Theory Of Collective Intelligence Research Articles

Application of Comprehensive Evaluation of Line Loss Lean Management Based on Big-Data-Driven Paradigm

Effective line loss management necessitates a model-driven evaluation method to assess its efficiency level thoroughly. This paper introduces a “model-driven + data-driven” approach based on collective intelligence theory to address the limitations of individual evaluation methods in conventional line loss assessments. Initially, eight different evaluation methods are used to form collective intelligence to evaluate the line loss management of power grid enterprises and generate a comprehensive dataset. Then, the data set is trained and evaluated using the random forest algorithm, with Spearman rank correlation coefficient as the test metric, to assess the power grid enterprise’s line loss management level. Combining model-driven and data-driven methods, this integrated approach efficiently leverages the informational value of indicator data while thoroughly considering the causal and associative attributes within the dataset. Based on data from 61 municipal grid enterprises, both the comparison of multiple AI methods and correlation tests of results verify the superiority of the proposed method.

How Collective Intelligence Restrains the Rumor Spreading in Emergencies?

When communicating online under COVID-19 epidemic in 2020, all kinds of rumors are widely spread on the Internet. Coming with the spread of virus, the rumor spreading not only threaten the public health, but also facilitate panic and destroy the harmony of society. This study took the COVID-19 epidemic, a serious public health emergency, as an example. Firstly, we build up a Structural Equation Modeling (SEM) containing the influential factors of Collective Intelligence on unconfirmed information dissemination (UID) on social media, and then conduct an empirical test based on the survey data. The results show that the use of Collective Intelligence can restrain the spread of rumor in public health emergencies, among which critical thinking, collective refutation and proof, crowdsourcing and member heterogeneity are the main restraining factors. However, opinion leaders would facilitate the spread of rumor. This paper not only enriches the theory of Collective Intelligence and rumor management, but also has important practical significance for suppressing rumor spreading during the emergencies.

The Crowd Classification Problem: Social Dynamics of Binary-Choice Accuracy

Decades of research suggest that information exchange in groups and organizations can reliably improve judgment accuracy in tasks such as financial forecasting, market research, and medical decision making. However, we show that improving the accuracy of numeric estimates does not necessarily improve the accuracy of decisions. For binary-choice judgments, also known as classification tasks—for example, yes/no or build/buy decisions—social influence is most likely to grow the majority vote share, regardless of the accuracy of that opinion. As a result, initially, inaccurate groups become increasingly inaccurate after information exchange, even as they signal stronger support. We term this dynamic the “crowd classification problem.” Using both a novel data set and a reanalysis of three previous data sets, we study this process in two types of information exchange: (1) when people share votes only, and (2) when people form and exchange numeric estimates prior to voting. Surprisingly, when people exchange numeric estimates prior to voting, the binary-choice vote can become less accurate, even as the average numeric estimate becomes more accurate. Our findings recommend against voting as a form of decision making when groups are optimizing for accuracy. For those cases where voting is required, we discuss strategies for managing communication to avoid the crowd classification problem. We close with a discussion of how our results contribute to a broader contingency theory of collective intelligence. This paper was accepted by Lamar Pierce, organizations.

