scholarly journals Beyond collective intelligence: Collective adaptation

2022 ◽  
Author(s):  
Mirta Galesic ◽  
Daniel Barkoczi ◽  
Andrew Berdahl ◽  
Dora Biro ◽  
Giuseppe Carbone ◽  
...  
Keyword(s):  
Conceptual Framework ◽  
Statistical Physics ◽  
Computational Models ◽  
Collective Intelligence ◽  
Social Systems ◽  
Cognitive Strategies ◽  
Social Environments ◽  
Collective Phenomena ◽  
Integrate Research

We develop a conceptual framework for studying collective adaptation: the process of iterative co-adaptation of cognitive strategies, social environments, and problem structures. Going beyond searching for “intelligent” collectives, we integrate research from different disciplines to show how collective adaptation perspective can help explain why similar collectives can follow very different and sometimes counter-intuitive trajectories. We further discuss how this perspective explains why successful collectives appear to have a general collective intelligence factor, why collectives rarely optimize their behaviour for a single problem, why their behaviours can appear myopic, and why playful exploration of alternative social systems can be useful. We describe different approaches for the study of collective adaptation, including computational models inspired by evolution and statistical physics. The framework of collective adaptation enables the integration and formalization of knowledge about human collective phenomena and opens doors to a rigorous transdisciplinary pursuit of important outstanding questions about human sociality.

scholarly journals Models of Identity Signaling

2022 ◽  
Author(s):  
Paul E. Smaldino
Keyword(s):  
Public Health ◽  
Social Learning ◽  
Human Behavior ◽  
Disease Transmission ◽  
Computational Models ◽  
Social Systems ◽  
Formal Models ◽  
Social Environments ◽  
Identity Signaling ◽  
Encrypted Communication

Identity signals inform receivers of a signaler’s membership in a subset of individuals, and in doing so shape cooperation, conflict, and social learning. Understanding the use and consequences of identity signaling is therefore critical for a complete science of collective human behavior. And, as with all complex social systems, this understanding is aided by the use of formal mathematical and computational models. Here I review some formal models of identity signaling. I divide these models into two categories. The first concerns models that assert how identity functions as a signal and test the consequences of those assertions, with a focus on public health behavior and disease transmission. The second concerns models used to understand how identity signals operate strategically in different social environments, with a focus on covert or encrypted communication.

Harnessing Collective Intelligence Through Pattern Mining in Social Computational Systems

2018 ◽  
pp. 91-110
Author(s):  
Gaganmeet Kaur Awal ◽  
K. K. Bharadwaj
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Pattern Mining ◽  
Collective Intelligence ◽  
Social Systems ◽  
Big Data Analytics ◽  
Diverse Range ◽  
Business Applications ◽  
Computational Systems ◽  
The Web

Due to the digital nature of the web, the social web mimics the real-world social dynamics that manifest themselves as data and can be easily mined as patterns, making the web a fertile ground for business and research-oriented analytical applications. Collective intelligence (CI) is a multifaceted field with roots in sociology, biology, and many other disciplines. Various manifestations of CI support the successful existence of large-scale social systems. This chapter gives an overview of the principles of CI and the concept of “wisdom of crowds” and highlights how to maximize the potential of big data analytics for CI. Also, various techniques and approaches have been described that leverage these CI concepts across a diverse range of ever-evolving social systems for commercial business applications like influence mining, expertise discovery, etc.

Modeling Cognitive Agents for Social Systems and a Simulation in Urban Dynamics

2009 ◽  
pp. 104-124 ◽  
Author(s):  
Yu Zhang ◽  
Mark Lewis ◽  
Christine Drennon ◽  
Michael Pellon ◽  
Coleman
Keyword(s):  
Decision Making ◽  
Social Structure ◽  
Social Systems ◽  
Three Dimensional ◽  
Decision Making Process ◽  
Social Environments ◽  
Cognitive Agents ◽  
Agent Interactions ◽  
Multi Agent ◽  
Cognitive Decision

Multi-agent systems have been used to model complex social systems in many domains. The entire movement of multi-agent paradigm was spawned, at least in part, by the perceived importance of fostering human-like adjustable autonomy and behaviors in social systems. But, efficient scalable and robust social systems are difficult to engineer. One difficulty exists in the design of how society and agents evolve and the other diffi- culties exist in how to capture the highly cognitive decision-making process that sometimes follows intuition and bounded rationality. We present a multi-agent architecture called CASE (Cognitive Agents for Social Environments). CASE provides a way to embed agent interactions in a three-dimensional social structure. It also presents a computational model for an individual agent’s intuitive and deliberative decision-making process. This chapter also presents our work on creating a multi-agent simulation which can help social and economic scientists use CASE agents to perform their tests. Finally, we test the system in an urban dynamic problem. Our experiment results suggest that intuitive decision-making allows the quick convergence of social strategies, and embedding agent interactions in a three-dimensional social structure speeds up this convergence as well as maintains the system’s stability.

