Regret Averse Opinion Aggregation

It is often suggested that when opinions differ among individuals in a group, the opinions should be aggregated to form a compromise. This paper compares two approaches to aggregating opinions, linear pooling and what I call opinion agglomeration. In evaluating both strategies, I propose a pragmatic criterion, No Regrets, entailing that an aggregation strategy should prevent groups from buying and selling bets on events at prices regretted by their members. I show that only opinion agglomeration is able to satisfy the demand. I then proceed to give normative and empirical arguments in support of the pragmatic criterion for opinion aggregation, and that ultimately favor opinion agglomeration.

Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy

Measuring the specific kind, temporal ordering, diversity, and turnover rate of stories surrounding any given subject is essential to developing a complete reckoning of that subject’s historical impact. Here, we use Twitter as a distributed news and opinion aggregation source to identify and track the dynamics of the dominant day-scale stories around Donald Trump, the 45th President of the United States. Working with a data set comprising around 20 billion 1-grams, we first compare each day’s 1-gram and 2-gram usage frequencies to those of a year before, to create day- and week-scale timelines for Trump stories for 2016–2021. We measure Trump’s narrative control, the extent to which stories have been about Trump or put forward by Trump. We then quantify story turbulence and collective chronopathy—the rate at which a population’s stories for a subject seem to change over time. We show that 2017 was the most turbulent overall year for Trump. In 2020, story generation slowed dramatically during the first two major waves of the COVID-19 pandemic, with rapid turnover returning first with the Black Lives Matter protests following George Floyd’s murder and then later by events leading up to and following the 2020 US presidential election, including the storming of the US Capitol six days into 2021. Trump story turnover for 2 months during the COVID-19 pandemic was on par with that of 3 days in September 2017. Our methods may be applied to any well-discussed phenomenon, and have potential to enable the computational aspects of journalism, history, and biography.

Synchronising institutions and value systems: A model of opinion dynamics mediated by proportional representation

Individuals increasingly participate in online platforms where they copy, share and form they opinions. Social interactions in these platforms are mediated by digital institutions, which dictate algorithms that in turn affect how users form and evolve their opinions. In this work, we examine the conditions under which convergence on shared opinions can be obtained in a social network where connected agents repeatedly update their normalised cardinal preferences (i.e. value systems) under the influence of a non-constant reflexive signal (i.e. institution) that aggregates populations’ information using a proportional representation rule. We analyse the impact of institutions that aggregate (i) expressed opinions (i.e. opinion-aggregation institutions), and (ii) cardinal preferences (i.e. value-aggregation institutions). We find that, in certain regions of the parameter space, moderate institutional influence can lead to moderate consensus and strong institutional influence can lead to polarisation. In our randomised network, local coordination alone in the total absence of institutions does not lead to convergence on shared opinions, but very low levels of institutional influence are sufficient to generate a feedback loop that favours global conventions. We also show that opinion-aggregation may act as a catalyst for value change and convergence. When applied to digital institutions, we show that the best mechanism to avoid extremism is to increase the initial diversity of the value systems in the population.

Synchronising institutions and value systems: a model of opinion dynamics mediated by proportional representation

Individuals increasingly participate in online platforms where they copy, share and form they opinions. Social interactions in these platforms are mediated by digital institutions, which dictate algorithms that in turn affect how users form and evolve their opinions. In this work, we examine the conditions under which convergence on shared opinions can be obtained in a social network where connected agents repeatedly update their normalised cardinal preferences (i.e. value systems) under the influence of a non-constant reflexive signal (i.e. institution) that aggregates populations' information using a proportional representation rule. We analyse the impact of institutions that aggregate (i) expressed opinions (i.e. opinion-aggregation institutions), and (ii) cardinal preferences (i.e. value-aggregation institutions). We find that, in certain regions of the parameter space, moderate institutional influence can lead to moderate consensus and strong institutional influence can lead to polarisation. In our randomised network, local coordination alone in the total absence of institutions does not lead to convergence on shared opinions, but very low levels of institutional influence are sufficient to generate a feedback loop that favours global conventions. We also show that opinion-aggregation may act as a catalyst for value change and convergence. When applied to digital institutions, we show that the best mechanism to avoid extremism is to increase the initial diversity of the value systems in the population.

Revealing Opinions for COVID-19 Questions through Context Retriever and Opinion Aggregating Question-Answering (Preprint)

BACKGROUND The COVID-19 has caused severe challenges to global public health because it is highly contagious and can be lethal. Numerous ongoing and recently published researches have emerged. However, the research regarding COVID-19 is largely ongoing and inconclusive. OBJECTIVE A potential approach to accelerate COVID-19 research is to borrow information from the existing researches of the other viruses that belong to the same coronavirus family. We develop a natural language processing method for answering factoid questions related to COVID-19 using published articles as knowledge sources. METHODS Given a question, first, a BM25 based context retriever model is implemented to select the most relevant passages from the articles. Second, for each selected context passage, an answer is obtained using a pre-trained BERT question-answering model. Third, an opinion aggregator, which is a combination of biterm topic model (BTM) and k-means clustering, is applied to aggregating all answers into several opinions. RESULTS We apply the proposed pipeline to extract answers, opinions and the most frequent words to six questions from the COVID-19 Open Research Dataset Challenge (CORD-19). By showing the longitudinal distributions of the opinions, we uncover the trends of opinions and popular words in the publications during four periods: before 1990, during 1990-2000, 2000-2010, 2011-2019, and after 2019. The changes in the opinions and popular words agree with several distinct characteristics and challenges of COVID-19, including a higher risk for senior people and people with pre-existing medical conditions, high contagion and rapid transmission, and more urgent need of screening and testing. The opinions and the popular words also provide additional insights for the COVID-19 related questions. CONCLUSIONS Compared with other methods for literature retriever and answer generation, opinion aggregation in our method leads to more interpretable, robust and comprehensive question-specific literature reviews. The results demonstrate the usefulness of the proposed method in answering COVID-19 related questions with main opinions and capturing the trends of research about COVID-19 and other relevant strains of coronavirus in recent years.

Opinion Aggregation and Individual Expertise

Group judgments are often influenced by their members’ individual expertise. It is less clear, though, how individual expertise should affect the group judgments. This chapter surveys a wide range of models of opinion aggregation and group judgment: models where all group members have the same impact on the group judgment, models that take into account differences in individual accuracy, and models where group members revise their beliefs as a function of their mutual respect. The scope of these models covers the aggregation of propositional attitudes, probability functions, and numerical estimates. By comparing these different kinds of models and contrasting them with findings in psychology, management science, and the expert judgment literature, the chapter provides a better understanding of the role of expertise in group agency, both from a theoretical and from an empirical perspective.