This post is about collective behaviour, and in particular the sorts of fields of study that are of interest when thinking about how to defend a networked organisation from attack. That is the problem of creating a defence force for an open, decentralised society.
In this post I want to look at a couple of outlying fields. It is clear that a core area of study is the study of how real people behave in small or medium size organisational structures. Here however, I want to concentrate on the study of a couple of biological behaviours, from a mathematical or computational point of view. Why? Because, by looking at these fields we might come across some novel techniques that we can use to augment the more traditional forms of organisational structure that human beings use.
There are two areas of scientific research that are of interest here:
Swarm intelligence is the collective behaviour of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
Swarm behaviour is in some ways a danger, and in others an asset with regard to network organisations – either way the study of this subject is throwing up interesting technical results, with ethical implications (see below).
Artificial Immune Systems
A good metaphor for thinking about the design of a new network oriented politics, and it’s long-term viability, is the immune system. If we are able to learn from these biological systems techniques that we can use to prevent the inevitable attacks from both inside the organisation, and outside of the organisation (by this I mean any network of individuals), we may be able to avoid the all too common pitfalls that real groups face.
Artificial Immune Systems (AIS) are adaptive systems, inspired by theoretical immunology and observed immune functions, principles and models, which are applied to problem solving.
It is a common experience that informal p2p or networked structures decay over time – and usually into more traditional hierarchical structures. We see this in a wide range of social movements, whether political revolutions, religious movements or smaller social collectives – over time the original ideals, and forms of informal social organisation are subsumed by the need for action, in particular defensive action.
It is for this reason that a study of techniques that might prove useful to literally immunize a p2p network organisation is useful – particularly if we can embed this in the legal and technical code of the networks structure.
The field of Artificial Immune Systems (AIS) is concerned with abstracting the structure and function of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology. AIS is a sub-field of Biologically-inspired computing, and Natural computation, with interests in Machine Learning and belonging to the broader field of Artificial Intelligence.
An Immune Aesthetic
Visualisation of complex systems, can help to interpret complexity in ways that humans are better suited to process. If we truly want to keep hold of ethical, or deep human values – perhaps we need a way to experience the systemic properties in a way which would allow us to participate? This sort of abstraction has all sorts of potential consequences – not all of them benign.
Fugue (below), is an interactive art installation, based on the functioning of the human immune system.
At the heart of the piece is a complex piece of scientific software, an artificial immune system algorithm, accurately mimicking the changes and cascading responses of the human immune system. The artistic concept, inspired by the musical form of the Fugue, interprets, expresses and communicates these changes through independent channels of vision, using cell-like images, and sound. In the most recent version, a large-scale interactive installation, participants engage the system in a spontaneous non-verbal dialogue, influencing both the unfolding of the immune system drama and the nature of their own experience.
How altruism helps swarming robots fly better
Swarm intelligence is also being used to study the emergence (that is evolution) of altruism. If a behaviour emerges based on an evolutionary stable strategy – it is a good sign that it is robust. Studies like these can therefore point to how we my seek to design systems that have robust p2p ethical properties.
“Testing the evolution of altruism using quantitative studies in live organisms has been largely impossible because experiments need to span hundreds of generations and there are too many variables,” EPFL notes in a press release. “However, Floreano’s robots evolve rapidly using simulated gene and genome functions and allow scientists to measure the costs and benefits associated with the trait.”
Their paper was published in the Journal Public Library of Science (PLoS) Biology. It provides support for what is known as Hamilton’s rule of kin selection, developed in 1964 by WD Hamilton. He proposed a precise set of conditions under which altruistic behavior may evolve. EPFL describes it:
“If an individual family member shares food with the rest of the family, it reduces his or her personal likelihood of survival but increases the chances of family members passing on their genes, many of which are common to the entire family. Hamilton’s rule simply states that whether or not an organism shares its food with another depends on its genetic closeness (how many genes it shares) with the other organism.
‘We have shown that Hamilton’s kin selection theory always accurately predicts the relationship between the evolution of altruism and the relatedness of individuals in a species,’ explains Markus Waibel, lead author of the paper and former doctoral student of both Keller and Floreano.
Hamilton’s rule has long been a subject of much debate because its equation seems too simple to be true. ‘This study mirrors Hamilton’s rule remarkably well to ex-plain when an altruistic gene is passed on from one generation to the next, and when it is not,’ says Keller.”
The study will help biologists but it has already had an impact on other robots at EPFL, notably swarms of flying robots. “We have been able to take this experiment and extract an algorithm that we can use to evolve cooperation in any type of robot,” says Floreano. “We are using this altruism algorithm to improve the control system of our flying robots and we see that it allows them to effectively collaborate and fly in swarm formation more successfully.”
How robots become altruistic after 500 generations
- Group Keller: Evolutionary Genetics and Ecology of Social Life
- Learning Algorithms and Systems Laboratory (LASA)
- Promise Theory