Collective intelligence最新文献_第4页

Social insects and beyond: The physics of soft, dense invertebrate aggregations 群居昆虫及其他:柔软、密集的无脊椎动物聚集的物理学

Collective intelligence Pub Date : 2022-06-22 DOI: 10.1177/26339137221123758

Olga Shishkov, O. Peleg

{"title":"Social insects and beyond: The physics of soft, dense invertebrate aggregations","authors":"Olga Shishkov, O. Peleg","doi":"10.1177/26339137221123758","DOIUrl":"https://doi.org/10.1177/26339137221123758","url":null,"abstract":"Aggregation is a common behavior by which groups of organisms arrange into cohesive groups. Whether suspended in the air (like honey bee clusters), built on the ground (such as army ant bridges), or immersed in water (such as sludge worm blobs), these collectives serve a multitude of biological functions, from protection against predation to the ability to maintain a relatively desirable local environment despite a variable ambient environment. In this review, we survey dense aggregations of a variety of insects, other arthropods, and worms from a soft matter standpoint. An aggregation can be orders of magnitude larger than its individual organisms, consisting of tens to hundreds of thousands of individuals, and yet functions as a coherent entity. Understanding how aggregating organisms coordinate with one another to form a superorganism requires an interdisciplinary approach. We discuss how considering the physics of an aggregation can yield additional insights to those gained from ecological and physiological considerations, given that the aggregating individuals exchange information, energy, and matter continually with the environment and one another. While the connection between animal aggregations and the physics of non-living materials has been proposed since the early 1900s, the recent advent of physics of behavior studies provides new insights into social interactions governed by physical principles. Current efforts focus on eusocial insects; however, we show that these may just be the tip of an iceberg of superorganisms that take advantage of physical interactions and simple behavioral rules to adapt to changing environments. By bringing attention to a wide range of invertebrate aggregations, we wish to inspire a new generation of scientists to explore collective dynamics and bring a deeper understanding of the physics of dense living aggregations.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80384714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Evolution of beliefs in social networks 社会网络中信念的进化

Collective intelligence Pub Date : 2022-05-26 DOI: 10.1177/26339137221111151

P. Paranamana, Pei Wang, Patrick Shafto

引用次数: 0

Collective intelligence for deep learning: A survey of recent developments 深度学习的集体智能:近期发展综述

Collective intelligence Pub Date : 2021-11-29 DOI: 10.1177/26339137221114874

David R Ha, Yu Tang

{"title":"Collective intelligence for deep learning: A survey of recent developments","authors":"David R Ha, Yu Tang","doi":"10.1177/26339137221114874","DOIUrl":"https://doi.org/10.1177/26339137221114874","url":null,"abstract":"In the past decade, we have witnessed the rise of deep learning to dominate the field of artificial intelligence. Advances in artificial neural networks alongside corresponding advances in hardware accelerators with large memory capacity, together with the availability of large datasets enabled practitioners to train and deploy sophisticated neural network models that achieve state-of-the-art performance on tasks across several fields spanning computer vision, natural language processing, and reinforcement learning. However, as these neural networks become bigger, more complex, and more widely used, fundamental problems with current deep learning models become more apparent. State-of-the-art deep learning models are known to suffer from issues that range from poor robustness, inability to adapt to novel task settings, to requiring rigid and inflexible configuration assumptions. Collective behavior, commonly observed in nature, tends to produce systems that are robust, adaptable, and have less rigid assumptions about the environment configuration. Collective intelligence, as a field, studies the group intelligence that emerges from the interactions of many individuals. Within this field, ideas such as self-organization, emergent behavior, swarm optimization, and cellular automata were developed to model and explain complex systems. It is therefore natural to see these ideas incorporated into newer deep learning methods. In this review, we will provide a historical context of neural network research’s involvement with complex systems, and highlight several active areas in modern deep learning research that incorporate the principles of collective intelligence to advance its capabilities. We hope this review can serve as a bridge between the complex systems and deep learning communities.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80258176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 44

Collective decision-making under changing social environments among agents adapted to sparse connectivity 适应稀疏连通性的智能体在变化的社会环境下的集体决策

