Agent-based multi-edge network simulation model for knowledge diffusion through board interlocks

Sankar, C. Prem; Thumba, Drisya Alex; Ramamohan, T. R.; Chandra, Satish; Kumar, K. Satheesh

doi:10.1016/j.eswa.2019.112962

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…Within this network, the variables of interest are defined, along with their interrelationships, enabling a comprehensive exploration of the system's behaviour and dynamics. The Network Simulation Method has been used in a wide variety of applications, including the simulation of economic [38][39][40], social [41], and biological systems [42], among others [43][44][45][46][47].…”

Section: Resultsmentioning

confidence: 99%

Understanding Complex Traffic Dynamics with the Nondimensionalisation Technique

Sánchez-Pérez,

Oviedo-Casado,

García-Ros

et al. 2024

Mathematics

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Hydrodynamic traffic models are crucial to optimizing transportation efficiency and urban planning. They usually comprise a set of coupled partial differential equations featuring an arbitrary number of terms that aim to describe the different nuances of traffic flow. Consequently, traffic models quickly become complicated to solve and difficult to interpret. In this article, we present a general traffic model that includes a relaxation term and an inflow of vehicles term and utilize the mathematical technique of nondimensionalisation to obtain universal solutions to the model. Thus, we are able to show extreme sensitivity to initial conditions and parameter changes, a classical signature of deterministic chaos. Moreover, we obtain simple relations among the different variables governing traffic, thus managing to efficiently describe the onset of traffic jams. We validate our model by comparing different scenarios and highlighting the model’s applicability regimes in traffic equations. We show that extreme speed values, or heavy traffic inflow, lead to divergences in the model, showing its limitations but also demonstrating how the problem of traffic jams can be alleviated. Our results pave the way to simulating and predicting traffic accurately on a real-time basis.

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Section: Resultsmentioning

confidence: 99%

Understanding Complex Traffic Dynamics with the Nondimensionalisation Technique

Sánchez-Pérez,

Oviedo-Casado,

García-Ros

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…The Network Simulation Method has been used in a wide variety of applications, including the simulation of economic [34][35][36], social [37], and biological [38] systems, among others. Within the fields of physics and engineering, the network method has been successfully applied to problems of coupled flow and heat transport problems [39], electrochemical reactions [40,41], oxidation or corrosion problems [42], membranes [43], and soil mechanics [19], among others.…”

Section: Network Simulation Methodsmentioning

confidence: 99%

Methodology to Obtain Universal Solutions for Systems of Coupled Ordinary Differential Equations. Examples of a Continuous Flow Chemical Reactor and a Coupled Oscillator

et al. 2023

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This paper presents a concise and orderly methodology to obtain universal solutions to different problems in science and engineering using the nondimensionalization of the governing equations of the physical–chemical problem posed. For its application, a deep knowledge of the problem is necessary since it will facilitate the adequate choice of the references necessary for its resolution. In addition, the application of the methodology to examples of coupled ordinary differential equations is shown, resulting in an interesting tool to teach postgraduate students in the branches of physics, mathematics, and engineering. The first example used for a system of coupled ordinary differential equations is a model of a continuous flow chemical reactor, where it is worth noting; on the one hand, the methodology used to choose the reference (characteristic) time and, on the other, the equivalence between the characteristic times obtained for each one of the species. The following universal curves are obtained, which are validated by comparing them with the results obtained by numerical simulation, where it stands out that the universal solution includes an unknown that must be previously obtained. The resolution of this unknown implies having a deep knowledge of the problem, a common characteristic when using the methodology proposed in this work for different engineering or physicochemical problems. Finally, the second example is a coupled oscillator, where it is worth noting that the appearance of characteristic periods that implicitly or explicitly affect the particles’ movement is striking.

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“…Stummer and Kiesling [43] developed a commercial game simulation platform based on ABM theory, under the premise of considering social factors such as consumer heterogeneity and word-of-mouth transmission, helping students or junior managers to obtain first-hand experience in innovation and technology management. Sankar et al [44] proposed an agent-based simulation scheme, simulating knowledge diffusion of the Indian Company Affiliation Network (ICAN), analyzing the impacts of board member behaviors on the knowledge flow of the enterprise.…”

Section: Abm (Agent-based Method)mentioning

confidence: 99%

Increment of Heterogeneous Knowledge in Enterprise Innovation Ecosystem: An Agent‐Based Simulation Framework

Wang

Shi

2021

Complexity

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How can enterprises acquire knowledge resources quickly to maintain a competitive advantage in the market has become the focus of attention among academic circles and enterprises in the era of the knowledge economy. Based on the ecology theory, this paper analyzed the characteristics of the innovation subject and the rules of the heterogeneous knowledge interaction between innovation subjects. Then, this paper proposed a multiagent-based model of EIE (enterprise innovation ecosystem) heterogeneous knowledge increment to simulate the interactions between innovation subjects. By combining the biotic factors (government and service institutions) and abiotic factors (economical, social, and cultural), this paper designs eight different innovation environment scenarios to explore the dynamic impacts of these factors on EIE heterogeneous knowledge increment. The research findings were as follows: (1) In the introduction stage of innovation collaboration between innovation subjects, the same investment of the innovation elements in academic institutes is more beneficial to increasing system heterogeneous knowledge than investment in enterprises. (2) The cultivation of innovative biotic factors in service institutions is conducive to the rapid interaction of heterogeneous knowledge among the innovation subjects. Still, it is not suitable for areas with higher homogeneous knowledge because high homogeneous environment can easily lead to intensified market competition. (3) The cultivation of the abiotic innovation environment is conducive to maintain stable high-speed growth of EIE heterogeneous knowledge increment over a long period. (4) Without considering the cost of environmental cultivation, cultivating biotic and abiotic environments at the same time is the most beneficial choice to EIE heterogeneous knowledge increment.

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Agent-based multi-edge network simulation model for knowledge diffusion through board interlocks

Cited by 10 publications

References 32 publications

Understanding Complex Traffic Dynamics with the Nondimensionalisation Technique

Understanding Complex Traffic Dynamics with the Nondimensionalisation Technique

Methodology to Obtain Universal Solutions for Systems of Coupled Ordinary Differential Equations. Examples of a Continuous Flow Chemical Reactor and a Coupled Oscillator

Increment of Heterogeneous Knowledge in Enterprise Innovation Ecosystem: An Agent‐Based Simulation Framework

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