The concept of social interaction is at the core of embodied and enactive approaches to social cognitive processes, yet scientifically it remains poorly understood. Traditionally, cognitive science had relegated all behavior to being the end result of internal neural activity. However, the role of feedback from the interactions between agent and their environment has become increasingly important to understanding behavior. We focus on the role that social interaction plays in the behavioral and neural activity of the individuals taking part in it. Is social interaction merely a source of complex inputs to the individual, or can social interaction increase the individuals’ own complexity? Here we provide a proof of concept of the latter possibility by artificially evolving pairs of simulated mobile robots to increase their neural complexity, which consistently gave rise to strategies that take advantage of their capacity for interaction. We found that during social interaction, the neural controllers exhibited dynamics of higher-dimensionality than were possible in social isolation. Moreover, by testing evolved strategies against unresponsive ghost partners, we demonstrated that under some conditions this effect was dependent on mutually responsive co-regulation, rather than on the mere presence of another agent’s behavior as such. Our findings provide an illustration of how social interaction can augment the internal degrees of freedom of individuals who are actively engaged in participation.

 

Embodied Dyadic Interaction Increases Complexity of Neural Dynamics: A Minimal Agent-Based Simulation Model

Madhavun Candadai, Matt Setzler, Eduardo J. Izquierdo and Tom Froese

Front. Psychol., 21 March 2019 | https://doi.org/10.3389/fpsyg.2019.00540

Source: www.frontiersin.org

A good example of interactions generating relevant novel information that is not present in initial nor boundary conditions, inherently limiting the predictability of complex systems.

Complexity science explains why efforts to reject the mainstream merely result in a new conformity.

Source: www.technologyreview.com

A simulation of a half-million flock is studied using a simple boids model originally proposed by Craig Reynolds. It was modeled with a differential equation in 3D space with a periodic boundary. Flocking is collective behavior of active agents, which is often observed in the real world (e.g., starling swarms). It is, nevertheless, hard to rigorously define flocks (or their boundaries). First, even within the same swarm, the members are constantly updated, and second, flocks sometimes merge or divide dynamically. To define individual flocks and to capture their dynamic features, we applied a DBSCAN and a non-negative matrix factorization (NMF) to the boid dataset. Flocking behavior has different types of dynamics depending on the size of the flock. A function of different flocks is discussed with the result of NMF analysis.

 

Dynamic organization of flocking behaviors in a large-scale boids model
Norihiro Maruyama Daichi Saito Yasuhiro Hashimoto Takashi Ikegami

Journal of Computational Social Science

Source: link.springer.com

Elements composing complex systems usually interact in several different ways and as such the interaction architecture is well modelled by a multiplex network. However often this architecture is hidden, as one usually only has experimental access to an aggregated projection. A fundamental challenge is thus to determine whether the hidden underlying architecture of complex systems is better modelled as a single interaction layer or results from the aggregation and interplay of multiple layers. Here we show that using local information provided by a random walker navigating the aggregated network one can decide in a robust way if the underlying structure is a multiplex or not and, in the former case, to determine the most probable number of hidden layers. As a byproduct, we show that the mathematical formalism also provides a principled solution for the optimal decomposition and projection of complex, non-Markovian dynamics into a Markov switching combination of diffusive modes.
We validate the proposed methodology with numerical simulations of both (i) random walks navigating hidden multiplex networks (thereby reconstructing the true hidden architecture) and (ii) Markovian and non-Markovian continuous stochastic processes (thereby reconstructing an effective multiplex decomposition where each layer accounts for a different diffusive mode). We also state and prove two existence theorems guaranteeing that an exact reconstruction of the dynamics in terms of these hidden jump-Markov models is always possible for arbitrary finite-order Markovian and fully non-Markovian processes. Finally, we showcase the applicability of the method to experimental recordings from (i) the mobility dynamics of human players in an online multiplayer game and (ii) the dynamics of RNA polymerases at the single-molecule level.

 

Multiplex decomposition of non-Markovian dynamics and the hidden layer reconstruction problem

Lucas Lacasa, Inés P. Mariño, Joaquín Miguez, Vincenzo Nicosia, Edgar Roldán, Ana Lisica, Stephan W. Grill, Jesús Gómez-Gardeñes

Source: arxiv.org

Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology to engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. Finally, we discuss the usefulness of self-organization within ALife studies, point to perspectives for future research, and list open questions.

 

Self-Organization and Artificial Life
Carlos Gershenson, Vito Trianni, Justin Werfel, Hiroki Sayama

Source: arxiv.org