scholarly journals USING ASSOCIATIVE NETWORKS TO REPRESENT ADOPTERS' BELIEFS IN A MULTIAGENT MODEL OF INNOVATION DIFFUSION

2008 ◽  
Vol 11 (02) ◽  
pp. 261-272 ◽  
Author(s):  
SAMUEL THIRIOT ◽  
JEAN-DANIEL KANT
Keyword(s):  
Innovation Diffusion ◽  
Diffusion Of Innovations ◽  
Social Representations ◽  
Real Data ◽  
Diffusion Models ◽  
Product Launch ◽  
Agent Based Models ◽  
Associative Networks ◽  
Agent Based ◽  
The Social

A lot of agent-based models were built to study diffusion of innovations. In most of these models, beliefs of individuals about the innovation were not represented at all, or in a highly simplified way. In this paper, we argue that representing beliefs could help to tackle problematics identified for diffusion of innovations, like misunderstanding of information, which can lead to diffusion failure, or diffusion of linked inventions. We propose a formalization of beliefs and messages as associative networks. This representation allows one to study the social representations of innovations and to validate diffusion models against real data. It could also make models usable to analyze diffusion prior to the product launch. Our approach is illustrated by a simulation of iPod™ diffusion.

Multi-Agent–Based Simulation of University Email Communities

2011 ◽  
pp. 273-291
Author(s):  
David Rodrigues
Keyword(s):  
Communication Network ◽  
Network Formation ◽  
Real Data ◽  
Informal Communication ◽  
University Environment ◽  
Agent Based ◽  
Detection Techniques ◽  
The Social ◽  
Multi Agent ◽  
Full Knowledge

In this chapter, a study on informal communication network formation in a university environment is presented. The teacher communication network is analyzed through community detection techniques. It is evident that informal communication is an important process that traverses the vertical hierarchical structure of departments and courses in a university environment. A multi-agent model of the case study is presented here, showing the implications of using real data as training sets for multi-agent-based simulations. The influence of the “social neighborhood,” as a mechanism to create assortative networks of contacts without full knowledge of the network, is discussed. It is shown that the radius of this social neighborhood has an effect on the outcome of the network structure and that in a university’s case this distance is relatively small.

scholarly journals Special Issue on New Trends in Agent-Based Simulation

2011 ◽  
Vol 15 (2) ◽  
pp. 165-165
Author(s):  
Keiki Takadama ◽  
Kiyoshi Izumi
Keyword(s):  
Social Sciences ◽  
Social Science ◽  
Computer Science ◽  
Mutual Influence ◽  
Real Data ◽  
Special Issue ◽  
Learning Mechanisms ◽  
Agent Based Simulation ◽  
Agent Based ◽  
The Social

Agent-Based Simulation (ABS), an interdisciplinary area embracing both the computer science and the social science, has attracted much attention and aided the understanding of socially complex phenomena. A current important issue in this research area is how to improve ABS effectiveness and comprehension, which makes further mutual influence between the computer science and the social sciences indispensable - e.g., (1) agent modeling involving learning mechanisms in the computer science and (2) social dynamics analysis needed in the social science. Such integration of these two areas would help fulfill the great potential of ABS, first in solving complex engineering problems using agent-based technology and second in developing and testing new theories on socially complex systems. This special issue features ABS papers from both of these important areas exploring new trends in ABS. The 10 papers composing this special issue start with papers by Nobutada Fujii and Hiroyasu Inoue analyzing the relationship between the network structure and system dynamics. In these papers, an agent-based computational economics approach has been active in applying agent-based technologies to financial and economic systems. Papers by Biliana Alexandrova-Kabadjova, Isamu Okada, TomokoOhi, and Nariaki Nishino cover consumer and financial markets using agent-based models. They test economic theory and examine market phenomena for market design. Agent-based simulation is increasingly used in application fields in the social sciences. Papers by Kiyoshi Izumi, Hideki Fujii, Hiromitsu Hattori, and Shigeo Sagai propose solutions for actual social problems such as injury prevention, traffic, and electrical power. Models are created based on behavior data, and the integration of an agent-based model and real data is a hot topic in this area. As the beginning of these technical papers, this issue starts by a position paper to give an ABS overview for understanding important issues in ABS from an overall viewpoint and for understanding state-of-the-art ABS. The information presented is invaluable in helping readers grasp the important features of ABS.

