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Human-based computation

Human-based computation (HBC) is a computer science technique in which a machine performs its function by outsourcing certain steps to humans. This approach uses differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human-computer interaction. In traditional computation, a human employs a computer[1] to solve a problem; a human provides a formalized problem description and an algorithm to a computer, and receives a solution to interpret. Human-based computation frequently reverses the roles; the computer asks a person or a large group of people to solve a problem, then collects, interprets, and integrates their solutions. Early work[edit] Human-based computation (apart from the historical meaning of "computer") research has its origins in the early work on interactive evolutionary computation. A concept of the automatic Turing test pioneered by Moni Naor (1996) is another precursor of human-based computation. Alternative terms[edit] Related:  Complex Systems

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.

Systems Thinking Resources - The Donella Meadows Institute Concepts and Frameworks The Five Learning Disciplines Developed by renowned systems thinker Peter Senge, these five disciplines each enhance the ability of a person or organization to use learning effectively. The five learning disciplines are Personal MasteryMental ModelsShared VisionTeam LearningSystems Thinking For descriptions of each of these disciplines, visit the Society for Organizational Learning’s website. U Process U Process, also know as Theory U, is a useful methodology for collectively approaching difficult problems and developing innovative, appropriate solutions. For more information about U Process, visit the Presencing Institute. Biomimicry Biomimicry is the concept of using natural forms, materials, and processes as models to drive human innovation. The Biomimicry Guild has a great introduction to this approach to problem solving. Double Loop Learning Tools The Iceberg Model We have a copy of the iceberg model hanging in our office. The Bathtub Theorem Open Space World Café

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]

Evonet Wiki : Welcome to Evo* 2007 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.

Thinking in Systems by Donella Meadows "Dana Meadows' exposition in this book exhibits a degree of clarity and simplicity that can only be attained by one who profoundly and honestly understands the subject at hand--in this case systems modeling. Many thanks to Diana Wright for bringing this extra legacy from Dana to us."—Herman Daly, Professor, School of Public Policy, University of Maryland at College Park In the years following her role as the lead author of the international bestseller, Limits to Growth—the first book to show the consequences of unchecked growth on a finite planet— Donella Meadows remained a pioneer of environmental and social analysis until her untimely death in 2001. Meadows’ newly released manuscript, Thinking in Systems, is a concise and crucial book offering insight for problem solving on scales ranging from the personal to the global. Some of the biggest problems facing the world—war, hunger, poverty, and environmental degradation—are essentially system failures. About the Author Donella Meadows

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.

Gotcha! Criminal mugs captured in computer In Search of..... - TV.com www.tv.com/shows/in-search-of Narrarated by Leonard Nimoy, In search of was a 30 minute syndicated show that covered a wide range of paranormal topics. It pioneered a lot of the methodology that ... Search Engine - Download.com download.cnet.com/s/search-engine search engine free download - GSA Search Engine Ranker, Nomao - The personalized search engine, Zoom Search Engine, and many more programs Google Search - Download.com download.cnet.com/s/google-search google search free download - Google Search, Google Toolbar for Internet Explorer, Google Search, and many more programs Star Search - Episode Guide - TV.com www.tv.com/shows/star-search-2003/episodes Star Search episode guides on TV.com.

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...

IFT.org 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.

Related:  Computation