174 replies to this topic

[Delta Wye]

Learning curve

From Wikipedia, the free encyclopedia
Jump to: navigation, search
For other uses, see Learning curve (disambiguation).
A learning curve is a graphical representation of the changing rate of learning (in the average person) for a given activity or tool. Typically, the increase in retention of information is sharpest after the initial attempts, and then gradually evens out, meaning that less and less new information is retained after each repetition.
The learning curve can also represent at a glance the initial difficulty of learning something and, to an extent, how much there is to learn after initial familiarity. For example, the Windows program Notepad is extremely simple to learn, but offers little after this. On the other extreme is the UNIX terminal editor Vim, which is difficult to learn, but offers a wide array of features to master after the user has figured out how to work it. It is possible for something to be easy to learn, but difficult to master or hard to learn with little beyond this.^{[citation needed]}

Contents

• 1 Learning curve in psychology and economics
  
• 2 Broader interpretations of the learning curve
  
• 3 Common terms
  
• 4 Learning curve models
  
• 5 General learning limits
  
• 6 See also
  
• 7 References
  
• 8 External links

Learning curve in psychology and economics

The first person to describe the learning curve was Hermann Ebbinghaus in 1885. He found that the time required to memorize a nonsense word increased sharply as the number of syllables increased.^[1] Psychologist Arthur Bills gave a more detailed description of learning curves in 1934. He also discussed the properties of different types of learning curves, such as negative acceleration, positive acceleration, plateaus, and ogive curves.^[2] In 1936, Theodore Paul Wright described the effect of learning on labor productivity in the aircraft industry and proposed a mathematical model of the learning curve.^[3]
The economic learning of productivity and efficiency generally follows the same kinds of experience curves and have interesting secondary effects. Efficiency and productivity improvement can be considered as whole organization or industry or economy learning processes, as well as for individuals. The general pattern is of first speeding up and then slowing down, as the practically achievable level of methodology improvement is reached. The effect of reducing local effort and resource use by learning improved methods paradoxically often has the opposite latent effect on the next larger scale system, by facilitating its expansion, or economic growth, as discussed in the Jevons paradox in the 1880s and updated in the Khazzoom-Brookes Postulate in the 1980s.
Broader interpretations of the learning curve

Initially introduced in educational and behavioral psychology, the term has acquired a broader interpretation over time, and expressions such as "experience curve", "improvement curve", "cost improvement curve", "progress curve", "progress function", "startup curve", and "efficiency curve" are often used interchangeably. In economics the subject is rates of "development", as development refers to a whole system learning process with varying rates of progression. Generally speaking all learning displays incremental change over time, but describes an "S" curve which has different appearances depending on the time scale of observation. It has now also become associated with the evolutionary theory of punctuated equilibrium and other kinds of revolutionary change in complex systems generally, relating to innovation, organizational behavior and the management of group learning, among other fields.^[4] These processes of rapidly emerging new form appear to take place by complex learning within the systems themselves, which when observable, display curves of changing rates that accelerate and decelerate.
Common terms




Steep learning curve, where learning is achieved very quickly
The familiar expression "steep learning curve" may refer to either of two aspects of a pattern in which the marginal rate of required resource investment is initially low, perhaps even decreasing at the very first stages, but eventually increases without bound.
Early uses of the metaphor focused on the pattern's positive aspect, namely the potential for quick progress in learning (as measured by, e.g., memory accuracy or the number of trials required to obtain a desired result)^[5] at the introductory or elementary stage.^[6] Over time, however, the metaphor has become more commonly used to focus on the pattern's negative aspect, namely the difficulty of learning once one gets beyond the basics of a subject.^{[citation needed]}
In the former case, the "steep[ness]" metaphor is inspired by the initially high rate of increase featured by the function characterizing the overall amount learned versus total resources invested (or versus time when resource investment per unit time is held constant)—in mathematical terms, the initially high positive absolute value of the first derivative of that function. In the latter case, the metaphor is inspired by the pattern's eventual behavior, i.e., its behavior at high values of overall resources invested (or of overall time invested when resource investment per unit time is held constant), namely the high rate of increase in the resource investment required if the next item is to be learned—in other words, the eventually always-high, always-positive absolute value and the eventually never-decreasing status of the first derivative of that function. In turn, those properties of the latter function dictate that the function measuring the rate of learning per resource unit invested (or per unit time when resource investment per unit time is held constant) has a horizontal asymptote at zero, and thus that the overall amount learned, while never "plateauing" or decreasing, increases more and more slowly as more and more resources are invested.
This difference in emphasis has led to confusion and disagreements even among learned people.^[7]
Learning curve models

See also: Learning curves
The page on "learning & experience curve models" offers more discussion of the mathematical theory of representing them as deterministic processes, and provides a good group of empirical examples of how that technique has been applied.
General learning limits

Learning curves, also called experience curves, relate to the much broader subject of natural limits for resources and technologies in general. Such limits generally present themselves as increasing complications that slow the learning of how to do things more efficiently, like the well-known limits of perfecting any process or product or to perfecting measurements.^[8] These practical experiences match the predictions of the second law of thermodynamics for the limits of waste reduction generally. Approaching limits of perfecting things to eliminate waste meets geometrically increasing effort to make progress, and provides an environmental measure of all factors seen and unseen changing the learning experience. Perfecting things becomes ever more difficult despite increasing effort despite continuing positive, if ever diminishing, results. The same kind of slowing progress due to complications in learning also appears in the limits of useful technologies and of profitable markets applying to product life cycle management and software development cycles). Remaining market segments or remaining potential efficiencies or efficiencies are found in successively less convenient forms.
Efficiency and development curves typically follow a two-phase process of first bigger steps corresponding to finding things easier, followed by smaller steps of finding things more difficult. It reflects bursts of learning following breakthroughs that make learning easier followed by meeting constraints that make learning ever harder, perhaps toward a point of cessation.

