机器学习市场中的等弹性代理和财富更新
Isoelastic Agents and Wealth Updates in Machine Learning Markets
摘要：
最近，预测市场已经显示出为机器学习开发灵活机制的相当大的希望。在本文中，代理与等弹性效用被认为是。结果表明，与同质市场的代理商与isoelastic公用事业的成本产生均衡价格对应的α-混合物，与一个特定形式的混合成分有关的每个代理商的财富。我们还证明了对数和其他等弹性代理的财富积累（通过预测训练目标的回报）可以通过施加不同的市场回报结构来实现贝叶斯模型更新和混合权重更新。给出了市场均衡计算的迭代算法。我们证明了具有等弹性效用的代理商的非均匀市场在一些机器学习基准上优于最先进的聚合分类器，如随机森林，以及单个分类器（神经网络，决策树），并表明等弹性组合方法通常优于对数方法。
英文摘要：
Recently, prediction markets have shown considerable promise for developing flexible mechanisms for machine learning. In this paper, agents with isoelastic utilities are considered. It is shown that the costs associated with homogeneous markets of agents with isoelastic utilities produce equilibrium prices corresponding to alpha-mixtures, with a particular form of mixing component relating to each agent's wealth. We also demonstrate that wealth accumulation for logarithmic and other isoelastic agents (through payoffs on prediction of training targets) can implement both Bayesian model updates and mixture weight updates by imposing different market payoff structures. An iterative algorithm is given for market equilibrium computation. We demonstrate that inhomogeneous markets of agents with isoelastic utilities outperform state of the art aggregate classifiers such as random forests, as well as single classifiers (neural networks, decision trees) on a number of machine learning benchmarks, and show that isoelastic combination methods are generally better than their logarithmic counterparts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
