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Wednesday, 23 October 2013

Currency Speculation



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"Speculator" redirects here. For the Montana mining incident, see Speculator Mine disaster.
This article is about the financial term. For other uses, see Speculation (disambiguation).
Speculation is the practice of engaging in risky financial transactions in an attempt to profit from short or medium term fluctuations in the market value of a tradable good such as a financial instrument, rather than attempting to profit from the underlying financial attributes embodied in the instrument such as capital gains, interest, or dividends. Many speculators pay little attention to the fundamental value of a security and instead focus purely on price movements. Speculation can in principle involve any tradable good or financial instrument. Speculators are particularly common in the markets for stocks, bonds, commodity futures, currencies, fine art, collectibles, real estate, and derivatives.
Speculators play one of four primary roles in financial markets, along with hedgers who engage in transactions to offset some other pre-existing risk, arbitrageurs who seek to profit from situations where fungible instruments trade at different prices in different market segments, and investors who seek profit through long-term ownership of an instrument's underlying attributes. The role of speculators is to absorb excess risk that other participants do not want, and to provide liquidity in the marketplace by buying or selling when no participants from the other categories are available. Successful speculation entails collecting an adequate level of monetary compensation in return for providing immediate liquidity and assuming additional risk so that, over time, the inevitable losses are offset by larger profits.

History[edit]

With the appearance of the stock ticker machine in 1867, which abrogated the need for traders to be physically present on the floor of a stock exchange, stock speculation underwent a dramatic expansion through to the end of the 1920s, the number of shareholders increasing, perhaps, from 4.4 million in 1900 to 26 million in 1932.[1]

Speculation and investment[edit]

The view of what distinguishes investment from speculation and speculation from excessive speculation varies widely among pundits, legislators and academics. Some sources note that speculation is simply a higher risk form of investment. Others define speculation more narrowly as positions not characterized as hedging.[2] The U.S. Commodity Futures Trading Commission defines a speculator as "a trader who does not hedge, but who trades with the objective of achieving profits through the successful anticipation of price movements."[3] The agency emphasizes that speculators serve important market functions, but defines excessive speculation as harmful to the proper functioning of futures markets.[4]
According to Ben Graham in Intelligent Investor, the prototypical defensive investor is "...one interested chiefly in safety plus freedom from bother." He admits, however, that "...some speculation is necessary and unavoidable, for in many common-stock situations, there are substantial possibilities of both profit and loss, and the risks therein must be assumed by someone." Thus, many long-term investors, even those who buy and hold for decades, may be classified as speculators, excepting only the rare few who are primarily motivated by income or safety of principal and not eventually selling at a profit.[5]

The economic benefits of speculation[edit]

Sustainable consumption level[edit]


Speculation usually involves more risks than investment.
The speculator Victor Niederhoffer, in "The Speculator as Hero"[6] describes the benefits of speculation:
Let's consider some of the principles that explain the causes of shortages and surpluses and the role of speculators. When a harvest is too small to satisfy consumption at its normal rate, speculators come in, hoping to profit from the scarcity by buying. Their purchases raise the price, thereby checking consumption so that the smaller supply will last longer. Producers encouraged by the high price further lessen the shortage by growing or importing to reduce the shortage. On the other side, when the price is higher than the speculators think the facts warrant, they sell. This reduces prices, encouraging consumption and exports and helping to reduce the surplus.
Another service provided by speculators to a market is that by risking their own capital in the hope of profit, they add liquidity to the market and make it easier or even possible for others to offset risk, including those who may be classified as hedgers and arbitrageurs.

Market liquidity and efficiency[edit]

If any particular market—for example, pork bellies—had no speculators, then only producers (the hog farmers) and consumers (butchers, etc.) would participate in that market. With fewer players in the market, there would be a larger spread between the current bid and ask price of pork bellies. Any new entrant in the market who wanted to trade pork bellies would be forced to accept an illiquid market, may trade at market prices with large bid-ask spreads, or even face difficulty finding a co-party to buy or sell to.
A commodity speculator may exploit the difference in the spread and, in competition with other speculators, reduce the spread. Some schools of thought argue that speculators increase the liquidity in a market, and therefore promote an efficient market.[citation needed] But it is also true that, as more and more speculators participate in a market, underlying real demand and supply can become diminishingly small compared to trading volume, and prices can become distorted.[citation needed]

Bearing risks[edit]

Speculators also perform a very important risk bearing role that is beneficial to society. For example, a farmer might be considering planting corn on some unused farmland. Alas, he might not want to do so because he is concerned that the price might fall too far by harvest time. By selling his crop in advance at a fixed price to a speculator, the farmer can hedge the price risk and is now willing to plant the corn. Thus, speculators can actually increase production through their willingness to take on risk.