Domains for Computation in Mathematics, Physics and Exact Real Arithmetic

1997 ◽  
Vol 3 (4) ◽  
pp. 401-452 ◽  
Author(s):  
Abbas Edalat
Keyword(s):  
Statistical Physics ◽  
Computational Models ◽  
Metric Spaces ◽  
Probability Distributions ◽  
Period Doubling ◽  
Computer Arithmetic ◽  
Iterated Function Systems ◽  
Integration Theory ◽  
Elementary Functions ◽  
Invariant Distributions

AbstractWe present a survey of the recent applications of continuous domains for providing simple computational models for classical spaces in mathematics including the real line, countably based locally compact spaces, complete separable metric spaces, separable Banach spaces and spaces of probability distributions. It is shown how these models have a logical and effective presentation and how they are used to give a computational framework in several areas in mathematics and physics. These include fractal geometry, where new results on existence and uniqueness of attractors and invariant distributions have been obtained, measure and integration theory, where a generalization of the Riemann theory of integration has been developed, and real arithmetic, where a feasible setting for exact computer arithmetic has been formulated. We give a number of algorithms for computation in the theory of iterated function systems with applications in statistical physics and in period doubling route to chaos; we also show how efficient algorithms have been obtained for computing elementary functions in exact real arithmetic.

scholarly journals The dynamics of information-driven coordination phenomena: A transfer entropy analysis

2016 ◽  
Vol 2 (4) ◽  
pp. e1501158 ◽  
Author(s):  
Javier Borge-Holthoefer ◽  
Nicola Perra ◽  
Bruno Gonçalves ◽  
Sandra González-Bailón ◽  
Alex Arenas ◽  
...  
Keyword(s):  
Information Transfer ◽  
Social Systems ◽  
Transfer Entropy ◽  
System Level ◽  
Directed Network ◽  
Characteristic Time Scale ◽  
Entropy Analysis ◽  
Collective Phenomena ◽  
Social Phenomena ◽  
Social Events

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.

scholarly journals A functional paradigm for evaluating culture: An example with cetaceans

2012 ◽  
Vol 58 (2) ◽  
pp. 271-286 ◽  
Author(s):  
Alison Linda Greggor
Keyword(s):  
Social Learning ◽  
Social Systems ◽  
Social Environments ◽  
Species Analysis ◽  
Learning Capabilities ◽  
Physical Attributes ◽  
Fitness Benefits ◽  
The Individual ◽  
Taxonomic Groups ◽  
Learning Ecology

Abstract Nonhuman culture was first considered in nonhuman primates because they are genetically similar to humans. However, evolution is not progressive and therefore many species may occupy niches that favor socially transmitted, group specific behavior. Not surprisingly, evidence for culture has accrued in several taxonomic groups, including cetaceans. If culture is an adaptation, it is imperative we understand the factors that favor its formation. Understanding the evolutionary origin of culture will allow for a wider range of species to be studied, including those that are difficult to test in the laboratory. I propose a broad-based functional paradigm for evaluating nonhuman culture; based on the idea that while not all cultural behaviors may garner fitness benefits to the individual, the ecological and social environments in which cultural behaviors evolved must have favored the physical attributes and social learning capabilities that allow for cultural formation. Specifically this framework emphasizes the relationships between social learning, ecology, social systems, and biology in relation to culture. I illustrate the utility of the functional paradigm with evidence from the ceteacean group, while setting the stage for a stringent species by species analysis. By means of contextualizing culture, the Functional Paradigm can evaluate a species’ potential to exhibit culture and can investigate potentially cultural behaviors [Current Zoology 58 (2): 271–286, 2012].

