Complexes

My first philosophical text in years, comments welcome. Information theory has been developed for seventy years with technological applications that have transformed our societies. The increasing ability to store, transmit, and process information is having a revolutionary impact in most disciplines. The goal of this work is to compare the formal approach to information with Buddhist philosophy. Considering both approaches as compatible and complementary, I argue that information theory can improve our understanding of Buddhist philosophy and vice versa. The resulting synthesis leads to a worldview based on information that overcomes limitations of the currently dominating physics-based worldview. Gershenson, Carlos, Information in Science and Buddhist Philosophy: Towards a non-Materialistic Worldview (October 4, 2018). https://ssrn.com/abstract=3261381

We present a set of Matlab/Octave functions to compute measures of emergence, self-organization, and complexity applied to discrete and continuous data. These measures are based on Shannon’s information and differential entropy. Examples from different datasets and probability distributions are provided to show how to use our proposed code. Santamaría-Bonfil, G., Gershenson, C. & Fernández, N. (2017). A package for measuring emergence, self-organization, and complexity based on Shannon entropy. Frontiers in Robotics and AI , 4 :10. http://journal.frontiersin.org/article/10.3389/frobt.2017.00010/full

Highlights • The useful of quantitative indicators of ecological complexity is evaluated. • Chaos should not be confused with complexity. • Light and temperature cause different ranges of complexity in the gradient. • Homoeostasis variation is related to the seasonal changes and transitions. • Autopoiesis reveals groups with higher and lower degree of autonomy. Abstract Measuring complexity is fast becoming a key instrument to compare different ecosystems at various scales in ecology. To date there has been little agreement on how to properly describe complexity in terms of ecology. In this regard, this manuscript assesses the significance of using a set of proposed measures based on information theory. These measures are as follows: emergence, self-organization, complexity, homeostasis and autopoiesis. A combination of quantitative and qualitative approaches was used in the data analysis with the aim to apply these proposed measures. This study system...

Urban mobility systems are composed multiple elements with strong interactions, i.e. their future is co-determined by the state of other elements. Thus, studying components in isolation, i.e. using a reductionist approach, is inappropriate. I propose five recommendations to improve urban mobility based on insights from the scientific study of complex systems: use adaptation over prediction, regulate interactions to avoid friction, use sensors to recover real time information, develop adaptive algorithms to exploit that information, and deploy agents to act on the urban environment. Improving Urban Mobility by Understanding its Complexity Carlos Gershenson http://arxiv.org/abs/1603.04267

For millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into 14 themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning), ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields. Aguilar W, Santamaría-Bonfil G, Froese T and Gershenson C (2014) The past, present, and future of artificial life. Front. Robot. AI 1:8. http://dx....

Self-organisation occurs in natural phenomena when a spontaneous increase inorder is produced by the interactions of elements of a complex system. Thermodynamically,this increase must be offset by production of entropy which, broadly speaking, can beunderstood as a decrease in order. Ideally, self-organisation can be used to guide the systemtowards a desired regime or state, while “exporting” the entropy to the system’s exterior. Thus, Guided Self-Organisation (GSO) attempts to harness the order-inducing potentialof self-organisation for specific purposes. Not surprisingly, general methods developed tostudy entropy can also be applied to guided self-organisation. This special issue covers a broad diversity of GSO approaches which can be classified in three categories: informationtheory, intelligent agents, and collective behavior. The proposals make another step towardsa unifying theory of GSO which promises to impact numerous research fields. Entropy Methods in Guided Self-Organisat...

Education at all levels is facing several challenges in most countries [1-4], such as low quality, high costs, lack of educators, and unsatisfied student demand. Traditional approaches are becoming unable to deliver the required education. Several causes for this inefficiency can be identified. I argue that beyond specific causes, the lack of effective education is related to complexity [5, 6]. However, information technology is helping us overcome this complexity. Complexity can be measured with information theory and can be seen as the balance between stability and variability [7-10]: phenomena without change or with constant change cannot exhibit complex behavior. It has been noted that to actively control a complex system, the controller has to be at least as complex as the controlled [11, 12]. For example, a successful healthcare provider has to match the complexity of the patients she attends. Treatment is highly specific for different patients, so a general practitioner must ha...