Finding environmental and other risks[edit]

Hedge funds that do fundamental analysis "are far more likely than other investors to try to identify a firm’s off-balance-sheet exposures", including "environmental or social liabilities present in a market or company but not explicitly accounted for in traditional numeric valuation or mainstream investor analysis", and hence make the prices better reflect the true quality of operation of the firms.[7]

Shorting[edit]

Shorting may act as a "canary in a coal mine" to stop unsustainable practices earlier and thus reduce damages and forming market bubbles.[7]

The economic disadvantages of speculation[edit]

Winner's Curse[edit]

Auctions are a method of squeezing out speculators from a transaction, but they may have their own perverse effects; see winner's curse. The winner's curse is however not very significant to markets with high liquidity for both buyers and sellers, as the auction for selling the product and the auction for buying the product occur simultaneously, and the two prices are separated only by a relatively small spread. This mechanism prevents the winner's curse phenomenon from causing mispricing to any degree greater than the spread.[citation needed]

Economic Bubbles[edit]

Speculation is often associated with economic bubbles. A bubble occurs when the price for an asset exceeds its intrinsic value by a significant margin.[8] Although not all bubbles occur because of speculation.[9] Speculative bubbles are characterized by rapid market expansion driven by word-of-mouth feedback loops as initial rises in asset price attract new buyers and generate further inflation.[10] The creation of the bubble is followed by a precipitous collapse fueled by the same phenomenon.[8][11] Speculative bubbles are essentially social epidemics whose contagion is mediated by the structure of the market.[11] Some economists link asset price movements within a bubble to fundamental economic factors such as cash flows and discount rates.[12]
In 1936 John Maynard Keynes wrote: "Speculators may do no harm as bubbles on a steady stream of enterprise. But the situation is serious when enterprise becomes the bubble on a whirlpool of speculation. (1936:159)"[13] Mr Keynes himself enjoyed speculation to the fullest, running an early precursor of a hedge fund. As the Bursar of the Cambridge University King's College, he managed two investment funds, one of which, called Chest Fund, invested not only in the then 'emerging' market US stocks, but also periodically included commodity futures and foreign currencies, albeit to a smaller extent (see Chua and Woodward, 1983) . His fund achieved positive returns in almost every year, averaging 13% p.a., even during the Great Depression, thanks to very modern investment strategies, which included inter-market diversification (i.e., invested not only in stocks but also commodities and currencies) as well as shorting, i.e., selling borrowed stocks or futures to make money on falling prices, which Keynes advocated among the principles of successful investment in his 1933 report ("a balanced investment position [...] and if possible, opposed risks.") [14]

Volatility[edit]

According to Ziemba and Ziemba (2007), Keynes risk-taking reached 'cowboy' proportions, i.e. 80% of the maximum rationally justifiable levels (of the so-called Kelly criterion), with overall return volatility approximately three times higher than the stock market index benchmark. Such levels of volatility, responsible for his spectacular investment performance, would be achievable today only through the most aggressive instruments (such as 3:1 leveraged exchange-traded funds). He chose modern speculation techniques practiced today by hedge funds, which are quite different from the simple buy-and-hold long-term investing.[15]
It is a controversial point whether the presence of speculators increases or decreases the short-term volatility in a market. Their provision of capital and information may help stabilize prices closer to their true values. On the other hand, crowd behavior and positive feedback loops in market participants may also increase volatility at times.

Government responses and regulation[edit]

The economic disadvantages of speculators has resulted in a number of attempts over the years to introduce regulations and restrictions to try and limit or reduce the impact of speculators. Such financial regulation is often enacted in response to a crisis as was the case with the Bubble Act 1720 which was passed by the British government at the height of the South Sea Bubble to try stop speculation in such schemes. This act was left in place for over a hundred years and was repealed in 1825. Another example was the Glass–Steagall legislation passed in 1933 during the Great Depression in the United States, most of the Glass-Steagall provisions were repealed during the 1980s and 1990s. The Onion Futures Act bans the trading of futures contracts on onions in the United States, after speculators successfully cornered the market in the mid-1950s; it remains in effect as of 2013.

