PHYSICS OF THE MIND: OPINION DYNAMICS AND DECISION MAKING PROCESSES BASED ON A BINARY NETWORK MODEL

2008 ◽  
Vol 22 (25n26) ◽  
pp. 4482-4494 ◽  
Author(s):  
F. V. KUSMARTSEV ◽  
KARL E. KÜRTEN
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Chaotic Behavior ◽  
Opinion Dynamics ◽  
Human Mind ◽  
Critical Parameters ◽  
Opinion Formation ◽  
Natural Catastrophes ◽  
Risk Condition ◽  
Binary Network

We propose a new theory of the human mind. The formation of human mind is considered as a collective process of the mutual interaction of people via exchange of opinions and formation of collective decisions. We investigate the associated dynamical processes of the decision making when people are put in different conditions including risk situations in natural catastrophes when the decision must be made very fast or at national elections. We also investigate conditions at which the fast formation of opinion is arising as a result of open discussions or public vote. Under a risk condition the system is very close to chaos and therefore the opinion formation is related to the order disorder transition. We study dramatic changes which may happen with societies which in physical terms may be considered as phase transitions from ordered to chaotic behavior. Our results are applicable to changes which are arising in various social networks as well as in opinion formation arising as a result of open discussions. One focus of this study is the determination of critical parameters, which influence a formation of stable mind, public opinion and where the society is placed “at the edge of chaos”. We show that social networks have both, the necessary stability and the potential for evolutionary improvements or self-destruction. We also show that the time needed for a discussion to take a proper decision depends crucially on the nature of the interactions between the entities as well as on the topology of the social networks.

scholarly journals A review on trust propagation and opinion dynamics in social networks and group decision making frameworks

2019 ◽  
Vol 478 ◽  
pp. 461-475 ◽  
Author(s):  
Raquel Ureña ◽  
Gang Kou ◽  
Yucheng Dong ◽  
Francisco Chiclana ◽  
Enrique Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Group Decision Making ◽  
Group Decision ◽  
Opinion Dynamics ◽  
Trust Propagation

Persuasion process on social networks

2021 ◽  
Author(s):  
Haibo Hu ◽  
Nannan Xu
Keyword(s):  
Social Networks ◽  
Mass Media ◽  
Social Interactions ◽  
Relative Proportion ◽  
Opinion Dynamics ◽  
Theoretical Models ◽  
Vital Role ◽  
Opinion Formation ◽  
Media Effect ◽  
The Arab Spring

From the Rwandan genocide to the Arab Spring movement, it has been well known that social networks, offline or online, and mass media can collectively change and amplify public opinions, however there are few theoretical models to characterize the persuasion process. In this paper, we propose an opinion dynamics model based on invasion process with media effect and committed agents, and analytically obtain the fraction of each opinion at the steady state. We find that the relative proportion of committed agents plays a vital role in influencing corresponding opinion formation, and social networks can enhance the influence of committed agents through the interaction between individuals. Mass media can affect individuals not only directly due to exposure but indirectly due to social interactions. This paper reveals the influence of mass media and committed agents on the final distribution of opinions through a persuasion process, and lays the foundation for building more general models that consider individual heterogeneity and external influences.

A Hierarchical Decision-Making Framework in Social Networks for Efficient Disaster Management

2022 ◽  
Vol 32 (1) ◽  
pp. 1-26
Author(s):  
Seunghan Lee ◽  
Saurabh Jain ◽  
Young-Jun Son
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Disaster Management ◽  
Management Strategies ◽  
Management Policy ◽  
Strategy Development ◽  
Opinion Formation ◽  
Hierarchical Decision Making ◽  
Hierarchical Decision ◽  
Decision Making Framework

One of the major challenges faced by the current society is developing disaster management strategies to minimize the effects of catastrophic events. Disaster planning and strategy development phases of this urgency require larger amounts of cooperation among communities or individuals in society. Social networks have also been playing a crucial role in the establishment of efficient disaster management planning. This article proposes a hierarchical decision-making framework that would assist in analyzing two imperative information flow processes (innovation diffusion and opinion formation) in social networks under the consideration of community detection. The proposed framework was proven to capture the heterogeneity of individuals using cognitive behavior models and evaluate its impact on diffusion speed and opinion convergence. Moreover, the framework demonstrated the evolution of communities based on their inter-and intracommunication. The simulation results with real social network data suggest that the model can aid in establishing an efficient disaster management policy using social sensing and delivery.

Beérkezett könyvek

2008 ◽  
Vol 63 (3) ◽  
pp. 607-608
Author(s):  
Csaba Pléh
Keyword(s):  
Decision Making ◽  
Human Mind ◽  
Frankfurt Am Main ◽  
Conscious Decision ◽  
Better Than

ErősFerenc, LénárdKataés BókayAntal(szerk.) Typus Budapestiensis. Tanulmányok a pszichoanalízis budapesti iskolájának történetéről éshatásáról. Thalassa, Budapest, 2008, 447 oldalHargittaiIstván: Doktor DNS. Őszinte beszélgetések James D. Watsonnal. Vince Kiadó, Budapest, 2008, 223 oldalKutrovátzGábor,LángBenedekésZemplénGábor: A tudomány határa. Typotex,Budapest, 2008, 376 oldalEngerl, C. andSinger, W. (eds) Better than conscious? Decision making, the human mind, and implications for institutions . MIT Press, Cambridge, 2008, xiv + 449 oldalKondor, Zsuzsanna: Embedded thinking. Multimedia and the new rationality. Peter Lang, Frankfurt am Main, 2008, xi + 169 oldalSíklakiIstván(szerk.): Szóbeli befolyásolás. I–II. Typotex, Budapest,_n

Opinion Dynamics Optimization by Varying Susceptibility to Persuasion via Non-Convex Local Search

2021 ◽  
Vol 16 (2) ◽  
pp. 1-34
Author(s):  
Rediet Abebe ◽  
T.-H. HUBERT Chan ◽  
Jon Kleinberg ◽  
Zhibin Liang ◽  
David Parkes ◽  
...  
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Iterative Process ◽  
Large Scale ◽  
Opinion Dynamics ◽  
Theoretical Models ◽  
Global Optimum ◽  
Local Optimum ◽  
Opinion Formation ◽  
Long Line

A long line of work in social psychology has studied variations in people’s susceptibility to persuasion—the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people’s intrinsic opinions, it is also natural to consider interventions that modify people’s susceptibility to persuasion. In this work, motivated by this fact, we propose an influence optimization problem. Specifically, we adopt a popular model for social opinion dynamics, where each agent has some fixed innate opinion, and a resistance that measures the importance it places on its innate opinion; agents influence one another’s opinions through an iterative process. Under certain conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to modify the resistance of any number of agents (within some given range) such that the sum of the equilibrium opinions is minimized; for the budgeted variant, in addition the algorithm is given upfront a restriction on the number of agents whose resistance may be modified. We prove that the objective function is in general non-convex. Hence, formulating the problem as a convex program as in an early version of this work (Abebe et al., KDD’18) might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Finally, we propose and evaluate experimentally a family of heuristics for the budgeted variant of the problem.

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