We apply measures of complexity, emergence, and self-organization to an urban traffic model for comparing a traditional traffic-light coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only is traffic a non-stationary problem, requiring controllers to adapt constantly; controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures and extending Ashby’s law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system. Zubillaga, Darío; Cruz, Geovany; Aguilar, Luis D.; Zapotécatl, Jorge; Fernández, Nelson; Aguilar, José; Rosenblueth, David A.; Gershenson, Carlos. 2014. "Measuring the Complexit...

Upshot : The limitations of materialism for studying cognition have motivated alternative epistemologies based on information and computation. I argue that these alternatives are also inherently limited and that these limits can only be overcome by considering materialism, info-computationalism, and cognition at the same time. Open peer commentary on the article “ Info-computational Constructivism and Cognition ” by Gordana Dodig-Crnkovic. Gershenson C. (2014) Info-computationalism or Materialism? Neither and Both. Constructivist Foundations 9(2) : 241–242. Available at  http://www.univie.ac.at/constructivism/journal/9/2/241.gershenson

We apply measures of complexity, emergence and self-organization to an abstract city traffic model for comparing a traditional traffic coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only traffic is a non-stationary problem, which requires controllers to adapt constantly. Controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures, we can say that the self-organizing method achieves an adaptability level comparable to a living system. Measuring the Complexity of Self-organizing Traffic Lights Dario Zubillaga, Geovany Cruz, Luis Daniel Aguilar, Jorge Zapotecatl, Nelson Fernandez, Jose Aguilar, David A. Rosenblueth, Carlos Gershenson http://arxiv.org/abs/1402.01...

//Please forward to whom may be interested. The National Autonomous University of Mexico (UNAM) has an open call for postdoctoral fellowships to start in March, 2014 (with a close deadline!). Candidates should have obtained a PhD degree within the last three years and be under 36 years, both to the date of the beginning of the fellowship. The area of interests of candidates should fall within complex systems, artificial life, information, evolution, cognition, robotics, and/or philosophy. Interested candidates should send CV and a tentative project (1 paragraph) to cgg-at-unam.mx by Friday, August 2nd.   Full application package should be ready by Monday, August 5 at noon, Mexico City time. Projects can be inspired from:  http://turing.iimas.unam.mx/~cgg/projects.html Postdoctoral fellowships are between one and two years (after renewal). Spanish is not a requisite. Accepted candidates would be working at the Self-organizing Systems Lab ( http://turing.iimas.unam....

The goal of Guided Self-Organization (GSO) is to leverage the strengths of self-organization while still being able to direct the outcome of the self-organizing process. GSO typically has the following features: (i) an increase in organization (structure and/or functionality) over some time; (ii) the local interactions are not explicitly guided by any external agent; (iii) task-independent objectives are combined with task-dependent constraints. A number of attempts have been made to formalize aspects of GSO within information theory, thermodynamics and dynamical systems. However, the lack of a broadly applicable mathematical framework across multiple scales and contexts leaves GSO methodology incomplete. Devising such a framework and identifying common principles of guidance are the main themes of the GSO workshops. Of particular interest are well-founded, but general methods for characterizing GSO systems in a principled way, with the view of ultimately allowing them to be guided...

