Opinion dynamics and wisdom under out-group discrimination

Eger, Steffen

doi:10.1016/j.mathsocsci.2016.02.005

Cited by 15 publications

(11 citation statements)

References 39 publications

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“…Three types of biases have been studied in explanatory models of opinion dynamics in recent years. The first is opposition between members of different groups (e.g., due to group-identity) leading to distancing from certain subjects' opinions (Altafini, 2013;Eger, 2016). The second is homophily: individuals ignore opinions too different from their own (Deffuant et al, 2000;Hegselmann and Krause, 2002).…”

Section: Discussionmentioning

confidence: 99%

Does My Rebuttal Matter? Insights from a Major

Gao¹,

Eger²,

Kuznetsov³

et al. 2019

Proceedings of the 2019 Conference of the North

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Peer review is a core element of the scientific process, particularly in conference-centered fields such as ML and NLP. However, only few studies have evaluated its properties empirically. Aiming to fill this gap, we present a corpus that contains over 4k reviews and 1.2k author responses from ACL-2018. We quantitatively and qualitatively assess the corpus. This includes a pilot study on paper weaknesses given by reviewers and on quality of author responses. We then focus on the role of the rebuttal phase, and propose a novel task to predict after-rebuttal (i.e., final) scores from initial reviews and author responses. Although author responses do have a marginal (and statistically significant) influence on the final scores, especially for borderline papers, our results suggest that a reviewer's final score is largely determined by her initial score and the distance to the other reviewers' initial scores. In this context, we discuss the conformity bias inherent to peer reviewing, a bias that has largely been overlooked in previous research. We hope our analyses will help better assess the usefulness of the rebuttal phase in NLP conferences.

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Section: Discussionmentioning

confidence: 99%

Does My Rebuttal Matter? Insights from a Major

Gao¹,

Eger²,

Kuznetsov³

et al. 2019

Proceedings of the 2019 Conference of the North

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“…Contagion mechanisms would suggest that inclinations are infectious much like epidemics. Examples for recent work related to collective inclination dynamics include Dauhoo et al [2016] on stochastic evolution of rumor spreading and Eger [2016] on opinion dynamics and wisdom of crowds. Last but not least the network itself may be subject to dynamics, say, following laws of homophily such that the likelihood of a link increases with similarity of preferences.…”

Section: Discussionmentioning

confidence: 99%

Diffusion of behavior in network games with threshold dynamics

Huang

Koster

Lindner

2016

Mathematical Social Sciences

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Research Highlights: • The main novelty of the paper is to assume that the thresholds are endogenously determined. Agents change their inclination by exposition to other inclinations in the social network. • With our model we are able to explain a variety of adoption behavior. Of particular interest is the existence of non-monotonic behavior of the aggregate adoption rate which is not possible in the benchmark model without inclination. Our model is therefore able to explain "sudden" outbreaks of collective action. • This suggests to reinvent the common static and exogenous concept of a tipping point by defining it endogenously generated by the network.

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“…(2) is a time-series model, and, in particular, a vector-autoregressive (VAR) model with special structure. The model may also be seen in the socio-economic context of so-called "opinion dynamic models" (Golub and Jackson, 2010;Acemoglu and Ozdaglar, 2011;Eger, 2016). There it is assumed that agents are situated in network structures and continuously update their opinions/beliefs/actions according to their ties with other agents.…”

Section: A Linear Model Of Semantic Changementioning

confidence: 99%

On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models

Eger¹,

Mehler²

2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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We consider two graph models of semantic change. The first is a time-series model that relates embedding vectors from one time period to embedding vectors of previous time periods. In the second, we construct one graph for each word: nodes in this graph correspond to time points and edge weights to the similarity of the word's meaning across two time points. We apply our two models to corpora across three different languages. We find that semantic change is linear in two senses. Firstly, today's embedding vectors (= meaning) of words can be derived as linear combinations of embedding vectors of their neighbors in previous time periods. Secondly, self-similarity of words decays linearly in time. We consider both findings as new laws/hypotheses of semantic change.

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Opinion dynamics and wisdom under out-group discrimination

Cited by 15 publications

References 39 publications

Does My Rebuttal Matter? Insights from a Major

Does My Rebuttal Matter? Insights from a Major

Diffusion of behavior in network games with threshold dynamics

On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models

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