Wikinomics: How Mass Collaboration Changes Everything Concepts[edit] According to Tapscott, Wikinomics is based on four ideas: Openness, Peering, Sharing, and Acting Globally. The use of mass collaboration in a business environment, in recent history, can be seen as an extension of the trend in business to outsource: externalize formerly internal business functions to other business entities. The difference however is that instead of an organized business body brought into being specifically for a unique function, mass collaboration relies on free individual agents to come together and cooperate to improve a given operation or solve a problem. The book also discusses seven new models of mass collaboration, including: The last chapter is written by viewers, and was opened for editing on February 5, 2007. Central Concepts of Wikinomics in the Enterprise[edit] According to Tapscott and Williams, these four principles are the central concepts of wikinomics in the enterprise: Coase's Law[edit] Reception[edit] See also[edit] References[edit] Videos
Network effect Diagram showing the network effect in a few simple phone networks. The lines represent potential calls between phones. The classic example is the telephone. The expression "network effect" is applied most commonly to positive network externalities as in the case of the telephone. Over time, positive network effects can create a bandwagon effect as the network becomes more valuable and more people join, in a positive feedback loop. Origins[edit] Network effects were a central theme in the arguments of Theodore Vail, the first post patent president of Bell Telephone, in gaining a monopoly on US telephone services. The economic theory of the network effect was advanced significantly between 1985 and 1995 by researchers Michael L. According to Metcalfe, the rationale behind the sale of networking cards was that (1) the cost of the network was directly proportional to the number of cards installed, but (2) the value of the network was proportional to the square of the number of users.
Interactive evolutionary computation Interactive evolutionary computation (IEC) or aesthetic selection is a general term for methods of evolutionary computation that use human evaluation. Usually human evaluation is necessary when the form of fitness function is not known (for example, visual appeal or attractiveness; as in Dawkins, 1986[1]) or the result of optimization should fit a particular user preference (for example, taste of coffee or color set of the user interface). IEC design issues[edit] The number of evaluations that IEC can receive from one human user is limited by user fatigue which was reported by many researchers as a major problem. However IEC implementations that can concurrently accept evaluations from many users overcome the limitations described above. IEC types[edit] IEC methods include interactive evolution strategy,[3] interactive genetic algorithm,[4][5] interactive genetic programming,[6][7][8] and human-based genetic algorithm.[9] IGA[edit] See also[edit] References[edit] External links[edit]
Genetic algorithm The 2006 NASA ST5 spacecraft antenna. This complicated shape was found by an evolutionary computer design program to create the best radiation pattern. Genetic algorithms find application in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields. Methodology[edit] In a genetic algorithm, a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions. A typical genetic algorithm requires: a genetic representation of the solution domain,a fitness function to evaluate the solution domain. Once the genetic representation and the fitness function are defined, a GA proceeds to initialize a population of solutions and then to improve it through repetitive application of the mutation, crossover, inversion and selection operators. Initialization of genetic algorithm[edit] Selection[edit] Genetic operators[edit]
Mass collaboration Mass collaboration is a form of collective action that occurs when large numbers of people work independently on a single project, often modular in its nature. Such projects typically take place on the internet using social software and computer-supported collaboration tools such as wiki technologies, which provide a potentially infinite hypertextual substrate within which the collaboration may be situated. Factors[edit] Modularity[edit] Modularity enables a mass of experiments to proceed in parallel, with different teams working on the same modules, each proposing different solutions. Differences[edit] Cooperation[edit] Mass collaboration differs from mass cooperation in that the creative acts taking place require the joint development of shared understandings. Another important distinction is the borders around which a mass cooperation can be defined. Online forum[edit] Coauthoring[edit] Changes[edit] Business[edit] being openpeeringsharingacting globallyinterdependence See also[edit]
Critical mass (sociodynamics) In social dynamics, critical mass is a sufficient number of adopters of an innovation in a social system so that the rate of adoption becomes self-sustaining and creates further growth. It is an aspect of the theory of diffusion of innovations, written extensively on by Everett Rogers in his book Diffusion of Innovations.[1] Social factors influencing critical mass may involve the size, interrelatedness and level of communication in a society or one of its subcultures. Critical mass may be closer to majority consensus in political circles, where the most effective position is more often that held by the majority of people in society. Critical mass is a concept used in a variety of contexts, including physics, group dynamics, politics, public opinion, and technology. The concept of critical mass was originally created by game theorist Thomas Schelling and sociologist Mark Granovetter to explain the actions and behaviors of a wide range of people and phenomenon. Finally, Herbert A. In M.
