scholarly journals Emergence of a Snake-Like Structure in Mobile Distributed Agents: An Exploratory Agent-Based Modeling Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Muaz A. Niazi
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Dynamic Structure ◽  
Agent Based Modeling ◽  
The Body ◽  
Agent Based ◽  
Practical Applications ◽  
Computer Animations ◽  
Computationally Intensive ◽  
Self Organizing

The body structure of snakes is composed of numerous natural components thereby making it resilient, flexible, adaptive, and dynamic. In contrast, current computer animations as well as physical implementations of snake-like autonomous structures are typically designed to use either a single or a relatively smaller number of components. As a result, not only these artificial structures are constrained by the dimensions of the constituent components but often also require relatively more computationally intensive algorithms to model and animate. Still, these animations often lack life-like resilience and adaptation. This paper presents a solution to the problem of modeling snake-like structures by proposing an agent-based, self-organizing algorithm resulting in an emergent and surprisingly resilient dynamic structure involving a minimal of interagent communication. Extensive simulation experiments demonstrate the effectiveness as well as resilience of the proposed approach. The ideas originating from the proposed algorithm can not only be used for developing self-organizing animations but can also have practical applications such as in the form of complex, autonomous, evolvable robots with self-organizing, mobile components with minimal individual computational capabilities. The work also demonstrates the utility of exploratory agent-based modeling (EABM) in the engineering of artificial life-like complex adaptive systems.

Agent-Based Modeling

2017 ◽  
pp. 36-51
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Complexity Science ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Linear Behavior ◽  
Complex Adaptive ◽  
Economic Markets ◽  
The Common ◽  
Policy Problems

Agent based modeling is one of many tools, from the complexity sciences, available to investigate complex policy problems. Complexity science investigates the non-linear behavior of complex adaptive systems. Complex adaptive systems can be found across a broad spectrum of the natural and human created world. Examples of complex adaptive systems include various ecosystems, economic markets, immune response, and most importantly for this research, human social organization and competition / cooperation. The common thread among these types of systems is that they do not behave in a mechanistic way which has led to problems in utilizing traditional methods for studying them. Complex adaptive systems do not follow the Newtonian paradigm of systems that behave like a clock works whereby understanding the workings of each of the parts provides an understanding of the whole. By understanding the workings of the parts and a few external rules, predictions can be made about the behavior of the system as a whole under varying circumstances. Such systems are labeled deterministic (Zimmerman, Lindberg, & Plsek, 1998).

scholarly journals A Novel Formal Agent-Based Simulation Modeling Framework of an AIDS Complex Adaptive System

2013 ◽  
Vol 5 (3) ◽  
pp. 33-53 ◽  
Author(s):  
Amnah Siddiqa ◽  
Muaz Niazi
Keyword(s):  
Complex Adaptive Systems ◽  
Formal Specification ◽  
Simulation Modeling ◽  
Adaptive Systems ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based Simulation ◽  
Modeling Framework ◽  
Agent Based ◽  
Complex Adaptive

HIV/AIDS spread depends upon complex patterns of interaction among various subsets emerging at population level. This added complexity makes it difficult to study and model AIDS and its dynamics. AIDS is therefore a natural candidate to be modeled using agent-based modeling, a paradigm well-known for modeling Complex Adaptive Systems (CAS). While agent-based models are well-known to effectively model CAS, often times models can tend to be ambiguous and using only using text-based specifications (such as ODD) making models difficult to be replicated. Previous work has shown how formal specification may be used in conjunction with agent-based modeling to develop models of various CAS. However, to the best of the authors’ knowledge, no such model has been developed in conjunction with AIDS. In this paper, we present a Formal Agent-Based Simulation modeling framework (FABS-AIDS) for an AIDS-based CAS. FABS-AIDS employs the use of a formal specification model in conjunction with an agent-based model to reduce ambiguity as well as improve clarity in the model definition. The proposed model demonstrates the effectiveness of using formal specification in conjunction with agent-based simulation for developing models of CAS in general and, social network-based agent-based models, in particular.

