11,801 research outputs found
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
Three Minimal Market Institutions with Human and Algorithmic Agents: Theory and Experimental Evidence
We define and examine the performance of three minimal strategic market games (sell-all, buy-sell, and double auction) in laboratory relative to the predictions of theory. Unlike open or partial equilibrium settings of most other experiments, these closed exchange economies have limited amounts of cash to facilitate transactions and include feedback. General equilibrium theory, since it abstracts away from market mechanisms and has no role for money or credit, makes no predictions about how the paths of convergence to the competitive equilibrium may differ across alternative mechanisms. Introduction of markets and money as carriers of process creates the possibility of motion. The laboratory data reveal different paths, and different levels of allocative efficiency in the three settings. The results suggest that abstracting away from all institutional details does not help understand dynamic aspects of market behavior. For example, the oligopoly effect of feedback from buying an endowed good is missed. Inclusion of mechanism differences into theory may enhance our understanding of important aspects of markets and money and help link conventional equilibrium analysis with dynamics
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
MUDA: A Truthful Multi-Unit Double-Auction Mechanism
In a seminal paper, McAfee (1992) presented a truthful mechanism for double
auctions, attaining asymptotically-optimal gain-from-trade without any prior
information on the valuations of the traders. McAfee's mechanism handles
single-parametric agents, allowing each seller to sell a single unit and each
buyer to buy a single unit. This paper presents a double-auction mechanism that
handles multi-parametric agents and allows multiple units per trader, as long
as the valuation functions of all traders have decreasing marginal returns. The
mechanism is prior-free, ex-post individually-rational, dominant-strategy
truthful and strongly-budget-balanced. Its gain-from-trade approaches the
optimum when the market size is sufficiently large.Comment: Accepted to the AAAI2018 conferenc
Double Auctions in Markets for Multiple Kinds of Goods
Motivated by applications such as stock exchanges and spectrum auctions,
there is a growing interest in mechanisms for arranging trade in two-sided
markets. Existing mechanisms are either not truthful, or do not guarantee an
asymptotically-optimal gain-from-trade, or rely on a prior on the traders'
valuations, or operate in limited settings such as a single kind of good. We
extend the random market-halving technique used in earlier works to markets
with multiple kinds of goods, where traders have gross-substitute valuations.
We present MIDA: a Multi Item-kind Double-Auction mechanism. It is prior-free,
truthful, strongly-budget-balanced, and guarantees near-optimal gain from trade
when market sizes of all goods grow to at a similar rate.Comment: Full version of IJCAI-18 paper, with 2 figures. Previous names:
"MIDA: A Multi Item-type Double-Auction Mechanism", "A Random-Sampling
Double-Auction Mechanism". 10 page
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
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