From private attitude to public opinion: A dynamic theory of social impact.

Nowak, Andrzej; Szamrej, Jacek; Latané, Bibb

doi:10.1037/0033-295x.97.3.362

Cited by 826 publications

(584 citation statements)

References 85 publications

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“…Love it, or hate it (Binmore, 1994), the simulations of the Prisoner's Dilemma in the last three decades outweigh any other topic, and by a considerable margin (see Hoffmann, 2000;Axelrod & D'Ambrosio, 1996;Gotts et al, 2003). Public opinion dynamics (to cite just a few, (Nowak et al, 1990;Latane, 1996;Huckfeldt et al, 2004)) or competitive position taking by political parties Kollman et al, 1992Kollman et al, , 1998de Marchi, 1999;Laver, 2005;Laver & Schilperoord, 2007;Fowler & Laver, 2008 have also received a considerable amount of attention.…”

Section: Simulation Modeling and Hypothesis Constructionmentioning

confidence: 99%

Monte Carlo Analysis in Academic Research

Johnson

2013

Oxford Handbooks Online

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Monte Carlo analysis is a research strategy that incorporates randomness into the design, implementation or evaluation of theoretical models. It began in the 1940s, when the development of computer hardware and mathematical models made it possible to generate streams of random numbers. These random number streams are combined with mathematical models in order to create models and evaluate theories of random processes. This chapter attempts to tame this diverse, unmanageable collection of concepts and methods by dividing simulation projects into three types. The first, commonly called "Monte Carlo simulation," is used to evaluate statistical estimators. When an estimation procedure is proposed, it is standard procedure to test it against a variety of simulated research problems. A second type of project, referred to as "Markov chain Monte Carlo" (or MCMC), helps researchers to draw conclusions about complicated probability models for which conventional research strategies do not yield insights. The third 1 type of project arises in the study of complex systems, which are characterized by a large number of loosely interconnected, autonomous elements. Commonly known as agent-based models, these simulations have found enthusiastic advocates in environmental and social sciences.Keywords: Monte Carlo, Markov chain Monte Carlo (MCMC), pseudo random number generation (PRNG), Bayesian statistics, agent-based modeling Monte Carlo (MC) analysis is a general term that refers to research that employs random numbers, usually in the form of a computer model (or simulation). Although this research began in the natural sciences, computer science, and mathematics, it is now widely applied in social science as well. This essay attempts to explain the fundamental ideas that spurred the creation of these new procedures as well as their eventual adaptation for use in social science research.This chapter is not a "how to" guide for simulation, but rather it is a "what for" or "why you might want to" guide. Some of the difficulties that arise in MC research projects are considered as well. It begins with some background information on the development of computers and algorithms for random numbers. After that, the chapter takes up applications in the evaluation of proposed statistical estimators, the practice of Bayesian statistics via computer simulation, and investigation of complex systems through agent-based models. Some conclusions about the challenges that face the field are presented, along with a conclusion. A significant part of the presentation is about the exciting developments that have occurred since 1990. Rapid improvements in hardware and software have opened up opportunities for scholars to work with models that were previously prohibited by conceptual and technical barriers. At the current time, we are able to conceptualize and implement models that were simply impossible just 10 years ago. The extremely rapid progress has been driven by a fruitful interaction of substantive researchers in the natural and social science...

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Section: Simulation Modeling and Hypothesis Constructionmentioning

confidence: 99%

Monte Carlo Analysis in Academic Research

Johnson

2013

Oxford Handbooks Online

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“…This microscopic model is also widely applied to social systems. In fact, economic and social systems are not very different from the viewpoint of complex networks [21,22,23,24,25,26,27,28,29,30]. There are many interactions and communications between agents in the network, and these are subject to opinion dynamics.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of clustered opinions in complex networks

Jung

Moon

Stanley

2008

J Econ Interac Coord

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A simple model for simulating tug of war game as varying the player number in a team is discussed to identify the slow pace of fast change. This model shows that a large number of information sources leads slow change for the system. Also, we introduce an opinion diffusion model including the effect of a high degree of clustering. This model shows that the de facto standard and lock-in effect, well-known phenomena in economics and business management, can be explained by the network clusters.

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“…Equation (2) implies that the first bankruptcies have a more important effect (because of the novelty and surprise), while the effect per failed firm decreases as the number of failures increases (due to habituation/desensitization). To our knowledge, a similar mechanism has been studied in the social psychology domain (see Nowak et al 1990). As mentioned in section 2.1, a detailed implementation will be needed in the future in order to confront it quantitatively with economic reality.…”

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confidence: 99%

When the collective acts on its components: economic crisis autocatalytic percolation

Cantono

Solomon

2010

New J. Phys.

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Agent-based models have improved the standards for empirical support and validation criteria in social, biological, cognitive and human sciences. Yet, the inclusion, in these models, of vertical interactions between various aggregation levels remains a challenge. We study analytically, numerically and by simulation the generic consequences of interactions between the collective and its individual components: the appearance of an autocatalytic loop between the dynamics of the collective and its components; the system, which is dominated by a limited number of factors amplified by this collective↔individuals autocatalytic loop; the microscopic features, which are not involved in the autocatalytic loop and are irrelevant at the systemic level; and how the above clarify the interplay between macroscopic predictable features and the ones dependent on random unpredictable individual events. Using the social and market percolation framework, we study the dramatic effects of the collective↔individuals autocatalytic loop on economic crisis propagation: the percolation transition becomes discontinuous; there are a few relevant regions and regimes corresponding to a quite diverse range of response policy options; there are stability ranges where appropriate policies can help to avoid macroscopic crisis percolation; and beyond those regions the systemic crisis might become unstoppable.

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From private attitude to public opinion: A dynamic theory of social impact.

Cited by 826 publications

References 85 publications

Monte Carlo Analysis in Academic Research

Monte Carlo Analysis in Academic Research

Dynamics of clustered opinions in complex networks

When the collective acts on its components: economic crisis autocatalytic percolation

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