Klaus Jaffe

Emergence, information and energy are fundamental properties of nature. We know that it takes free energy to acquire information, and it takes information to increment free energy. Energy obeys all laws of thermodynamics, while information does not. Emergence occurs in dynamic complex systems: when more than one dimension of reality interacts and novel properties of energy and information emerge. Information can be either useful or not in producing free energy. Information can reveal itself in different forms (as entropy, physically encoded, mechanical, biological, structural, in neural or social networks, etc.). Information may increase free energy by reducing entropy in the system, or by capturing free energy from the surroundings. The interaction between information and energy has been studied mostly in physical-chemistry and engineering. Now we find it everywhere, including in computer sciences, genetics, biotechnology, experimental social sciences, and experimental law. In emergent systems new possibilities of increasing free energy and useful information appear. Emergent complexity is visible in the transitions from subatomic particles to atoms, from atoms to molecules, to cells, to organisms, to societies and ecosystems. General and simple concepts are presented to help untangle the forces behind evolutionary processes leading to ever more complexity with more free energy and useful information, giving birth to life. We need to quantify changes in energy and information to better understand the dynamics of emergence in complex far-from-equilibrium systems.

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R Alexander Bentley, Benjamin Horne, Joshua Borycz, Simon Carrignon, Garriy Shteynberg, Blai Vidiella, Sergi Valverde, Michael J O’Brien
Adaptive Behavior

Diversity of expertise is inherent to cultural evolution. When it is transparent, diversity of human knowledge is useful; when social conformity overcomes that transparency, “expertise” can lead to divisiveness. This is especially true today, where social media has increasingly allowed misinformation to spread by prioritizing what is recent and popular, regardless of validity or general benefit. Whereas in traditional societies there was diversity of expertise, contemporary social media facilitates homophily, which isolates true subject experts from each other and from the wider population. Diversity of knowledge thus becomes social division. Here, we discuss the potential of a cultural-evolutionary framework designed for the countless choices in contemporary media. Cultural-evolutionary theory identifies key factors that determine whether communication networks unify or fragment knowledge. Our approach highlights two parameters: transparency of information and social conformity. By identifying online spaces exhibiting aggregate patterns of high popularity bias and low transparency of information, we can help define the “safe limits” of social conformity and information overload in digital communications.

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Menachem Stern and Arvind Murugan

Annual Review of Condensed Matter Physics Vol. 14:417-441

Learning is traditionally studied in biological or computational systems. The power of learning frameworks in solving hard inverse problems provides an appealing case for the development of physical learning in which physical systems adopt desirable properties on their own without computational design. It was recently realized that large classes of physical systems can physically learn through local learning rules, autonomously adapting their parameters in response to observed examples of use. We review recent work in the emerging field of physical learning, describing theoretical and experimental advances in areas ranging from molecular self-assembly to flow networks and mechanical materials. Physical learning machines provide multiple practical advantages over computer designed ones, in particular by not requiring an accurate model of the system, and their ability to autonomously adapt to changing needs over time. As theoretical constructs, physical learning machines afford a novel perspective on how physical constraints modify abstract learning theory.

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Jisung Yoon, Chris Kempes, Vicky Chuqiao Yang, Geoffrey West, Hyejin Youn

For a collective to become greater than the sum of its parts, individuals’ efforts and activities must be coordinated or regulated. Not readily observable and measurable, this particular aspect often goes unnoticed and understudied in complex systems. Diving into the Wikipedia ecosystem, where people are free to join and voluntarily edit individual pages with no firm rules, we identified and quantified three fundamental coordination mechanisms and found they scale with an influx of contributors in a remarkably systemic way over three order of magnitudes. Firstly, we have found a super-linear growth in mutual adjustments (scaling exponent: 1.3), manifested through extensive discussions and activity reversals. Secondly, the increase in direct supervision (scaling exponent: 0.9), as represented by the administrators’ activities, is disproportionately limited. Finally, the rate of rule enforcement exhibits the slowest escalation (scaling exponent 0.7), reflected by automated bots. The observed scaling exponents are notably robust across topical categories with minor variations attributed to the topic complication. Our findings suggest that as more people contribute to a project, a self-regulating ecosystem incurs faster mutual adjustments than direct supervision and rule enforcement. These findings have practical implications for online collaborative communities aiming to enhance their coordination efficiency. These results also have implications for how we understand human organizations in general.

Read the full article at: arxiv.org