Uri Hertz , Raphael Köster , Marco A. Janssen , Joel Z. Leibo
{"title":"超越矩阵:研究社会生态系统中认知代理的实验方法。","authors":"Uri Hertz , Raphael Köster , Marco A. Janssen , Joel Z. Leibo","doi":"10.1016/j.cognition.2024.105993","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48455,"journal":{"name":"Cognition","volume":"254 ","pages":"Article 105993"},"PeriodicalIF":2.8000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Beyond the matrix: Experimental approaches to studying cognitive agents in social-ecological systems\",\"authors\":\"Uri Hertz , Raphael Köster , Marco A. Janssen , Joel Z. Leibo\",\"doi\":\"10.1016/j.cognition.2024.105993\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":48455,\"journal\":{\"name\":\"Cognition\",\"volume\":\"254 \",\"pages\":\"Article 105993\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognition\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0010027724002798\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognition","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010027724002798","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
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.
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.