scholarly journals A modular multi-agent framework for innovation diffusion in changing business environments: conceptualization, formalization and implementation

2020 ◽  
Author(s):  
Simon Johanning ◽  
Fabian Scheller ◽  
Daniel Abitz ◽  
Claudius Wehner ◽  
Thomas Bruckner
Keyword(s):  
Innovation Diffusion ◽  
Decision Support Tool ◽  
Agent Based Models ◽  
Implementation Framework ◽  
Support Tool ◽  
Agent Based ◽  
Business Environments ◽  
Formal Framework ◽  
Common Framework ◽  
Model Components

Abstract Understanding how innovations are accepted in a dynamic and complex market environment is a crucial factor for competitive advantage. To understand the relevant factors for this diffusion and to predict success, empirically grounded agent-based models have become increasingly popular in recent years. Despite the popularity of these innovation diffusion models, no common framework that integrates their diversity exists. This article presents a flexible, modular and extensible common description and implementation framework that allows to depict the large variety of model components found in existing models. The framework aims to provide a theoretically grounded description and implementation framework for empirically grounded agent-based models of innovation diffusion. It identifies 30 component requirements to conceptualize an integrated formal framework description. Based on this formal description, a java-based implementation allowing for flexible configuration of existing and future models of innovation diffusion is developed. As a variable decision support tool in decision-making processes on the adoption of innovations the framework is valuable for the investigation of a range of research questions on innovation diffusion, business model evaluation and infrastructure transformation.

Self-organizing moral systems

2017 ◽  
Vol 17 (2) ◽  
pp. 119-147 ◽  
Author(s):  
Gerald Gaus
Keyword(s):  
Social Contract ◽  
Agent Based Models ◽  
Top Down ◽  
Agent Based ◽  
Moral Systems ◽  
Individual Mode ◽  
The Social ◽  
Central Plan ◽  
Do So ◽  
Self Organizing

This essay examines two different modes of reasoning about justice: an individual mode in which each individual judges what we all ought to do and a social mode in which we seek to reconcile our judgments of justice so that we can share common rules of justice. Social contract theory has traditionally emphasized the second, reconciliation mode, devising a central plan (the contract) to do so. However, I argue that because we disagree not only in our judgments of justice but also about the degree of reconciliation justice calls for, the social contract presupposes a single, controversial, answer to the proper degree of reconciliation. In place of the social contract’s ‘top-down’ approach, this article explores the idea of self-organizing moral systems, in which each individual, acting on her own views of justice (including the importance of reconciliation), responds to the decisions of others, forming systems of shared justice. Several basic agent-based models are explored to begin to understand the dynamics under which individuals with diverse views of justice may come to share common rules. It is found that, surprisingly, by increasing the diversity in a system, we can sometimes increase the possibility of agreement.

MEASURING SOLIDARITY IN AGENT-BASED MODELS OF RESOURCE SHARING SITUATIONS

2008 ◽  
Vol 11 (02) ◽  
pp. 337-356 ◽  
Author(s):  
PIETER W. G. BOTS ◽  
OLIVIER BARRETEAU ◽  
GERALDINE ABRAMI
Keyword(s):  
Social Behavior ◽  
Rural Areas ◽  
Resource Sharing ◽  
Management Practice ◽  
Land Resource ◽  
Agent Based Models ◽  
Agent Based ◽  
The Social ◽  
Urban And Rural Areas ◽  
Future Work

In this paper we present a first attempt to represent the social behavior of actors in a resource sharing context in such a way that different forms of solidarity can be detected and measured. We expect that constructing agent-based models of water-related interactions at the interface of urban and rural areas, and running social simulations to study the occurrence and consequences of solidary behavior, will produce insights that may eventually contribute to water and land resource management practice. We propose a typology for solidary behavior, present the agent-based architecture that we are using, show some illustrative results, and formulate some questions that will guide our future work.

scholarly journals A modular multi-agent framework for innovation diffusion in changing business environments: conceptualization, formalization and implementation

