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Showing posts with the label Big Data

Decision Making as Output and Bounded Rationality

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  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
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Algorithmic Black Boxes, Data Scientists and Trade-offs

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There is a thought provoking article published in the Forbes on the directions in data science and their probable implications. Its title is quite catchy ‘How Data Scientists turned against Statistics”. At least on the title, it seems to capture a conflict that seems ongoing between the data scientists resting on their tools and the statistical methods and kit that one has got used to in the days of the small datasets. The article seems to posit a trade-off. The trade-off is the larger the datasets, the lower the statistical rigour by an individual analyst who will instead prefer to use automated tools. The sophisticated construction of statistical methodology and consequent inferences give way to automated construction of data collection methodological, analytical and inferential tools with little control in the hands of the data scientist. In the words of the author, the giant leap into the world of data has been accompanied by a giant leap of faith the outcome of which is there is
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Big Data and Diminishing Returns

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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 the
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