Peer-to-peer and mass communication effect on opinion shifts

Kindler, Alex; Solomon, Sorin; Stauffer, Dietrich

doi:10.1016/j.physa.2012.10.038

Cited by 8 publications

(9 citation statements)

References 24 publications

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“…, N. Following [10] and [18], we call these agents spinsons (spin+person) to reflect their dichotomous nature originating in spin models of statistical physics and humanly features and interpretation. We should mention that the idea of using binary variables in modeling innovation diffusion is not new [19,20,21,22,23]. In fact Watts and Dodds [12] argue that binary decisions can be applied to a surprisingly wide range of real world situations and are particularly useful for modeling processes in which 'adopted' and 'not adopted' are natural states.…”

Section: Modelmentioning

confidence: 99%

Rewiring the network. What helps an innovation to diffuse?

Sznajd-Weron¹,

Szwabiński²,

Weron³

et al. 2014

J. Stat. Mech.

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Abstract. A fundamental question related to innovation diffusion is how the social network structure influences the process. Empirical evidence regarding real-world influence networks is very limited. On the other hand, agent-based modeling literature reports different and at times seemingly contradictory results. In this paper we study innovation diffusion processes for a range of Watts-Strogatz networks in an attempt to shed more light on this problem. Using the so-called Sznajd model as the backbone of opinion dynamics, we find that the published results are in fact consistent and allow to predict the role of network topology in various situations. In particular, the diffusion of innovation is easier on more regular graphs, i.e. with a higher clustering coefficient. Moreover, in the case of uncertainty -which is particularly high for innovations connected to public health programs or ecological campaigns -a more clustered network will help the diffusion. On the other hand, when social influence is less important (i.e. in the case of perfect information), a shorter path will help the innovation to spread in the society and -as a result -the diffusion will be easiest on a random graph.

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Section: Modelmentioning

confidence: 99%

Rewiring the network. What helps an innovation to diffuse?

Sznajd-Weron¹,

Szwabiński²,

Weron³

et al. 2014

J. Stat. Mech.

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show abstract

“…In this version, the stress is less on the question of the static existence of macroscopic clusters, but more on the 'contagion' process that sweeps across the population, creating macroscopic clusters of 'contaminated' agents. This allows, in turn, feedback between the growth of the contaminated cluster and processes that it influences ] [Cantono 2010], [Cantono 2012], [Kindler 2013] which can, in their turn, feedback on the growth of the cluster.…”

Section: Propagation Of Distress By Contagion Crisis Percolation Acrmentioning

confidence: 99%

“…1. As mentioned previously, it is not even clear that the simple percolation (rather than the Ising or bootstrap percolation [Kindler 2013]) picture is the correct one. 2. the links of the network have in reality very different weights.…”

Section: Dealing With Uncertainty Of the Global Stochastic Parametersmentioning

confidence: 99%

“…Moreover, in the case in which each company has many contacts it becomes non-realistic to assume that it can be failed by the fall of just one partner and one has to take into account the weighted influence of all its connections as described in [Kindler 2013]. The relevant formalism for that case has been shown in [Kindler 2013] to be 'bootstrap percolation' which is outside the scope of the present exposition.…”

Section: Examples Of Models Relaxing / Modifying Assumptions Of the Pmentioning

confidence: 99%

“…The conditions required for a company's distress to become public may vary. For instance in [Kindler 2013] one considered the cumulative influence of its neighbors.…”

Section: Evolution Of the Minsky Accelerator Process Based On The Nmentioning

confidence: 99%

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Minsky Financial Instability, Interscale Feedback, Percolation and Marshall–Walras Disequilibrium

Solomon¹,

Golo

2013

Accounting, Economics and Law

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We study analytically and numerically Minsky instability as a combination of top-down, bottom-up and peer-to-peer positive feedback loops. The peer-to-peer interactions are represented by the links of a network formed by the connections between firms; contagion leading to avalanches and percolation phase transitions propagating across these links. The global parameter in the top-bottom -bottom-up feedback loop is the interest rate. Before the Minsky Moment, in the 'Minsky loans accelerator' stage the relevant "bottom" parameter representing the individual firms' micro-states, is the quantity of loans. After the Minsky Moment, in the 'Minsky crisis accelerator' stage, the relevant 'bottom' parameters are the number of ponzi units / quantity of failures / defaults. We represent the top-bottom, bottom-up interactions on a plot similar to the Marshall-Walras diagram for quantity-price market equilibrium (where the interest rate is the analog of the price). The Minsky instability is then simply emerging as a consequence of the fixed point (the intersection of the supply and demand curves) being unstable (repulsive). In the presence of network effects, one obtains more than one fixed point and a few dynamic regimes (phases). We describe them and their implications for understanding, predicting and steering economic instability.

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