34 research outputs found
A Market-based Approach to Multi-factory Scheduling
In this paper, we report on the design of a novel market-based approach for decentralised scheduling across multiple factories. Specifically, because of the limitations of scheduling in a centralised manner -- which requires a center to have complete and perfect information for optimality and the truthful revelation of potentially commercially private preferences to that center -- we advocate an informationally decentralised approach that is both agile and dynamic. In particular, this work adopts a market-based approach for decentralised scheduling by considering the different stakeholders representing different factories as self-interested, profit-motivated economic agents that trade resources for the scheduling of jobs. The overall schedule of these jobs is then an emergent behaviour of the strategic interaction of these trading agents bidding for resources in a market based on limited information and their own preferences. Using a simple (zero-intelligence) bidding strategy, we empirically demonstrate that our market-based approach achieves a lower bound efficiency of 84%. This represents a trade-off between a reasonable level of efficiency (compared to a centralised approach) and the desirable benefits of a decentralised solution
An Ontology for Formalising Agreement Patterns in Auction Markets
Knowledge and best practices on auction systems are cur- rently disseminated across the research literature, which limits its access, reuse, evaluation and feedback by practitioners. This article presents a systematic approach to collect this knowledge as design patterns, in order to provide assistance to software developers. An ontology has been de- _ned for formalising design patterns in auction systems, with the aim of improving its searchability by software developers. Finally, a case study illustrates how the proposed pattern ontology provides assistance in the development of a dynamic pricing model for an e-commerce servic
Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market
We report successful results from using deep learning neural networks (DLNNs)
to learn, purely by observation, the behavior of profitable traders in an
electronic market closely modelled on the limit-order-book (LOB) market
mechanisms that are commonly found in the real-world global financial markets
for equities (stocks & shares), currencies, bonds, commodities, and
derivatives. Successful real human traders, and advanced automated algorithmic
trading systems, learn from experience and adapt over time as market conditions
change; our DLNN learns to copy this adaptive trading behavior. A novel aspect
of our work is that we do not involve the conventional approach of attempting
to predict time-series of prices of tradeable securities. Instead, we collect
large volumes of training data by observing only the quotes issued by a
successful sales-trader in the market, details of the orders that trader is
executing, and the data available on the LOB (as would usually be provided by a
centralized exchange) over the period that the trader is active. In this paper
we demonstrate that suitably configured DLNNs can learn to replicate the
trading behavior of a successful adaptive automated trader, an algorithmic
system previously demonstrated to outperform human traders. We also demonstrate
that DLNNs can learn to perform better (i.e., more profitably) than the trader
that provided the training data. We believe that this is the first ever
demonstration that DLNNs can successfully replicate a human-like, or
super-human, adaptive trader operating in a realistic emulation of a real-world
financial market. Our results can be considered as proof-of-concept that a DLNN
could, in principle, observe the actions of a human trader in a real financial
market and over time learn to trade equally as well as that human trader, and
possibly better.Comment: 8 pages, 4 figures. To be presented at IEEE Symposium on
Computational Intelligence in Financial Engineering (CIFEr), Bengaluru; Nov
18-21, 201
Fostering efficiency of computational resource allocation - Integrating information services into markets
The application of market mechanisms for the allocation of computing services is a demanding task,
which requires bridging economic and associated technical challenges. Even if the market-based
approach promises an efficient allocation of computing services, the wide heterogeneity of consumer
requirements and the diversity of computational services on provider side are challenging the
processes of finding, allocating, and using an appropriate service in an autonomous way. The focus of
the most papers is mainly devoted to the optimization embedded in the allocation process itself.
However, we think that the optimization process starts much earlier and contains the information
gathering until the final market-based resource allocations.
In this paper we introduce an integrated framework for market-based allocation of computing
services, integrating information retrieval of market information, prediction models, bidding
strategies and marked mechanisms. As proof-of-concept, we implemented a first prototype of the
framework. Furthermore, we propose a methodology for evaluating strategic behavior in market
mechanisms with bidding strategies using market information and statistical prediction techniques.
First simulation results show strategic behavior in selected market mechanisms by applying the
proposed techniques
Which Trading Agent is Best? Using a Threaded Parallel Simulation of a Financial Market Changes the Pecking-Order
This paper presents novel results generated from a new simulation model of a
contemporary financial market, that cast serious doubt on the previously widely
accepted view of the relative performance of various well-known public-domain
automated-trading algorithms. Various public-domain trading algorithms have
been proposed over the past 25 years in a kind of arms-race, where each new
trading algorithm was compared to the previous best, thereby establishing a
"pecking order", i.e. a partially-ordered dominance hierarchy from best to
worst of the various trading algorithms. Many of these algorithms were
developed and tested using simple minimal simulations of financial markets that
only weakly approximated the fact that real markets involve many different
trading systems operating asynchronously and in parallel. In this paper we use
BSE, a public-domain market simulator, to run a set of experiments generating
benchmark results from several well-known trading algorithms. BSE incorporates
a very simple time-sliced approach to simulating parallelism, which has obvious
known weaknesses. We then alter and extend BSE to make it threaded, so that
different trader algorithms operate asynchronously and in parallel: we call
this simulator Threaded-BSE (TBSE). We then re-run the trader experiments on
TBSE and compare the TBSE results to our earlier benchmark results from BSE.
Our comparison shows that the dominance hierarchy in our more realistic
experiments is different from the one given by the original simple simulator.
We conclude that simulated parallelism matters a lot, and that earlier results
from simple simulations comparing different trader algorithms are no longer to
be entirely trusted.Comment: 6 pages, 2 tables, 3 figures, to be presented at European Modelling
and Simulation Symposium (EMSS2020
Evaluating the impacts of farmersâ behaviors on a hypothetical agricultural water market based on double auction
Agricultural water markets are considered effective instruments to mitigate the impacts of water scarcity and to increase crop production. However, previous studies have limited understanding of how farmersâ behaviors affect the performance of water markets. This study develops an agent-based model to explicitly incorporate farmersâ behaviors, namely irrigation behavior (represented by farmersâ sensitivity to soil water deficit k) and bidding behavior (represented by farmersâ rent seeking l and learning rate b), in a hypothetical water market based on a double auction. The model is applied to the Guadalupe River Basin in Texas to simulate a hypothetical agricultural water market under various hydrological conditions. It is found that the joint impacts of the behavioral parameters on the water market are strong and complex. In particular, among the three behavioral parameters, k affects the water market potential and its impacts on the performance of the water market are significant under most scenarios. The impacts of l or b on the performance of the water market depend on the other two parameters. The water market could significantly increase crop production only when the following conditions are satisfied: (1) k is small and (2) l is small and/or b is large. The first condition requires efficient irrigation scheduling, and the second requires well-developed water market institutions that provide incentives to bid true valuation of water permits
Market Making via Reinforcement Learning.
APPEAL FROM A JUDGMENT AND CONVICTION RENDERED AGAINST DEFENDANT/APPELLANT BY THE HONORABLE ROBERT F. OWENS, CIRCUIT COURT JUDGE OF THE FIFTH CIRCUIT COURT, ST. GEORGE DEPARTMENT IN AND FOR WASHINGTON COUNTY, STATE OF UTAH, AND APPEAL FROM THAT TRIAL COURT\u27S DENIAL OF A NEW TRIAL FOR DEFENDAN