• Natural Limits One of the key studies in the area concerns diminishing returns on investments generally, either physical or financial, pointing to whole system limits for resource development or other efforts. The most studied of these may be Energy Return on Energy Invested or EROEI, discussed at length in an Encyclopedia of the Earth article and in an OilDrum article and series also referred to as Hubert curves. The energy needed to produce energy is a measure of our difficulty in learning how to make remaining energy resources useful in relation to the effort expended. Energy returns on energy invested have been in continual decline for some time, caused by natural resource limits and increasing investment. Energy is both nature's and our own principal resource for making things happen. The point of diminishing returns is when increasing investment makes the resource more expensive. As natural limits are approached, easily used sources are exhausted and ones with more complications need to be used instead. As an environmental signal persistently dimishing EROI indicates an approach of whole system limits in our ability to make things happen.

• Useful Natural Limits EROEI measures the return on invested effort as a ratio of R/I or learning progress. The inverse I/R measures learning difficulty. The simple difference is that if R approaches zero R/I will too, but I/R will approach infinity. When complications emerge to limit learning progress the limit of useful returns, uR, is approached and R-uR approaches zero. The difficulty of useful learning I/(R-uR) approaches infinity as increasingly difficult tasks make the effort unproductive. That point is approached as a vertical asymptote, at a particular point in time, that can be delayed only by unsustainable effort. It defines a point at which enough investment has been made and the task is done, usually planned to be the same as when the task is complete. For unplanned tasks it may be either foreseen or discovered by surprise. The usefulness measure, uR, is affected by the complexity of environmental responses that can only be measured when they occur unless they are foreseen.

See also

• Experience curve effects
  
• Learning
  
• Learning speed
  
• Labor productivity
  
• Learning-by-doing (disambiguation)
  
• Population growth

References

• ^ Wozniak, R.H. (1999). Introduction to memory: Hermann Ebbinghaus (1885/1913). Classics in the history of psychology
  
• ^ Bills, A.G. (1934). General experimental psychology. Longmans Psychology Series. (pp. 192-215). New York, NY: Longmans, Green and Co.
  
• ^ Wright, T.P., "Factors Affecting the Cost of Airplanes", Journal of Aeronautical Sciences, 3(4) (1936): 122–128.
  
• ^ Connie JG Gersick 1991 "Revolutionary Change Theories: A Multilevel Exploration of the Punctuated Equilibrium Paradigm" The Academy of Management Review, Vol. 16, No. 1 pp. 10-36 1
  
• ^ Y. Kenneth and S. Gerald, "Sparse Representations for Fast, One-Shot Learning". MIT AI Lab Memo 1633, May 1998.
  
• ^ Ritter, F. E., & Schooler, L. J. The learning curve. In International Encyclopedia of the Social and Behavioral Sciences (2002), 8602-8605. Amsterdam: Pergamon
  
• ^ "Laparoscopic Colon Resection Early in the Learning Curve", Ann Surg. 2006 June; 243(6): 730–737, see the "Discussions" section, Dr. Smith's remark about the usage of the term "steep learning curve": "First, semantics. A steep learning curve is one where you gain proficiency over a short number of trials. That means the curve is steep. I think semantically we are really talking about a prolonged or long learning curve. I know it is a subtle distinction, but I can't miss the opportunity to make that point."
  
• ^ "Towards the Limits of Precision and Accuracy in Measurement".

[Shibas]

TheShadowWalker, on 22 January 2013 - 05:22 PM, said:

All i know is, i've played 8 matches in 3 and a half months because of the horrible development of the game. i am massively disappointed. and will NOT return unless they get their heads back on straight. PGI's record thus far has been horrid. and they clearly haven't improved. I invested 120 dollars into a game i love the mechanics and basis of, but can't play enjoyably because their too busy adding content to fix preexisting issues. It's to the point now, that if PGI doesn't have some of the major bugs fixed in the patch following the one on the 23rd, I WILL uninstall and never look back. I'm fed up, Sick and tired of the money grubbing content push that they've done since closed beta. major bugs fixed since closed beta? 5 maybe? major bugs created due to pushing content in hopes of increasing MC sales? Countless.
I want more content just as much as the next guy. But it's not worth releasing content for a game that is only being half assed by the development team. Memory leak from closed beta STILL exists. 90 days after open beta launch, most of us screamed this game was FAR from ready for open beta. most new come'rs leave within an hour or two of playing. i've played thousands of matches in closed beta, but will not commit that kind of time to this game with this dev team. Even if 6 months a year later they finally have it worked out. There's other games out there. bigger and better adventures with dev teams that DO what is needed to provide a stable client, not holding their hands out saying MORE MORE MORE. A lot of us have paid twice the price of a retail game for this. and this is the product we get in return after being playable for so long? As far as im concerned at this moment in time, they need to fire their entire dev team and hire in some rookies straight out of school, i can promise you they'd more than likely be a hundred fold more productive in the foundation and development of a working game, then milking their players for all they can before said product is even live. Again PGI. You've lost my respect, You've lost me as a source of income and you've lost 2 other people in my house hold from being paying players as well. And those are just the people I can personally speak for. Not to mention the hundreds or thousands of other players who feel and see things as i do. PGI you need to man up or sit down and shut the heck up. because you've destroyed one major love in many of our lives with your greed.