Resilience and transparency in social systems

Kybernetes ◽  
2019 ◽  
Vol 48 (4) ◽  
pp. 715-726 ◽  
Author(s):  
Dai Griffiths
Keyword(s):  
Organizational Design ◽  
Empirical Studies ◽  
Social Systems ◽  
Full Range ◽  
Social Environments ◽  
Content Type ◽  
Management Interventions ◽  
The Relationship ◽  
Practical Implications ◽  
Transparency And Accountability

Purpose This paper draws on the literature of cybernetics to argue that the resilience of organizations can be diminished by an unconsidered maximization of transparency and accountability. In doing so, it critically examines the concept of resilience and the relationship of resilience to neoliberalism. Design/methodology/approach A conceptual analysis of resilience is carried out at two levels. First, the use of the concepts of resilience, viability, transparency, accountability and neoliberalism is considered, together with the relationship between them. Second, the management interventions that result from the application of these related ideas are critiqued from the perspective of cybernetics and particularly of variety and black boxes. Findings It is shown that within complex social environments, the unconsidered imposition of transparency and accountability as a management strategy may constrain the resilience of the organizations and individuals rather than enhance it. The use of data analytics enhances this tendency. Research limitations/implications The theoretical analysis of the relationship between transparency and resilience offers a basis for carrying out empirical studies. Practical implications There are practical implications for organizational managers, employees and stakeholders, offering them a means of understanding the systemic threat posed by organizational design decisions which enhance transparency and accountability without taking into consideration the full range of interactions which act to maintain organizational viability. Social implications The analysis provides a rationale for resisting the imposition of social policies inspired by neoliberalism. Originality/value The bringing together of the concepts of resilience, neoliberalism, transparency and accountability, and their exposure to cybernetic analysis, provides a novel perspective on resilience, and new insights into way that organizations maintain their viability.

Representing development: models, meaning, and the challenge of complexity

2008 ◽  
Vol 31 (3) ◽  
pp. 342-343 ◽  
Author(s):  
Robert Lickliter
Keyword(s):  
Computational Models ◽  
Subjective Experience ◽  
Levels Of Analysis ◽  
Fundamental Feature ◽  
Development Models ◽  
Developmental Science ◽  
Different Levels ◽  
Integrate Research ◽  
Nonlinear Nature

AbstractNeuroconstructivism (Mareschal et al. 2007a) provides a useful framework for how to integrate research from different levels of analysis to model the multidimensional dynamics of development. However, the authors overlook the topic of meaning, a fundamental feature of cognition and subjective experience and also downplay the nonlinear nature of developmental causality. Neuroconstructivism is overly optimistic on the point of how well current computational models can address the challenge of complexity in developmental science.

A Bayesian Belief Network Methodology for Modeling Social Systems in Virtual Communities

2010 ◽  
pp. 125-152
Author(s):  
Ben K. Daniel ◽  
Juan-Diego Zapata-Rivera ◽  
Gordon I. McCalla
Keyword(s):  
Graphical Models ◽  
Bayesian Model ◽  
Computational Models ◽  
Virtual Communities ◽  
Knowledge Engineering ◽  
Social Systems ◽  
Bayesian Belief Networks ◽  
Current Approach ◽  
Interactive Simulation ◽  
Engineering Effort

Bayesian Belief Networks (BBNs) are increasingly used for understanding and simulating computational models in many domains. Though BBN techniques are elegant ways of capturing uncertainties, knowledge engineering effort required to create and initialize the network has prevented many researchers from using them. Even though the structure of the network and its conditional & initial probabilities could be learned from data, data is not always available and/or too costly to obtain. Further, current algorithms that can be used to learn relationships among variables, initial and conditional probabilities from data are often complex and cumbersome to employ. Qualitative-based approaches applied to the creation of graphical models can be used to create initial computational models that can help researchers analyze complex problems and provide guidance/support for decision-making. Once created, initial BBN models can be refined once appropriate data is obtained. This chapter extends the use of BBNs to help experts make sense of complex social systems (e.g., social capital in virtual communities) using a Bayesian model as an interactive simulation tool. Scenarios are used to update the model and to find out whether the model is consistent with the expert’s beliefs. A sensitivity analysis was conducted to help explain how the model reacted to different sets of evidence. Currently, we are in the process of refining the initial probability values presented in the model using empirical data and developing more authentic scenarios to further validate the model. We will elaborate on how database technologies were used to support the current approach and will describe opportunities for future database tools needed to support this type of work.

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