THE METHOD OF DEMOCRACY: JOHN DEWEY’S THEORY OF COLLECTIVE INTELLIGENCE BY DAVID RIDLEY

Reviewed by: The Method of Democracy: John Dewey's Theory of Collective Intelligence by David Ridley Veli-Mikko Kauppi By David Ridley The Method of Democracy: John Dewey's Theory of Collective Intelligence Oxford: Peter Lang, 2021, 220pp., incl. index. John Dewey's writings on social intelligence, collective intelligence and the intelligence of the public have gained renascent attention, especially within democratic theory and democratic education. It has been proposed that pragmatism in general and Dewey in particular, offer an alternative model for democratic participation. This model shares many of the goals of deliberative democratic theory or critical theory, but is proposed to be capable of dodging some of the problems often affiliated with them—such as the powerlessness in the face of the rise of non-democratic populist movements that exploit the very means and apparatus of democracy. It is in part this allegedly non-democratic and non-intelligent populism that has motivated David Ridley to search for a 'forgotten alternative' model for populism: one that relies on the popular expressions of the contradictions of society, but one that is democratic and intelligent. In Ridley's thought neoliberalism presents the essential problem affecting and corrupting all aspects of life—a problem which must be solved in its multifariousness. However, so far the suggestions of how to solve it have been bound to fail due to the denial of collective intelligence, which can be observed in both right-wing populist solutions as well as their traditional leftist counterparts (p. 3). [End Page 295] The book is in three parts. Part one: Theoretical Foundations; Part two: A Problematic Situation; and Part three: Reconstruction. In part one Ridley first travels through the history of western intellectual Marxism and the so-called Frankfurt School. Starting with Lukács and his concept of reification, Ridley draws a rather grim picture of the pessimism and retreat from practice to theory, to which the Frankfurt School ended up in by its first generation, most notably Adorno and Horkheimer. This pessimism and distrust towards the potential of masses followed as an essential undercurrent also to its second generation, of which Jurgen Habermas is the most notable representative. As a way out of this dead-end, Ridley examines John Dewey's contribution to critical social theory in the second chapter of his book. This contribution was, at least during the first generation of Frankfurt School and to some extent even today, ignorantly underrated in Europe. This has been a loss for both critical theory and pragmatism, as both apparently share, among other things, the central interest in a just practice of social inquiry as interdisciplinary social research. It is in the second chapter of part one that Ridley presents his main argument: that uncritical everyday experiences can be transformed into critical opinion, or in Ridley's terms to 'intelligent populism', by means of Dewey's notion of collective intelligence. "Democratic social inquiry is in a primitive sense already in the public's hands," Ridley points out (p. 183). As members of democratic societies, we already are inquirers, but the quality of our inquiries defines the quality of our decisions. Co-operative social inquiry is the method of democracy, a procedure to actualize the aims of democracy: "On one level, Dewey's theory of social inquiry is very abstract and banal. Socialising formal practices of inquiry, developed to sophistication by academics and scientists, would help the public develop its collective intelligence, avoid co-optation by reactionary political parties and interests, free it from the manipulative consumption of the culture industry and, in time, reclaim democracy for the people." (p. 183). In Ridley's presentation, accusations of too much optimism regarding the intelligence of the common man in Dewey's work can be objected to as misinterpretation of his ideas, both in their simplicity and in their nuanced complexity. This nuanced complexity of Dewey's method of democracy is then examined in detail, producing the philosophically most interesting contribution of the book. Following the Deweyan pattern of inquiry, Part two focuses on elaborating what the problem is, once an indeterminate situation has emerged. This is done in two chapters, of which the first one focuses on neoliberalism being...

Leveraging Artificial Intelligence for Collective Intelligence

Society faces an increasing number of problems whose scale and complexity are growing exponentially (e.g. global warming, global health crisis/pandemic, food distribution etc.). The advent of artificial intelligence (AI) holds the promise to help us bring together the knowledge and associated action to solve them. However, our ability to tap into the distributed creative potential of humans and AI necessarily involves figuring out how to make them work together as a collective. In this symposium, we bring together diverse perspectives from cross-disciplinary scholars attacking this problem using diverse theoretical lenses and research methods. Together these five papers (qualitative, computational, empirical, and theoretical) create an opportunity to discuss multiple ways for leveraging AI for augmenting individual cognition, interactive processes, and collective functioning. Our goal is to provoke discussion at the intersection of computational social science, complexity sciences, information science, and management science to stimulate progress toward the human-AI systems that provide the greatest hope of solving the major problems threatening humanity. Humans Plus Machines: Diverging Discourse and Structuring of AI Technologies Among Senior Executives Presenter: Rebecca Hinds; Stanford U. Presenter: Melissa Valentine; Stanford U. Designing Diversity for Collective Intelligence with Expert-Aware AI Presenter: James Evans; U. Of Chicago Presenter: Jamshid Sourati; U. Of Chicago Online Diversity improves Geopolitical Forecasting Accuracy as a Function of Group size Presenter: Niccolo Pescetelli; Max Planck Institute for Human Development Presenter: Alex Rutherford; Max Planck Institute for Human Development Presenter: Iyad Rahwan; Max Planck Institute for Human Development Understanding how Machine Behavior Shapes Group Interaction and Effectiveness Presenter: Malte Friedrich Jung; Stanford U. A Functional Systems Theory of Collective Intelligence: Articulating a role for AI in CI Presenter: Pranav Gupta; Carnegie Mellon U. Presenter: Anita Williams Woolley; Carnegie Mellon U.

