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The analytics of social compatibility

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India is the land of arranged marriages and the protagonist story had recently met five prospective brides who were equally eligible and evenly matched. All the girls have at least one unique attribute which he wanted his spouse to posses and each of these attribute was equally important to him; one of them was very beautiful; another was highly educated; another had a good sense of humour and so on. It was difficult for him to choose one girl over another. The protagonist met a data scientist, to discuss his problem of choices.

Data Scientist: This is similar to social choice theory, a framework for weighting individual interests, values, or welfares as an aggregate towards collective decision using symbolic logic. Let’s make an algorithm to evaluate the social compatibility between people. Then we will use it to find your best match form your prospective brides.

Protagonist: Why bother about evaluating social compatibility?

DS: Do you realize its immense business potential? The top two Indian matrimonial sites draw 2 million visitors accessing over 15 million pages daily. If these two websites implement the algorithm then assuming that only 5% of the visitors actually use it, you have a 100,000 daily user in India alone. In the future, if the top matrimonial and dating websites across world implement the algorithm then you can hit half a million daily users. On a pay per use or fixed month rate revenue model, look at the expected income.

P: So how can we quantify compatibility?

DS: A simple approach is to rank the girls in order of each attribute and then combine the individual ranks into a composite rank using the known methods of combining ranks. The top composite ranked girl is your first choice.

DS: Unfortunately a ranking based approach is conceptually flawed. Economics Nobel Laureate Dr. Kenneth Arrow proved the Arrow’s Impossibility Theorem, a pioneering theorem of social choice theory, which states that no rank-order voting system satisfies all fairness criterions. Moreover for critical social decisions psychology could prevail over statistics. The ideal methodology should be able to quantify the physiological aspect of human behaviour.

DS: Ideally you would want all the desired attributes of a dream spouse in one person. But in reality, the desired attributes will be distributed across different girls. So you have to give up on one attribute to gain on another. Thus the attributes are competing against each other so you have to make competitive choices.

DS: Assume that you have a total of twenty points to allocate across the attributes. How much are you willing to give up on the beauty to gain on the educational qualification of your spouse? If you allocate 15 points to beauty, you have only 5 points to allocate to education. When faced with scarce resources (points) you will be much more judicious in spending. Hence competitive choices are a better quantification of your actual psychological preference.

P: And how do we quantify competitive choices?

DS: By using conjoint analysis. It is based on mathematical psychology and is widely used in psychophysics, perception, decision-making and the quantitative analysis of behaviour. I will create a social compatibility algorithm and use your data to see what your actual psychological preferences; then we will find your most suitable match.

P: Really? You can build such a algorithm?

DS: Rest assured; I have learned conjoint analysis from one of the pioneers of the subject, Dr. V. Srinivasan. Give me two days.

(Two days later)

DS: The social compatibility algorithm is ready; and based on your competitive choices, it suggests that your most suitable match is the second girl. Hmmm … she is a teacher but you didn’t tell me what she teaches?

P: Well, she teaches statistics in a college.

DS: Statistics! I knew the algorithm was right.

Claimer: Based on a true incident. Both the protagonist and the data scientist work in the analytics industry. The protagonist and the lady statistician are now seeing each other.

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