God does not play with the dice. Kamal Haasan-starrer Dasavathaaram movie is making waves. The basic concept behind the movie is Chaos Theory and application of Chaos Theory.
In mathematics, chaos theory describes the behavior of certain dynamical systems. That is, systems whose state evolves with time – that may exhibit dynamics that are highly sensitive to initial conditions (popularly referred to as the butterfly effect). As a result of this sensitivity, which manifests itself as an exponential growth of perturbations in the initial conditions, the behavior of chaotic systems appears to be random. This happens even though these systems are deterministic, meaning that their future dynamics are fully defined by their initial conditions, with no random elements involved. This behavior is known as deterministic chaos, or simply chaos.
Chaotic behaviour is also observed in natural systems, such as the weather. This may be explained by a chaos-theoretical analysis of a mathematical model of such a system, embodying the laws of physics that are relevant for the natural system.
Chaotic behavior has been observed in the laboratory in a variety of systems including electrical circuits, lasers, oscillating chemical reactions, fluid dynamics, and mechanical and magneto-mechanical devices. Observations of chaotic behaviour in nature include the dynamics of satellites in the solar system, the time evolution of the magnetic field of celestial bodies, population growth in ecology, the dynamics of the action potentials in neurons, and molecular vibrations. Every day examples of chaotic systems include weather and climate. There is some controversy over the existence of chaotic dynamics in the plate tectonics and in economics.
Systems that exhibit mathematical chaos are deterministic and thus orderly in some sense; this technical use of the word chaos is at odds with common parlance, which suggests complete disorder. A related field of physics called quantum chaos theory studies systems that follow the laws of quantum mechanics. Recently, another field, called relativistic chaos,has emerged to describe systems that follow the laws of general relativity.
As well as being orderly in the sense of being deterministic, chaotic systems usually have well defined statistics.For example, the Lorenz system pictured is chaotic, but has a clearly defined structure. Bounded chaos is a useful term for describing models of disorder
Chaos theory is applied in many scientific disciplines: mathematics, biology, computer science, economics, engineering, finance, philosophy, physics, politics, population dynamics, psychology, and robotics.
One of the most successful applications of chaos theory has been in ecology, where dynamical systems such as the Ricker model have been used to show how population growth under density dependence can lead to chaotic dynamics.
Chaos theory is also currently being applied to medical studies of epilepsy, specifically to the prediction of seemingly random seizures by observing initial conditions.
Coming to Derivatives markets and more specifically our commodity futures markets,
Network effect Network Externalities and the two sided Network : The macro economic purpose and existence of derivatives markets. Network effect is a term used narrowly to describe business phenomena, or more broadly to describe non-business phenomena.
In the narrow usage, a network effect is a characteristic that causes a good or service to have a value to a potential customer which depends on the number of other customers who own the good or are users of the service. In other words, the number of prior adopters is a term in the value available to the next adopter.
One consequence of a network effect is that the purchase of a good by one individual indirectly benefits others who own the good — for example by purchasing a telephone a person makes other telephones more useful. This type of side-effect in a transaction is known as an externality in economics, and externalities arising from network effects are known as network externalities. The resulting bandwagon effect is an example of a positive feedback loop.
Stock exchanges and derivatives exchanges feature a network effect. Market liquidity is a major determinant of transaction cost in the sale or purchase of a security, as a bid-ask spread exists between the price at which a purchase can be done versus the price at which the sale of the same security can be done. As the number of buyers and sellers on an exchange increases, liquidity increases, and transaction costs decrease. This then attracts a larger number of buyers and sellers to the exchange.
The network advantage of financial exchanges is apparent in the difficulty that startup exchanges have in dislodging a dominant exchange. For example, the Chicago Board of Trade has retained overwhelming dominance of trading in US Treasury Bond futures despite the startup of Eurex US trading of identical futures contracts. Similarly, the Chicago Mercantile Exchange has maintained a dominance in trading of Eurobond interest rate futures despite a challenge from Euronext.Liffe.
There are two kinds of economic value to be concerned about when thinking of network effects:
Inherent — my value from me using the product
Network — my value from you using the product
Network value itself can be direct or indirect.
Direct network value is an immediate result of other users adopting the same system. Some examples of this are fax machines and email.
Indirect is a secondary result of many people using the same system. For example, complementary goods are cheaper or more available when many people adopt a standard. Toner may be cheaper for widely used printers.
Negative and positive network effectsPositive network effects are obvious. More people means more interaction.Negative network effects beyond lock-in also exist.
Negative network effects result from resource limits. Consider the connection that overloads the freeway or trading portals — or the competition for bandwidth. In fact, the automobile and ethernet congestion examples illustrate that there can be threshold limits. In this case, the n+1 person begins to decrease the value of a network if additional resources are not provided.The result is that in some networks there is an exclusion value. This is clear to anyone who has considered problems of authentication or trust on the modern internet.
Another negative network effect is provider complacency. The absence of viable competitors in a successful network can cause a provider to restrict resources, consider fee increases, monopolistic tantrums or otherwise create an environment contrary to the end as well as intermediary users’ benefit.
These situations are typically accompanied by vocal complaints from the users. (In a competitive environment the users would simply change vendors rather than complain.)
Classic examples are the United States Postal Service or telephone companies during the 1960s and 1970s. More recent examples include the National Stock Exchange of India, Microsoft’s operating system and Ebay’s auction site.
Two-sided markets, also called two-sided networks, are economic networks having two distinct user groups that provide each other with network benefits.
Example markets include our commodity derivatives markets comprised of commercials(hedgers,producers,supply lines or people who have an inherent commercial implication of the markets) and non commercials(investors,speculators,traders and arbitrageurs , who are there only for a profit motive),credit cards, comprised of cardholders and merchants; HMOs (patients and doctors); operating systems (end-users and developers), travel reservation services (travelers and airlines); video games (gamers and game developers); and communication networks, such as the Internet. Benefits to each group exhibit demand economies of scale. Consumers, for example, prefer credit cards honored by more merchants, while merchants prefer cards carried by more consumers.
Structural Characteristics
In some networks, users are homogeneous, that is, they all perform similar functions. For example, although participants in a telephone network originate and receive calls, these roles are transient. Almost all phone users play both roles at different times. Likewise, almost all instant messaging, FAX, and email users both. Networks with homogenous users are called one-sided to distinguish them from two-sided networks, which have two distinct user groups whose respective members consistently play the same role in transactions.
In a two-sided network, members of each group exhibit a preference regarding the number of users in the other group; these are called cross-side network effects. Each group’s members may also have preferences regarding the number of users in their own group; these are called same-side network effects. Cross-side network effects are usually positive, but can be negative (as with consumer reactions to advertising or with non commercial in derivatives markets losing money on zero sum transactions).
Same-side network effects may be either positive (e.g., the benefit from swapping video games with more peers) or negative (e.g., the desire to exclude direct rivals from an online business-to-business marketplace). Figure 1 depicts these relationships.
In two-sided networks, users on each side typically require very different functionality from their common platform. In derivatives markets for example the commercial user are participating in order to reduce is inherent risk in his actual business whereas the non commercial has only the motive to profit out of the same.
Other examples include credit card networks, for example, consumers require a unique account, a plastic card, access to phone-based customer service, a monthly bill, etc. Merchants require terminals for authorizing transactions, procedures for submitting charges and receiving payment, “signage” (decals that show the card is accepted), etc. Given these different requirements, platform providers may specialize in serving users on just one side of a two-sided network.
Srinivasan Venkataraghavan is Chief Executive Officer, Altos Advisory Services
