Month: February 2020

Self-reported willingness to share political news articles in online surveys correlates with actual sharing on Twitter

Mohsen Mosleh, Gordon Pennycook, David G. Rand 

 

There is an increasing imperative for psychologists and other behavioral scientists to understand how people behave on social media. However, it is often very difficult to execute experimental research on actual social media platforms, or to link survey responses to online behavior in order to perform correlational analyses. Thus, there is a natural desire to use self-reported behavioral intentions in standard survey studies to gain insight into online behavior. But are such hypothetical responses hopelessly disconnected from actual sharing decisions? Or are online survey samples via sources such as Amazon Mechanical Turk (MTurk) so different from the average social media user that the survey responses of one group give little insight into the on-platform behavior of the other? Here we investigate these issues by examining 67 pieces of political news content. We evaluate whether there is a meaningful relationship between (i) the level of sharing (tweets and retweets) of a given piece of content on Twitter, and (ii) the extent to which individuals (total N = 993) in online surveys on MTurk reported being willing to share that same piece of content. We found that the same news headlines that were more likely to be hypothetically shared on MTurk were also shared more frequently by Twitter users, r = .44. For example, across the observed range of MTurk sharing fractions, a 20 percentage point increase in the fraction of MTurk participants who reported being willing to share a news headline on social media was associated with 10x as many actual shares on Twitter. We also found that the correlation between sharing and various features of the headline was similar using both MTurk and Twitter data. These findings suggest that self-reported sharing intentions collected in online surveys are likely to provide some meaningful insight into what content would actually be shared on social media.

Source: journals.plos.org

Adoption Dynamics and Societal Impact of AI Systems in Complex Networks 

Pedro M. Fernandes, Francisco C. Santos, Manuel Lopes

AIES ’20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and SocietyFebruary 2020 Pages 258–264

 

We propose a game-theoretical model to simulate the dynamics of AI adoption in adaptive networks. This formalism allows us to understand the impact of the adoption of AI systems for society as a whole, addressing some of the concerns on the need for regulation. Using this model we study the adoption of AI systems, the distribution of the different types of AI (from selfish to utilitarian), the appearance of clusters of specific AI types, and the impact on the fitness of each individual. We suggest that the entangled evolution of individual strategy and network structure constitutes a key mechanism for the sustainability of utilitarian and human-conscious AI. Differently, in the absence of rewiring, a minority of the population can easily foster the adoption of selfish AI and gains a benefit at the expense of the remaining majority.

Source: dl.acm.org

Artificial Life—Next Generation Perspectives: Echoes from the 2018 Conference in Tokyo

Olaf Witkowski, Takashi Ikegami, Nathaniel Virgo, Mizuki Oka and Hiroyuki Iizuka

 

Artificial life is a research field devoted to the theoretical study of features of living systems, such as evolution and the brain. The field has developed philosophical concepts such as autopoiesis and emergence, alongside a large range of computational and experimental setups, from evolutionary simulations to robotics and chemical experiments.

The complexity and diversity of the artificial life field is crucial to its community. Many researchers consider the community as a real source of creativity and free-minded exchange of ideas on important questions. For ideas that donʼt fit neatly into a single “mainstream” field of science, there is value in examining and discussing them in a context free from departmental or disciplinary constraints, with the purpose of reaching a better knowledge of the fundamental mechanisms that govern living systems.

Source: www.mitpressjournals.org

Ecosystem antifragility: beyond integrity and resilience

We review the concept of ecosystem resilience in its relation to ecosystem integrity from an information theory approach. We summarize the literature on the subject identifying three main narratives: ecosystem properties that enable them to be more resilient; ecosystem response to perturbations; and complexity. We also include original ideas with theoretical and quantitative developments with application examples. The main contribution is a new way to rethink resilience, that is mathematically formal and easy to evaluate heuristically in real-world applications: ecosystem antifragility. An ecosystem is antifragile if it benefits from environmental variability. Antifragility therefore goes beyond robustness or resilience because while resilient/robust systems are merely perturbation-resistant, antifragile structures

 

Equihua M, Espinosa Aldama M, Gershenson C, López-Corona O, Munguía M, Pérez-Maqueo O, Ramírez-Carrillo E. 2020. Ecosystem antifragility: beyond integrity and resilience. PeerJ 8:e8533 https://doi.org/10.7717/peerj.8533

Source: peerj.com

Dynamics of a birth–death process based on combinatorial innovation

Mike Steel, Wim Hordijk, Stuart A. Kauffman

Journal of Theoretical Biology

 

A feature of human creativity is the ability to take a subset of existing items (e.g. objects, ideas, or techniques) and combine them in various ways to give rise to new items, which, in turn, fuel further growth. Occasionally, some of these items may also disappear (extinction). We model this process by a simple stochastic birth–death model, with non-linear combinatorial terms in the growth coefficients to capture the propensity of subsets of items to give rise to new items. In its simplest form, this model involves just two parameters (P, α). This process exhibits a characteristic ‘hockey-stick’ behaviour: a long period of relatively little growth followed by a relatively sudden ‘explosive’ increase. We provide exact expressions for the mean and variance of this time to explosion and compare the results with simulations. We then generalise our results to allow for more general parameter assignments, and consider possible applications to data involving human productivity and creativity.

 

Source: www.sciencedirect.com