The Nonlinear Library AF - Can Kauffman's NK Boolean networks make humans swarm? by Yori Ong
May 8, 2024
40:57
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Can Kauffman's NK Boolean networks make humans swarm?, published by Yori Ong on May 8, 2024 on The AI Alignment Forum.
With this article, I intend to initiate a discussion with the community on a remarkable (thought) experiment and its implications. The experiment is to conceptualize Stuart Kauffman's NK Boolean networks as a digital social communication network, which introduces a thus far unrealized method for strategic information transmission. From this premise, I deduce that such a technology would enable people to 'swarm', i.e.: engage in self-organized collective behavior without central control.
Its realization could result in a powerful tool for bringing about large-scale behavior change. The concept provides a tangible connection between network topology, common knowledge and cooperation, which can improve our understanding of the logic behind prosocial behavior and morality. It also presents us with the question of how the development of such a technology should be pursued and how the underlying ideas can be applied to the alignment of AI with human values.
The intention behind sharing these ideas is to test whether they are correct, create common knowledge of unexplored possibilities, and to seek concrete opportunities to move forward. This article is a more freely written form of a paper I recently submitted to the arXiv, which can be found here.
Introduction
Random NK Boolean networks were first introduced by Stuart Kauffman in 1969 to model gene regulatory systems.[1] The model consists of N automata which are either switched ON (1) or OFF (0). The next state of each automaton is determined by a random boolean function that takes the current state of K other automata as input, resulting in a dynamic network underpinned by a semi-regular and directed graph.
It can be applied to model gene regulation, in which the activation of some leads to the activation or suppression of others, but also to physical systems, in which a configuration of spins acting on another will determine whether it flips up or down.
NK Boolean networks evolve deterministically: each following state can be computed based on its preceding state. Since the total number of possible states of the network is finite (although potentially very large), the network must eventually return to a previously visited state, resulting in cyclic behavior. The possible instances of Boolean networks can be subdivided between an ordered and a chaotic regime, which is mainly determined by the number of inputs for each node, K.
In the ordered regime, the behavior of the network eventually gets trapped in cycles (attractors) that are relatively short and few in number. When a network in the ordered phase is perturbed by an externally induced 'bit-flip', the network eventually returns to the same or slightly altered ordered behavior. If the connectivity K is increased beyond a certain critical threshold, the network's behavior transitions from ordered to chaotic.
States of the network become part of many and long cycles and minute external perturbations can easily change the course of the network state's evolution to a different track. This is popularly called the 'butterfly effect'.
It has been extensively demonstrated that human behavior is not just determined by our 'own' decisions. Both offline and online social networks determine the input we receive, and causally influence the choices we make and opinions we adopt autonomously.[2] However, social networks are not regular, social ties are often reciprocal instead of directed and people are no automata. NK Boolean networks are therefore not very suitable for modeling an existing reality.
What is nevertheless possible in the digital age, is to conceptualize and realize online communication networks based on its logic: just give N people a 'lightbulb app...
With this article, I intend to initiate a discussion with the community on a remarkable (thought) experiment and its implications. The experiment is to conceptualize Stuart Kauffman's NK Boolean networks as a digital social communication network, which introduces a thus far unrealized method for strategic information transmission. From this premise, I deduce that such a technology would enable people to 'swarm', i.e.: engage in self-organized collective behavior without central control.
Its realization could result in a powerful tool for bringing about large-scale behavior change. The concept provides a tangible connection between network topology, common knowledge and cooperation, which can improve our understanding of the logic behind prosocial behavior and morality. It also presents us with the question of how the development of such a technology should be pursued and how the underlying ideas can be applied to the alignment of AI with human values.
The intention behind sharing these ideas is to test whether they are correct, create common knowledge of unexplored possibilities, and to seek concrete opportunities to move forward. This article is a more freely written form of a paper I recently submitted to the arXiv, which can be found here.
Introduction
Random NK Boolean networks were first introduced by Stuart Kauffman in 1969 to model gene regulatory systems.[1] The model consists of N automata which are either switched ON (1) or OFF (0). The next state of each automaton is determined by a random boolean function that takes the current state of K other automata as input, resulting in a dynamic network underpinned by a semi-regular and directed graph.
It can be applied to model gene regulation, in which the activation of some leads to the activation or suppression of others, but also to physical systems, in which a configuration of spins acting on another will determine whether it flips up or down.
NK Boolean networks evolve deterministically: each following state can be computed based on its preceding state. Since the total number of possible states of the network is finite (although potentially very large), the network must eventually return to a previously visited state, resulting in cyclic behavior. The possible instances of Boolean networks can be subdivided between an ordered and a chaotic regime, which is mainly determined by the number of inputs for each node, K.
In the ordered regime, the behavior of the network eventually gets trapped in cycles (attractors) that are relatively short and few in number. When a network in the ordered phase is perturbed by an externally induced 'bit-flip', the network eventually returns to the same or slightly altered ordered behavior. If the connectivity K is increased beyond a certain critical threshold, the network's behavior transitions from ordered to chaotic.
States of the network become part of many and long cycles and minute external perturbations can easily change the course of the network state's evolution to a different track. This is popularly called the 'butterfly effect'.
It has been extensively demonstrated that human behavior is not just determined by our 'own' decisions. Both offline and online social networks determine the input we receive, and causally influence the choices we make and opinions we adopt autonomously.[2] However, social networks are not regular, social ties are often reciprocal instead of directed and people are no automata. NK Boolean networks are therefore not very suitable for modeling an existing reality.
What is nevertheless possible in the digital age, is to conceptualize and realize online communication networks based on its logic: just give N people a 'lightbulb app...