Network Connectivity Dynamics, Cognitive Biases, and the Evolution of Cultural Diversity in Round‐Robin Interactive Micro‐Societies

Segovia-Martín, José; Walker, Bradley; Fay, Nicolas; Tamariz, Mónica

doi:10.1111/cogs.12852

Cited by 14 publications

(20 citation statements)

References 86 publications

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“…The experiment carried out by Segovia-Martín et al [ 70 ] can also help us to understand our findings regarding the diffusion of traits in the system. They found that content bias is a predictor of population convergence in a single cultural variant (which tends to be more adaptive), and convergence in certain cultural traits is affected by the connectivity of individuals and populations.…”

Section: Discussionmentioning

confidence: 98%

Cultural Evolution and Digital Media: Diffusion of Fake News About COVID-19 on Twitter

Oliveira

Albuquerque

2021

SN COMPUT. SCI.

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Disinformation (fake news) is a major problem that affects modern populations, especially in an era when information can be spread from one corner of the world to another in just one click. The diffusion of misinformation becomes more problematic when it addresses issues related to health, as it can affect people at both the individual and population levels. Through the ideas proposed by cultural evolution theory, in this study, we seek to understand the dynamics of disseminating messages (cultural traits) with untrue content (maladaptive traits). For our investigation, we used the scenario caused by the Coronavirus Disease 2019 (COVID-19) pandemic as a model. The instability caused by the pandemic provides a good model for the study of adapted and maladaptive traits, as the information can directly affect individual and population fitness. Through data collected on the Twitter platform (259,176 tweets) and using machine learning techniques and web scraping, we built a predictive model to analyze the following questions: (1) Is false information more shared? (2) Is false information more adopted? (3) Do people with social prestige influence the dissemination of maladaptive traits of COVID-19? We observed that fake news features contained in messages with false information were shared and adopted as unblemished messages. We also observed that social prestige was not a determining factor for the diffusion of maladaptive traits. Even with the ability to allow connections between individuals participating in social media, some factors such as attachment to cultural traits and the formation of social bubbles can favor isolation and decrease connectivity between individuals. Consequently, in the scenario of isolation between groups and low connectivity between individuals, there is a reduction in cultural exchange between people, which interferes with the dynamics of the selection of cultural traits. Thus, maladaptive (harmful) traits are favored and maintained in the cultural system. We also argue that the local Brazilian cultural context can be a determining factor for maintaining maladaptive traits. We conclude that in an unstable (pandemic) scenario, the information transmitted on Twitter is not reliable in relation to the increase in fitness, which may occur because of the low cultural exchange promoted by the personalization of the social network and cultural context of the population. Supplementary Information The online version contains supplementary material available at 10.1007/s42979-021-00836-w.

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Section: Discussionmentioning

confidence: 98%

Cultural Evolution and Digital Media: Diffusion of Fake News About COVID-19 on Twitter

Oliveira

Albuquerque

2021

SN COMPUT. SCI.

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“…We look closely at this model but also move away from it in two important aspects. First, instead of modelling using differential equations, we build on a tradition of agent-based models of cultural evolution that aims at representing the micro-level behavior of individual agents [2,28,36,37]. Our approach to modelling multilevel selection is endogenous, as our institutional structures and group interactions emerge from individual characteristics and processes.…”

Section: Modelling Opinion Dynamics In Social Networkmentioning

confidence: 99%

“…Our model is also related to a number of models that explore the spread of cultural variants in social learning scenarios where agents are endowed with cognitive biases that affect the likelihood of adopting a given variant [34][35][36][37]. These models implement a content-based bias, also termed direct bias by Boyd and Richerson [2], which in our case refers to individual's sensitivity for opinion's value.…”

Section: Modelling Opinion Dynamics In Social Networkmentioning

confidence: 99%

“…Content-biased models generally assume that all agents in the population assign the same value to the different cultural variants. A population of agents with high content bias, for instance, leads to a reduction in cultural diversity [36][37][38]. However, in real life not everyone shares the same values, and different subpopulations may develop dissimilar value systems (e.g.…”

Section: Modelling Opinion Dynamics In Social Networkmentioning

confidence: 99%

“…This parameter is limited by the agent's memory size m, that is, the maximum amount of history (in rounds) that can influence the opinion choice. We fix m to a value of 3 (associated with the best fit in[36,37]), which means that only opinions observed or produced in the most recent 3 rounds are taken into account for frequency bias. Given an agent a n and a variant x o , then f (x o | h nr|M,m ) is the relative frequency of variant x o in the memory of an agent a n by round r. Thus, f (x n | h nr|M,3 ) corresponds to the relative frequency of variant x o in an agent memory for the last 3 rounds.…”

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confidence: 99%

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

Segovia-Martín¹,

Tamariz²

2021

Preprint

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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.

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Pandemic and infodemic: the spread of misinformation about COVID-19 from a cultural evolutionary perspective

Häusler,

Baraghith

2023

Biol Philos

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In this paper, we critically consider the analogy between “infodemic” and “pandemic”, i.e. the spread of fake news about COVID-19 as a medial virus and the infection with the biological virus itself from the perspective of cultural evolutionary theory (CET). After confronting three major shortcomings of the ‘infodemic’ concept, we use CET as a background framework to analyze this phenomenon. To do so, we summarize which bi-ases are crucial for transmission in terms of cultural selection and how transmission is restricted by filter bubbles or echo chambers acting as TRIMS (transmission isolating mechanisms) post “infection”, which isolate false from trustworthy scientific information in the context of the Corona pandemic. This is followed by a demonstration of the threat to biological fitness posed by the effects of an infection with fake news, which leads to a reduced willingness to vaccinate and follow health measures. We identify fake news on Covid as pseudoscience, trying to immunize itself from external influences. We then address the question of how to combat the infodemic. Since debunking strategies, such as warnings by fact-checking, have proven relatively ineffective in combating fake news, the inoculation theory from psychology might offer an alternative solution. Through its underlying ‘prebunking strategy’, which educates individuals about the risks and tactics of fake news prior to a potential infection, they could be ‘immunized’ in advance, similar to a virological vaccination. Although we recognize that the pandemic/infodemic analogy is in fact far from perfect, we believe that CET could provide a theoretical underpinning in order to give much more semantic depth to the concept ‘infodemic’.

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Network Connectivity Dynamics, Cognitive Biases, and the Evolution of Cultural Diversity in Round‐Robin Interactive Micro‐Societies

Cited by 14 publications

References 86 publications

Cultural Evolution and Digital Media: Diffusion of Fake News About COVID-19 on Twitter

Cultural Evolution and Digital Media: Diffusion of Fake News About COVID-19 on Twitter

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

Pandemic and infodemic: the spread of misinformation about COVID-19 from a cultural evolutionary perspective

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