20,321 research outputs found
An Evolutionary Framework for Determining Heterogeneous Strategies in Multi-Agent Marketplaces
We propose an evolutionary approach for studying the dynamics of interaction of strategic agents that interact in a marketplace. The goal is to learn which agent strategies are most suited by observing the distribution of the agents that survive in the market over extended periods of time. We present experimental results from a simulated market, where multiple service providers compete for customers using different deployment and pricing schemes. The results show that heterogeneous strategies evolve and co-exist in the same market.marketing;simulation;multi-agent systems;complexity economics;trading agents
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
A group-theoretic approach to formalizing bootstrapping problems
The bootstrapping problem consists in designing agents that learn a model of themselves and the world, and utilize it to achieve useful tasks. It is different from other learning problems as the agent starts with uninterpreted observations and commands, and with minimal prior information about the world. In this paper, we give a mathematical formalization of this aspect of the problem. We argue that the vague constraint of having "no prior information" can be recast as a precise algebraic condition on the agent: that its behavior is invariant to particular classes of nuisances on the world, which we show can be well represented by actions of groups (diffeomorphisms, permutations, linear transformations) on observations and commands. We then introduce the class of bilinear gradient dynamics sensors (BGDS) as a candidate for learning generic robotic sensorimotor cascades. We show how framing the problem as rejection of group nuisances allows a compact and modular analysis of typical preprocessing stages, such as learning the topology of the sensors. We demonstrate learning and using such models on real-world range-finder and camera data from publicly available datasets
Modeling Linkages Between Climate Policy and Land Use: An Overview
Agriculture and forestry play an important role in emitting and storing greenhouse gases. For an efficient and cost-effective climate policy it is therefore important to explicitly include land use, land use change, and forestry (LULUCF) in economy-climate models. This paper gives an overview and assessment of existing approaches to include land use, land-use change, and forestry into climate-economy models or to link economy-climate models to land-use models.Climate Change, Climate Policy, Modeling, Land Use
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