Controlling Collective Behavior in Complex Systems

2021 ◽  
pp. 441-450
Author(s):  
Mario di Bernardo
Keyword(s):  
Complex Systems ◽  
Collective Behavior

scholarly journals Unsupervised manifold learning of collective behavior

2020 ◽  
Author(s):  
Mathew Titus ◽  
George Hagstrom ◽  
James R. Watson
Keyword(s):  
Complex Systems ◽  
Prior Knowledge ◽  
Collective Behavior ◽  
Generative Models ◽  
Emergent Behavior ◽  
New Method ◽  
Collective Behaviors ◽  
State Classification ◽  
Macro Scale ◽  
Fish Schooling

AbstractCollective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative models, or other supervised learning techniques. Each of these cases requires knowledge of and a method for characterizing the macro-state(s) of the system. This presents a challenge for studying novel systems where there may be little prior knowledge. Here, we present a new unsupervised method of detecting emergent behavior in complex systems, and discerning between distinct collective behaviors. We require only metrics, d(1), d(2), defined on the set of agents, X, which measure agents’ nearness in variables of interest. We apply the method of diffusion maps to the systems (X, d(i)) to recover efficient embeddings of their interaction networks. Comparing these geometries, we formulate a measure of similarity between two networks, called the map alignment statistic (MAS). A large MAS is evidence that the two networks are codetermined in some fashion, indicating an emergent relationship between the metrics d(1) and d(2). Additionally, the form of the macro-scale organization is encoded in the covariances among the two sets of diffusion map components. Using these covariances we discern between different modes of collective behavior in a data-driven, unsupervised manner. This method is demonstrated on empirical fish schooling data. We show that our state classification subdivides the known behaviors of the school in a meaningful manner, leading to a finer description of the system’s behavior.Author summaryMany complex systems in society and nature exhibit collective behavior where individuals’ local interactions lead to system-wide organization. One challenge we face today is to identify and characterize these emergent behaviors, and here we have developed a new method for analyzing data from individuals, to detect when a given complex system is exhibiting system-wide organization. Importantly, our approach requires no prior knowledge of the fashion in which the collective behavior arises, or the macro-scale variables in which it manifests. We apply the new method to an agent-based model and empirical observations of fish schooling. While we have demonstrated the utility of our approach to biological systems, it can be applied widely to financial, medical, and technological systems for example.

Collective Behavior and Team Performance

1992 ◽  
Vol 34 (3) ◽  
pp. 277-288 ◽  
Author(s):  
James E. Driskell ◽  
Eduardo Salas
Keyword(s):  
Complex Systems ◽  
Team Performance ◽  
Collective Behavior ◽  
Experimental Results ◽  
Anecdotal Evidence ◽  
Critical Component ◽  
Team Members ◽  
Team Interaction ◽  
Effective Teams

Modern complex systems require effective team performance, yet the question of which factors determine effective teams remains to be answered. Group researchers suggest that collective or interdependent behavior is a critical component of team interaction. Furthermore, anecdotal evidence suggests that some team members are less collectively oriented than others and that the tendency to ignore task inputs from others is one factor that contributes to poor team performance. In this study we develop a procedure for differentiating collectively oriented versus egocentric team members. Experimental results confirm that collectively oriented team members were more likely to attend to the task inputs of other team members and to improve their performance during team interaction than were egocentric team members.

scholarly journals Unraveling Hidden Interactions in Complex Systems with Deep Learning

2020 ◽  
Author(s):  
Seungwoong Ha ◽  
Hawoong Jeong
Keyword(s):  
Complex Systems ◽  
Collective Behavior ◽  
Training Data ◽  
Data Driven ◽  
Velocity Correlation ◽  
General Process ◽  
Vicsek Model ◽  
Model Free ◽  
Free Data ◽  
Insight Into

