39,322 research outputs found
Rational bidding using reinforcement learning: an application in automated resource allocation
The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems.
The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized
Some Econometric Evidence on the Effectiveness of Active Labour Market Programmes in East Germany
In this paper we summarise our previous results on the effectiveness of different kinds of labour market training programmes as well as employment programmes in East Germany after unification. All the studies use the microeconometric evaluation approach and are based on different types of matching estimators. We find some positive earnings effect for on-the-job training and also some positive employment effects for employment programmes. No such effects appear for public sector sponsored (off-the-job) training programmes. Generally, the scope of such analysis is very much hampered by the insufficient quality and quantity of the data available for East Germany. Although in particular the results for public sector sponsored training programmes raise serious doubts about the effectiveness of these programmes, any definite policy conclusion from this and other studies about active labour market policy in East Germany would probably be premature.http://deepblue.lib.umich.edu/bitstream/2027.42/39702/3/wp318.pd
CONSUMERS' WILLINGNESS TO PAY FOR THE COLOR OF SALMON:A CHOICE EXPERIMENT WITH REAL ECONOMIC INCENTIVES
We designed an experimental market with posted prices to investigate consumers' willingness to pay for the color of salmon. Salmon fillets varying in color and price were displayed in 20 choice scenarios. In each scenario, the participants chose which of two salmon fillets they wanted to buy. To induce real economic incentives, each participant drew one unique binding scenario; the participants then had to buy the salmon fillet they had chosen in their binding scenario.Consumer/Household Economics,
Stochastic simulation framework for the Limit Order Book using liquidity motivated agents
In this paper we develop a new form of agent-based model for limit order
books based on heterogeneous trading agents, whose motivations are liquidity
driven. These agents are abstractions of real market participants, expressed in
a stochastic model framework. We develop an efficient way to perform
statistical calibration of the model parameters on Level 2 limit order book
data from Chi-X, based on a combination of indirect inference and
multi-objective optimisation. We then demonstrate how such an agent-based
modelling framework can be of use in testing exchange regulations, as well as
informing brokerage decisions and other trading based scenarios
Measuring and Optimizing Cultural Markets
Social influence has been shown to create significant unpredictability in
cultural markets, providing one potential explanation why experts routinely
fail at predicting commercial success of cultural products. To counteract the
difficulty of making accurate predictions, "measure and react" strategies have
been advocated but finding a concrete strategy that scales for very large
markets has remained elusive so far. Here we propose a "measure and optimize"
strategy based on an optimization policy that uses product quality, appeal, and
social influence to maximize expected profits in the market at each decision
point. Our computational experiments show that our policy leverages social
influence to produce significant performance benefits for the market, while our
theoretical analysis proves that our policy outperforms in expectation any
policy not displaying social information. Our results contrast with earlier
work which focused on showing the unpredictability and inequalities created by
social influence. Not only do we show for the first time that dynamically
showing consumers positive social information under our policy increases the
expected performance of the seller in cultural markets. We also show that, in
reasonable settings, our policy does not introduce significant unpredictability
and identifies "blockbusters". Overall, these results shed new light on the
nature of social influence and how it can be leveraged for the benefits of the
market
A Collaborative Mechanism for Crowdsourcing Prediction Problems
Machine Learning competitions such as the Netflix Prize have proven
reasonably successful as a method of "crowdsourcing" prediction tasks. But
these competitions have a number of weaknesses, particularly in the incentive
structure they create for the participants. We propose a new approach, called a
Crowdsourced Learning Mechanism, in which participants collaboratively "learn"
a hypothesis for a given prediction task. The approach draws heavily from the
concept of a prediction market, where traders bet on the likelihood of a future
event. In our framework, the mechanism continues to publish the current
hypothesis, and participants can modify this hypothesis by wagering on an
update. The critical incentive property is that a participant will profit an
amount that scales according to how much her update improves performance on a
released test set.Comment: Full version of the extended abstract which appeared in NIPS 201
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