I know right, I'm totally with you. PGI programs like old people having the sex. If GOD wanted this game done, he would have miracled their a ss.

[Coolwhoami]

Playing video games is hard... work?

[Nekki Basara]

Delta Wye, on 22 January 2013 - 06:11 PM, said:

Learning curve

From Wikipedia, the free encyclopedia
Jump to: navigation, search
For other uses, see Learning curve (disambiguation).
A learning curve is a graphical representation of the changing rate of learning (in the average person) for a given activity or tool. Typically, the increase in retention of information is sharpest after the initial attempts, and then gradually evens out, meaning that less and less new information is retained after each repetition.
The learning curve can also represent at a glance the initial difficulty of learning something and, to an extent, how much there is to learn after initial familiarity. For example, the Windows program Notepad is extremely simple to learn, but offers little after this. On the other extreme is the UNIX terminal editor Vim, which is difficult to learn, but offers a wide array of features to master after the user has figured out how to work it. It is possible for something to be easy to learn, but difficult to master or hard to learn with little beyond this.^{[citation needed]}

Contents

• 1 Learning curve in psychology and economics
  
• 2 Broader interpretations of the learning curve
  
• 3 Common terms
  
• 4 Learning curve models
  
• 5 General learning limits
  
• 6 See also
  
• 7 References
  
• 8 External links

Learning curve in psychology and economics


The first person to describe the learning curve was Hermann Ebbinghaus in 1885. He found that the time required to memorize a nonsense word increased sharply as the number of syllables increased.^[1] Psychologist Arthur Bills gave a more detailed description of learning curves in 1934. He also discussed the properties of different types of learning curves, such as negative acceleration, positive acceleration, plateaus, and ogive curves.^[2] In 1936, Theodore Paul Wright described the effect of learning on labor productivity in the aircraft industry and proposed a mathematical model of the learning curve.^[3]
The economic learning of productivity and efficiency generally follows the same kinds of experience curves and have interesting secondary effects. Efficiency and productivity improvement can be considered as whole organization or industry or economy learning processes, as well as for individuals. The general pattern is of first speeding up and then slowing down, as the practically achievable level of methodology improvement is reached. The effect of reducing local effort and resource use by learning improved methods paradoxically often has the opposite latent effect on the next larger scale system, by facilitating its expansion, or economic growth, as discussed in the Jevons paradox in the 1880s and updated in the Khazzoom-Brookes Postulate in the 1980s.
Broader interpretations of the learning curve

Initially introduced in educational and behavioral psychology, the term has acquired a broader interpretation over time, and expressions such as "experience curve", "improvement curve", "cost improvement curve", "progress curve", "progress function", "startup curve", and "efficiency curve" are often used interchangeably. In economics the subject is rates of "development", as development refers to a whole system learning process with varying rates of progression. Generally speaking all learning displays incremental change over time, but describes an "S" curve which has different appearances depending on the time scale of observation. It has now also become associated with the evolutionary theory of punctuated equilibrium and other kinds of revolutionary change in complex systems generally, relating to innovation, organizational behavior and the management of group learning, among other fields.^[4] These processes of rapidly emerging new form appear to take place by complex learning within the systems themselves, which when observable, display curves of changing rates that accelerate and decelerate.
Common terms




Steep learning curve, where learning is achieved very quickly
The familiar expression "steep learning curve" may refer to either of two aspects of a pattern in which the marginal rate of required resource investment is initially low, perhaps even decreasing at the very first stages, but eventually increases without bound.
Early uses of the metaphor focused on the pattern's positive aspect, namely the potential for quick progress in learning (as measured by, e.g., memory accuracy or the number of trials required to obtain a desired result)^[5] at the introductory or elementary stage.^[6] Over time, however, the metaphor has become more commonly used to focus on the pattern's negative aspect, namely the difficulty of learning once one gets beyond the basics of a subject.^{[citation needed]}
In the former case, the "steep[ness]" metaphor is inspired by the initially high rate of increase featured by the function characterizing the overall amount learned versus total resources invested (or versus time when resource investment per unit time is held constant)—in mathematical terms, the initially high positive absolute value of the first derivative of that function. In the latter case, the metaphor is inspired by the pattern's eventual behavior, i.e., its behavior at high values of overall resources invested (or of overall time invested when resource investment per unit time is held constant), namely the high rate of increase in the resource investment required if the next item is to be learned—in other words, the eventually always-high, always-positive absolute value and the eventually never-decreasing status of the first derivative of that function. In turn, those properties of the latter function dictate that the function measuring the rate of learning per resource unit invested (or per unit time when resource investment per unit time is held constant) has a horizontal asymptote at zero, and thus that the overall amount learned, while never "plateauing" or decreasing, increases more and more slowly as more and more resources are invested.
This difference in emphasis has led to confusion and disagreements even among learned people.^[7]
Learning curve models

