7,354 research outputs found
PROJEKTOWANIE SYSTEMÓW ANALIZY DANYCH DO AUTOMATYZACJI PROCESÓW BIZNESOWYCH
The paper deals with the design of data analysis systems for business process automation. The main goal of the project is to develop an innovative system for analyzing multisource data, business data mining processes, and as a result the creation and sharing of new improved procedures and solutions.Artykuł dotyczy projektowania systemów analizy danych do automatyzacji procesów biznesowych. Głównym celem projektu jest opracowanie innowacyjnego systemu do analizy danych wieloźródłowych, procesów eksploracji danych biznesowych, a co za tym idzie tworzenie i udostępnianie nowych ulepszonych procedur i rozwiązań
THE EFFECT OF TRUST ON INFORMATION DIFFUSION IN ONLINE SOCIAL NETWORKS
online social networks have a explosive growth in recent years and they provide a perfect platform for information diffusion. Many models have been given to explore the information diffusion procedure and its dynamics. But the trust relationship and memory effect are ignored. Based on the complex network theory, The information diffusion model is proposed and the network users, considered as agents, are classified into susceptible, infected and recovered individuals. The users’ behaviour rule and diffusion process are designed. The proposed agent-based model is tested by simulation experiments in four different complex networks: regular network, small world network, random network and scale-free network. Moreover, the effect of four immunization strategies are explored. The research results show that the influence of users’ trust relationship on different networks is varied, and the vertex weight priority immunization strategy is the best one in all four networks
A Population Dynamics Approach to Viral Marketing
Souto, P. C., Silva, L. V., Pinto, D. C., & Santos, F. C. (2020). A Population Dynamics Approach to Viral Marketing. In H. Cherifi, S. Gaito, J. F. Mendes, E. Moro, & L. M. Rocha (Eds.), Complex Networks and Their Applications VIII : Proceedings of the 8th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2019 (Vol. 1, pp. 399-411). (Studies in Computational Intelligence; Vol. 881 SCI). Springer. https://doi.org/10.1007/978-3-030-36687-2_33The symbiosis of Social Media and viral campaigns has recently become ubiquitous. In many recent phenomena (e.g., the Cambridge Analytica scandal), rumours in viral marketing programs are present without being even noticed by consumers. Yet, the study of population dynamics and its complex patterns of interaction remains largely elusive. Here, we propose an agent-based Marketing referral model to study the impact on firms’ dissemination and profitability of biased behavior in a population of opportunistic individuals. We show that those agents only interested in collecting rewards without any brand recognition are responsible for most of Marketing campaign success and dissemination, for a large range of different cost structures, network characteristics, and number of invites. This effect is further amplified whenever the difference between the cost of using the service and the reward collected after bringing a new customer is higher.authorsversionpublishe
The role of homophily in the emergence of opinion controversies
Understanding the emergence of strong controversial issues in modern
societies is a key issue in opinion studies. A commonly diffused idea is the
fact that the increasing of homophily in social networks, due to the modern
ICT, can be a driving force for opinion polariation. In this paper we address
the problem with a modelling approach following three basic steps. We first
introduce a network morphogenesis model to reconstruct network structures where
homophily can be tuned with a parameter. We show that as homophily increases
the emergence of marked topological community structures in the networks
raises. Secondly, we perform an opinion dynamics process on homophily dependent
networks and we show that, contrary to the common idea, homophily helps
consensus formation. Finally, we introduce a tunable external media pressure
and we show that, actually, the combination of homophily and media makes the
media effect less effective and leads to strongly polarized opinion clusters.Comment: 24 pages, 10 figure
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An Agent-Based Simulation for Emergency Warning Dissemination in a Multiplex Social Network
The variety of natural disasters provide different sets of characteristics and properties with unique challenges. One significant difference between hazard types is prewarning lead time, the amount of time individuals have from a potential warning to the disaster occurring. Rapid onset disasters may not provide an official warning about a hazard at all; social cues such as others evacuating or environmental cues such as an earthquake may be the only indication of an incoming tsunami for many individuals. Slow onset disasters such as hurricanes may provide much more of an official broadcast, allowing the public to plan and warn others. When individuals from the public warn others, they produce a "contagion process" which allows for people who would otherwise be uninformed to become informed and potentially spread the information themselves. However, since not everyone communicates to their connections when they learn new information, there is some average probability of spreading information which may be below a necessary critical percolation threshold to guarantee network permeation. This can be mitigated in part with a significant initial official broadcast process. This paper addresses the relationship between the official