Extending the Hegselmann-Krause Model I

Douven, Igor; Riegler, Alexander

doi:10.1093/jigpal/jzp059

Cited by 45 publications

(48 citation statements)

References 6 publications

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“…As mentioned in the introduction, we here continue work that we have started elsewhere (Douven, 2010; Douven and Riegler, 2010, and especially Wenmackers et al, 2012). Specifically, we present a formal model for studying a community of agents that update their belief states by “averaging” (in a certain well-defined sense) over the belief states of agents that are close enough to their own belief state (where “close enough” will also receive a precise definition).…”

Section: Theoretical Backgroundmentioning

confidence: 80%

Rationality: a social-epistemology perspective

2014

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Both in philosophy and in psychology, human rationality has traditionally been studied from an “individualistic” perspective. Recently, social epistemologists have drawn attention to the fact that epistemic interactions among agents also give rise to important questions concerning rationality. In previous work, we have used a formal model to assess the risk that a particular type of social-epistemic interactions lead agents with initially consistent belief states into inconsistent belief states. Here, we continue this work by investigating the dynamics to which these interactions may give rise in the population as a whole.

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Section: Theoretical Backgroundmentioning

confidence: 80%

Rationality: a social-epistemology perspective

2014

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“…The model's ability to generate disagreement is however regained, allowing some non-local random interactions between the agents (an agent's opinion is not confined to the confidence interval) [80,[164][165][166]. Some extensions of the "truth-seeking" model (32), allowing random noises, have been proposed in [160,167,168].…”

Section: Other Extensionsmentioning

confidence: 99%

A tutorial on modeling and analysis of dynamic social networks. Part II

Proskurnikov

Tempo

2018

Annual Reviews in Control

265

135

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Recent years have witnessed a significant trend towards filling the gap between Social Network Analysis (SNA) and control theory. This trend was enabled by the introduction of new mathematical models describing dynamics of social groups, the development of algorithms and software for data analysis and the tremendous progress in understanding complex networks and multi-agent systems (MAS) dynamics. The aim of this tutorial is to highlight a novel chapter of control theory, dealing with dynamic models of social networks and processes over them, to the attention of the broad research community. In its first part [1], we have considered the most classical models of social dynamics, which have anticipated and to a great extent inspired the recent extensive studies on MAS and complex networks. This paper is the second part of the tutorial, and it is focused on more recent models of social processes that have been developed concurrently with MAS theory. Future perspectives of control in social and techno-social systems are also discussed.touched by control theory, despite the long-term studies on social group dynamics [24][25][26] and "sociocybernetics" [27][28][29][30]. This gap between SNA and control can be explained, to a great extent, by the lack of dynamic models of social processes and mathematical armamentarium for their analysis. Focusing on topological properties of networks, SNA and network science have paid much less attention to dynamics over them, except for some special processes such as e.g. random walks, branching and queueing processes, percolation and contagion dynamics [19,20].The recent years have witnessed an important tendency towards filling the gap between SNA and control theory, enabled by the rapid progress in multiagent systems and dynamic networks. The emerging branch of control theory, studying social processes, is very young and even has no name yet. However, the interest of sociologists to this new area and understanding that "coordination and control of social systems is the foundational problem of sociology" [31] leaves no doubt that it should become a key instrument to examine social networks and dynamics over them. Without aiming to provide a exhaustive survey of "social control theory" at its dawn, this tutorial fo-

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“…Structural results in the BC-model were obtained with respect to convergence and its rate (Dittmer 2001;Krause 2006;Lorenz 2006), thresholds for the confidence radius (Fortunato 2005b), the identification of the really crucial topological and metric structures (Krause 2006), or the influence of the underlying network (Weisbuch 2004). The influence of a 'true' opinion, to which (some of) the individuals are attracted, received special attention (Hegselmann & Krause 2006;Malarz 2006;Douven & Riegler 2010;Douven & Kelp 2011;Kurz & Rambau 2011;Wenmackers et al 2012Wenmackers et al , 2014. With a grain of salt, the true opinion can also be interpreted as a control that is constant over time and that is contained in each individual's confidence interval.…”

Section: Introductionmentioning

confidence: 99%

Optimal Opinion Control: The Campaign Problem

et al. 2014

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Opinion dynamics is nowadays a very common field of research. In this article we formulate and then study a novel, namely strategic perspective on such dynamics: There are the usual 'normal' agents that update their opinions, for instance according the well-known bounded confidence mechanism. But, additionally, there is at least one strategic agent. That agent uses opinions as freely selectable strategies to get control on the dynamics: The strategic agent of our benchmark problem tries, during a campaign of a certain length, to influence the ongoing dynamics among normal agents with strategically placed opinions (one per period) in such a way, that, by the end of the campaign, as much as possible normals end up with opinions in a certain interval of the opinion space. Structurally, such a problem is an optimal control problem. That type of problem is ubiquitous. Resorting to advanced and partly nonstandard methods for computing optimal controls, we solve some instances of the campaign problem. But even for a very small number of normal agents, just one strategic agent, and a ten-period campaign length, the problem turns out to be extremely difficult. Explicitly we discuss moral and political concerns that immediately arise, if someone starts to analyze the possibilities of an "optimal opinion control".

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Extending the Hegselmann-Krause Model I

Cited by 45 publications

References 6 publications

Rationality: a social-epistemology perspective

Rationality: a social-epistemology perspective

A tutorial on modeling and analysis of dynamic social networks. Part II

Optimal Opinion Control: The Campaign Problem

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