#15 Game Theory and Data Science

The opening scene of the movie “Crazy Rich Asians”, features the main character in a game of cards with one of her students, being a professor at NYU (New York University). It was her way of teaching the subject, game theory, that caught my interest in the same.

So, when the subject was offered to us in my last semester in college at Delhi Technological University, I opted for it as an elective course. Only 25-30 students in a batch of 160 opted for it. Most of the them also happened to be top scores. Sitting in class the very first day, I felt classic imposter syndrome. Fortunately, one of my friends took it as well (thank you Tripti 🤗 ).

Game theory, by its very nature is a captivating subject. No, it does not mean you can just play games with your friends and call it a day. It is a sophisticated branch of applied mathematics and involves applying tools and models to analyse situations. It consists of terms such as players, who make use of something called strategies, to maximise their payoffs.

Some common terms include:

• Player: An individual or entity who makes decisions within the game.

• Game: A mathematical model of a situation involving players who make decisions to achieve specific outcomes.

• Strategy: A complete plan of action a player will follow during the game, specifying the moves they will make in every possible situation.

• Payoff: The outcome a player receives from a particular strategy or set of strategies. The players will always try to maximise their payoffs.

• Nash Equilibrium: A situation where no player can benefit by unilaterally changing their strategy, given the strategies of other players.

• Dominant Strategy: A strategy that always provides a higher payoff than any other strategy, regardless of what the other players do.

• Pareto Efficiency: A state where no player can be made better off without making at least one player worse off.

• Cooperative Game: A game where players can form coalitions and negotiate collective strategies to improve their payoffs.

One thing that caught my attention was when my professor mentioned how closely the subject is intersected with algorithms and data science. Many commonalities between the two include optimization, data-driven decision making, and predictive analysis.

In risk management, game theory helps to model the behaviours of investors and firms in financial markets, taking assistance from data science to analyse financial data, market trends, and other economic indicators. Businesses then utilize this data to understand and manage risk, as well as consider different scenarios and their probabilities, and hence, make informed decisions.

Economics, in simple terms, is defined as the balance between limited resources and unlimited wants. Game theory offers us insights into the efficient management and optimization of these resources. With data science, it allows for the development of algorithms that can analyse vast data to identify optimal resource allocation strategies. This is particularly useful in supply chain management, where businesses need to allocate resources such as inventory, transportation, and labour effectively to minimize costs and maximize profits.

In the competitive world of marketing, understanding consumer behaviour and rationale and the strategies of competitors is crucial. Game theory helps businesses model these interactions, while data science provides the necessary tools to analyse consumer data and market trends. Companies can leverage these fields and develop more effective marketing strategies.

More than often, game theory also finds it’s applications in games such as chess, poker etc. When studied thoroughly, it is a powerful and useful discipline, especially when combined along with data science.

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Note: The terms explained above have been taken from an AI tool. Let me know if this style of writing suits you all better.

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