See also: Learning curves
The page on "learning & experience curve models" offers more discussion of the mathematical theory of representing them as deterministic processes, and provides a good group of empirical examples of how that technique has been applied.
General learning limits

Learning curves, also called experience curves, relate to the much broader subject of natural limits for resources and technologies in general. Such limits generally present themselves as increasing complications that slow the learning of how to do things more efficiently, like the well-known limits of perfecting any process or product or to perfecting measurements.^[8] These practical experiences match the predictions of the second law of thermodynamics for the limits of waste reduction generally. Approaching limits of perfecting things to eliminate waste meets geometrically increasing effort to make progress, and provides an environmental measure of all factors seen and unseen changing the learning experience. Perfecting things becomes ever more difficult despite increasing effort despite continuing positive, if ever diminishing, results. The same kind of slowing progress due to complications in learning also appears in the limits of useful technologies and of profitable markets applying to product life cycle management and software development cycles). Remaining market segments or remaining potential efficiencies or efficiencies are found in successively less convenient forms.
Efficiency and development curves typically follow a two-phase process of first bigger steps corresponding to finding things easier, followed by smaller steps of finding things more difficult. It reflects bursts of learning following breakthroughs that make learning easier followed by meeting constraints that make learning ever harder, perhaps toward a point of cessation.

• Natural Limits One of the key studies in the area concerns diminishing returns on investments generally, either physical or financial, pointing to whole system limits for resource development or other efforts. The most studied of these may be Energy Return on Energy Invested or EROEI, discussed at length in an Encyclopedia of the Earth article and in an OilDrum article and series also referred to as Hubert curves. The energy needed to produce energy is a measure of our difficulty in learning how to make remaining energy resources useful in relation to the effort expended. Energy returns on energy invested have been in continual decline for some time, caused by natural resource limits and increasing investment. Energy is both nature's and our own principal resource for making things happen. The point of diminishing returns is when increasing investment makes the resource more expensive. As natural limits are approached, easily used sources are exhausted and ones with more complications need to be used instead. As an environmental signal persistently dimishing EROI indicates an approach of whole system limits in our ability to make things happen.

• Useful Natural Limits EROEI measures the return on invested effort as a ratio of R/I or learning progress. The inverse I/R measures learning difficulty. The simple difference is that if R approaches zero R/I will too, but I/R will approach infinity. When complications emerge to limit learning progress the limit of useful returns, uR, is approached and R-uR approaches zero. The difficulty of useful learning I/(R-uR) approaches infinity as increasingly difficult tasks make the effort unproductive. That point is approached as a vertical asymptote, at a particular point in time, that can be delayed only by unsustainable effort. It defines a point at which enough investment has been made and the task is done, usually planned to be the same as when the task is complete. For unplanned tasks it may be either foreseen or discovered by surprise. The usefulness measure, uR, is affected by the complexity of environmental responses that can only be measured when they occur unless they are foreseen.

See also

• Experience curve effects
  
• Learning
  
• Learning speed
  
• Labor productivity
  
• Learning-by-doing (disambiguation)
  
• Population growth

References

• ^ Wozniak, R.H. (1999). Introduction to memory: Hermann Ebbinghaus (1885/1913). Classics in the history of psychology
  
• ^ Bills, A.G. (1934). General experimental psychology. Longmans Psychology Series. (pp. 192-215). New York, NY: Longmans, Green and Co.
  
• ^ Wright, T.P., "Factors Affecting the Cost of Airplanes", Journal of Aeronautical Sciences, 3(4) (1936): 122–128.
  
• ^ Connie JG Gersick 1991 "Revolutionary Change Theories: A Multilevel Exploration of the Punctuated Equilibrium Paradigm" The Academy of Management Review, Vol. 16, No. 1 pp. 10-36 1
  
• ^ Y. Kenneth and S. Gerald, "Sparse Representations for Fast, One-Shot Learning". MIT AI Lab Memo 1633, May 1998.
  
• ^ Ritter, F. E., & Schooler, L. J. The learning curve. In International Encyclopedia of the Social and Behavioral Sciences (2002), 8602-8605. Amsterdam: Pergamon
  
• ^ "Laparoscopic Colon Resection Early in the Learning Curve", Ann Surg. 2006 June; 243(6): 730–737, see the "Discussions" section, Dr. Smith's remark about the usage of the term "steep learning curve": "First, semantics. A steep learning curve is one where you gain proficiency over a short number of trials. That means the curve is steep. I think semantically we are really talking about a prolonged or long learning curve. I know it is a subtle distinction, but I can't miss the opportunity to make that point."
  
• ^ "Towards the Limits of Precision and Accuracy in Measurement".

Voted 6. Because 6 is better than 5.

[Delta Wye]

Att. PGI: As a gamer who plays a lot of games to the point of excluding all other hobbies and pursuits, I am an expert in the creation of games. Your buggy netcode, while functional, is a sign that you are ignorant about designing, building, testing, marketing, and being successful as a gaming company. As someone who plays a tremendous amount of games, some with functional netcode, rest assured at my expert opinion on previously said subjects. If, within the span of 3 weeks, these bugs are all fixed, I will continue to play your free game and post long-winded poorly formatted posts on your free forum. If they are not fixed, I will take my money and not spend it elsewhere.

You have been warned.