Abstract Rich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by human scientists. In this study, we propose AgentNet, a model-free data-driven framework consisting of deep neural networks to reveal and analyze the hidden interactions in complex systems from observed data alone. AgentNet utilizes a graph attention network with novel variable-wise attention to model the interaction between individual agents, and employs various encoders and decoders that can be selectively applied to any desired system. Our model successfully captured a wide variety of simulated complex systems, namely cellular automata (discrete), the Vicsek model (continuous), and active Ornstein--Uhlenbeck particles (non-Markovian) in which, notably, AgentNet's visualized attention values coincided with the true variable-wise interaction strengths and exhibited collective behavior that was absent in the training data. A demonstration with empirical data from a flock of birds showed that AgentNet could identify hidden interaction ranges exhibited by real birds, which cannot be detected by conventional velocity correlation analysis. We expect our framework to open a novel path to investigating complex systems and to provide insight into general process-driven modeling.

scholarly journals Unraveling hidden interactions in complex systems with deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seungwoong Ha ◽  
Hawoong Jeong
Keyword(s):  
Complex Systems ◽  
Collective Behavior ◽  
Training Data ◽  
Data Driven ◽  
Velocity Correlation ◽  
General Process ◽  
Vicsek Model ◽  
Model Free ◽  
Free Data ◽  
Insight Into

AbstractRich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by scientists with human ingenuity. In this study, we propose AgentNet, a model-free data-driven framework consisting of deep neural networks to reveal and analyze the hidden interactions in complex systems from observed data alone. AgentNet utilizes a graph attention network with novel variable-wise attention to model the interaction between individual agents, and employs various encoders and decoders that can be selectively applied to any desired system. Our model successfully captured a wide variety of simulated complex systems, namely cellular automata (discrete), the Vicsek model (continuous), and active Ornstein–Uhlenbeck particles (non-Markovian) in which, notably, AgentNet’s visualized attention values coincided with the true variable-wise interaction strengths and exhibited collective behavior that was absent in the training data. A demonstration with empirical data from a flock of birds showed that AgentNet could identify hidden interaction ranges exhibited by real birds, which cannot be detected by conventional velocity correlation analysis. We expect our framework to open a novel path to investigating complex systems and to provide insight into general process-driven modeling.

scholarly journals Unsupervised manifold learning of collective behavior

2021 ◽  
Vol 17 (2) ◽  
pp. e1007811
Author(s):  
Mathew Titus ◽  
George Hagstrom ◽  
James R. Watson
Keyword(s):  
Complex Systems ◽  
Collective Behavior ◽  
Generative Models ◽  
Emergent Behavior ◽  
Collective Behaviors ◽  
State Classification ◽  
Learning Techniques ◽  
Macro Scale ◽  
Map Alignment ◽  
Observation Training

Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative models, or other supervised learning techniques. Each of these cases requires knowledge of and a method for characterizing the macro-state(s) of the system. This presents a challenge for studying novel systems where there may be little prior knowledge. Here, we present a new unsupervised method of detecting emergent behavior in complex systems, and discerning between distinct collective behaviors. We require only metrics, d(1), d(2), defined on the set of agents, X, which measure agents’ nearness in variables of interest. We apply the method of diffusion maps to the systems (X, d(i)) to recover efficient embeddings of their interaction networks. Comparing these geometries, we formulate a measure of similarity between two networks, called the map alignment statistic (MAS). A large MAS is evidence that the two networks are codetermined in some fashion, indicating an emergent relationship between the metrics d(1) and d(2). Additionally, the form of the macro-scale organization is encoded in the covariances among the two sets of diffusion map components. Using these covariances we discern between different modes of collective behavior in a data-driven, unsupervised manner. This method is demonstrated on a synthetic flocking model as well as empirical fish schooling data. We show that our state classification subdivides the known behaviors of the school in a meaningful manner, leading to a finer description of the system’s behavior.

scholarly journals Low rattling: A predictive principle for self-organization in active collectives