Collective intelligence evolution using ant colony optimization and neural networks

Recently, theory of collective intelligence (CI) evolution is proposed as a meta algorithm toward artificial general intelligence. But the only implementation of the CI algorithm of the theory is the Monte Carlo tree search (MCTS) used by AlphaZero. Since ant colony optimization (ACO) is an extensively used CI algorithm, it is useful to implement CI evolution using ACO. A genetic version of ACO is adapted to satisfy the CI evolution theory by two methods. One method is realized by using a policy network, namely policy network guided ACO (P-ACO). The other method is realized by using a policy network and a value network, namely policy and value network guided ACO (PV-ACO). Both methods of ACO evolution algorithm are applied to Tic-Tac-Toe and Four in a Row, where traditional ACO played poorly compared to the tree search algorithm, e.g., MCTS. Computational experiments are done to compare both methods with pure ACO and MCTS. As a result, the intelligence level of ACO evolution algorithm quickly exceeds pure ACO and MCTS. In this article, the performance of ACO evolution algorithm is analyzed and the feasibility of applying the CI evolution theory to a specific application is verified.

Neuroevolutionary paradigm as a model for the formation of large nanotechnological projects

Neuroevolutionary approach is a branch of collective intelligence theory. Social group in this approach is compared to mathematical neural network with people as neurons and social connections as synapses. It is argued here that large scientific projects like nanotechnological one can be analysed from this point of view. Annual conference of the Nanotechnological Society of Russia has been taken as an example.

Probability Collectives: A multi-agent approach for solving combinatorial optimization problems

Complex systems generally have many components. It is not possible to understand such complex systems only by knowing the individual components and their behavior. This is because any move by a component affects the further decisions/moves by other components and so on. In a complex system, as the number of components grows, complexity also grows exponentially, making the entire system to be seen as a collection of subsystems or a Multi-Agent System (MAS). The major challenge is to make these agents work in a coordinated way, optimizing their local utilities and contributing the maximum towards optimization of the global objective. This paper discusses the theory of Collective Intelligence (COIN) using the modified version of Probability Collectives (PC) to achieve the global goal. The paper successfully demonstrated this approach by optimizing the Rosenbrock function in which the coupled variables are seen as autonomous agents working collectively to achieve the function optimum. To demonstrate the PC approach on combinatorial optimization problems, two test cases of the Multi-Depot Multiple Traveling Salesmen Problem (MDMTSP) with 3 depots, 3 vehicles and 15 nodes are solved. In these cases, the vehicles are considered as autonomous agents collectively searching the minimum cost path. PC is successfully accompanied with insertion, elimination and swapping heuristic techniques. The optimum results to the Rosenbrock function and both the MDMTSP test cases are obtained at reasonable computational costs.

Cyber Ethics: Bodies or Bytes?

Information ethics requires: (1) an ethical theory that recognizes the importance of the body; and (2) a materialist theory of information. Alasdair MacIntyre's ethics of acknowledged dependence is an example of the former. It holds that the virtues we need to achieve both personal and common moral goods recognize networks of dependence based upon bodily vulnerability and disability. Pierre Lévy's theory of collective intelligence is an example of a view that disregards both requirements. Based upon a secular appropriation of medieval angelology, it holds that in cyberspace, social relations are dematerialized, and that information is an incorporeal substance flowing between disembodied minds. The new anthropological space of virtuality that Lévy holds to be ushered in by new electronic information technologies proposes a radical rethinking of ethics. But on close inspection, his information ethics turns out to be stripped of any serious moral dimension. To address serious moral issues, information ethics cannot abstract from our bodily animal nature. Several recent studies recognize that information is an effect of social relations between embodied subjects. They provide the materialist information theory required for meaningful information ethics.

Cyber Ethics: Bodies or Bytes?

Information ethics requires: (1) an ethical theory that recognizes the importance of the body; and (2) a materialist theory of information. Alasdair MacIntyre's ethics of acknowledged dependence is an example of the former. It holds that the virtues we need to achieve both personal and common moral goods recognize networks of dependence based upon bodily vulnerability and disability. Pierre Lévy's theory of collective intelligence is an example of a view that disregards both requirements. Based upon a secular appropriation of medieval angelology, it holds that in cyberspace, social relations are dematerialized, and that information is an incorporeal substance flowing between disembodied minds. The new anthropological space of virtuality that Lévy holds to be ushered in by new electronic information technologies proposes a radical rethinking of ethics. But on close inspection, his information ethics turns out to be stripped of any serious moral dimension. To address serious moral issues, information ethics cannot abstract from our bodily animal nature. Several recent studies recognize that information is an effect of social relations between embodied subjects. They provide the materialist information theory required for meaningful information ethics.