Decision Making as Output and Bounded Rationality

  The classical economics theories proceed on the assumption of rational agents. Rationality implies the economic agents undertake actions or exercise choices based on the cost-benefit analysis they undertake. The assumption further posits that there exists no information asymmetry and thus the agent is aware of all the costs and benefits associated with the choice he or she has exercised. The behavioral school contested the decision stating the decisions in practice are often irrational. Implied there is a continuous departure from rationality. Rationality in the views of the behavioral school is more an exception to the norm rather a rule. The past posts have discussed the limitations of this view by the behavioral school. Economics has often posited rationality in the context in which the choices are exercised rather than theoretical abstract view of rational action. Rational action in theory seems to be grounded in zero restraint situation yet in practice, there are numerous restra

Big Data and Diminishing Returns



In many industries has become the buzzword. Scarcely a discussion seems to happen without touching upon the perceived advantages of big data. Firms seem to outcompeting with each other in collecting reams of data. The question however is the effectiveness of this data. The firm’s outcomes are determined primarily by the utilization of big data rather than collection of data per se. Ferreting out big data is a challenging task.

Although the big data presents a data set that shows 10X or 100X in relation to existing mechanisms, it does not necessarily convey 10X or 100X worth of increase in insight. While the implication of big data is that quantity is paramount, the returns generated do not match the quantity of data generated. Big data too is subject to diminishing returns.

Experts point out, it is not per se the data that should be big, but the primary factor that counts is the diversity of data. Even if datasets may be small, the amount of richness they provide when they are diverse, they have the potential to create an output subject to increasing returns.

As Hal Varian points out, the statistical limit of accuracy with which one can measure data as it increases is the square root of its sample size. To obtain an estimate that is twice as good as current estimates, a firm needs four times more data than the current levels of data.  For a company like Google, the demand side ( query traffic) is growing at around 40% per year ( in other words doubles every 2-2.5 years), the supply side (increasing the relevance of search) is experiencing the law of diminishing returns.  Big data was crucial to Google in the initial years to mine the user behavior and the links used but the passage of time necessitated better algorithms to mine the data than data alone per se

Source: Nicholas Carr, “The diminishing returns on data”, Posted August 14, 2009, Accessed on March 31, 2012, http://www.roughtype.com/archives/2009/08/the_diminishing.php; In Defense Of Small Data. By: Upbin, Bruce, Forbes.com, 3/30/2012; Nathan Eddy, “Diving into Data Pool Demands Solid Business Strategy”, Posted April 12, 2012, Accessed April 15, 2012, http://slashdot.org/topic/bi/diving-into-the-data-pool-business-strategy/;

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