theory. These closed exchange economies have some cash to facilitate transactions, and include feedback. The experiment reveals that (1) the competitive general equilibrium (CGE) and non-cooperative (NCE) models are reasonable anchors to locate most but not all the observed outcomes of the three market mechanisms; (2) outcomes tend to get closer to CGE predictions as the number of players increases; (3) prices and allocations in double auctions deviate persistently from CGE predictions; (4) the outcome paths across the three market mechanisms differ significantly and persistently; (5) importance of market structures for outcomes is reinforced by algorithmic trader simulations; and (6) none of the three markets dominates the others across six measures of performance. Inclusion of some mechanism differences into theory may enhance our understanding of important aspects of markets.Strategic market games, Laboratory experiments, Minimally intelligent agents, Adaptive learning agents, General equilibrium
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