Collective intelligence Pub Date : 2021-10-26 DOI: 10.1177/26339137221121347

R. Mann

{"title":"Collective decision-making under changing social environments among agents adapted to sparse connectivity","authors":"R. Mann","doi":"10.1177/26339137221121347","DOIUrl":"https://doi.org/10.1177/26339137221121347","url":null,"abstract":"Humans and other animals often follow the decisions made by others because these are indicative of the quality of possible choices, resulting in ‘social response rules’, that is, observed relationships between the probability that an agent will make a specific choice and the decisions other individuals have made. The form of social responses can be understood by considering the behaviour of rational agents that seek to maximise their expected utility using both social and private information. Previous derivations of social responses assume that agents observe all others within a group, but real interaction networks are often characterised by sparse connectivity. Here, I analyse the observable behaviour of rational agents that attend to the decisions made by a subset of others in the group. This reveals an adaptive strategy in sparsely connected networks based on highly simplified social information, that is, the difference in the observed number of agents choosing each option. Where agents employ this strategy, collective outcomes and decision-making efficacy are controlled by the social connectivity at the time of the decision, rather than that to which the agents are accustomed, providing an important caveat for sociality observed in the laboratory and suggesting a basis for the social dynamics of highly connected online communities.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"193 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75533278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Searching for or reviewing evidence improves crowdworkers’ misinformation judgments and reduces partisan bias 搜索或审查证据可以提高众包工作者对错误信息的判断，减少党派偏见

Collective intelligence Pub Date : 2021-08-17 DOI: 10.1177/26339137231173407

P. Resnick, Aljoharah Alfayez, Jane Im, Eric Gilbert

{"title":"Searching for or reviewing evidence improves crowdworkers’ misinformation judgments and reduces partisan bias","authors":"P. Resnick, Aljoharah Alfayez, Jane Im, Eric Gilbert","doi":"10.1177/26339137231173407","DOIUrl":"https://doi.org/10.1177/26339137231173407","url":null,"abstract":"Can crowd workers be trusted to judge whether news-like articles circulating on the Internet are misleading, or does partisanship and inexperience get in the way? And can the task be structured in a way that reduces partisanship? We assembled pools of both liberal and conservative crowd raters and tested three ways of asking them to make judgments about 374 articles. In a no research condition, they were just asked to view the article and then render a judgment. In an individual research condition, they were also asked to search for corroborating evidence and provide a link to the best evidence they found. In a collective research condition, they were not asked to search, but instead to review links collected from workers in the individual research condition. Both research conditions reduced partisan disagreement in judgments. The individual research condition was most effective at producing alignment with journalists’ assessments. In this condition, the judgments of a panel of sixteen or more crowd workers were better than that of a panel of three expert journalists, as measured by alignment with a held out journalist’s ratings.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91293396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings 在分散的多智能体环境中，从公共制裁中获得社会规范的学习型智能体

Collective intelligence Pub Date : 2021-06-16 DOI: 10.1177/26339137231162025

Eugene Vinitsky, R. Koster, J. Agapiou, Edgar A. Duéñez-Guzmán, A. Vezhnevets, Joel Z. Leibo

引用次数: 14

A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machines 社会物理感知调查:一种新兴的感知范式，探索人类和机器的集体智慧

Collective intelligence Pub Date : 2021-04-03 DOI: 10.1177/26339137231170825

Md. Tahmid Rashid, Na Wei, Dong Wang

{"title":"A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machines","authors":"Md. Tahmid Rashid, Na Wei, Dong Wang","doi":"10.1177/26339137231170825","DOIUrl":"https://doi.org/10.1177/26339137231170825","url":null,"abstract":"Propelled by the omnipresence of versatile data capture, communication, and computing technologies, physical sensing has revolutionized the avenue for decisively interpreting the real world. However, various limitations hinder physical sensing’s effectiveness in critical scenarios such as disaster response and urban anomaly detection. Meanwhile, social sensing is contriving as a pervasive sensing paradigm leveraging observations from human participants equipped with portable devices and ubiquitous Internet connectivity to perceive the environment. Despite its virtues, social sensing also inherently suffers from a few drawbacks (e.g., inconsistent reliability and uncertain data provenance). Motivated by the complementary strengths of the two sensing modes, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that explores the collective intelligence of humans and machines to reconstruct the “state of the world,” both physically and socially. While a good number of interesting SPS applications have been studied, several critical unsolved challenges still exist in SPS. In this paper, we provide a comprehensive survey of SPS, emphasizing its definition, key enablers, state-of-the-art applications, potential research challenges, and roadmap for future work. This paper intends to bridge the knowledge gap of existing sensing-focused survey papers by thoroughly examining the various aspects of SPS crucial for building potent SPS systems.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87286631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Interdisciplinarity can aid the spread of better methods between scientific communities 跨学科可以帮助更好的方法在科学界之间传播

Collective intelligence Pub Date : 2020-11-05 DOI: 10.1177/26339137221131816

P. Smaldino, Cailin O’Connor

引用次数: 10