Human-based genetic algorithm In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute solution suggestions to the evolutionary process. For this purpose, a HBGA has human interfaces for initialization, mutation, and recombinant crossover. As well, it may have interfaces for selective evaluation. In short, a HBGA outsources the operations of a typical genetic algorithm to humans. Evolutionary genetic systems and human agency[edit] Among evolutionary genetic systems, HBGA is the computer-based analogue of genetic engineering (Allan, 2005). One obvious pattern in the table is the division between organic (top) and computer systems (bottom). Looking to the right, the selector is the agent that decides fitness in the system. The innovator is the agent of genetic change. HBGA is roughly similar to genetic engineering. Differences from a plain genetic algorithm[edit] Functional features[edit] HBGA is a method of collaboration and knowledge exchange.
Evolutionary algorithm Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape; this generality is shown by successes in fields as diverse as engineering, art, biology, economics, marketing, genetics, operations research, robotics, social sciences, physics, politics and chemistry[citation needed]. In most real applications of EAs, computational complexity is a prohibiting factor. In fact, this computational complexity is due to fitness function evaluation. A possible limitation [according to whom?] Implementation of biological processes[edit] Evolutionary algorithm types[edit] Similar techniques differ in the implementation details and the nature of the particular applied problem. Related techniques[edit] Swarm algorithms, including: Ant colony optimization - Based on the ideas of ant foraging by pheromone communication to form paths. [edit] See also[edit] References[edit] Bibliography[edit]
Tipping point (sociology) In sociology, a tipping point is a point in time when a group —or a large number of group members— rapidly and dramatically changes its behavior by widely adopting a previously rare practice. The idea was expanded and built upon by Nobel Prize-winner Thomas Schelling in 1972. A similar idea underlies Mark Granovetter's threshold model of collective behavior. The phrase has extended beyond its original meaning and been applied to any process in which, beyond a certain point, the rate of the process increases dramatically. Journalists and academics have applied the phrase to dramatic changes in governments, such as during the Arab Spring[2] The concept of at tipping point is described in an article in an academic journal, the Journal of Democracy, entitled China at the Tipping Point? Regime transitions belong to that paradoxical class of events whichare inevitable but not predictable. Mathematically, the angle of repose may be seen as a bifurcation.
Notes on Factors in Collective Intelligence | There are probably hundreds of factors we could identify as important for the generation of collective intelligence in different types of human system. We find these factors wherever we see collective intelligence being exercised, and when we support them (especially in combination) we often find collective intelligence increasing. From my work with reflective forms of CI in groups, communities and societies, I find that about fifteen factors stand out most vividly, and I’ve listed them with brief descriptions here. _ _ _ _ _ __ _ Some Factors Which Support Collective Intelligence DIVERSITY – To the extent everyone is the same, their intelligence can’t collectively add up to something more than any of them individually. Like this: Like Loading...
Environmental Modelling & Software - Putting humans in the loop: Social computing for Water Resources Management Abstract The advent of online services, social networks, crowdsourcing, and serious Web games has promoted the emergence of a novel computation paradigm, where complex tasks are solved by exploiting the capacity of human beings and computer platforms in an integrated way. Water Resources Management systems can take advantage of human and social computation in several ways: collecting and validating data, complementing the analytic knowledge embodied in models with tacit knowledge from individuals and communities, using human sensors to monitor the variation of conditions at a fine grain and in real time, activating human networks to perform search tasks or actuate management actions. This exploratory paper overviews different forms of human and social computation and analyzes how they can be exploited to enhance the effectiveness of ICT-based Water Resources Management. Keywords Copyright © 2012 Elsevier Ltd.
Artificial neural network An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background[edit] There is no single formal definition of what an artificial neural network is. History[edit] and
Social dynamics Social dynamics can refer to the behavior of groups that results from the interactions of individual group members as well to the study of the relationship between individual interactions and group level behaviors.[1] The field of social dynamics brings together ideas from Economics, Sociology, Social Psychology, and other disciplines, and is a sub-field of complex adaptive systems or complexity science. The fundamental assumption of the field is that individuals are influenced by one another's behavior. The field is closely related to system dynamics. Like system dynamics, social dynamics is concerned with changes over time and emphasizes the role of feedbacks. Topics[edit] See also[edit] References[edit] Weidlich, W. (1997) "Sociodynamics applied to the evolution of urban and regional structures". Further reading[edit] Easley, David; Klienberg, Jon (2010). External links[edit] Watts, D.J.; Strogatz, S.H. (1998).
Socially distributed cognition Distributed cognition is a psychological theory that knowledge lies not only within the individual, but also in the individual's social and physical environment. This theory was developed in the mid-1980s by Edwin Hutchins. Using insights from sociology, cognitive science, and the psychology of Vygotsky (cf. cultural-historical psychology) it emphasizes the social aspects of cognition. Embodiment of information that is embedded in representations of interactionCoordination of enaction among embodied agentsEcological contributions to a cognitive ecosystem Distributed cognition is a branch of cognitive science that proposes that human knowledge and cognition are not confined to the individual. This abstraction can be categorized into three distinct types of processes: Early research[edit] John Milton Roberts thought that social organization could be seen as cognition through a community (Roberts 1964). Daniel L. Applications[edit] Metaphors and examples[edit] Quotes[edit] On cognitive science: