Network formation with heterogeneous players

Galeotti, Andrea; Goyal, Sanjeev; Kamphorst, Jurjen

doi:10.1016/j.geb.2005.02.003

Cited by 223 publications

(131 citation statements)

References 25 publications

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“…(v) The random graph topology results from randomly assigning links to various nodes (agents). Figures 2f) and 2g) illustrate two random graphs which have been generated by assuming that every possible link occurs independently with (uniform) wiring probability wp = 0,10 and wp = 0,50, respectively 6 .…”

Section: Description Of Alternative Topologiesmentioning

confidence: 99%

“…The interaction dynamics depends on the topology of communication between agents, as the degree of connectedness and position (or centrality) of each (type of) agent in the network can decisively affect final outcomes, in terms of efficiency, equilibrium and other network properties [6][7][8][9][10]. Specifically, agents change/update their own opinion about a subject (e.g., an economic perception about an investment decision or a political view), in accordance with some type of learning process, which will lead to the formation of a belief on that subject and affect their final decision.…”

Section: Introductionmentioning

confidence: 99%

“…Other applications with economic perspective encompass the study of interaction dynamics among consumer agents [4] as well as among company executives within a firm and between different firms [5], to represent changes in organizational structure, price formation and competition conditions in the market. Furthermore, such models can provide insight into agents' voting behavior, the rise and fall of political parties and others.The interaction dynamics depends on the topology of communication between agents, as the degree of connectedness and position (or centrality) of each (type of) agent in the network can decisively affect final outcomes, in terms of efficiency, equilibrium and other network properties [6][7][8][9][10]. Specifically, agents change/update their own opinion about a subject (e.g., an economic perception about an investment decision or a political view), in accordance with some type of learning process, which will lead to the formation of a belief on that subject and affect their final decision.…”

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confidence: 99%

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Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks

Koulouris¹,

Katerelos²,

Tsekeris³

2013

: Interdiscip. Descr. Complex Syst.

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This article investigates the Multiple Equilibria Regulation (MER) model, i.e., an agent-based simulation model, to represent opinion dynamics in social networks. It relies on a small set of microprerequisites (intra-individual balance and confidence bound), leading to emergence of (non)stationary macro-outcomes. These outcomes may refer to consensus, polarization or fragmentation of opinions about taxation (e.g., congestion pricing) or other policy measures, according to the way communication is structured. In contrast with other models of opinion dynamics, it allows for the impact of both the regulation of intra-personal discrepancy and the interpersonal variability of opinions on social learning and network dynamics. Several simulation experiments are presented to demonstrate, through the MER model, the role of different network structures (complete, star, cellular automata, small-world and random graphs) on opinion formation dynamics and the overall evolution of the system. The findings can help to identify specific topological characteristics, such as density, number of neighbourhoods and critical nodes-agents, that affect the stability and system dynamics. This knowledge can be used to better organize the information diffusion and learning in the community, enhance the predictability of outcomes and manage possible conflicts. It is shown that a small-world organization, which depicts more realistic aspects of real-life and virtual social systems, provides increased predictability and stability towards a less fragmented and more manageable grouping of opinions, compared to random networks. Such macro-level organizations may be enhanced with use of web-based technologies to increase the density of communication and public acceptability of policy measures. KEY WORDS INTRODUCTIONIn recent years, social sciences have embraced simulation techniques as a new powerful tool to explore the dynamics of social systems. Agent-based models (ABMs) constitute a fruitful approach to simulate and analyze complex phenomena observed in social networks. They typically rely on a set of simple rules pertaining to the behavior of agents, in order to determine the minimal conditions under which these phenomena emerge. A basic problem encountered by researchers is that of understanding emergence and, especially, the relationship between micro and macro properties of complex systems [1,2]. Such systems can be described either in terms of the properties and behavior of their individual agents or the system as a whole. The explanation of the emergence of macroscopic societal regularities, such as norms or price equilibria, from the micro level behavior of agents requires some generative ('bottom-up') mechanism [3], through which decentralized local interactions of heterogeneous autonomous agents generate the given regularity.In this context, ABMs of social networks can simulate the emergence of community-wide economic and political outcomes, based on the individual behavior and interaction dynamics of network agents. The agen...

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Section: Description Of Alternative Topologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks

Koulouris¹,

Katerelos²,

Tsekeris³

2013

: Interdiscip. Descr. Complex Syst.

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“…The model permits two-way flow of information. We draw and expand on previous work by Bala and Goyal (2000a, b), Galeotti, Goyal, and Kamphorst (2004), and Haller and Sarangi (2004). We provide examples of non-existence and present instances of existence of Nash networks.…”

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confidence: 92%

(Non-)existence and Scope of Nash Networks

2006

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“…The two-way flow model is first presented in Bala and Goyal (2000) and is extended in Galeotti et al (2006), Hojman andSzeidl (2004), andMac Bride (2006). In this model, the cost of link formation is onesided (while paying for forming a link, the agents do not need the agreement of the partner), whereas the positive spillovers induced by the formation of a link are two-sided.…”

Section: Introductionmentioning

confidence: 99%