摘要翻译：
我们研究了在一个人工投资场景中各种agent策略的性能。代理配备了预算$X(t)$，并在每个时间步骤中投入预算的特定部分$Q(t)$。投资回报率(RoI)$r(t)$是一个具有不同类型和水平噪声的周期函数。规避风险的代理选择与预期的正RoI成正比的分数$q(t)$,而寻求风险的代理如果预测RoI为正（“一切都在红色”），则总是选择最大值$q_{max}$。除了这些不同的策略之外，代理还具有不同的能力来预测未来$r(t)$，这取决于它们内部的复杂性。这里，我们比较使用技术分析（如移动最小二乘法）的“零智能”智能体与使用强化学习或遗传算法预测$R(t)$的智能体。代理的性能是通过其在一定时间步数后的平均预算增长来衡量的。我们给出了大量的计算机模拟结果，结果表明，对于给定的人工环境，(i)风险寻求策略优于风险规避策略，(ii)遗传算法能够自己找到最优策略，从而优于其他考虑的预测方法。
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英文标题：
《Risk-Seeking versus Risk-Avoiding Investments in Noisy Periodic
  Environments》
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作者：
J. Emeterio Navarro Barrientos, Frank E. Walter, Frank Schweitzer
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最新提交年份：
2008
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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一级分类：Computer Science        计算机科学
二级分类：Computational Engineering, Finance, and Science        计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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英文摘要：
  We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, $x(t)$, and at each time step invest a particular fraction, $q(t)$, of their budget. The return on investment (RoI), $r(t)$, is characterized by a periodic function with different types and levels of noise. Risk-avoiding agents choose their fraction $q(t)$ proportional to the expected positive RoI, while risk-seeking agents always choose a maximum value $q_{max}$ if they predict the RoI to be positive ("everything on red"). In addition to these different strategies, agents have different capabilities to predict the future $r(t)$, dependent on their internal complexity. Here, we compare 'zero-intelligent' agents using technical analysis (such as moving least squares) with agents using reinforcement learning or genetic algorithms to predict $r(t)$. The performance of agents is measured by their average budget growth after a certain number of time steps. We present results of extensive computer simulations, which show that, for our given artificial environment, (i) the risk-seeking strategy outperforms the risk-avoiding one, and (ii) the genetic algorithm was able to find this optimal strategy itself, and thus outperforms other prediction approaches considered.
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PDF链接：
https://arxiv.org/pdf/0801.4305