A Diffusion Model for Maximizing Influence Spread in Large Networks

Associative Diffusion and the Pitfalls of Structural Reductionism

American Sociological Review ◽

10.1177/00031224211057150 ◽

2021 ◽

pp. 000312242110571

Author(s):

Amir Goldberg

Keyword(s):

Diffusion Model ◽

Network Size ◽

Diffusion Models ◽

Large Networks ◽

Cultural Differentiation ◽

Fully Connected ◽

Fully Connected Networks

In their insightful comment, DellaPosta and Davoodi argue that our finding (Goldberg and Stein 2018) that segmented networks inhibit cultural differentiation does not generalize to large networks. However, their demonstration rests on an incorrect implementation of the preference updating process in the associative diffusion model. We show that once this discrepancy is corrected, cultural differentiation is more pronounced in fully connected networks, irrespective of network size and even under extreme assumptions about cognitive decay. We use this as an opportunity to discuss the associative diffusion model’s assumptions and scope conditions, as well as to critically reassess prevailing contagion-based diffusion models.

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A new lithium diffusion model in layered oxides based on asymmetric but reversible transition metal migration

Energy & Environmental Science ◽

10.1039/c9ee04123k ◽

2020 ◽

Vol 13 (4) ◽

pp. 1269-1278 ◽

Cited By ~ 5

Author(s):

Kyojin Ku ◽

Byunghoon Kim ◽

Sung-Kyun Jung ◽

Yue Gong ◽

Donggun Eum ◽

...

Keyword(s):

Transition Metal ◽

Diffusion Model ◽

Layered Oxides ◽

Layered Oxide ◽

Reversible Transition ◽

Lithium Diffusion ◽

Metal Migration

We propose a new lithium diffusion model involving coupled lithium and transition metal migration, peculiarly occurring in a lithium-rich layered oxide.

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Does the Name-Race Implicit Association Test Measure Racial Prejudice?

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000093 ◽

2011 ◽

Vol 58 (4) ◽

pp. 271-277 ◽

Cited By ~ 26

Author(s):

Don van Ravenzwaaij ◽

Han L. J. van der Maas ◽

Eric-Jan Wagenmakers

Keyword(s):

Diffusion Model ◽

Implicit Association Test ◽

Alternative Explanation ◽

Group Membership ◽

Racial Prejudice ◽

Decision Time ◽

Association Test ◽

Implicit Association ◽

Model Decomposition ◽

Test Measure

Research using the Implicit Association Test (IAT) has shown that names labeled as Caucasian elicit more positive associations than names labeled as non-Caucasian. One interpretation of this result is that the IAT measures latent racial prejudice. An alternative explanation is that the result is due to differences in in-group/out-group membership. In this study, we conducted three different IATs: one with same-race Dutch names versus racially charged Moroccan names; one with same-race Dutch names versus racially neutral Finnish names; and one with Moroccan names versus Finnish names. Results showed equivalent effects for the Dutch-Moroccan and Dutch-Finnish IATs, but no effect for the Finnish-Moroccan IAT. This suggests that the name-race IAT-effect is not due to racial prejudice. A diffusion model decomposition indicated that the IAT-effects were caused by changes in speed of information accumulation, response conservativeness, and non-decision time.

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Impact of Context Information on Metaphor Elaboration

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000422 ◽

2018 ◽

Vol 65 (6) ◽

pp. 370-384 ◽

Cited By ~ 5

Author(s):

Veronika Lerche ◽

Ursula Christmann ◽

Andreas Voss

Keyword(s):

Diffusion Model ◽

Processing Speed ◽

A Priori ◽

Model Fit ◽

Verbal Task ◽

Context Information ◽

Speed Of Processing ◽

Good Account ◽

Dependent Variables ◽

Decision Settings

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

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