Science ◽  
2020 ◽  
Vol 371 (6524) ◽  
pp. 90-95
Author(s):  
Pavel Chvykov ◽  
Thomas A. Berrueta ◽  
Akash Vardhan ◽  
William Savoie ◽  
Alexander Samland ◽  
...  
Keyword(s):  
Complex Systems ◽  
Collective Behavior ◽  
Molecular Motor ◽  
Self Organization ◽  
Active Particle ◽  
Dynamical Response ◽  
External Forcing ◽  
Response Properties ◽  
Emergent Order ◽  
And Control

Self-organization is frequently observed in active collectives as varied as ant rafts and molecular motor assemblies. General principles describing self-organization away from equilibrium have been challenging to identify. We offer a unifying framework that models the behavior of complex systems as largely random while capturing their configuration-dependent response to external forcing. This allows derivation of a Boltzmann-like principle for understanding and manipulating driven self-organization. We validate our predictions experimentally, with the use of shape-changing robotic active matter, and outline a methodology for controlling collective behavior. Our findings highlight how emergent order depends sensitively on the matching between external patterns of forcing and internal dynamical response properties, pointing toward future approaches for the design and control of active particle mixtures and metamaterials.

Is Statistical Process Control (SPC) Obsolete?

2021 ◽  
Vol 10 (2) ◽  
pp. 1-3
Author(s):  
Anastasius S. Moumtzoglou
Keyword(s):  
Complex Systems ◽  
Statistical Process Control ◽  
21St Century ◽  
Collective Behavior ◽  
Network Science ◽  
Daily Life ◽  
Critical Role ◽  
Statistical Process ◽  
Human Ability ◽  
The World

The human ability to reason and comprehend the world requires the coherent activity of billions of neurons. At the same time, the biological existence is rooted in seamless interactions between thousands of genes and metabolites within the cells. These are complex systems which capture the fact that it is challenging to derive their collective behavior from the knowledge of the system's components. Given the critical role, complex systems play in daily life, their understanding, mathematical description, prediction, and eventually, control is one of the significant intellectual and scientific challenges of the 21st century. The advent of network science is a clear demonstration that science can live up to this challenge.

Introduction to Modern Dynamics

2019 ◽  
Author(s):  
David D. Nolte
Keyword(s):  
Nonlinear Dynamics ◽  
Complex Systems ◽  
Chaos Theory ◽  
Collective Behavior ◽  
Final Section ◽  
Evolutionary Dynamics ◽  
Metric Spaces ◽  
Geometric Mechanics ◽  
Neural Dynamics ◽  
Modern Perspective

Introduction to Modern Dynamics: Chaos, Networks, Space and Time (2nd Edition) combines the topics of modern dynamics—chaos theory, dynamics on complex networks and the geometry of dynamical spaces—into a coherent framework. This text is divided into four parts: Geometric Mechanics, Nonlinear Dynamics, Complex Systems, and Relativity. These topics share a common and simple mathematical language that helps students gain a unified physical intuition. Geometric mechanics lays the foundation and sets the tone for the rest of the book by emphasizing dynamical spaces, like state space and phase space, whose geometric properties define the set of all trajectories through those spaces. The section on nonlinear dynamics has chapters on chaos theory, synchronization, and networks. Chaos theory provides the language and tools to understand nonlinear systems, introducing fixed points that are classified through stability analysis and nullclines that shepherd system trajectories. Synchronization and networks are central paradigms in this book because they demonstrate how collective behavior emerges from the interactions of many individual nonlinear elements. The section on complex systems contains chapters on neural dynamics, evolutionary dynamics, and economic dynamics. The final section contains chapters on metric spaces and the special and general theories of relativity. In the second edition, sections on conventional topics, like applications of Lagrangians, have been strengthened, as well as being updated to provide a modern perspective. Several of the introductory chapters have been rearranged for improved logical flow and there are expanded homework problems at the end of each chapter.

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