[octatonic]

Delta Wye, on 22 January 2013 - 06:11 PM, said:

Learning curve

From Wikipedia, the free encyclopedia
Jump to: navigation, search
For other uses, see Learning curve (disambiguation).
A learning curve is a graphical representation of the changing rate of learning (in the average person) for a given activity or tool. Typically, the increase in retention of information is sharpest after the initial attempts, and then gradually evens out, meaning that less and less new information is retained after each repetition.
The learning curve can also represent at a glance the initial difficulty of learning something and, to an extent, how much there is to learn after initial familiarity. For example, the Windows program Notepad is extremely simple to learn, but offers little after this. On the other extreme is the UNIX terminal editor Vim, which is difficult to learn, but offers a wide array of features to master after the user has figured out how to work it. It is possible for something to be easy to learn, but difficult to master or hard to learn with little beyond this.^{[citation needed]}

Contents

• 1 Learning curve in psychology and economics
  
• 2 Broader interpretations of the learning curve
  
• 3 Common terms
  
• 4 Learning curve models
  
• 5 General learning limits
  
• 6 See also
  
• 7 References
  
• 8 External links

Learning curve in psychology and economics


The first person to describe the learning curve was Hermann Ebbinghaus in 1885. He found that the time required to memorize a nonsense word increased sharply as the number of syllables increased.^[1] Psychologist Arthur Bills gave a more detailed description of learning curves in 1934. He also discussed the properties of different types of learning curves, such as negative acceleration, positive acceleration, plateaus, and ogive curves.^[2] In 1936, Theodore Paul Wright described the effect of learning on labor productivity in the aircraft industry and proposed a mathematical model of the learning curve.^[3]
The economic learning of productivity and efficiency generally follows the same kinds of experience curves and have interesting secondary effects. Efficiency and productivity improvement can be considered as whole organization or industry or economy learning processes, as well as for individuals. The general pattern is of first speeding up and then slowing down, as the practically achievable level of methodology improvement is reached. The effect of reducing local effort and resource use by learning improved methods paradoxically often has the opposite latent effect on the next larger scale system, by facilitating its expansion, or economic growth, as discussed in the Jevons paradox in the 1880s and updated in the Khazzoom-Brookes Postulate in the 1980s.
Broader interpretations of the learning curve

Initially introduced in educational and behavioral psychology, the term has acquired a broader interpretation over time, and expressions such as "experience curve", "improvement curve", "cost improvement curve", "progress curve", "progress function", "startup curve", and "efficiency curve" are often used interchangeably. In economics the subject is rates of "development", as development refers to a whole system learning process with varying rates of progression. Generally speaking all learning displays incremental change over time, but describes an "S" curve which has different appearances depending on the time scale of observation. It has now also become associated with the evolutionary theory of punctuated equilibrium and other kinds of revolutionary change in complex systems generally, relating to innovation, organizational behavior and the management of group learning, among other fields.^[4] These processes of rapidly emerging new form appear to take place by complex learning within the systems themselves, which when observable, display curves of changing rates that accelerate and decelerate.
Common terms




Steep learning curve, where learning is achieved very quickly
The familiar expression "steep learning curve" may refer to either of two aspects of a pattern in which the marginal rate of required resource investment is initially low, perhaps even decreasing at the very first stages, but eventually increases without bound.
Early uses of the metaphor focused on the pattern's positive aspect, namely the potential for quick progress in learning (as measured by, e.g., memory accuracy or the number of trials required to obtain a desired result)^[5] at the introductory or elementary stage.^[6] Over time, however, the metaphor has become more commonly used to focus on the pattern's negative aspect, namely the difficulty of learning once one gets beyond the basics of a subject.^{[citation needed]}
In the former case, the "steep[ness]" metaphor is inspired by the initially high rate of increase featured by the function characterizing the overall amount learned versus total resources invested (or versus time when resource investment per unit time is held constant)—in mathematical terms, the initially high positive absolute value of the first derivative of that function. In the latter case, the metaphor is inspired by the pattern's eventual behavior, i.e., its behavior at high values of overall resources invested (or of overall time invested when resource investment per unit time is held constant), namely the high rate of increase in the resource investment required if the next item is to be learned—in other words, the eventually always-high, always-positive absolute value and the eventually never-decreasing status of the first derivative of that function. In turn, those properties of the latter function dictate that the function measuring the rate of learning per resource unit invested (or per unit time when resource investment per unit time is held constant) has a horizontal asymptote at zero, and thus that the overall amount learned, while never "plateauing" or decreasing, increases more and more slowly as more and more resources are invested.
This difference in emphasis has led to confusion and disagreements even among learned people.^[7]
Learning curve models

See also: Learning curves
The page on "learning & experience curve models" offers more discussion of the mathematical theory of representing them as deterministic processes, and provides a good group of empirical examples of how that technique has been applied.
General learning limits

Learning curves, also called experience curves, relate to the much broader subject of natural limits for resources and technologies in general. Such limits generally present themselves as increasing complications that slow the learning of how to do things more efficiently, like the well-known limits of perfecting any process or product or to perfecting measurements.^[8] These practical experiences match the predictions of the second law of thermodynamics for the limits of waste reduction generally. Approaching limits of perfecting things to eliminate waste meets geometrically increasing effort to make progress, and provides an environmental measure of all factors seen and unseen changing the learning experience. Perfecting things becomes ever more difficult despite increasing effort despite continuing positive, if ever diminishing, results. The same kind of slowing progress due to complications in learning also appears in the limits of useful technologies and of profitable markets applying to product life cycle management and software development cycles). Remaining market segments or remaining potential efficiencies or efficiencies are found in successively less convenient forms.
Efficiency and development curves typically follow a two-phase process of first bigger steps corresponding to finding things easier, followed by smaller steps of finding things more difficult. It reflects bursts of learning following breakthroughs that make learning easier followed by meeting constraints that make learning ever harder, perhaps toward a point of cessation.