Agent-based Modeling

2021 ◽  
Keyword(s):  
Complex Systems ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Agent Based Modeling ◽  
Urban Systems ◽  
Dynamic Processes ◽  
Modeling Tools ◽  
Individual Agent ◽  
Bottom Up ◽  
Agent Based

Agent-based modeling (ABM) has become widely accepted as a methodological tool to model and simulate dynamic processes of geographical phenomena. A growing number of ABM studies across a variety of domains and disciplines is partially explained by the development of agent-modeling tools and platforms, the availability of micro-data, and the advancement in computer technology and cyberinfrastructure. In addition to these technical reasons, another key motivation underlying ABM research is to address challenges embedded in conventional modeling approaches being relatively coarse, aggregate, static, normative, and inflexible across scales with a reductionist viewpoint (Batty 2005 cited under Application: Urban Systems.” With complexity science, including complex systems, complex adaptive systems, and artificial life, providing theoretical foundations and rationales, ABM is a computational methodology for simulating dynamic processes of nature and human systems driven by disaggregated, heterogeneous, and autonomous entities, i.e., agents, that interact among themselves and their environments. A key fundamental concept of the ABM framework is that a system emerges from the dynamic individual-level interactions from bottom-up, where the simulated outcome is more than the sum of its components. This bottom-up approach enables ABM to exhibit complex system dynamics, properties of which could include feedback effect, path-dependence, phase shift, non-linearity, adaptation, self-organization, tipping points, and emergence. Three key components of ABM are agents, their environment, and their decision rules. Agents are the crucial component in ABM where each individual agent has its own characteristics and agenda, assesses its surrounded situation, and makes decisions. Agents reside in an environment, which can represent a geographic space in case for spatially explicit agent-based models. Agents’ behavioral decisions and interactions within their environment are defined based on a set of rules, which can alter their status and location over time. The purpose of ABM research can be classified into theoretical exploration and empirical investigation as well as the combination of two. In the latter case, ABM can be used as an artificial laboratory experiment to explore what-if scenarios and to investigate how changes in agents, environments, and/or rules affect the macro-level outcomes. ABM has been applied to represent a wide variety of geographic processes and behaviors including but not limited to urban system, land-use/land-cover change, ecology, transportation, animal/human movement, behavioral geography, spatial cognition, transportation, and disease epidemiology. While the growing interest in ABM as a modeling methodology to simulate complex systems is remarkable, there exist various conceptual, methodological, and technical challenges.

scholarly journals Conceptualizing a circular framework of supply chain resource sustainability

2017 ◽  
Vol 37 (10) ◽  
pp. 1520-1540 ◽  
Author(s):  
S.C. Lenny Koh ◽  
Angappa Gunasekaran ◽  
Jonathan Morris ◽  
Raymond Obayi ◽  
Seyed Mohammad Ebrahimi
Keyword(s):  
Supply Chain ◽  
Decision Support ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Decision Rules ◽  
Theory Building ◽  
Theory Of Constraints ◽  
Content Type ◽  
Practical Applications ◽  
Resource Sustainability

Purpose In response to calls for conceptual frameworks and generic theory building toward the advancement of sustainability in supply chain resource utilization and management, the purpose of this paper is to advance a circular framework for supply chain resource sustainability (SCRS), and a decision-support methodology for assessing SCRS against the backdrop of five foundational premises (FPs) deduced from the literature on resource sustainability. Design/methodology/approach Taking a conceptual theory-building approach, the paper advances a set of SCRS decision-support criteria for each of the theoretical premises advanced, and applies the theory of constraints to illustrate the conceptual and practical applications of the framework in SCRS decision making. Findings This study uses recent conceptualizations of supply chains as “complex adaptive systems” to provide a robust and novel frame and a set of decision rules with which to assess the interconnectedness of environmental, economic, and social capital of supply chain resources from pre-production to post-production. Research limitations/implications The paper contributes to theory building in sustainability research, and the SCRS decision framework developed could be applied in tandem with existing quantitative hybrid life-cycle and input-output approaches to facilitate targeted resource sustainability assessments, with implications for research and practice. Originality/value The novel SCRS framework proposed serves as a template for evaluating SCRS and provides a decision-support methodology for assessing SCRS against the five theorized FPs.

Applying complex adaptive systems to agent-based models for social programme evaluation

2018 ◽  
pp. 312-326 ◽  
Author(s):  
Professor Michael E. Wolf-Branigin ◽  
Dr William G. Kennedy ◽  
Dr Emily S. Ihara ◽  
Dr Catherine J. Tompkins
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Programme Evaluation ◽  
Agent Based Models ◽  
Agent Based ◽  
Complex Adaptive ◽  
Social Programme
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