Modeling a Simple Self-Organizing System

2014 ◽  
pp. 134-148
Author(s):  
Nicholas C. Georgantzas ◽  
Evangelos Katsamakas
Keyword(s):  
System Dynamics ◽  
Simulation Model ◽  
Distributed Control ◽  
Negative Feedback ◽  
Positive Feedback ◽  
Model Analysis ◽  
Self Organization ◽  
Agent Based ◽  
Complex Adaptive ◽  
Self Organizing

This chapter presents a System Dynamics (SD) simulation model that not only replicates self-organizing system uncertainty results but also looks at self-organization causally. The SD simulation and model analysis results show exactly how distributed control leads positive feedback to explosive growth, which ends when all dynamics have been absorbed into an attractor, leaving the system in a stable, negative feedback state. The chapter's SD model analysis helps explain why phenomena of interest emerge in agent-based models, a topic crucial in understanding and designing Complex Adaptive Self-Organizing Systems (CASOS).

Prominent Causal Paths in a Simple Self-Organizing System

2012 ◽  
Vol 5 (2) ◽  
pp. 25-40 ◽  
Author(s):  
Nicholas C. Georgantzas ◽  
Evangelos Katsamakas
Keyword(s):  
Model Analysis ◽  
Feedback Loops ◽  
Self Organization ◽  
Biologically Inspired ◽  
Dynamic Interactions ◽  
Agent Based ◽  
Causal Pathways ◽  
Complex Adaptive ◽  
System Entropy ◽  
Self Organizing

The dynamic interactions of interdependent components in complex, adaptive, self-organizing systems (CASOS) often seem to sequester system entropy or uncertainty through distributed, as opposed to central, control. This article presents a system dynamics (SD) simulation model that not only replicates self-organizing system uncertainty results, but also looks at self-organization causally. The model analysis articulates how circular causal pathways or feedback loops in CASOS produce nonlinear dynamics spontaneously out of local interactions. The SD simulation and model analysis results show exactly how distributed control leads positive feedback to explosive growth, which ends when all dynamics have been absorbed into an attractor, leaving the system in a stable, negative feedback state. Cast as a methodological contribution, the article’s SD model analysis explains why phenomena of interest emerge in agent-based models, a topic crucial in understanding and designing CASOS. Moreover, CASOS concepts inspired by nature and biology can motivate biologically-inspired IS research.

scholarly journals System Dynamics versus Agent-Based Modeling: A Review of Complexity Simulation in Construction Waste Management

2018 ◽  
Vol 10 (7) ◽  
pp. 2484 ◽  
Author(s):  
Zhikun Ding ◽  
Wenyan Gong ◽  
Shenghan Li ◽  
Zezhou Wu
Keyword(s):  
Waste Management ◽  
System Dynamics ◽  
Complex Adaptive System ◽  
Agent Based Modeling ◽  
Effective Management ◽  
Construction Waste ◽  
Agent Based ◽  
Complex Adaptive ◽  
Research Findings ◽  
Novel Model

The environmental impacts caused by construction waste have attracted increasing attention in recent years. The effective management of construction waste is essential in order to reduce negative environmental influences. Construction waste management (CWM) can be viewed as a complex adaptive system, as it involves not only various factors (e.g., social, economic, and environmental), but also different stakeholders (such as developers, contractors, designers, and governmental departments) simultaneously. System dynamics (SD) and agent-based modeling (ABM) are the two most popular approaches to deal with the complexity in CWM systems. However, the two approaches have their own advantages and drawbacks. The aim of this research is to conduct a comprehensive review and develop a novel model for combining the advantages of both SD and ABM. The research findings revealed that two options can be considered when combining SD with ABM; the two options are discussed.

scholarly journals Smart Method for Self-Organization in Last-Mile Parcel Delivery

2020 ◽  
pp. 036119812097606
Author(s):  
J.H.R. van Duin ◽  
T.S. Vlot ◽  
L.A. Tavasszy ◽  
M.B. Duinkerken ◽  
B. van Dijk
Keyword(s):  
Simulation Model ◽  
Real Time ◽  
New Method ◽  
Self Organization ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Last Mile ◽  
Service Areas ◽  
Delivery Services ◽  
Delivery Vehicles

Parcel delivery operators experience an increasing pressure to meet the strongly growing demand for delivery services, while protecting city livability and the environment. Improving the performance of the last mile of delivery is considered key in meeting this challenge as it forms the most inefficient, expensive, and environmentally unfriendly part of delivery operations. A primary cause is a significant duplication of service areas, resulting in redundant vehicle kilometers traveled. In this paper, a new method is presented that allows for the allocation of parcels to delivery vehicles and construction of vehicle routes in real time through an auctioning system. These tasks are performed in a self-organizing manner by vehicles, parcels, and a supporting platform, to allow for collaborative and intermodal delivery. The performance of this new method is tested and compared against the currently used techniques using an agent-based simulation model. The new method manages to greatly improve the efficiency, robustness, and flexibility of delivery operations.