Food security[edit]

Some nations have moved to limit foreign ownership of cropland in order to ensure that food is available for local consumption while others have sold food land and depend on the World Food Programme.[16]
In 1935 the Indian government passed a law allowing the government to in part restrict and directly control food production (Defence of India Act, 1935). This included the ability to restrict or ban the trading in derivatives on those food commodities. Post independence, in the 1950s, India continued to struggle with feeding its population and the government increasingly restricting trading in food commodities. Just at the time the Forward Markets Commission (India) was established, the government felt that derivative markets increased speculation which led to increased costs and price instabilities. And in 1953 finally prohibited options and futures trading altogether.[17] These restrictions were not lifted until the 1980s.

Regulations[edit]

In the US following passage of the Dodd-Frank Wall Street Reform and Consumer Protection Act, the U.S., Commodity Futures Trading Commission (CFTC) has proposed regulations aimed at limiting speculation in futures markets by instituting position limits. The CFTC offers three basic elements for their regulatory framework: "the size (or levels) of the limits themselves; the exemptions from the limits (for example, hedged positions) and; the policy on aggregating accounts for purposes of applying the limits."[18] The proposed position limits apply to 28 physical commodities traded in various exchanges across the U.S.[19]
Another part of the Dodd-Frank Act established the Volcker Rule which deals with speculative investments of banks that don't benefit their customers. The Volcker Rule passed on 21 January 2010 states that these investments played a key role in the financial crisis of 2007–2010.[20]

Proposals[edit]

See also: Speculative attack, Currency crisis, Black Wednesday, Fictitious capital, Financial transaction tax, Currency transaction tax, Tobin tax, and Spahn tax
A number of proposals have been made in the past to try and limit speculation that were never enacted, these have included:
  • The Tobin tax is a tax intended to reduce short-term currency speculation, ostensibly to stabilize foreign exchange.
  • In May 2008 German leaders planned to propose a worldwide ban on oil trading by speculators, blaming the 2008 oil price rises on manipulation by hedge funds.[21]

Books[edit]

See also[edit]

Wikiquote has a collection of quotations related to: Speculation

References[edit]

Notes
  1. Jump up ^ Stäheli 2013, p. 4.
  2. Jump up ^ Szado, Edward (2011). "Defining Speculation: The First Step toward a Rational Dialogue". The Journal of Alternative Investments. CAIA Association. 
  3. Jump up ^ "CFTC Glossary: A guide to the language of the futures industry". cftc.gov. Commodity Futures Trading Commission. Retrieved 28 August 2012. 
  4. Jump up ^ "Staff Report on Commodity Swap Dealers & Index Traders with Commission Recommendations". U.S. Commodity Futures Trading Commission. 2008. Retrieved 27 August 2012. 
  5. Jump up ^ Graham, Benjamin (1973). Intelligent Investor. HarperCollins Books. ISBN 0-06-055566-1.
  6. Jump up ^ Victor Niederhoffer, The Wall Street Journal, 10 February 1989 Daily Speculations
  7. ^ Jump up to: a b Unlikely heroes - Can hedge funds save the world? One pundit thinks so, The Economist, 16 February 2010
  8. ^ Jump up to: a b Hollander, Barbara Gottfried (2011). Booms, Bubbles, & Busts (The Global Marketplace). Heinemann Library. pp. 40–41. ISBN 1432954776. 
  9. Jump up ^ Lei, Noussair & Plott 2001, p. 831: "In a setting in which speculation is not possible, bubbles and crashes are observed. The results suggest that the departures from fundamental values are not caused by the lack of common knowledge of rationality leading to speculation, but rather by behavior that itself exhibits elements of irrationality."
  10. Jump up ^ Rosser, J. Barkley (2000). From Catastrophe to Chaos: A General Theory of Economic Discontinuities: Mathematics, Microeconomics, Macroeconomics, and Finance. p. 107. 
  11. ^ Jump up to: a b Shiller, Robert J. (23 July 2012). "Bubbles without Markets". Retrieved 29 August 2012. 
  12. Jump up ^ Siegel, Journal (2003). "What Is an Asset Price Bubble? An Operation Definition". European Financial Management 9 (1): 11–24. 
  13. Jump up ^ Dr. Stephen Spratt of Intelligence Capital (September 2006). "A Sterling Solution". Stamp Out Poverty report. Stamp Out Poverty Campaign. p. 15. Retrieved 2 January 2010. 
  14. Jump up ^ Chua, J. H. and R. S. Woodward (1983). The Investment Wizardry of J.M. Keynes. Financial Analysts Journal 39 (3). pp. 35–37. JSTOR 4478643. 
  15. Jump up ^ Ziemba, Rachel and William Ziemba (2007). "Good and Bad properties of the Kelly criterion". John Wiley & Sons. pp. 29–31. Retrieved 26 January 2010. 
  16. Jump up ^ Valente, Marcela. "Curbing foreign ownership of farmland." IPS, 22 May 2011.
  17. Jump up ^ Frida Youssef (October 2000). "Integrated report on Commodity Exchanges And Forward Market Commission (FMC)". FMC. 
  18. Jump up ^ "Speculative Limits". U.S. Commodity Futures Trading Commission. Retrieved 21 August 2012. 
  19. Jump up ^ "CFTC Approves Notice of Proposed Rulemaking Regarding Regulations on Aggregation for Position Limits for Futures and Swaps". U.S. Commodity Futures Trading Commission. Retrieved 21 August 2012. 
  20. Jump up ^ David Cho, and Binyamin Appelbaum (22 January 22). "Obama's 'Volcker Rule' shifts power away from Geithner". The Washington Post. Retrieved 13 February 2010. 
  21. Jump up ^ Evans-Pritchard, Ambrose (26 May 2008). "Germany in call for ban on oil speculation". The Daily Telegraph (The Daily Telegraph). Retrieved 28 May 2008. 
Bibliography
  • Lei, Vivian; Noussair, Charles N.; Plott, Charles R. (2001). "Nonspeculative Bubbles in Experimental Asset Markets: Lack of Common Knowledge of Rationality Vs. Actual Irrationality". Econometrica 69 (4): 831–859. JSTOR 2692246. 
  • Stäheli, Urs (2013). Spectacular Speculation: Thrills, the Economy, and Popular Discourse. Stanford, CA: Stanford University Press. ISBN 978-0-804-77131-3. 

External links[edit]

Look up speculation in Wiktionary, the free dictionary.

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Wednesday, 20 March 2013

System dynamics

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Dynamic stock and flow diagram of model New product adoption (model from article by John Sterman 2001)
System dynamics is an approach to understanding the behaviour of complex systems over time. It deals with internal feedback loops and time delays that affect the behaviour of the entire system.[1] What makes using system dynamics different from other approaches to studying complex systems is the use of feedback loops and stocks and flows. These elements help describe how even seemingly simple systems display baffling nonlinearity.

Contents

 [hide] 

[edit] Overview

System Dynamics (SD) is a methodology and mathematical modeling technique for framing, understanding, and discussing complex issues and problems. Originally developed in the 1950s to help corporate managers improve their understanding of industrial processes, system dynamics is currently being used throughout the public and private sector for policy analysis and design.[2]
Convenient GUI system dynamics software developed into user friendly versions by the 1990s and have been applied to diverse systems. SD models solve the problem of simultaneity (mutual causation) by updating all variables in small time increments with positive and negative feedbacks and time delays structuring the interactions and control. The best known SD model is probably the 1972 The Limits to Growth. This model forecast that exponential growth would lead to economic collapse during the 21st century under a wide variety of growth scenarios.
System Dynamics is an aspect of systems theory as a method for understanding the dynamic behavior of complex systems. The basis of the method is the recognition that the structure of any system — the many circular, interlocking, sometimes time-delayed relationships among its components — is often just as important in determining its behavior as the individual components themselves. Examples are chaos theory and social dynamics. It is also claimed that because there are often properties-of-the-whole which cannot be found among the properties-of-the-elements, in some cases the behavior of the whole cannot be explained in terms of the behavior of the parts.

