Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraint-based interpretation of emergence and arguing that such accounts recover many of the features of more traditional accounts. We show that the constraint-based account of emergence also leads naturally into a meaningful definition of self-organization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper.
Self-Organization, Emergence, and Constraint in Complex Natural Systems Jonathan Lawhead
Evolutionary Robotics is a field that “aims to apply evolutionary computation techniques to evolve the overall design or controllers, or both, for real and simulated autonomous robots” (Vargas et al., 2014). This approach is “useful both for investigating the design space of robotic applications and for testing scientific hypotheses of biological mechanisms and processes” (Floreano et al., 2008). However, as noted in Bongard (2013) “the use of metaheuristics (i.e., evolution) sets this subfield of robotics apart from the mainstream of robotics research,” which “aims to continuously generate better behavior for a given robot, while the long-term goal of Evolutionary Robotics is to create general, robot-generating algorithms.”
We humans set a premium on our own free will and independence ... and yet there's a shadowy influence we might not be considering. As science writer Ed Yong explains in this fascinating, hilarious and disturbing talk, parasites have perfected the art of manipulation to an incredible degree. So are they influencing us? It's more than likely.
Mark Waser's insight:
Ends with exactly the parasite you'd expect (no, not the one that causes red dots)
One aspect of intelligence is the ability to restructure your own environment so that the world you live in becomes more beneficial to you. In this paper we investigate how the information-theoretic measure of agent empowerment can provide a task-independent, intrinsic motivation to restructure the world. We show how changes in embodiment and in the environment change the resulting behaviour of the agent and the artefacts left in the world. For this purpose, we introduce an approximation of the established empowerment formalism based on sparse sampling, which is simpler and significantly faster to compute for deterministic dynamics. Sparse sampling also introduces a degree of randomness into the decision making process, which turns out to beneficial for some cases. We then utilize the measure to generate agent behaviour for different agent embodiments in a Minecraft-inspired three dimensional block world. The paradigmatic results demonstrate that empowerment can be used as a suitable generic intrinsic motivation to not only generate actions in given static environments, as shown in the past, but also to modify existing environmental conditions. In doing so, the emerging strategies to modify an agent’s environment turn out to be meaningful to the specific agent capabilities, i.e., de facto to its embodiment.
Changing the Environment Based on Empowerment as Intrinsic Motivation Christoph Salge , Cornelius Glackin and Daniel Polani
Requests are at the core of many social media systems such as question & answer sites and online philanthropy communities. While the success of such requests is critical to the success of the community, the factors that lead community members to satisfy a request are largely unknown. Success of a request depends on factors like who is asking, how they are asking, when are they asking, and most critically what is being requested, ranging from small favors to substantial monetary donations. We present a case study of altruistic requests in an online community where all requests ask for the very same contribution and do not offer anything tangible in return, allowing us to disentangle what is requested from textual and social factors. Drawing from social psychology literature, we extract high-level social features from text that operationalize social relations between recipient and donor and demonstrate that these extracted relations are predictive of success. More specifically, we find that clearly communicating need through the narrative is essential and that that linguistic indications of gratitude, evidentiality, and generalized reciprocity, as well as high status of the asker further increase the likelihood of success. Building on this understanding, we develop a model that can predict the success of unseen requests, significantly improving over several baselines. We link these findings to research in psychology on helping behavior, providing a basis for further analysis of success in social media systems.
How to Ask for a Favor: A Case Study on the Success of Altruistic Requests Tim Althoff, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky
Pedro DomingosDepartment of Computer Science and EngineeringUniversity of Washington
Mark Waser's insight:
A good overview/non-technical summary -- "Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other fields. However,developing successful machine learning applications requiresa substantial amount of “black art” that is hard to find in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions."
Social, technological, and biological networks are known to organize into modules or “communities.” Characterizing and identifying modules is highly nontrivial and still an outstanding problem in networks research. A new approach uses both the concept of modular hierarchy for network construction and the methods of statistical inference to address this problem, succeeding where the existing approaches see difficulties.
Hierarchical Block Structures and High-Resolution Model Selection in Large Networks Tiago P. Peixoto Phys. Rev. X 4, 011047 (2014)
Tere’s a simple theory underlying much of American politics. It sits hopefully at the base of almost every speech, every op-ed, every article, and every panel discussion. It courses through the Constitution and is a constant in President Obama’s most stirring addresses. It’s what we might call the More Information Hypothesis: the belief that many of our most bitter political battles are mere misunderstandings. The cause of these misunderstandings? Too little information — be it about climate change, or taxes, or Iraq, or the budget deficit. If only the citizenry were more informed, the thinking goes, then there wouldn’t be all this fighting.It’s a seductive model. It suggests our fellow countrymen aren’t wrong so much as they’re misguided, or ignorant, or — most appealingly — misled by scoundrels from the other party. It holds that our debates are tractable and that the answers to our toughest problems aren’t very controversial at all. The theory is particularly prevalent in Washington, where partisans devote enormous amounts of energy to persuading each other that there’s really a right answer to the difficult questions in American politics — and that they have it.
