EB-DEVS: A Formal Framework for Modeling and Simulation of Emergent
Behavior in Dynamic Complex Systems
- URL: http://arxiv.org/abs/2010.05042v2
- Date: Tue, 13 Oct 2020 22:54:20 GMT
- Title: EB-DEVS: A Formal Framework for Modeling and Simulation of Emergent
Behavior in Dynamic Complex Systems
- Authors: Daniel J. Foguelman, Philipp Henning, Adelinde Uhrmacher, and Rodrigo
Castro
- Abstract summary: We introduce Emergent Behavior-DEVS (EB-DEVS) a Modeling and Simulation (M&S) formalism.
EB-DEVS builds on the DEVS formalism, adding upward/downward communication channels to well-established capabilities.
We present three case studies: flocks of birds with learning, population epidemics with vaccination and sub-cellular dynamics with homeostasis.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Emergent behavior is a key feature defining a system under study as a complex
system. Simulation has been recognized as the only way to deal with the study
of the emergency of properties (at a macroscopic level) among groups of system
components (at a microscopic level), for the manifestations of emergent
structures cannot be deduced from analysing components in isolation. A
systems-oriented generalisation must consider the presence of feedback loops
(micro components react to macro properties), interaction among components of
different classes (modular composition) and layered interaction of subsystems
operating at different spatio-temporal scales (hierarchical organisation). In
this work we introduce Emergent Behavior-DEVS (EB-DEVS) a Modeling and
Simulation (M&S) formalism that permits reasoning about complex systems where
emergent behavior is placed at the forefront of the analysis activity. EB-DEVS
builds on the DEVS formalism, adding upward/downward communication channels to
well-established capabilities for modular and hierarchical M&S of heterogeneous
multi-formalism systems. EB-DEVS takes a minimalist stance on expressiveness,
introducing a small set of extensions on Classic DEVS that can cope with
emergent behavior, and making both formalisms interoperable (the modeler
decides which subsystems deserve to be expressed via micro-macro dynamics). We
present three case studies: flocks of birds with learning, population epidemics
with vaccination and sub-cellular dynamics with homeostasis, through which we
showcase how EB-DEVS performs by placing emergent properties at the center of
the M&S process.
Related papers
- A process algebraic framework for multi-agent dynamic epistemic systems [55.2480439325792]
We propose a unifying framework for modeling and analyzing multi-agent, knowledge-based, dynamic systems.
On the modeling side, we propose a process algebraic, agent-oriented specification language that makes such a framework easy to use for practical purposes.
arXiv Detail & Related papers (2024-07-24T08:35:50Z) - Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
In this study, we first construct the brain-effective network via the dynamic causal model.
We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE)
This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
arXiv Detail & Related papers (2024-05-21T20:37:07Z) - Benchmarking formalisms for dynamic structure system Modeling and Simulation [3.2268447897914943]
We identify criteria for a smooth flow from model creation to its simulation for dynamic structure systems.
We benchmark the existing modeling formalisms focusing more on DEVS extensions and use the results to identify approaches gaps and discuss them.
arXiv Detail & Related papers (2024-01-25T09:13:40Z) - Discrete, compositional, and symbolic representations through attractor dynamics [51.20712945239422]
We introduce a novel neural systems model that integrates attractor dynamics with symbolic representations to model cognitive processes akin to the probabilistic language of thought (PLoT)
Our model segments the continuous representational space into discrete basins, with attractor states corresponding to symbolic sequences, that reflect the semanticity and compositionality characteristic of symbolic systems through unsupervised learning, rather than relying on pre-defined primitives.
This approach establishes a unified framework that integrates both symbolic and sub-symbolic processing through neural dynamics, a neuroplausible substrate with proven expressivity in AI, offering a more comprehensive model that mirrors the complex duality of cognitive operations
arXiv Detail & Related papers (2023-10-03T05:40:56Z) - Capturing Actionable Dynamics with Structured Latent Ordinary
Differential Equations [68.62843292346813]
We propose a structured latent ODE model that captures system input variations within its latent representation.
Building on a static variable specification, our model learns factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space.
arXiv Detail & Related papers (2022-02-25T20:00:56Z) - Simulation of emergence in artificial societies: a practical model-based
approach with the EB-DEVS formalism [0.11470070927586014]
We apply EB-DEVS, a novel formalism tailored for the modelling, simulation and live identification of emergent properties.
This work provides case study-driven evidence for the neatness and compactness of the approach to modelling communication structures.
arXiv Detail & Related papers (2021-10-15T15:55:16Z) - Meta-brain Models: biologically-inspired cognitive agents [0.0]
We propose a computational approach we call meta-brain models.
We will propose combinations of layers composed using specialized types of models.
We will conclude by proposing next steps in the development of this flexible and open-source approach.
arXiv Detail & Related papers (2021-08-31T05:20:53Z) - Divide and Rule: Recurrent Partitioned Network for Dynamic Processes [25.855428321990328]
Many dynamic processes are involved with interacting variables, from physical systems to sociological analysis.
Our goal is to represent a system with a part-whole hierarchy and discover the implied dependencies among intra-system variables.
The proposed architecture consists of (i) a perceptive module that extracts a hierarchical and temporally consistent representation of the observation at multiple levels, (ii) a deductive module for determining the relational connection between neurons at each level, and (iii) a statistical module that can predict the future by conditioning on the temporal distributional estimation.
arXiv Detail & Related papers (2021-06-01T06:45:56Z) - GEM: Group Enhanced Model for Learning Dynamical Control Systems [78.56159072162103]
We build effective dynamical models that are amenable to sample-based learning.
We show that learning the dynamics on a Lie algebra vector space is more effective than learning a direct state transition model.
This work sheds light on a connection between learning of dynamics and Lie group properties, which opens doors for new research directions.
arXiv Detail & Related papers (2021-04-07T01:08:18Z) - An active inference model of collective intelligence [0.0]
This paper posits a minimal agent-based model that simulates the relationship between local individual-level interaction and collective intelligence.
Results show that stepwise cognitive transitions increase system performance by providing complementary mechanisms for alignment between agents' local and global optima.
arXiv Detail & Related papers (2021-04-02T14:32:01Z) - S2RMs: Spatially Structured Recurrent Modules [105.0377129434636]
We take a step towards exploiting dynamic structure that are capable of simultaneously exploiting both modular andtemporal structures.
We find our models to be robust to the number of available views and better capable of generalization to novel tasks without additional training.
arXiv Detail & Related papers (2020-07-13T17:44:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.