In this chapter review measures of emergence, self-organization, complexity, homeostasis, and autopoiesis based on information theory. These measures are derived from proposed axioms and tested in two case studies: random Boolean networks and an Arctic lake ecosystem. Emergence is defined as the information produced by a system or process. Self-organization is defined as the opposite of emergence, while complexity is defined as the balance between emergence and self-organization. Homeostasis reflects the stability of a system. Autopoiesis is defined as the ratio between the complexity of a system and the complexity of its environment. The proposed measures can be applied at multiple scales, which can be studied with multi-scale profiles. Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis Nelson Fernandez, Carlos Maldonado, Carlos Gershenson http://arxiv.org/abs/1304.1842

In this chapter, concepts related to information and computation are reviewed in the context of human computation. A brief introduction to information theory and different types of computation is given. Two examples of human computation systems, online social networks and Wikipedia, are used to illustrate how these can be described and compared in terms of information and computation. Full text at  http://arxiv.org/abs/1304.1428 Draft of a chapter to be published in Michelucci, P. (Ed.) Handbook of Human Computation , Springer.

From today's seminar [in Spanish] Your browser does not support iframes. http://bambuser.com/v/3140519 Based on: Gershenson, C. (In Press) The Implications of Interactions for Science and Philosophy. Foundations of Science. http://dx.doi.org/10.1007/s10699-012-9305-8

In evolutionary biology, attention to the relationship between stochastic organisms and their stochastic environments has leaned towards the adaptability and learning capabilities of the organisms rather than toward the properties of the environment. This article is devoted to the algorithmic aspects of the environment and its interaction with living organisms. We ask whether one may use the fact of the existence of life to establish how far nature is removed from algorithmic randomness. The paper uses a novel approach to behavioral evolutionary questions, using tools drawn from information theory, algorithmic complexity and the thermodynamics of computation to support an intuitive assumption about the near optimal structure of a physical environment that would prove conducive to the evolution and survival of organisms, and sketches the potential of these tools, at present alien to biology, that could be used in the future to address different and deeper questions. We contribute to the...

We extend the concept that life is an informational phenomenon, at every level of organisation, from molecules to the global ecological system. According to this thesis: (a) living is information processing, in which memory is maintained by both molecular states and ecological states as well as the more obvious nucleic acid coding; (b) this information processing has one overall function - to perpetuate itself; and (c) the processing method is filtration (cognition) of, and synthesis of, information at lower levels to appear at higher levels in complex systems (emergence). We show how information patterns, are united by the creation of mutual context, generating persistent consequences, to result in `functional information'. This constructive process forms arbitrarily large complexes of information, the combined effects of which include the functions of life. Molecules and simple organisms have already been measured in terms of functional information content; we show how quantifica...

Complexity and Information: Measuring Emergence, Self-organization, Homeostasis, and Autopoiesis at Multiple Scales Keynote talk at the 5th International Workshop on Guided Self-Organization . University of Sydney, Australia, September 26th, 2012. youtu.be/Ba0zSNYkWtw?a   Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to ambiguity and confusion in their meaning. We use information theory to provide abstract and concise measures of complexity, emergence, self-organization, homeostasis, and autopoiesis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focusing on the information produced by a system), emergence becomes the opposite of self- organization, while complexity represents their balance. Homeostasis can be seen as a measure of the stability of the system. Autopoiesis can be measured as the ratio between the ...

Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to ambiguity and confusion in their meaning. In this paper we use information theory to provide abstract and concise measures of complexity, emergence, self-organization, and homeostasis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focussing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance. We use computational experiments on random Boolean networks and elementary cellular automata to illustrate our measures at multiple scales. Gershenson, C. & N. Fernández (2012). Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales. C3 Report 2012.03.  http://arxiv.org/abs/1205.2026

1. Law of  Information Transformation . I nformation will potentially be transformed by interacting with other information. 2.  Law of Information Propagation .  Information propagates as fast as possible.  3.  Law of Requisite Complexity . M ore complex information will require more complex agents to perceive, act on, and propagate it. 4.  Law of Information Criticality .  Transforming and propagating information will tend to a critical balance be- tween its stability and its variability. 5.  Law of Information Organization .  Information produces constraints that regulate information production.  6.  Law of Information Self-organization .  Information tends to its preferred, most probable state.  7.  Law of Information Potentiality .  An agent can give different potential meanings to information.  8.  Law of Information Perception .  The meaning of information is unique for an agent percei...