22,747 research outputs found
Impact of considering need for work and risk on performance of construction contractors: An agent-based approach
Competitive bidding is the main mechanism of allocating projects in the construction market. In the traditional single criterion bidding method, the markup decision has a significant impact on a contractor's business success. Contractors usually take into consideration several factors in the process of determining their markup. This study has reviewed the literature and identified a range of contractors' behaviors when making their markup decision within a competitive bidding environment. An additive markup function consisting of three components, namely competition, risk, and need for work, was developed in order to replicate markup behaviors of contractors. Then, agent-based modeling has been employed for simulating the bidding process within a market formed of a set of heterogeneous contractors with different risk attitudes and defined markup behaviors. This model was used to study the impact of considering need for work and risk allowance in markup determination on financial performance of contractors in various market scenarios. Results suggest that the optimal policy is moderation in both dimensions of risk attitude and need for work
Real-Time Bidding by Reinforcement Learning in Display Advertising
The majority of online display ads are served through real-time bidding (RTB)
--- each ad display impression is auctioned off in real-time when it is just
being generated from a user visit. To place an ad automatically and optimally,
it is critical for advertisers to devise a learning algorithm to cleverly bid
an ad impression in real-time. Most previous works consider the bid decision as
a static optimization problem of either treating the value of each impression
independently or setting a bid price to each segment of ad volume. However, the
bidding for a given ad campaign would repeatedly happen during its life span
before the budget runs out. As such, each bid is strategically correlated by
the constrained budget and the overall effectiveness of the campaign (e.g., the
rewards from generated clicks), which is only observed after the campaign has
completed. Thus, it is of great interest to devise an optimal bidding strategy
sequentially so that the campaign budget can be dynamically allocated across
all the available impressions on the basis of both the immediate and future
rewards. In this paper, we formulate the bid decision process as a
reinforcement learning problem, where the state space is represented by the
auction information and the campaign's real-time parameters, while an action is
the bid price to set. By modeling the state transition via auction competition,
we build a Markov Decision Process framework for learning the optimal bidding
policy to optimize the advertising performance in the dynamic real-time bidding
environment. Furthermore, the scalability problem from the large real-world
auction volume and campaign budget is well handled by state value approximation
using neural networks.Comment: WSDM 201
Statistical Arbitrage Mining for Display Advertising
We study and formulate arbitrage in display advertising. Real-Time Bidding
(RTB) mimics stock spot exchanges and utilises computers to algorithmically buy
display ads per impression via a real-time auction. Despite the new automation,
the ad markets are still informationally inefficient due to the heavily
fragmented marketplaces. Two display impressions with similar or identical
effectiveness (e.g., measured by conversion or click-through rates for a
targeted audience) may sell for quite different prices at different market
segments or pricing schemes. In this paper, we propose a novel data mining
paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and
exploiting price discrepancies between two pricing schemes. In essence, our
SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per
action)-based campaigns and CPM (cost per mille impressions)-based ad
inventories; it statistically assesses the potential profit and cost for an
incoming CPM bid request against a portfolio of CPA campaigns based on the
estimated conversion rate, bid landscape and other statistics learned from
historical data. In SAM, (i) functional optimisation is utilised to seek for
optimal bidding to maximise the expected arbitrage net profit, and (ii) a
portfolio-based risk management solution is leveraged to reallocate bid volume
and budget across the set of campaigns to make a risk and return trade-off. We
propose to jointly optimise both components in an EM fashion with high
efficiency to help the meta-bidder successfully catch the transient statistical
arbitrage opportunities in RTB. Both the offline experiments on a real-world
large-scale dataset and online A/B tests on a commercial platform demonstrate
the effectiveness of our proposed solution in exploiting arbitrage in various
model settings and market environments.Comment: In the proceedings of the 21st ACM SIGKDD international conference on
Knowledge discovery and data mining (KDD 2015
Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services
The application of autonomous agents by the provisioning and usage of computational services 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 service provisioning and usage of Grid services, 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 bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms
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
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