[edit] History

System dynamics was created during the mid-1950s[3] by Professor Jay Forrester of the Massachusetts Institute of Technology. In 1956, Forrester accepted a professorship in the newly-formed MIT Sloan School of Management. His initial goal was to determine how his background in science and engineering could be brought to bear, in some useful way, on the core issues that determine the success or failure of corporations. Forrester's insights into the common foundations that underlie engineering, which led to the creation of system dynamics, were triggered, to a large degree, by his involvement with managers at General Electric (GE) during the mid-1950s. At that time, the managers at GE were perplexed because employment at their appliance plants in Kentucky exhibited a significant three-year cycle. The business cycle was judged to be an insufficient explanation for the employment instability. From hand simulations (or calculations) of the stock-flow-feedback structure of the GE plants, which included the existing corporate decision-making structure for hiring and layoffs, Forrester was able to show how the instability in GE employment was due to the internal structure of the firm and not to an external force such as the business cycle. These hand simulations were the beginning of the field of system dynamics.[2]
During the late 1950s and early 1960s, Forrester and a team of graduate students moved the emerging field of system dynamics from the hand-simulation stage to the formal computer modeling stage. Richard Bennett created the first system dynamics computer modeling language called SIMPLE (Simulation of Industrial Management Problems with Lots of Equations) in the spring of 1958. In 1959, Phyllis Fox and Alexander Pugh wrote the first version of DYNAMO (DYNAmic MOdels), an improved version of SIMPLE, and the system dynamics language became the industry standard for over thirty years. Forrester published the first, and still classic, book in the field titled Industrial Dynamics in 1961.[2]
From the late 1950s to the late 1960s, system dynamics was applied almost exclusively to corporate/managerial problems. In 1968, however, an unexpected occurrence caused the field to broaden beyond corporate modeling. John Collins, the former mayor of Boston, was appointed a visiting professor of Urban Affairs at MIT. The result of the Collins-Forrester collaboration was a book titled Urban Dynamics. The Urban Dynamics model presented in the book was the first major non-corporate application of system dynamics.[2]
The second major noncorporate application of system dynamics came shortly after the first. In 1970, Jay Forrester was invited by the Club of Rome to a meeting in Bern, Switzerland. The Club of Rome is an organization devoted to solving what its members describe as the "predicament of mankind"—that is, the global crisis that may appear sometime in the future, due to the demands being placed on the Earth's carrying capacity (its sources of renewable and nonrenewable resources and its sinks for the disposal of pollutants) by the world's exponentially growing population. At the Bern meeting, Forrester was asked if system dynamics could be used to address the predicament of mankind. His answer, of course, was that it could. On the plane back from the Bern meeting, Forrester created the first draft of a system dynamics model of the world's socioeconomic system. He called this model WORLD1. Upon his return to the United States, Forrester refined WORLD1 in preparation for a visit to MIT by members of the Club of Rome. Forrester called the refined version of the model WORLD2. Forrester published WORLD2 in a book titled World Dynamics.[2]

[edit] Topics in systems dynamics

The elements of system dynamics diagrams are feedback, accumulation of flows into stocks and time delays.
As an illustration of the use of system dynamics, imagine an organisation that plans to introduce an innovative new durable consumer product. The organisation needs to understand the possible market dynamics in order to design marketing and production plans.

[edit] Causal loop diagrams

Main article: Causal loop diagram
In the System Dynamics methodology, a problem or a system (e.g., ecosystem, political system or mechanical system) is first represented as a causal loop diagram.[4] A causal loop diagram is a simple map of a system with all its constituent components and their interactions. By capturing interactions and consequently the feedback loops (see figure below), a causal loop diagram reveals the structure of a system. By understanding the structure of a system, it becomes possible to ascertain a system’s behavior over a certain time period.[5]
The causal loop diagram of the new product introduction may look as follows:
Causal loop diagram of New product adoption model
There are two feedback loops in this diagram. The positive reinforcement (labeled R) loop on the right indicates that the more people have already adopted the new product, the stronger the word-of-mouth impact. There will be more references to the product, more demonstrations, and more reviews. This positive feedback should generate sales that continue to grow.
The second feedback loop on the left is negative reinforcement (or "balancing" and hence labeled B). Clearly growth can not continue forever, because as more and more people adopt, there remain fewer and fewer potential adopters.
Both feedback loops act simultaneously, but at different times they may have different strengths. Thus one would expect growing sales in the initial years, and then declining sales in the later years.
Causal loop diagram of New product adoption model with nodes values after calculus
In this dynamic causal loop diagram :
  • step1 : (+) green arrows show that Adoption rate is function of Potential Adopters and Adopters
  • step2 : (-) red arrow shows that Potential adopters decreases by Adoption rate
  • step3 : (+) blue arrow shows that Adopters increases by Adoption rate

