Social Behaviour of Machines
Artificial Intelligence (AI) has become the buzz word in academia, industry, and even public sector. While there is a surge in AI research that examines various aspects of machine intelligence, there has been little discussion on collective behaviour of machines. The very core idea behind complex systems theory is that the emergent behaviour of a complex system can be different and difficult to predict based on the linear sum of its parts. We already have built systems in which multiple automated agents interact with each other: for example, the editing bots in Wikipedia that simultaneously revise the articles in the encyclopaedia. Our past research on Wikipedia bots demonstrated that even though the bots share the same goal that is to improve the encyclopaedia and they have a very low level of intelligence, still conflict and disorganisation are often inevitable, (mostly because they learn from humans).
AI Enabled Collective Intelligence
Collective Intelligence (CI) is the emergent outcome of the collective efforts of many individuals. Existence of CI, superior to the intelligence of any of the individuals who contributed to it, was scientifically illustrated in Galton’s famous Wisdom of the Crowd experiment in 1906. Since then there have been numerous examples of successful deployment of collective intelligence to solve problems that have been unsolvable by either individuals or machines. With the recent surge of Artificial Intelligence (AI) and its applications in decision making and forecasting, it is important to examine how AI can be used to improve the state of the art in collective intelligence. AI can be used in matching particular tasks to responders, training of entire “crowds”, combining the contributions of crowds and translating it into a collective decision, to name a few.
Gender and Sexuality in Digital Age
Gender differences in human sexual behaviour have been reported extensively; compared to women, men masturbate more, use more pornography, are more permissive and more reactive to visual cues, and experience sexual desire more spontaneously. Some of these differences however have been reported to have shrunk recently. Today, considerable amount of our sexual behaviour happens online. A notable aspect of internet-based technologies is that they produce massive amount of transactional data (aka big data) that can be studied in the framework of computational social science. Some of the research questions regarding gender differences in sexual behaviour could be addressed in an unprecedented way by using the big data generated in online platforms.
Online Political Behaviour
One of the main challenges in representative democracy is the divergence between decision makers and the people that they represent. The divergence can exist both in the agenda, and in the opinions on a specific issue. Simply put, what matters to policy makers might be different to what matters to the citizen. Even when they agree on what matters, the representatives might have different opinion to the opinion of people who elected them. In modern days expression of opinion by citizens is much more common due to the freedom of speech and public media. In the age of the Internet, when people produce more than 2.5 quintillion of data each day, this divergence, that some argue has grown even in established democracies, seems paradoxical.