Is Game Theory important for Data Scientists?

Is Game Theory important for Data Scientists?

Imagine you are driving down a lane in heavy traffic, you observe that the traffic in your lane is moving slowly. You switch to another lane where the traffic seems to move faster. However, after a while you observe that the traffic in the prior lane is now moving at a faster pace. This is when you have to take a strategic decision – should you stay where you are or should you switch back to the previous lane?

Game theory, deals with understanding strategic situations- where, how well a person performs, depends on what others do and vice-versa. The basic principle of game theory is to find out an optimal solution for a given situation. It is not just the games like Poker, Football and Chess that fit into Game theory but there are many other important decisions like investing, customer engagement, deciding what job to take, etc. Game theory applications can be found in various strategic decision making expanses like Sports, Economics, Politics, Geosciences, etc.

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Game Theory Data Science


Is Game Theory important for Data Scientists?

Data scientists can use game theory to analyse competitive situations in a structured way. Big Data analytics is one of the core technologies used by businesses today for decision making and applying game theory data science for strategic decision making, is definitely an intelligent move that will help enterprises predict likely outcomes for businesses, individuals and societies.

Game Theory might not be an important general knowledge concept for all the data scientists but is somehow tangentially related to algorithm design. Game theory data science is an additional concept data scientists can master to predict how rational people will make decisions that help them make effective data-driven decisions under strategic circumstances. Data scientists can apply game theory based on the type of decision making data problem they are dealing with.

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The major components that help in analysing a data-driven decision making problem are-

  • The set of options or choices available
  • The set of outcomes based on the above options
  • Outcomes valuation

Data-driven decision making in analytics is classified into 4 types based on the above components-

  • Decision making under uncertainty. (The probabilities are not known but the set of outcomes are known)
  • Decision making with a risk factor(The probability for each choice is known )
  • Decision making under certainty ( The possible outcome for each option is known)
  • Decision making in an interactive context.

Here is an interesting short clip from the movie "A Beautiful Mind" that explains the Game Theory Concept-



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Data scientists who are solving interactive context problems are most likely to apply game theory in their analytics algorithms, together with machine learning and AI - this helps them make an optimal choice possible. Customer Engagement is an interactive context problem that involves organizations and individuals. Customer engagement entails different levels of cooperation and conflicts. Game theory can be applied by data scientists to such problems to obtain a mutually beneficial outcome.

Game theory takes into account discrete variables such as –events, actions, and outcomes rather than using continuous variables. This forms an integral part of data science. Game theory makes pre-assumptions that the engagements can be modelled with business interactions involving rational or unbiased decision makers and provided the decision making problem has deterministic outcomes.

Data scientists who are deeply into drawing business insights from analytics should leverage game theory strategy to help organizations make strategic decisions from raw data, however this solely depends on the domain of the raw data. Game theory is fun to learn for any data scientists as it can replace intuitive interpretations of  big data analysis with quantifiable data-driven decision making.

Game Theory Approach to Competitive Business

Data scientists have used game theory approach to competitive business in some real-time applications –

  • Data scientists at Armorway have developed patented game- theoretic algorithms by exploiting data analysis and machine learning that uses big data to draw meaningful visualizations and develop intelligence driven deployment strategies. This game-theoretic algorithm is being used by a high-profile Hollywood event production and University of Southern California for improving campus security during the major Hollywood event. The algorithm will be used to classify and categorize different types of situational vulnerabilities during the Hollywood event.

“Preventing crime is like a game of chess, and we use big data and game theory analytics to help our clients  outsmart the bad guys.”-said Armorway CEO Zare’ Baghdasarian

  • Game-theoretic algorithm developed by Armorway data scientists has also been used to enhance the effectiveness of patrolling at the US Coastguard through real-time incidents. There has been 60% improvement in the effectiveness of patrols at the coastguard after the application of this algorithm.
  • A statistician and a popular New York Times blogger Nate Silver used Game theory strategy, and predictive analytics to predict that President Barack Obama would be re-elected. His algorithmic predictions have not just brought victory to Obama but also victory for analytics.

To sum it up, game theory strategy is a specialized concept of data science and is not a core part of traditional data science but it helps organizations leverage behavioural analytics with various set of approaches. Game theory is a nice to have and an interesting concept for data scientists but not a must have. Data scientist can effectively use game theory strategy to enhance the predilection of behavioural models that will add value in delivering rich insights for a business from  big data. Using machine intelligence, game theory, data mining and predictive analytics-data scientists will soon be able to make predictions about future events.

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