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April 29-30, 2018: Training
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New York, NY

Gamifying strategy: Enterprise AI use cases on agent-based simulation and learning

Anand Rao (PwC)
1:45pm–2:25pm Wednesday, May 2, 2018
Implementing AI
Location: Sutton North/Center

Who is this presentation for?

  • Chief strategy officers and chief analytics officers

Prerequisite knowledge

  • A basic understanding of simulation (system dynamic or agent-based) and reinforcement learning

What you'll learn

  • Understand how to formulate a strategic decision as a game and determine the key drivers and feedback loops within the given game
  • Learn how to build large-scale agent-based simulations and how to encode the consumer choices within the agent, run large-scale simulations and use classical machine learning algorithms to find optimal or no-regret strategies, and convert the agent-based simulation into a reinforcement learning problem to learn the strategic choices of agents


There are many enterprise AI use cases for automation and operational decision making, but when it comes to strategic decision making—especially for new products or when entering new markets—there are very few successful use cases. How can AI techniques help tackle strategic questions? Gamifying strategy—building large-scale agent-based simulations and combining them with reinforcement learning—offers powerful ways of using AI for strategic decisions. This method is particularly useful when one does not have large volumes of data to train the machine learning.

Anand Rao presents four successful use cases on gamifying strategy and applying agent-based simulation in the auto, payments, medical devices, and airlines industries. All four examples involve billion-dollar strategic questions without much direct historical data. In each case, the solution was a large-scale multiagent model, literally involving millions of agents to simulate and understand the behavior of these problems. The rich structure of the interactions between the different entities involved in the decision making, the environmental uncertainties, and the strategic actions under the company’s control are captured within an agent-based model as a game. A “decision cockpit,” which allows strategic decision makers to move the different strategic levers under their control, helps them evaluate the options and project what is likely to happen to consumer adoption, revenues, and costs. Simulations of hundreds of thousands of scenarios are run to capture the impact of different strategic choices under varying environmental or competitive scenarios. This synthetic simulated data is then analyzed using machine learning techniques to find optimal or no-regret strategies that are robust under uncertainty. The agents within these strategy games can also learn policies through reinforcement learning. Anand explains how to use postsimulation learning of strategies and determine optimal strategies based on reinforcement learning.

Case studies include:

  • A large auto manufacturer trying to decide if there is a profitable business in ridesharing, electric vehicles, and autonomous vehicles, and if it does, how to enter the market and maximize the return on investments
  • A large retail bank analyzing if it wants to enter the mobile wallet business, and if so, what strategy to use to maximize ROI: build market share first and then aim for profitability or build a profitable business from the start
  • An airline, the result of a merger between two entities, contemplating a series of policies (e.g., pricing, fleet standardization) from the two firms and trying to select the policy (or modify the policy) that best captures the synergy between the two
  • A large medical device manufacturer of genome sequencing machine contemplating the bundled pricing of a new machine that is significantly cheaper and more powerful than the previous version
Photo of Anand Rao

Anand Rao


Anand Rao is a partner in PwC’s Advisory Practice and the innovation lead for the Data and Analytics Group, where he leads the design and deployment of artificial intelligence and other advanced analytical techniques and decision support systems for clients, including natural language processing, text mining, social listening, speech and video analytics, machine learning, deep learning, intelligent agents, and simulation. Anand is also responsible for open source software tools related to Apache Hadoop and packages built on top of Python and R for advanced analytics as well as research and commercial relationships with academic institutions and startups, research, development, and commercialization of innovative AI, big data, and analytic techniques. Previously, Anand was the chief research scientist at the Australian Artificial Intelligence Institute; program director for the Center of Intelligent Decision Systems at the University of Melbourne, Australia; and a student fellow at IBM’s T.J. Watson Research Center. He has held a number of board positions at startups and currently serves as a board member for a not-for-profit industry association. Anand has coedited four books and published over 50 papers in refereed journals and conferences. He was awarded the most influential paper award for the decade in 2007 from Autonomous Agents and Multi-Agent Systems (AAMAS) for his work on intelligent agents. He’s a frequent speaker on AI, behavioral economics, autonomous cars and their impact, analytics, and technology topics in academic and trade forums. Anand holds an MSc in computer science from Birla Institute of Technology and Science in India, a PhD in artificial intelligence from the University of Sydney, where he was awarded the university postgraduate research award, and an MBA with distinction from Melbourne Business School.