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首页> 外文期刊>ACM transactions on intelligent systems >Automated Pricing in a Multiagent Prediction Market Using a Partially Observable Stochastic Game
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Automated Pricing in a Multiagent Prediction Market Using a Partially Observable Stochastic Game

机译：使用部分可观察的随机博弈的多主体预测市场中的自动定价

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Prediction markets offer an efficient market-based mechanism to aggregate large amounts of dispersed or distributed information from different people to predict the possible outcome of future events. Recently, automated prediction markets where software trading agents perform market operations such as trading and updating beliefs on behalf of humans have been proposed. A challenging aspect in automated prediction markets is to develop suitable techniques that can be used by automated trading agents to update the price at which they should trade securities related to an event so that they can increase their profit. This problem is nontrivial, as the decision to trade and the price at which trading should occur depends on several dynamic factors, such as incoming information related to the event for which the security is being traded, the belief-update mechanism and risk attitude of the trading agent, and the trading decision and trading prices of other agents. To address this problem, we have proposed a new behavior model for trading agents based on a game-theoretic framework called partially observable stochastic game with information (POSGI). We propose a correlated equilibrium (CE)-based solution strategy for this game that allows each agent to dynamically choose an action (to buy or sell or hold) in the prediction market. We have also performed extensive simulation experiments using the data obtained from the Intrade prediction market for four different prediction markets. Our results show that our POSGI model and CE strategy produces prices that are strongly correlated with the prices of the real prediction markets. Results comparing our CE strategy with five other strategies commonly used in similar market show that our CE strategy improves price predictions and provides higher utilities to the agents compared to other existing strategies.
机译：预测市场提供了一种有效的基于市场的机制，可以汇总来自不同人员的大量分散或分布的信息，以预测未来事件的可能结果。最近，已经提出了自动预测市场，在该市场中，软件交易代理人代表人类进行诸如交易和更新信念的市场操作。自动化预测市场中一个具有挑战性的方面是开发适合的技术，自动化交易代理可以使用这些技术来更新交易与事件相关的证券的价格，从而增加利润。这个问题并非易事，因为交易决策和应该进行的交易价格取决于几个动态因素，例如与交易证券的事件有关的传入信息，交易者的信念更新机制和风险态度。交易代理商，以及其他代理商的交易决策和交易价格。为了解决这个问题，我们提出了一种基于博弈论框架的交易代理行为模型，该博弈论框架被称为带有信息的部分可观察的随机博弈（POSGI）。我们为此游戏提出了一种基于相关均衡（CE）的解决方案策略，该策略允许每个代理在预测市场中动态选择一个动作（购买或出售或持有）。我们还使用从Intrade预测市场获得的四个不同预测市场的数据进行了广泛的模拟实验。我们的结果表明，我们的POSGI模型和CE策略产生的价格与真实预测市场的价格密切相关。将我们的消费电子产品策略与同类市场中常用的其他五种策略进行比较的结果表明，与其他现有策略相比，我们的消费电子产品策略改善了价格预测并为代理商提供了更高的效用。

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