[edit] Stock and flow diagrams

Main article: Stock and flow
Causal loop diagrams aid in visualizing a system’s structure and behavior, and analyzing the system qualitatively. To perform a more detailed quantitative analysis, a causal loop diagram is transformed to a stock and flow diagram. A Stock and flow model helps in studying and analyzing the system in a quantitative way, such models are usually built and simulated using computer software.
A stock is the term for any entity that accumulates or depletes over time. A flow is the rate of change in a stock.
A flow is the rate of accumulation of the stock
In our example, there are two stocks: Potential adopters and Adopters. There is one flow: New adopters. For every new adopter, the stock of potential adopters declines by one, and the stock of adopters increases by one.
Stock and flow diagram of New product adoption model

[edit] Equations

The real power of system dynamics is utilised through simulation. Although it is possible to perform the modeling in a spreadsheet, there are a variety of software packages that have been optimised for this.
The steps involved in a simulation are:
  • Define the problem boundary
  • Identify the most important stocks and flows that change these stock levels
  • Identify sources of information that impact the flows
  • Identify the main feedback loops
  • Draw a causal loop diagram that links the stocks, flows and sources of information
  • Write the equations that determine the flows
  • Estimate the parameters and initial conditions. These can be estimated using statistical methods, expert opinion, market research data or other relevant sources of information.[6]
  • Simulate the model and analyse results.
In this example, the equations that change the two stocks via the flow are:
 \ \mbox{Potential adopters} = \int_{0} ^{t} \mbox{-New adopters }\,dt  \ \mbox{Adopters} = \int_{0} ^{t} \mbox{New adopters }\,dt

[edit] Equations in discrete time

List of all the equations in discrete time, in their order of execution in each year, for years 1 to 15 :
1) \ \mbox{Probability that contact has not yet adopted}=\mbox{Potential adopters} / (\mbox{Potential adopters } + \mbox{ Adopters}) 2) \ \mbox{Imitators}=q \cdot \mbox{Adopters} \cdot \mbox{Probability that contact has not yet adopted}3) \ \mbox{Innovators}=p \cdot \mbox{Potential adopters} 4) \ \mbox{New adopters}=\mbox{Innovators}+\mbox{Imitators} 4.1) \ \mbox{Potential adopters}\ -= \mbox{New adopters }\ 4.2) \ \mbox{Adopters}\ += \mbox{New adopters }\ 
\ p=0.03 \ q=0.4

[edit] Dynamic simulation results

The dynamic simulation results show that the behaviour of the system would be to have growth in adopters that follows a classical s-curve shape.
The increase in adopters is very slow initially, then exponential growth for a period, followed ultimately by saturation.
Dynamic stock and flow diagram of New product adoption model
Stocks and flows values for years = 0 to 15

[edit] Equations in continuous time

To get intermediate values and better accuracy, the model can run in continuous time : we multiply the number of units of time and we proportionally divide values that change stock levels. In this example we multiply the 15 years by 4 to obtain 60 trimesters, and we divide the value of the flow by 4.
Dividing the value is the simplest with the Euler method, but other methods could be imployed instad, such as Runge–Kutta methods.
List of the equations in continuous time for trimesters = 1 to 60 :
  • They are the same equations as in the section Equation in discrete time above, except equations 4.1 and 4.2 replaced by following :
10) \ \mbox{Valve New adopters}\ = \mbox{New adopters} \cdot TimeStep 10.1) \ \mbox{Potential adopters}\ -= \mbox{Valve New adopters} 10.2) \ \mbox{Adopters}\ += \mbox{Valve New adopters } 
 \ TimeStep = 1/4
  • In the below stock and flow diagram, the intermediate flow 'Valve New adopters' calculates the equation :
 \ \mbox{Valve New adopters}\ = \mbox{New adopters } \cdot TimeStep
Dynamic stock and flow diagram of New product adoption model in continuous time

