Strategically biased Learning in Market Interactions

IF 0.7 4区 数学 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
G. Bottazzi, Daniele Giachini
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引用次数: 0

Abstract

We consider a market economy where two rational agents are able to learn the distribution of future events. In this context, we study whether moving away from the standard Bayesian belief updating, in the sense of under-reaction to some degree to new information, may be strategically convenient for traders. We show that, in equilibrium, strong under-reaction occurs, thus rational agents may strategically want to bias their learning process. Our analysis points out that the underlying mechanism driving exante strategical decisions is diversity seeking. Finally, we show that, even if robust with respect to strategy selection, strong under-reaction can generate low realized welfare levels because of a long transient phase in which the agent makes poor predictions. JEL Classification: C60, D53, D81, D83, G11, G12
市场互动中的战略偏差学习
我们考虑一个市场经济,其中两个理性主体能够了解未来事件的分布。在这种情况下,我们研究是否从标准的贝叶斯信念更新,在某种程度上对新信息的反应不足的意义上,可能在策略上方便交易者。我们表明,在均衡状态下,会出现强烈的反应不足,因此理性主体可能会在策略上想要偏向他们的学习过程。我们的分析指出,驱动外延战略决策的潜在机制是寻求多样性。最后,我们表明,即使在策略选择方面是稳健的,强烈的反应不足也会产生较低的实现福利水平,因为代理在长时间的瞬态阶段做出较差的预测。JEL分类:C60、D53、D81、D83、G11、G12
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来源期刊
Advances in Complex Systems
Advances in Complex Systems 综合性期刊-数学跨学科应用
CiteScore
1.40
自引率
0.00%
发文量
121
审稿时长
6-12 weeks
期刊介绍: Advances in Complex Systems aims to provide a unique medium of communication for multidisciplinary approaches, either empirical or theoretical, to the study of complex systems. The latter are seen as systems comprised of multiple interacting components, or agents. Nonlinear feedback processes, stochastic influences, specific conditions for the supply of energy, matter, or information may lead to the emergence of new system qualities on the macroscopic scale that cannot be reduced to the dynamics of the agents. Quantitative approaches to the dynamics of complex systems have to consider a broad range of concepts, from analytical tools, statistical methods and computer simulations to distributed problem solving, learning and adaptation. This is an interdisciplinary enterprise.
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