Phil 11.22.18

Listening to How CRISPR Gene Editing Is Changing the World, where Jennifer Kahn discusses the concept of Fitness Cost, where mutations (CRISPR or otherwise) often decrease the fitness of the modified organism. I’m thinking that this relates to the conflicting fitness mechanisms of diverse and monolithic systems. Diverse systems are resilient in the long run. Monolithic systems are effective in the short run. That stochastic interaction between those two time scales is what makes the problem of authoritarianism so hard.

Fitness cost is explicitly modeled here: Kinship, reciprocity and synergism in the evolution of social behaviour

  • There are two ways to model the genetic evolution of social behaviour. Population genetic models using personal fitness may be exact and of wide applicability, but they are often complex and assume very different forms for different kinds of social behaviour. The alternative, inclusive fitness models, achieves simplicity and clarity by attributing all fitness effects of a behaviour to an expanded fitness of the actor. For example, Hamilton’s rule states that an altruistic behaviour will be favoured when -c + rb > 0, where c is the fitness cost to the altruist, b is the benefit to Its partner, and r is their relatedness. But inclusive fitness results are often inexact for interactions between kin, and they do not address phenomena such as reciprocity and synergistic effects that may either be confounded with kinship or operate in its absence. Here I develop a model the results of which may be expressed in terms of either personal or inclusive fitness, and which combines the advantages of both; it Is general, exact, simple and empirically useful. Hamilton’s rule is shown to hold for reciprocity as well as kin selection. It fails because of synergistic effects, but this failure can be corrected through the use of coefficients of synergism, which are analogous to the coefficient of relatedness.

The spread of low-credibility content by social bots

  • The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.

Using Machine Learning to map the field of Collective Intelligence research cluster_enhance-width-1200

  • As part of our new research programme we have used machine learning and literature search to map key trends in collective intelligence research. This helps us build on the existing body of knowledge on collective intelligence, as well as identify some of the gaps in research that can be addressed to advance the field.

Working on 810 meta-reviews today. Done-ish!

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.