超越矩阵:研究社会生态系统中认知代理的实验方法。

IF 2.8 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Uri Hertz , Raphael Köster , Marco A. Janssen , Joel Z. Leibo
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引用次数: 0

摘要

在社会生态系统中,行为主体之间及其与环境之间的互动对于可持续性应用和了解人类认知如何在环境中发挥作用都非常重要。在这类系统中,环境影响着行为主体的经验和行动,而行为主体的集体行动反过来又改变着环境的社会和物理方面。在此,我们回顾了当前的研究方法,这些方法依赖于精益设计,具有离散的行动和结果,几乎不考虑不同的环境参数和认知需求。然后,我们介绍了多代理强化学习(MARL)方法,该方法以现代人工智能技术为基础,为复杂的社会世界建模提供了新的途径,同时预设了更多的社会世界特征,并允许它们捕捉各种社会现象。这些技术可以反馈到实验室,使复杂社会情境下的实验设计变得更加容易,同时又不影响计算建模的可操作性。我们将通过讨论最近使用 MARL 的几项研究来展示 MARL 的潜力,详细介绍环境设置和认知限制如何导致复杂合作策略的出现。这种新颖的方法可以帮助研究人员将人类认知、可持续发展和人工智能的见解结合起来,解决现实世界中的社会生态系统问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Beyond the matrix: Experimental approaches to studying cognitive agents in social-ecological systems
Social-ecological systems, in which agents interact with each other and their environment are important both for sustainability applications and for under- standing how human cognition functions in context. In such systems, the en- vironment shapes the agents' experience and actions, and in turn collective action of agents changes social and physical aspects of the environment. Here we review current investigation approaches, which rely on a lean design, with discrete actions and outcomes and little scope for varying environmental pa- rameters and cognitive demands. We then introduce multiagent reinforcement learning (MARL) approach, which builds on modern artificial intelligence tech- niques, which provides new avenues to model complex social worlds, while pre- serving more of their characteristics, and allowing them to capture a variety of social phenomena. These techniques can be fed back to the laboratory where they make it easier to design experiments in complex social situations without compromising their tractability for computational modeling. We showcase the potential MARL by discussing several recent studies that have used it, detail- ing the way environmental settings and cognitive constraints can lead to the emergence of complex cooperation strategies. This novel approach can help re- searchers bring together insights from human cognition, sustainability, and AI, to tackle real world problems of social-ecological systems.
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来源期刊
Cognition
Cognition PSYCHOLOGY, EXPERIMENTAL-
CiteScore
6.40
自引率
5.90%
发文量
283
期刊介绍: Cognition is an international journal that publishes theoretical and experimental papers on the study of the mind. It covers a wide variety of subjects concerning all the different aspects of cognition, ranging from biological and experimental studies to formal analysis. Contributions from the fields of psychology, neuroscience, linguistics, computer science, mathematics, ethology and philosophy are welcome in this journal provided that they have some bearing on the functioning of the mind. In addition, the journal serves as a forum for discussion of social and political aspects of cognitive science.
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