Know your enemies and know yourself in the real-time bidding function optimisation

Real-time bidding (RTB) is a popular method to sell online ad space inventory using real-time auctions to determine which advertiser gets to make the ad impression. Advertisers can take user information into account when making their bids and get more control over the process. The goal of an optimal bidding function is to maximise the overall effectiveness of the ad campaigns defined by the advertisers under a certain budget constraint. A straightforward solution would be to model the bidding function in an explicit form. However, such functional solutions lack generality in practice and are insensitive to the stochastic behaviour of other bidders in the environment. In this paper, we propose to formulate the online auctions into a general mean field multi-agent framework, in which the agents compete with each other and each agent's best response strategy depends on its opponents' actions. We firstly introduce a novel Deep Attentive Survival Analysis (DASA) model to estimate the opponent's action distribution on the ad impression level which outperforms state-of-the-art survival analysis. Furthermore, we introduce the DASA model as the opponent model into the Mean Field Deep Deterministic Policy Gradients (DDPG) algorithm for each agent to learn the optimal bidding strategy and converge to the mean field equilibrium. The experiments have shown that with the inference of the market, the market converges to the equilibrium faster while playing against both fixed strategy agents and dynamic learning agents

Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime†models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches.dynamic pricing;trading agent competition;agent-mediated electronic commerce;dynamic markets;economic regimes;enabling technologies;price forecasting;supply-chain