I hate the terms "stupid" or "irrational" when they are describing human traits that have evolved because they are better in the long term than any of the known alternatives that criticize them *cough*REDUCTIONISM*cough.
Abstract: We explore the role of cognitive dissonance in dictator and public goods games. Specifically, we motivate cognitive dissonance between one's perception of “fair treatment” and self-interested behaviour by having participants answer a question about fairness. Utilizing two manipulations (reminding participants about their answer to the fairness question and publicly reporting aggregate answers to the question), we find that there is greater cognitive dissonance and behavioural change when there is a social component (i.e., reporting of aggregate answers). When a participant's answer to the fairness question is private, there is less dissonance and hence no behavioural change.
Group-level cognitive states are widely observed in human social systems, but their discussion is often ruled out a priori in quantitative approaches. In this paper, we show how reference to the irreducible mental states and psychological dynamics of a group is necessary to make sense of large scale social phenomena. We introduce the problem of mental boundaries by reference to a classic problem in the evolution of cooperation. We then provide an explicit quantitative example drawn from ongoing work on cooperation and conflict among Wikipedia editors. We show the limitations of methodological individualism, and the substantial benefits that come from being able to refer to collective intentions and attributions of cognitive states of the form "what the group believes" and "what the group values".
In this exclusive interview, behavioral economist Dan Ariely explains why smartphone apps could be the perfect solution to staying motivated, and why most app developers simply aren't getting it right.
Neuroeconomics applies models from economics and psychology to inform neurobiological studies of choice. This approach has revealed neural signatures of concepts like value, risk, and ambiguity, which are known to influence decision making. Such observations have led theorists to hypothesize a single, unified decision process that mediates choice behavior via a common neural currency for outcomes like food, money, or social praise. In parallel, recent neuroethological studies of decision making have focused on natural behaviors like foraging, mate choice, and social interactions. These decisions strongly impact evolutionary fitness and thus are likely to have played a key role in shaping the neural circuits that mediate decision making. This approach has revealed a suite of computational motifs that appear to be shared across a wide variety of organisms. We argue that the existence of deep homologies in the neural circuits mediating choice may have profound implications for understanding human decision making in health and disease.
Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically embodied systems like robots, as the behavioral dynamics are results of interactions among their controller, mechanical dynamics of the body, and the environment. This paper presents a guided self-organization approach for dynamic robots based on a coupling between the system mechanical dynamics with an internal control structure known as the attractor selection mechanism. The mechanism enables the robot to gracefully shift between random and deterministic behaviors, represented by a number of attractors, depending on internally generated stochastic perturbation and sensory input. The robot used in this paper is a simulated curved beam hopping robot: a system with a variety of mechanical dynamics which depends on its actuation frequencies. Despite the simplicity of the approach, it will be shown how the approach regulates the probability of the robot to reach a goal through the interplay among the sensory input, the level of inherent stochastic perturbation, i.e., noise, and the mechanical dynamics.
Guided Self-Organization in a Dynamic Embodied System Based on Attractor Selection Mechanism Surya G. Nurzaman , Xiaoxiang Yu, Yongjae Kim and Fumiya Iida
This article is based on the keynote address presented to the European Meetings on Cybernetics and Systems Research (EMCSR) in 2012, on the occasion of Edgar Morin receiving the Bertalanffy Prize in Complexity Thinking, awarded by the Bertalanffy Centre for the Study of Systems Science (BCSSS). The following theses will be elaborated on: (a) The whole is at the same time more and less than its parts; (b) We must abandon the term "object" for systems because all the objects are systems and parts of systems; (c) System and organization are the two faces of the same reality; (d) Eco-systems illustrate self-organization.
Complex Thinking for a Complex World – About Reductionism, Disjunction and Systemism Edgar Morin
Systema: connecting matter, life, culture and technology Vol 2, No 1 (2014)
Ten of the most influential social psychology experiments.
“I have been primarily interested in how and why ordinary people do unusual things, things that seem alien to their natures. Why do good people sometimes act evil? Why do smart people sometimes do dumb or irrational things?” –Philip Zimbardo. Like eminent social psychologist Professor Philip Zimbardo (author ofThe Lucifer Effect: Understanding How Good People Turn Evil), I’m also obsessed with why we do dumb or irrational things. The answer quite often is because of other people – something social psychologists have comprehensively shown.
Over the past few months I’ve been describing 10 of the most influential social psychology studies. Each one tells a unique, insightful story relevant to all our lives, every day.
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