scholarly journals Regret Averse Opinion Aggregation

2021 ◽  
Vol 8 (0) ◽  
Author(s):  
Lee Elkin
Keyword(s):  
Opinion Aggregation ◽  
Linear Pooling

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.

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

2021 ◽  
Author(s):  
José Segovia-Martín ◽  
Monica Tamariz
Keyword(s):  
Social Interactions ◽  
Feedback Loop ◽  
Opinion Dynamics ◽  
Proportional Representation ◽  
Value Systems ◽  
Opinion Aggregation ◽  
Local Coordination ◽  
Institutional Influence ◽  
Low Levels ◽  
The Impact

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.

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257525
Author(s):  
Jose Segovia-Martin ◽  
Monica Tamariz
Keyword(s):  
Social Interactions ◽  
Feedback Loop ◽  
Opinion Dynamics ◽  
Proportional Representation ◽  
Value Systems ◽  
Opinion Aggregation ◽  
Local Coordination ◽  
Institutional Influence ◽  
Low Levels ◽  
The Impact

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.

scholarly journals Multispectral Periocular Classification With Multimodal Compact Multi-Linear Pooling

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 14572-14578 ◽  
Author(s):  
Faisal M. Algashaam ◽  
Kien Nguyen ◽  
Mohamed Alkanhal ◽  
Vinod Chandran ◽  
Wageeh Boles ◽  
...  
Keyword(s):  
Linear Pooling

scholarly journals IMAGING ALL THE PEOPLE

Episteme ◽  
2016 ◽  
Vol 14 (4) ◽  
pp. 463-479 ◽  
Author(s):  
Hannes Leitgeb
Keyword(s):  
Decision Theory ◽  
Belief Function ◽  
Belief Functions ◽  
Degree Of Belief ◽  
Causal Decision ◽  
The People ◽  
Formal Result ◽  
Linear Pooling ◽  
The Individual ◽  
Commutativity Property

AbstractIt is well known that aggregating the degree-of-belief functions of different subjects by linear pooling or averaging is subject to a commutativity dilemma: other than in trivial cases, conditionalizing the individual degree-of-belief functions on a piece of evidence E followed by linearly aggregating them does not yield the same result as first aggregating them linearly and then conditionalizing the resulting social degree-of-belief function on E. In the present paper we suggest a novel way out of this dilemma: adapting the method of update or learning such that linear pooling commutes with it. As it turns out, the resulting update scheme – (general) imaging on the evidence – is well-known from areas such as the study of conditionals and causal decision theory, and a formal result from which the required commutativity property is derivable was supplied already by Gärdenfors (1982) in a different context. We end up determining under which conditions imaging would seem to be right method of update, and under which conditions, therefore, group update would not be affected by the commutativity dilemma.

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260592
Author(s):  
Peter Sheridan Dodds ◽  
Joshua R. Minot ◽  
Michael V. Arnold ◽  
Thayer Alshaabi ◽  
Jane Lydia Adams ◽  
...  
Keyword(s):  
The United States ◽  
Journalism History ◽  
Specific Kind ◽  
Data Set ◽  
Opinion Aggregation ◽  
Story Generation ◽  
The Us ◽  
Computational Aspects ◽  
Us Presidential Election ◽  
Narrative Control

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.

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