Evolutionary Design of Cellular Self-Organizing Systems

2013 ◽  
Author(s):  
James Humann ◽  
Yan Jin
Keyword(s):  
Fitness Function ◽  
Emergent Behavior ◽  
Local Interaction ◽  
System Level ◽  
Self Organization ◽  
Agent Based ◽  
Simple Set ◽  
Central Controller ◽  
Multi Agent ◽  
Self Organizing

In this paper, a genetic algorithm (GA) is used to discover interaction rules for a cellular self-organizing (CSO) system. The CSO system is a group of autonomous, independent agents that perform tasks through self-organization without any central controller. The agents have a local neighborhood of sensing and react only to other agents within this neighborhood. Their interaction rules are a simple set of direction vectors based on a flocking model. The five local interaction rules are assigned relative weights, and the agents self-organize to display some emergent behavior at the system level. The engineering challenge is to identify which sets of local rules will cause certain desired global behaviors. The global required behaviors of the system, such as flocking or exploration, are translated into a fitness function that can be evaluated at the end of a multi-agent based simulation run. The GA works by tuning the relative weights of the local interaction rules so that the desired global behavior emerges, judged by the fitness function. The GA approach is shown to be successful in tuning the weights of these interaction rules on simulated CSO systems, and, in some cases, the GA actually evolved qualitatively different local interaction “strategies” that displayed equivalent emergent capabilities.

scholarly journals Promoting Disaster Resilience: Operation Mechanisms and Self-Organizing Processes of Crowdsourcing

2020 ◽  
Vol 12 (5) ◽  
pp. 1862 ◽  
Author(s):  
Zhijun Song ◽  
Hui Zhang ◽  
Chris Dolan
Keyword(s):  
Natural Disaster ◽  
Adaptive System ◽  
Complex Adaptive System ◽  
The Self ◽  
Theoretical Research ◽  
Self Organization ◽  
Disaster Resilience ◽  
Depth Analysis ◽  
Complex Adaptive ◽  
Self Organizing

It is often difficult to realize effective governance and management within the inherent complexity and uncertainty of disasters. The application of crowdsourcing, through encouraging voluntary support from the general public, advances efficient disaster governance. Twelve international case studies of crowdsourcing and natural disaster governance were collected for in-depth analysis. Influenced by Complex Adaptive System theory, we explored the self-organizing operation mechanisms and self-organization processes of crowdsourcing within disaster governance. The self-organizing operation mechanisms of crowdsourcing are influenced by the multi-directional interaction between the crowdsourcing platform, the initiator (who commences the crowdsourcing process) and the contractor (who undertakes disaster reduction tasks). The benefits of crowdsourcing for governance structure and self-organization processes in natural disaster governance are reflected in three perspectives: strengthening communication and coordination, optimizing emergency decision-making, and improving the ability to learn and adapt. This paper discusses how crowdsourcing can promote disaster resilience from the perspective of the complex adaptive system to enrich the theoretical research on crowdsourcing and disaster resilience.

EMERGENT FEATURES IN A GENERAL FOOD WEB SIMULATION: LOTKA–VOLTERRA, GAUSE'S LAW, AND THE PARADOX OF ENRICHMENT

2013 ◽  
Vol 16 (08) ◽  
pp. 1350014 ◽  
Author(s):  
TED CARMICHAEL ◽  
MIRSAD HADZIKADIC
Keyword(s):  
Simulation Model ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Marine Ecosystem ◽  
System Level ◽  
Computer Simulation Model ◽  
Agent Based ◽  
Predator Prey ◽  
Paradox Of Enrichment ◽  
Complex Adaptive

Computer simulations of complex food-webs are important tools for deepening our understanding of these systems. Yet most computer models assume, rather than generate, key system-level patterns, or use mathematical modeling approaches that make it difficult to fully account for nonlinear dynamics. In this paper, we present a computer simulation model that addresses these concerns by focusing on assumptions of agent attributes rather than agent outcomes. Our model utilizes the techniques of complex adaptive systems and agent-based modeling so that system level patterns of a marine ecosystem emerge from the interactions of thousands of individual computer agents. This methodology is validated by using this general simulation model to replicate fundamental properties of a marine ecosystem, including: (i) the predator–prey oscillations found in Lotka–Volterra; (ii) the stepped pattern of biomass accrual from resource enrichment; (iii) the Paradox of Enrichment; and (iv) Gause's Law.

Self-organization

2018 ◽  
Author(s):  
Stuart P. Wilson
Keyword(s):  
Large Deviation ◽  
Natural World ◽  
Self Organization ◽  
Global Order ◽  
Deterministic Systems ◽  
Working Definition ◽  
Complex Forms ◽  
Robust To Noise ◽  
Organizing Principles ◽  
Self Organizing

Self-organization describes a dynamic in a system whereby local interactions between individuals collectively yield global order, i.e. spatial patterns unobservable in their entirety to the individuals. By this working definition, self-organization is intimately related to chaos, i.e. global order in the dynamics of deterministic systems that are locally unpredictable. A useful distinction is that a small perturbation to a chaotic system causes a large deviation in its trajectory, i.e. the butterfly effect, whereas self-organizing patterns are robust to noise and perturbation. For many, self-organization is as important to the understanding of biological processes as natural selection. For some, self-organization explains where the complex forms that compete for survival in the natural world originate from. This chapter outlines some fundamental ideas from the study of simulated self-organizing systems, before suggesting how self-organizing principles could be applied through biohybrid societies to establish new theories of living systems.

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