沉思vs.直觉:强化学习视角

IF 2.3 Q3 MANAGEMENT
In-Koo Cho , Anna Rubinchik
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引用次数: 2

摘要

为了寻找具有可观察原语的积极决策模型,我们依靠认知神经科学中新兴的文献来构建一个三要素机器(agent)。它的控制单元启动冲动或认知元素来解决固定马尔可夫环境中的问题,所选择的元素取决于问题是平凡的还是新奇的,过去成功的记忆,以及抑制的强度。我们的预测是基于记忆的平稳渐近分布,根据参数的不同,它可以产生不同的“特征”,例如,一个紧张的笨蛋,他可以通过更少的抑制更经常地成功,以及一个悠闲的聪明人,他可以通过更强的抑制冲动(直觉)反应获得更多。正如人们所预料的那样,更强的抑制和更低的认知成本增加了认知因素做出决策的频率。更令人惊讶的是,增加“胡萝卜”和减少“大棒”(在一个更支持性的环境中)可以增强警觉代理(即经常发现新问题的代理)的深思熟虑决策(由认知单元做出)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Contemplation vs. intuition: a reinforcement learning perspective

In a search for a positive model of decision-making with observable primitives, we rely on the burgeoning literature in cognitive neuroscience to construct a three-element machine (agent). Its control unit initiates either impulsive or cognitive elements to solve a problem in a stationary Markov environment, the element chosen depends on whether the problem is mundane or novel, memory of past successes, and the strength of inhibition. Our predictions are based on a stationary asymptotic distribution of the memory, which, depending on the parameters, can generate different “characters”, e.g., an uptight dimwit, who could succeed more often with less inhibition, as well as a laid-back wise-guy, who could gain more with a stronger inhibition of impulsive (intuitive) responses. As one would expect, stronger inhibition and lower cognitive costs increase the frequency of decisions made by the cognitive element. More surprisingly, increasing the “carrot” and reducing the “stick” (being in a more supportive environment) enhance contemplative decisions (made by the cognitive unit) for an alert agent, i.e., the one who identifies novel problems frequently enough.

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来源期刊
CiteScore
2.70
自引率
10.00%
发文量
15
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