The diamond model of social response within an agent-based approach

Models of social response concern the identification and delineation of possible responses to social pressure. Most models are based on simple one-dimensional conceptualizations of conformity and its alternatives even though more sophisticated models have been available for a number of years. The diamond model is perhaps the most refined of the two-dimensional formulations. It is particularly useful in building agent-based models of opinion dynamics because it gives clear and explicit operational definitions of basic types of social response. In fact, the diamond model is actually a ready recipe for a microscopic model of opinion dynamics. Moreover, it fits quite well Einstein's "simple but no simpler" strategy. In this work, we will present the logic of the diamond model as well as its implications for agent-based modeling.

[1]  G. Macdonald,et al.  Proposal of a Double Diamond Model of Social Response , 2013 .

[2]  M. A. Muñoz,et al.  Nonlinear q-voter model. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  K. Kacperski,et al.  Phase transitions and hysteresis in a cellular automata-based model of opinion formation , 1996 .

[4]  S. Galam Sociophysics: A Physicist's Modeling of Psycho-political Phenomena , 2012 .

[5]  Katarzyna Sznajd-Weron,et al.  Phase transition in the Sznajd model with independence , 2011, ArXiv.

[6]  Katarzyna Sznajd-Weron,et al.  Mapping the q-voter model: From a single chain to complex networks , 2015, 1501.05091.

[7]  Albert Einstein,et al.  The Ultimate Quotable Einstein , 2010 .

[8]  Pawel Sobkowicz,et al.  Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality , 2009, J. Artif. Soc. Soc. Simul..

[9]  Marco Alberto Javarone,et al.  Conformism-driven phases of opinion formation on heterogeneous networks: the q-voter model case , 2014, 1410.7300.

[10]  Krzysztof Kulakowski,et al.  Opinion polarization in the Receipt–Accept–Sample model , 2008, 0806.1204.

[11]  Krzysztof Kulakowski,et al.  Opinion formation in an open system and the spiral of silence , 2014, ArXiv.

[12]  Romualdo Pastor-Satorras,et al.  Mean-Field Analysis of the q-Voter Model on Networks , 2013, 1301.7563.

[13]  R. H. Willis,et al.  Conformity, Independence, and Anticonformity , 1965 .

[14]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[15]  B. Latané,et al.  Statistical mechanics of social impact. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[16]  P. Sobkowicz Discrete Model of Opinion Changes Using Knowledge and Emotions as Control Variables , 2012, PloS one.

[17]  Frank Schweitzer,et al.  Phase transitions in social impact models of opinion formation , 2000 .

[18]  Guillaume Deffuant,et al.  Mixing beliefs among interacting agents , 2000, Adv. Complex Syst..

[19]  André M Timpanaro,et al.  Analytical expression for the exit probability of the q-voter model in one dimension. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Katarzyna Sznajd-Weron,et al.  Phase transitions in the q-voter model with two types of stochastic driving. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Katarzyna Sznajd-Weron,et al.  Phase transitions in the q-voter model with noise on a duplex clique. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  K. Sznajd-Weron,et al.  Anticonformity or Independence?—Insights from Statistical Physics , 2013 .