• Natural Limits One of the key studies in the area concerns diminishing returns on investments generally, either physical or financial, pointing to whole system limits for resource development or other efforts. The most studied of these may be Energy Return on Energy Invested or EROEI, discussed at length in an Encyclopedia of the Earth article and in an OilDrum article and series also referred to as Hubert curves. The energy needed to produce energy is a measure of our difficulty in learning how to make remaining energy resources useful in relation to the effort expended. Energy returns on energy invested have been in continual decline for some time, caused by natural resource limits and increasing investment. Energy is both nature's and our own principal resource for making things happen. The point of diminishing returns is when increasing investment makes the resource more expensive. As natural limits are approached, easily used sources are exhausted and ones with more complications need to be used instead. As an environmental signal persistently dimishing EROI indicates an approach of whole system limits in our ability to make things happen.

• Useful Natural Limits EROEI measures the return on invested effort as a ratio of R/I or learning progress. The inverse I/R measures learning difficulty. The simple difference is that if R approaches zero R/I will too, but I/R will approach infinity. When complications emerge to limit learning progress the limit of useful returns, uR, is approached and R-uR approaches zero. The difficulty of useful learning I/(R-uR) approaches infinity as increasingly difficult tasks make the effort unproductive. That point is approached as a vertical asymptote, at a particular point in time, that can be delayed only by unsustainable effort. It defines a point at which enough investment has been made and the task is done, usually planned to be the same as when the task is complete. For unplanned tasks it may be either foreseen or discovered by surprise. The usefulness measure, uR, is affected by the complexity of environmental responses that can only be measured when they occur unless they are foreseen.

See also

• Experience curve effects
  
• Learning
  
• Learning speed
  
• Labor productivity
  
• Learning-by-doing (disambiguation)
  
• Population growth

References

• ^ Wozniak, R.H. (1999). Introduction to memory: Hermann Ebbinghaus (1885/1913). Classics in the history of psychology
  
• ^ Bills, A.G. (1934). General experimental psychology. Longmans Psychology Series. (pp. 192-215). New York, NY: Longmans, Green and Co.
  
• ^ Wright, T.P., "Factors Affecting the Cost of Airplanes", Journal of Aeronautical Sciences, 3(4) (1936): 122–128.
  
• ^ Connie JG Gersick 1991 "Revolutionary Change Theories: A Multilevel Exploration of the Punctuated Equilibrium Paradigm" The Academy of Management Review, Vol. 16, No. 1 pp. 10-36 1
  
• ^ Y. Kenneth and S. Gerald, "Sparse Representations for Fast, One-Shot Learning". MIT AI Lab Memo 1633, May 1998.
  
• ^ Ritter, F. E., & Schooler, L. J. The learning curve. In International Encyclopedia of the Social and Behavioral Sciences (2002), 8602-8605. Amsterdam: Pergamon
  
• ^ "Laparoscopic Colon Resection Early in the Learning Curve", Ann Surg. 2006 June; 243(6): 730–737, see the "Discussions" section, Dr. Smith's remark about the usage of the term "steep learning curve": "First, semantics. A steep learning curve is one where you gain proficiency over a short number of trials. That means the curve is steep. I think semantically we are really talking about a prolonged or long learning curve. I know it is a subtle distinction, but I can't miss the opportunity to make that point."
  
• ^ "Towards the Limits of Precision and Accuracy in Measurement".

same.

[Rannos]

I saw the wall of text and knew exactly what to do

[Nekki Basara]

Rannos, on 22 January 2013 - 06:23 PM, said:

I saw the wall of text and knew exactly what to do

Vote checkbox?

[Silent]

Delta Wye, on 22 January 2013 - 06:11 PM, said:

Learning curve

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For other uses, see Learning curve (disambiguation).
A learning curve is a graphical representation of the changing rate of learning (in the average person) for a given activity or tool. Typically, the increase in retention of information is sharpest after the initial attempts, and then gradually evens out, meaning that less and less new information is retained after each repetition.
The learning curve can also represent at a glance the initial difficulty of learning something and, to an extent, how much there is to learn after initial familiarity. For example, the Windows program Notepad is extremely simple to learn, but offers little after this. On the other extreme is the UNIX terminal editor Vim, which is difficult to learn, but offers a wide array of features to master after the user has figured out how to work it. It is possible for something to be easy to learn, but difficult to master or hard to learn with little beyond this.^{[citation needed]}