communication size and the probability of the public to share information, identifying approximate probabilities which are significantly affected by the broadcast process. I develop an interdisciplinary agent-based simulation of a multiplex social network with Monte Carlo iterations to model this relationship. This simulation takes a novel approach to the problem by considering social networks in a multiplex context, where different forms of communication have unique attributes associated with them. Each agent in the simulation is an individual from the hazard-affected community who, once informed, potentially informs others in their social network. The probability of an individual informing others is based on who has told them the information previously and the lead time to the disaster, among others. Simulation parameter values are chosen from previous literature along with the spatial aspects of the Coos Bay, OR and Seaside, OR communities. Results indicate that the initial broadcast size has a negative correlation with the critical percolation threshold. The threshold varies from approximately 1-5%, depending on the size of an initial broadcast. A sensitivity analysis on simulation parameters indicates that, along with sharing probability and initial broadcast size, prewarning lead time and confidence in information significantly affect the total number of informed individuals in the public. The results generated from this study will inform officials and community leaders with the behavior of community characteristics on their response to hazards and natural disasters specific to the community
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
DYNAMOD – A dynamic agent based modelling framework for digital businesses
Digital Businesses have become a major driver for economic growth and have seen an
explosion of new startups. At the same time, it also includes mature enterprises that have
become global giants in a relatively short period of time. Digital Businesses have unique characteristics that make the running and management of a Digital Business much different from traditional offline businesses. Digital businesses respond to online users who are highly interconnected and networked. This enables a rapid flow of word of mouth, at a pace far greater than ever envisioned when dealing with traditional products and services. The
relatively low cost of incremental user addition has led to a variety of innovation in pricing of digital products, including various forms of free and freemium pricing models. This thesis explores the unique characteristics and complexities of Digital Businesses and its implications on the design of Digital Business Models and Revenue Models.
The thesis proposes an Agent Based Modeling Framework that can be used to develop
Simulation Models that simulate the complex dynamics of Digital Businesses and the user
interactions between users of a digital product. Such Simulation models can be used for a variety of purposes such as simple forecasting, analysing the impact of market disturbances, analysing the impact of changes in pricing models and optimising the pricing for maximum revenue generation or a balance between growth in usage and revenue generation. These models can be developed for a mature enterprise with a large historical record of user growth rate as well as for early stage enterprises without much historical data. Through three case studies, the thesis demonstrates the applicability of the Framework and its potential applications.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/87286/201
Examining the Supply Chain Impact on Labor Social Security in Indonesia: A Systems Thinking Analysis
Abstract—Supply chains, production networks and other complex inter-organisational relationships are defining features of contemporary business organisations. This article reviews the impact of supply chain pressures on work, employment relations and human resource management, with a particular emphasis on domestic-oriented supply chains. By utilizing systems thinking, we identify and translate several variables into the Causal Loop Diagram (CLD) to clarify the linkages among them. We found seven determinants that have contributed to the low level of labor social security membership namely: competition, lack of insurance minded, ineffective communication, incompetent marketing agents, dis-harmonized regulations, weak law enforcement, and unattractive incentives for marketing agents. This paper could benefit supply chain to evaluate the existing membership program and to improve its quality and approach. Moreover, policymakers could utilize findings and recommendations from this study to perfecting the policy based on evidence
Modelling the impact of beliefs and communication on attitude dynamics : a cognitive agent-based approach
In the context of military training for stabilization operation of a crisis zone with civilian population, understanding the formation of attitude and its dynamics is a key issue. This paper presents a multi-agent model for simulating attitude formation and change based on individual's perception of information and its diffusion through communication. We represent the attitude as object-evaluation associations of varying strength proposed by Fazio [1]. Individuals observe military operations. They exchange and revise beliefs about social objects depending on multiple criteria deriving from social psychology theories. They compute their attitude value based on analytic assessment of these beliefs. We illustrate, through several simulation experiments, the role of communication on attitude dynamics
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