Inferring models of opinion dynamics from aggregated jury data

Burghardt, Keith; Rand, William; Girvan, Michelle

doi:10.1371/journal.pone.0218312

Cited by 6 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Often, however, social influence can reduce crowd wisdom. Corporate earnings predictions [16], jury decisions [15,33], and other guesses can degrade with influence [40,41,55], and malevolent individuals can manipulate people to make particular collective decisions [6,46]. Too much influence by a single individual can also reduce the wisdom of collective decisions [4,9,25], and deferring to friends can sometimes make unpopular (and potentially low-quality) ideas appear popular [38].…”

Section: Crowdsourcingmentioning

confidence: 99%

Origins of Algorithmic Instabilities in Crowdsourced Ranking

Burghardt¹,

Hogg

D’Souza

et al. 2020

Proc. ACM Hum.-Comput. Interact.

Self Cite

View full text Add to dashboard Cite

Crowdsourcing systems aggregate decisions of many people to help users quickly identify high-quality options, such as the best answers to questions or interesting news stories. A long-standing issue in crowdsourcing is how option quality and human judgement heuristics interact to affect collective outcomes, such as the perceived popularity of options. We address this limitation by conducting a controlled experiment where subjects choose between two ranked options whose quality can be independently varied. We use this data to construct a model that quantifies how judgement heuristics and option quality combine when deciding between two options. The model reveals popularity-ranking can be unstable: unless the quality difference between the two options is sufficiently high, the higher quality option is not guaranteed to be eventually ranked on top. To rectify this instability, we create an algorithm that accounts for judgement heuristics to infer the best option and rank it first. This algorithm is guaranteed to be optimal if data matches the model. When the data does not match the model, however, simulations show that in practice this algorithm performs better or at least as well as popularity-based and recency-based ranking for any two-choice question. Our work suggests that algorithms relying on inference of mathematical models of user behavior can substantially improve outcomes in crowdsourcing systems. CCS Concepts: • Human-centered computing → User models; Laboratory experiments; Heuristic evaluations; Empirical studies in visualization; Empirical studies in interaction design.

show abstract

Section: Crowdsourcingmentioning

confidence: 99%

Origins of Algorithmic Instabilities in Crowdsourced Ranking

Burghardt¹,

Hogg

D’Souza

et al. 2020

Proc. ACM Hum.-Comput. Interact.

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, it cannot be applied to long time scales and it may not easily be generalised to the whole society level. On the other hand, data from political polls or elections [23][24][25] have a poor time resolution and no information on the individual agents, the only measurable observable being the share n + (t) of voters who support the proposition at each time step. Concurrently, data are available up to the time scale of centuries (for elections) and describe the average opinion of the whole society and not only a subgroup.…”

Section: Introductionmentioning

confidence: 99%

Time scales in the dynamics of political opinions and the voter model

Meyer,

Metzler

2024

New J. Phys.

View full text Add to dashboard Cite

Opinions in human societies are measured by political polls on time scales ofmonths to years. Such opinion polls do not resolve the effects of individualinteractions but constitute a stochastic process. Voter models with zealots(individuals who do not change their opinions) can describe the mean-fielddynamics in systems where no consensus is reached. We show that for largepopulations, the voter model with zealots is equivalent to the noisy votermodel and it has a single characteristic time scale associated with thenumber of zealots in the population. We discuss which parameters areobservable in real data by analysing time series of approval ratings ofseveral political leaders that match the statistical behaviour of the votermodel using the technique of the time-averaged mean squared displacement.The characteristic time scale of political opinions in societies is around 12months, so it cannot be resolved by analysing election data, for which theresolution is several years. The effective population size in all fitteddata sets is much smaller than the real population size, which indicatespositive correlations of successive voter model steps. We also discussthe heterogeneity of voters as a cause of subdiffusion on long time scales,i.e., slow changes in the society.

show abstract

“…In this sense, agents are not completely open-minded, being persistently driven by an individual attachment due, for example, to the influence from a specific ideology [5,6]. The key ingredient for estimating this stubbornness and, consequently, for offering insights in efficient control strategies for steering social behaviors towards desired patterns, is the development of new technically sound tools, able to extract low-dimensional information from social data [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A new metric for understanding hidden political influences from voting records

et al. 2020

View full text Add to dashboard Cite

Inspired by the increasing attention of the scientific community towards the understanding of human relationships and actions in social sciences, in this paper we address the problem of inferring from voting data the hidden influence on individuals from competing ideology groups. As a case study, we present an analysis of the closeness of members of the Italian Senate to political parties during the XVII Legislature. The proposed approach is aimed at automatic extraction of the relevant information by disentangling the actual influences from noise, via a two step procedure. First, a sparse principal component projection is performed on the standardized voting data. Then, the projected data is combined with a generative mixture model, and an information theoretic measure, which we refer to as Political Data-aNalytic Affinity (Political DNA), is finally derived. We show that the definition of this new affinity measure, together with suitable visualization tools for displaying the results of analysis, allows a better understanding and interpretability of the relationships among political groups.

show abstract

Inferring models of opinion dynamics from aggregated jury data

Cited by 6 publications

References 45 publications

Origins of Algorithmic Instabilities in Crowdsourced Ranking

Origins of Algorithmic Instabilities in Crowdsourced Ranking

Time scales in the dynamics of political opinions and the voter model

A new metric for understanding hidden political influences from voting records

Contact Info

Product

Resources

About