Contents

• 1 Learning curve in psychology and economics
  
• 2 Broader interpretations of the learning curve
  
• 3 Common terms
  
• 4 Learning curve models
  
• 5 General learning limits
  
• 6 See also
  
• 7 References
  
• 8 External links

Learning curve in psychology and economics


The first person to describe the learning curve was Hermann Ebbinghaus in 1885. He found that the time required to memorize a nonsense word increased sharply as the number of syllables increased.^[1] Psychologist Arthur Bills gave a more detailed description of learning curves in 1934. He also discussed the properties of different types of learning curves, such as negative acceleration, positive acceleration, plateaus, and ogive curves.^[2] In 1936, Theodore Paul Wright described the effect of learning on labor productivity in the aircraft industry and proposed a mathematical model of the learning curve.^[3]
The economic learning of productivity and efficiency generally follows the same kinds of experience curves and have interesting secondary effects. Efficiency and productivity improvement can be considered as whole organization or industry or economy learning processes, as well as for individuals. The general pattern is of first speeding up and then slowing down, as the practically achievable level of methodology improvement is reached. The effect of reducing local effort and resource use by learning improved methods paradoxically often has the opposite latent effect on the next larger scale system, by facilitating its expansion, or economic growth, as discussed in the Jevons paradox in the 1880s and updated in the Khazzoom-Brookes Postulate in the 1980s.
Broader interpretations of the learning curve

Initially introduced in educational and behavioral psychology, the term has acquired a broader interpretation over time, and expressions such as "experience curve", "improvement curve", "cost improvement curve", "progress curve", "progress function", "startup curve", and "efficiency curve" are often used interchangeably. In economics the subject is rates of "development", as development refers to a whole system learning process with varying rates of progression. Generally speaking all learning displays incremental change over time, but describes an "S" curve which has different appearances depending on the time scale of observation. It has now also become associated with the evolutionary theory of punctuated equilibrium and other kinds of revolutionary change in complex systems generally, relating to innovation, organizational behavior and the management of group learning, among other fields.^[4] These processes of rapidly emerging new form appear to take place by complex learning within the systems themselves, which when observable, display curves of changing rates that accelerate and decelerate.
Common terms




Steep learning curve, where learning is achieved very quickly
The familiar expression "steep learning curve" may refer to either of two aspects of a pattern in which the marginal rate of required resource investment is initially low, perhaps even decreasing at the very first stages, but eventually increases without bound.
Early uses of the metaphor focused on the pattern's positive aspect, namely the potential for quick progress in learning (as measured by, e.g., memory accuracy or the number of trials required to obtain a desired result)^[5] at the introductory or elementary stage.^[6] Over time, however, the metaphor has become more commonly used to focus on the pattern's negative aspect, namely the difficulty of learning once one gets beyond the basics of a subject.^{[citation needed]}
In the former case, the "steep[ness]" metaphor is inspired by the initially high rate of increase featured by the function characterizing the overall amount learned versus total resources invested (or versus time when resource investment per unit time is held constant)—in mathematical terms, the initially high positive absolute value of the first derivative of that function. In the latter case, the metaphor is inspired by the pattern's eventual behavior, i.e., its behavior at high values of overall resources invested (or of overall time invested when resource investment per unit time is held constant), namely the high rate of increase in the resource investment required if the next item is to be learned—in other words, the eventually always-high, always-positive absolute value and the eventually never-decreasing status of the first derivative of that function. In turn, those properties of the latter function dictate that the function measuring the rate of learning per resource unit invested (or per unit time when resource investment per unit time is held constant) has a horizontal asymptote at zero, and thus that the overall amount learned, while never "plateauing" or decreasing, increases more and more slowly as more and more resources are invested.
This difference in emphasis has led to confusion and disagreements even among learned people.^[7]
Learning curve models

See also: Learning curves
The page on "learning & experience curve models" offers more discussion of the mathematical theory of representing them as deterministic processes, and provides a good group of empirical examples of how that technique has been applied.
General learning limits

Learning curves, also called experience curves, relate to the much broader subject of natural limits for resources and technologies in general. Such limits generally present themselves as increasing complications that slow the learning of how to do things more efficiently, like the well-known limits of perfecting any process or product or to perfecting measurements.^[8] These practical experiences match the predictions of the second law of thermodynamics for the limits of waste reduction generally. Approaching limits of perfecting things to eliminate waste meets geometrically increasing effort to make progress, and provides an environmental measure of all factors seen and unseen changing the learning experience. Perfecting things becomes ever more difficult despite increasing effort despite continuing positive, if ever diminishing, results. The same kind of slowing progress due to complications in learning also appears in the limits of useful technologies and of profitable markets applying to product life cycle management and software development cycles). Remaining market segments or remaining potential efficiencies or efficiencies are found in successively less convenient forms.
Efficiency and development curves typically follow a two-phase process of first bigger steps corresponding to finding things easier, followed by smaller steps of finding things more difficult. It reflects bursts of learning following breakthroughs that make learning easier followed by meeting constraints that make learning ever harder, perhaps toward a point of cessation.

• Natural Limits One of the key studies in the area concerns diminishing returns on investments generally, either physical or financial, pointing to whole system limits for resource development or other efforts. The most studied of these may be Energy Return on Energy Invested or EROEI, discussed at length in an Encyclopedia of the Earth article and in an OilDrum article and series also referred to as Hubert curves. The energy needed to produce energy is a measure of our difficulty in learning how to make remaining energy resources useful in relation to the effort expended. Energy returns on energy invested have been in continual decline for some time, caused by natural resource limits and increasing investment. Energy is both nature's and our own principal resource for making things happen. The point of diminishing returns is when increasing investment makes the resource more expensive. As natural limits are approached, easily used sources are exhausted and ones with more complications need to be used instead. As an environmental signal persistently dimishing EROI indicates an approach of whole system limits in our ability to make things happen.