2020 ◽  
Author(s):  
Simon Johanning ◽  
Fabian Scheller ◽  
Daniel Abitz ◽  
Claudius Wehner ◽  
Thomas Bruckner
Keyword(s):  
Innovation Diffusion ◽  
Decision Support Tool ◽  
Agent Based Models ◽  
Implementation Framework ◽  
Support Tool ◽  
Agent Based ◽  
Business Environments ◽  
Formal Framework ◽  
Common Framework ◽  
Model Components

Abstract Understanding how innovations are accepted in a dynamic and complex market environment is a crucial factor for competitive advantage. To understand the relevant factors for this diffusion and to predict success, empirically grounded agent-based models have become increasingly popular in recent years. Despite the popularity of these innovation diffusion models, no common framework that integrates their diversity exists. This article presents a flexible, modular and extensible common description and implementation framework that allows to depict the large variety of model components found in existing models. The framework aims to provide a theoretically grounded description and implementation framework for empirically grounded agent-based models of innovation diffusion. It identifies 30 component requirements to conceptualize an integrated formal framework description. Based on this formal description, a java-based implementation allowing for flexible configuration of existing and future models of innovation diffusion is developed. As a variable decision support tool in decision-making processes on the adoption of innovations the framework is valuable for the investigation of a range of research questions on innovation diffusion, business model evaluation and infrastructure transformation.

scholarly journals A modular multi-agent framework for innovation diffusion in changing business environments: conceptualization, formalization and implementation

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Simon Johanning ◽  
Fabian Scheller ◽  
Daniel Abitz ◽  
Claudius Wehner ◽  
Thomas Bruckner
Keyword(s):  
Innovation Diffusion ◽  
Decision Support Tool ◽  
Agent Based Models ◽  
Implementation Framework ◽  
Support Tool ◽  
Agent Based ◽  
Business Environments ◽  
Formal Framework ◽  
Common Framework ◽  
Model Components

Abstract Understanding how innovations are accepted in a dynamic and complex market environment is a crucial factor for competitive advantage. To understand the relevant factors for this diffusion and to predict success, empirically grounded agent-based models have become increasingly popular in recent years. Despite the popularity of these innovation diffusion models, no common framework that integrates their diversity exists. This article presents a flexible, modular and extensible common description and implementation framework that allows to depict the large variety of model components found in existing models. The framework aims to provide a theoretically grounded description and implementation framework for empirically grounded agent-based models of innovation diffusion. It identifies 30 component requirements to conceptualize an integrated formal framework description. Based on this formal description, a java-based implementation allowing for flexible configuration of existing and future models of innovation diffusion is developed. As a variable decision support tool in decision-making processes on the adoption of innovations the framework is valuable for the investigation of a range of research questions on innovation diffusion, business model evaluation and infrastructure transformation.

Agent-Based Modeling

2010 ◽  
pp. 28-57 ◽  
Author(s):  
Brian L. Heath ◽  
Raymond R. Hill
Keyword(s):  
Real Life ◽  
Agent Based Modeling ◽  
Model Verification ◽  
Verification And Validation ◽  
Future Research ◽  
Agent Based Models ◽  
Agent Based ◽  
Philosophical Foundations ◽  
The Social ◽  
Model Verification And Validation

Models and simulations have been widely used as a means to predict the performance of systems. Agentbased modeling and agent distillations have recently found tremendous success particularly in analyzing ground force employment and doctrine. They have also seen wide use in the social sciences modeling a plethora of real-life scenarios. The use of these models always raises the question of whether the model is correctly encoded (verified) and accurately or faithfully represents the system of interest (validated). The topic of agent-based model verification and validation has received increased interest. This chapter traces the historical roots of agent-based modeling. This review examines the modern influences of systems thinking, cybernetics as well as chaos and complexity on the growth of agent-based modeling. The chapter then examines the philosophical foundations of simulation verification and validation. Simulation verification and validation can be viewed from two quite different perspectives: the simulation philosopher and the simulation practitioner. Personnel from either camp are typically unaware of the other camp’s view of simulation verification and validation. This chapter examines both camps while also providing a survey of the literature and efforts pertaining to the verification and validation of agent-based models. The chapter closes with insights pertaining to agent-based modeling, the verification and validation of agent-based models, and potential directions for future research.

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