[edit] Application

System dynamics has found application in a wide range of areas, for example population, ecological and economic systems, which usually interact strongly with each other.
System dynamics have various "back of the envelope" management applications. They are a potent tool to:
  • Teach system thinking reflexes to persons being coached
  • Analyze and compare assumptions and mental models about the way things work
  • Gain qualitative insight into the workings of a system or the consequences of a decision
  • Recognize archetypes of dysfunctional systems in everyday practice
Computer software is used to simulate a system dynamics model of the situation being studied. Running "what if" simulations to test certain policies on such a model can greatly aid in understanding how the system changes over time. System dynamics is very similar to systems thinking and constructs the same causal loop diagrams of systems with feedback. However, system dynamics typically goes further and utilises simulation to study the behaviour of systems and the impact of alternative policies.[7]
System dynamics has been used to investigate resource dependencies, and resulting problems, in product development.[8][9]

[edit] Example

Causal loop diagram of a model examining the growth or decline of a life insurance company.[10]
The figure above is a causal loop diagram of a system dynamics model created to examine forces that may be responsible for the growth or decline of life insurance companies in the United Kingdom. A number of this figure's features are worth mentioning. The first is that the model's negative feedback loops are identified by "C's," which stand for "Counteracting" loops. The second is that double slashes are used to indicate places where there is a significant delay between causes (i.e., variables at the tails of arrows) and effects (i.e., variables at the heads of arrows). This is a common causal loop diagramming convention in system dynamics. Third, is that thicker lines are used to identify the feedback loops and links that author wishes the audience to focus on. This is also a common system dynamics diagramming convention. Last, it is clear that a decision maker would find it impossible to think through the dynamic behavior inherent in the model, from inspection of the figure alone.[10]

[edit] Example of piston motion

  • 1.Objective : study of a crank-connecting rod system.
We want to model a crank-connecting rod system through a system dynamic model. Two different full descriptions of the physical system with related systems of equations can be found hereafter (English) and hereafter (French) : they give the same results. In this example, the crank, with variable radius and angular frequency, will drive a piston with a variable connecting rod length.
  • 2.System dynamic modeling : the system is now modelled, according to a stock and flow system dynamic logic.
Below figure shows stock and flow diagram :
Stock and flow diagram for crank-connecting rod system dynamic
  • 3.Simulation : the behavior of the crank-connecting rod dynamic system can then be simulated.
Next figure is a 3D simulation, created using the Procedural animation technic. Variables of the model animate all parts of this animation : crank, radius, angular frequency, rod length, piston position.
3D Procedural animation of the crank-connecting rod system modeled in 2

[edit] See also

[edit] References

  1. ^ MIT System Dynamics in Education Project (SDEP)
  2. ^ a b c d e Michael J. Radzicki and Robert A. Taylor (2008). "Origin of System Dynamics: Jay W. Forrester and the History of System Dynamics". In: U.S. Department of Energy's Introduction to System Dynamics. Retrieved 23 Oktober 2008.
  3. ^ Forrester, Jay (1971). Counterintuitive behavior of social systems. Technology Review 73(3): 52–68
  4. ^ Sterman, John D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. New York: McGraw
  5. ^ Meadows, Donella. (2008). Thinking in Systems: A Primer. Earthscan
  6. ^ Sterman, John D. (2001). "System dynamics modeling: Tools for learning in a complex world". California management review 43 (4): 8–25.
  7. ^ System Dynamics Society
  8. ^ Repenning, Nelson P. (2001). "Understanding fire fighting in new product development". The Journal of Product Innovation Management 18 (5): 285–300. doi:10.1016/S0737-6782(01)00099-6.
  9. ^ Nelson P. Repenning (1999). Resource dependence in product development improvement efforts, Massachusetts Institute of Technology Sloan School of Management Department of Operations Management/System Dynamics Group, dec 1999.
  10. ^ a b Michael J. Radzicki and Robert A. Taylor (2008). "Feedback". In: U.S. Department of Energy's Introduction to System Dynamics. Retrieved 23 October 2008.

[edit] Further reading

[edit] External links

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