• Useful Natural Limits EROEI measures the return on invested effort as a ratio of R/I or learning progress. The inverse I/R measures learning difficulty. The simple difference is that if R approaches zero R/I will too, but I/R will approach infinity. When complications emerge to limit learning progress the limit of useful returns, uR, is approached and R-uR approaches zero. The difficulty of useful learning I/(R-uR) approaches infinity as increasingly difficult tasks make the effort unproductive. That point is approached as a vertical asymptote, at a particular point in time, that can be delayed only by unsustainable effort. It defines a point at which enough investment has been made and the task is done, usually planned to be the same as when the task is complete. For unplanned tasks it may be either foreseen or discovered by surprise. The usefulness measure, uR, is affected by the complexity of environmental responses that can only be measured when they occur unless they are foreseen.

See also

• Experience curve effects
  
• Learning
  
• Learning speed
  
• Labor productivity
  
• Learning-by-doing (disambiguation)
  
• Population growth

References

• ^ Wozniak, R.H. (1999). Introduction to memory: Hermann Ebbinghaus (1885/1913). Classics in the history of psychology
  
• ^ Bills, A.G. (1934). General experimental psychology. Longmans Psychology Series. (pp. 192-215). New York, NY: Longmans, Green and Co.
  
• ^ Wright, T.P., "Factors Affecting the Cost of Airplanes", Journal of Aeronautical Sciences, 3(4) (1936): 122–128.
  
• ^ Connie JG Gersick 1991 "Revolutionary Change Theories: A Multilevel Exploration of the Punctuated Equilibrium Paradigm" The Academy of Management Review, Vol. 16, No. 1 pp. 10-36 1
  
• ^ Y. Kenneth and S. Gerald, "Sparse Representations for Fast, One-Shot Learning". MIT AI Lab Memo 1633, May 1998.
  
• ^ Ritter, F. E., & Schooler, L. J. The learning curve. In International Encyclopedia of the Social and Behavioral Sciences (2002), 8602-8605. Amsterdam: Pergamon
  
• ^ "Laparoscopic Colon Resection Early in the Learning Curve", Ann Surg. 2006 June; 243(6): 730–737, see the "Discussions" section, Dr. Smith's remark about the usage of the term "steep learning curve": "First, semantics. A steep learning curve is one where you gain proficiency over a short number of trials. That means the curve is steep. I think semantically we are really talking about a prolonged or long learning curve. I know it is a subtle distinction, but I can't miss the opportunity to make that point."
  
• ^ "Towards the Limits of Precision and Accuracy in Measurement".

not an emptyquote

[Rannos]

Nekki, look inside yourself. I think you'll find the answer.

[Lemming]

I agree. Preorder cancelled.

[Don Dongington]

Never not vote checkbox

[TheShadowWalker]

kingcom, on 22 January 2013 - 06:09 PM, said:

This is a man of the people, hire him PGI.

TheShadowWalker, what would be your first act as president in chief of PGI?

I would focus all developers on straightening out the net code and cleaning up the current bugs that make the game unplayable for some and, improve the overall experience for everyone by doing so...then i would worry about content additions

[Rannos]

Yeah get those modelers and artists on the net code ASAP!

[Grraarrgghh]

TheShadowWalker, on 22 January 2013 - 06:34 PM, said:

I would focus all developers on straightening out the net code and cleaning up the current bugs that make the game unplayable for some and, improve the overall experience for everyone by doing so...then i would worry about content additions

YOU'RE IN FAR COUNTRY NOW BOY.

[Nekki Basara]

Rannos, on 22 January 2013 - 06:28 PM, said:

Nekki, look inside yourself. I think you'll find the answer.

Squawk?

[Roland]

TheShadowWalker, on 22 January 2013 - 05:22 PM, said:

All i know is, i've played 8 matches in 3 and a half months because of the horrible development of the game. i am massively disappointed. and will NOT return unless they get their heads back on straight.

[TheShadowWalker]

Rannos, on 22 January 2013 - 06:38 PM, said:

Yeah get those modelers and artists on the net code ASAP!

Let's be realistic here, by developers that's geared towards the database and software engineers. as far as the artist... they could do better on the overall designs, paint pallet and c'mon most of the hero mech's paint jobs have been well... disappointing at best, with the exclusion of maybe flame and fang, which i find moderately reasonable.

@roland. This is more about the community and on going issues with the game in previous states and it's current. as well as future. not really myself. I threw the fact that I won't continue to support PGI at this rate as a flag for them, not for you and other members of this community. Quit thinking it's all about you or i, and start thinking about thing's as a whole. You gain a +1 to ignorance.

[Silent]

Fire everyone.

Salt the land.

[Rannos]

TheShadowWalker, on 22 January 2013 - 06:41 PM, said:

Let's be realistic here, by developers that's geared towards the database and software engineers. as far as the artist... they could do better on the overall designs, paint pallet and c'mon most of the hero mech's paint jobs have been well... disappointing at best, with the exclusion of maybe flame and fang, which i find moderately reasonable.

Nah man don't back pedal. You're five year plan to make college students till the fields and care for the livestock will work great. It'll free up resources so that farmers can build the factories. **** experience and expertise, that's just more propaganda from the MAN.