Social systems can be defined as autopoietic networks of distinctions and rules that specify which actions should be performed under which conditions. Social systems have an enormous power over human individuals, as they can “program” them to sacrifice resources, happiness, loved ones and even themselves to the perpetuation of the system—as exemplified by religious celibacy, honor killings and suicide bombings. Such overriding of the biological instincts of survival and procreation demands powerful control mechanisms. The present paper surveys some of the basic neural and behavioral mechanisms used for programming within social systems, and is followed by another paper surveying emotional and structural mechanism. Basic conditioning happens through rewarding or reinforcement of socially sanctioned actions. Its power is extended by the conformist transmission of narratives that promise as yet virtual rewards, and by ritualized behaviors that suppress non-conforming beliefs through cognitive dissonance. Through such mechanisms, social systems commonly impede individual emancipation, self-actualization and societal progress.

Source: www.academia.edu

It should be noted that similar mechanisms are also useful for promoting cooperation and limiting selfish behaviors.

There are (at least) four ways that an agent can acquire information concerning the state of the universe: via observation, control, prediction, or via retrodiction, i.e., memory. Each of these four ways of acquiring information seems to rely on a different kind of physical device (resp., an observation device, a control device, etc.). However it turns out that certain mathematical structure is common to those four types of device. Any device that possesses a certain subset of that structure is known as an “inference device” (ID).
Here I review some of the properties of IDs, including their relation with Turing machines, and (more loosely) quantum mechanics. I also review the bounds of the joint abilities of any set of IDs to know facts about the physical universe that contains them. These bounds constrain the possible properties of any universe that contains agents who can acquire information concerning that universe.
I then extend this previous work on IDs, by adding to the definition of IDs some of the other mathematical structure that is common to the four ways of acquiring information about the universe but is not captured in the (minimal) definition of IDs. I discuss these extensions of IDs in the context of epistemic logic (especially possible worlds formalisms like Kripke structures and Aumann structures). In particular, I show that these extensions of IDs are not subject to the problem of logical omniscience that plagues many previously studied forms of epistemic logic.

Constraints on physical reality arising from a formalization of knowledge
David Wolpert

Source: arxiv.org

Friends and colleagues who knew Ilya Prigogine well called him “A poet of thermodynamics.” It is an apt description. When Prigogine talked about thermodynamics and irreversible processes, one had the sense he understood or knew more than what his words conveyed. Natural processes all around us are irreversible; it is a fact. Their consequence is not merely to increase the entropy of the universe and destroy order. They can also do the opposite: create highly ordered complex structures with extraordinary properties and create life itself. Prigogine saw this as a profound aspect of nature that thermodynamics has revealed. When he came across the famed South Indian sculpture of Nataraja, the dancing Shiva, that depicts as a cosmic dance the perfect balance between creation and destruction that originate from the same source, he made sure he had a bronze statue of Nataraja of highest artistic quality in his art collection. A picture of it became the cover art for the book Thermodynamic Theory of Structure Stability and Fluctuations, that he coauthored with Paul Glansdorff. It was poetry of thermodynamics, creation and destruction emerging from a common source, a perfectly balanced cosmic dance. One could surmise all this from Prigogine’s discourses on thermodynamics.

Dissipative structures and irreversibility in nature: Celebrating 100th birth anniversary of Ilya Prigogine (1917–2003)
Chaos 27, 104501 (2017); https://doi.org/10.1063/1.5008858
Dilip Kondepudi, Tomio Petrosky, and John A. Pojman

Source: aip.scitation.org

As the annual flu season approaches, medical professionals are again encouraging people to get flu shots. Perhaps you are among those who rationalize skipping the shot on the grounds that “I never get the flu” or “if I get sick, I get sick” or “I’m healthy, so I’ll get over it.” What you might not realize is that these vaccination campaigns for flu and other diseases are about much more than your health. They’re about achieving a collective resistance to disease that goes beyond individual well-being — and that is governed by mathematical principles unforgiving of unwise individual choices.

Source: www.quantamagazine.org

The structure of complex networks has been of interest in many scientific and engineering disciplines over the decades. A number of studies in the field have been focused on finding the common properties among different kinds of networks such as heavy-tail degree distribution, small-worldness and modular structure and they have tried to establish a theory of structural universality in complex networks. However, there is no comprehensive study of network structure across a diverse set of domains in order to explain the structural diversity we observe in the real-world networks. In this paper, we study 986 real-world networks of diverse domains ranging from ecological food webs to online social networks along with 575 networks generated from four popular network models. Our study utilizes a number of machine learning techniques such as random forest and confusion matrix in order to show the relationships among network domains in terms of network structure. Our results indicate that there are some partitions of network categories in which networks are hard to distinguish based purely on network structure. We have found that these partitions of network categories tend to have similar underlying functions, constraints and/or generative mechanisms of networks even though networks in the same partition have different origins, e.g., biological processes, results of engineering by human being, etc. This suggests that the origin of a network, whether it’s biological, technological or social, may not necessarily be a decisive factor of the formation of similar network structure. Our findings shed light on the possible direction along which we could uncover the hidden principles for the structural diversity of complex networks.

Characterizing the structural diversity of complex networks across domains
Kansuke Ikehara, Aaron Clauset

Source: arxiv.org