Robust Project-Based Organizations for the Design of Complex Engineered Systems

2021 ◽  
pp. 875697282110143
Author(s):  
Valerie Maier-Speredelozzi ◽  
Bryan Still
Keyword(s):  
Real World ◽  
Organizational Forms ◽  
Agent Based Models ◽  
Agent Based ◽  
Complex Engineered Systems ◽  
Established Relationship ◽  
Engineered Systems ◽  
Collaboration And Communication ◽  
Multiscale Networks

Cost and schedule overruns have become increasingly common in projects that set out to deliver complex engineered systems. Considering the well-established relationship between systems and organizations that design them, this article compares real-world, project-based organizational forms to idealized forms using agent-based models. It identifies multiscale networks as the preferred theoretical form and structures based on military staffs as the preferred practical form for organizations that design complex engineered systems. Matrix organizations are particularly susceptible to congestion failure, whereas military staffs are more robust and better suited to meeting demands for cross-functional collaboration and communication.

scholarly journals Data assimilation in Agent-based models using creation and annihilation operators

2019 ◽  
Author(s):  
Daniel Tang
Keyword(s):  
Real World ◽  
Null Model ◽  
The State ◽  
Actual State ◽  
Agent Based Models ◽  
Agent Based ◽  
Predator Prey ◽  
Predator Prey Model ◽  
The One ◽  
The Relationship

Agent-based models are a powerful tool for studying the behaviour of complex systems that can be described in terms of multiple, interacting ``agents''. However, because of their inherently discrete and often highly non-linear nature, it is very difficult to reason about the relationship between the state of the model, on the one hand, and our observations of the real world on the other. In this paper we consider agents that have a discrete set of states that, at any instant, act with a probability that may depend on the environment or the state of other agents. Given this, we show how the mathematical apparatus of quantum field theory can be used to reason probabilistically about the state and dynamics the model, and describe an algorithm to update our belief in the state of the model in the light of new, real-world observations. Using a simple predator-prey model on a 2-dimensional spatial grid as an example, we demonstrate the assimilation of incomplete, noisy observations and show that this leads to an increase in the mutual information between the actual state of the observed system and the posterior distribution given the observations, when compared to a null model.

Exploring Emergence within Social Systems with Agent Based Models

2014 ◽  
pp. 52-71 ◽  
Author(s):  
Marcia R. Friesen ◽  
Richard Gordon ◽  
Robert D. McLeod
Keyword(s):  
Modeling And Simulation ◽  
Real World ◽  
Research Group ◽  
Social Systems ◽  
Emergent Behavior ◽  
Agent Based Models ◽  
Social Modeling ◽  
Agent Based ◽  
Global Parameters ◽  
Representative Samples

In this chapter, the authors examine manifestations of emergence or apparent emergence in agent based social modeling and simulation, and discuss the inherent challenges in building real world models and in defining, recognizing and validating emergence within these systems. The discussion is grounded in examples of research on emergence by others, with extensions from within our research group. The works cited and built upon are explicitly chosen as representative samples of agent-based models that involve social systems, where observation of emergent behavior is a sought-after outcome. The concept of the distinctiveness of social from abiotic emergence in terms of the use of global parameters by agents is introduced.

OPEN PROBLEMS IN USING AGENT-BASED MODELS IN INDUSTRIAL AND LABOR DYNAMICS

2004 ◽  
Vol 07 (02) ◽  
pp. 285-288 ◽  
Author(s):  
NIGEL GILBERT
Keyword(s):  
Real World ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Open Problems ◽  
Agent Based ◽  
The Real ◽  
Methodological Problems ◽  
Explanatory Success ◽  
Labor Dynamics ◽  
Main Attraction

The preceding papers have shown the impressive versatility and potential of agent-based modeling in developing an understanding of industrial and labor dynamics. The main attraction of agent-based models is that the actors — firms, workers, and networks — that are the objects of study in the 'real world,' can be represented directly in the model. This one-to-one correspondence between model agents and economic actors provides greater clarity and more opportunities for analysis than many alternative modeling approaches. However, the advantages of agent-based modeling have to be tempered by disadvantages and as yet unsolved methodological problems. In this brief summary drawn from the discussion at the closing session of WILD@ACE, we review three of these open problems in the context of the papers presented at the conference: How can agent-based models be empirically validated? What criteria should be used to evaluate the explanatory success of agent-based models? And how can the conclusions of research on similar topics be integrated?

scholarly journals Agent Based Models of Polymicrobial Biofilms and the Microbiome—A Review

2021 ◽  
Vol 9 (2) ◽  
pp. 417
Author(s):  
Sherli Koshy-Chenthittayil ◽  
Linda Archambault ◽  
Dhananjai Senthilkumar ◽  
Reinhard Laubenbacher ◽  
Pedro Mendes ◽  
...  
Keyword(s):  
Species Interactions ◽  
Computational Models ◽  
Human Microbiome ◽  
Agent Based Modeling ◽  
Autonomous Agent ◽  
Agent Based Models ◽  
Agent Based ◽  
Complex Interactions ◽  
Mucosal Surfaces ◽  
Living Organisms

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.

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