Our recent research can be categorized into the following clusters:

• Mechanism design (MD) provides a game theoretic framework to explore if the given social choice function may be implemented as an equilibrium outcome of an induced game. On the other hand, machine learning (ML) seeks to learn the preferences or types of the agents through available data or through intelligent exploration. Modern web applications require both ML and MD for a complete solution. ML and MD are well investigated as individual problems but not together; interesting research questions arise when we try to handle them together. In this context, we are currently exploring multi-armed bandit problems where the arms are controlled by strategic agents. Immediate applications include Internet advertsing, crowdsourcing, demanad management in smart grids, procurement, etc.

Mechanisms with learning

• Shweta Jain, Satyanath Bhat, Ganesh Ghalme, Divya Padmanabhan, and Y. Narahari. Mechanism design for stochastic multi-armed bandit problems. Indian Journal of Pure and Applied Mathematics. 2016 (To appear).
• Satyanath Bhat, Shweta Jain, Sujit Gujar, Y. Narahari. An optimal bidimensional multi-armed bandit auction for multi-unit procurement. Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2015), Istanbul, May 4-8, 2015, pp. 1789-1790.
• Arpita Biswas, Shweta Jain, Debmalya Mandal, and Narahari Yadati. A Truthful Budget Feasible Multi-Armed Bandit Mechanism for Crowdsourcing Time Critical Tasks. International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015), Istanbul, Turkey.
• Shweta Jain, B. Narayanaswamy, and Y. Narahari. A Multiarmed Bandit Incentive Mechanism for Crowdsourcing Demand Response in Smart Grids. Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI-14). Quebec City, Canada, July 27-31, 2014.
• Shweta Jain, Sujit Gujar, Onno Zoeter, and Y. Narahari. A Quality Assuring Multi-Armed-Bandit Crowdsourcing Mechanism with Incentive Compatible Learning. Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2014), Paris, France, May 2014.
• Debmalya Mandal and Y. Narahari. A Novel Ex-Post Truthful Mechanism for Multi-Slot Sponsored Search Auctions. Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2014), Paris, France, May 2014.

• There is a rich variety of algorithmic and complexity theoretic problems in the area of social choice theory in general and voting theory in particular. The bag of problems we are investigating includes: manipulability of voting rules; detecting manipulators; kernelization complexity of manipulation problems in voting; sampling techniques for voting problems; and frugal bribery problems.

COMPUTATIONAL SOCIAL CHOICE

• Siddharth Barman, Arpita Biswas, Sanath Krishnamurthy, and Narahari Yadati. Groupwise Maximin Fair Allocation of Indivisible Goods. AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, Louisiana, USA.
• Arpita Biswas and Siddharth Barman. Fair Division under Cardinality Constraints. To appear in the proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden.
• Palash Dey, Neeldhara Mishra, and Y. Narahari. Frugal bribery in voting. Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16). Phoenix, Arizona, USA, February 2016.
• Palash Dey and Y. Narahari. Estimating the Margin of Victory of an Election using Sampling. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-2015), Brazil, 2015, pp. 1120-1126.
• Palash Dey, Neeldhara Mishra, Y. Narahari. Kernelization Complexity of Possible Winner and Coalitional Manipulation Problems in Voting. Theoretical Computer Science. 2016, pp. 111-125.
• Palash Dey and Y. Narahari. Asymptotic Collusion-proofness of Voting Rules: The Case of Large Number of Candidates. Studies in Microeconomics. Special Issue on Game Theory and its Applications to Social and Economic Networks. Volume 3, Number 3, 2015, pp. 120-139.
• Palash Dey, Neeldhara Mishra, Y. Narahari. Kernelization Complexity of Possible Winner and Coalitional Manipulation Problems in Voting. Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2015), Istanbul, May 4-8, 2015, pp. 87-96.
• Palash Dey, Neeldhara Mishra, Y. Narahari. Detecting possible manipulators in elections. Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2015), Istanbul, May 4-8, 2015, pp. 1441-1450.
• Palash Dey and Arnab Bhattacharyya. Sample Complexity for Winner Prediction in Elections. Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2015), Istanbul, May 4-8, 2015, pp. 1421-1430.
• Palash Dey and Y. Narahari. Asymptotic Collusion-Proofness of Voting Rules: The Case of Large Number of Candidates. Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2014), Paris, France, May 2014.

• We have been engaged with a variety of problems in social network analysis where we are exploring the use of game theoretic and other techniques for solving these problems better. The following problems continue to engage our attention: influence maximization for viral marketing; influence limitation for spread of virus and rumor; modeling the spread of preferences in social networks; game theoretic models for social network analysis problems; etc.

SOCIAL NETWORK ANALYSIS

• Swapnil Dhamal, K.J. Prabuchandran, and Y. Narahari. A multi-phase approach for improving information diffusion in social networks. Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2015), Istanbul, May 4-8, 2015, pp. 1787-1788.
• Swapnil Dhamal and Y. Narahari. Formation of Stable Strategic Networks with Desired Topologies. Studies in Microeconomics. Special Issue on Game Theory and its Applications to Social and Economic Networks. Volume 3, Number 3, 2015, pp. 158-213.
• Rohith D. Vallam, C.A. Subramanian, Ramasuri Narayanam, Y. Narahari, and N. Srinath. Strategic Network Formation with Localized Payoffs. Studies in Microeconomics. Volume 2, Number 1, 2014, pp. 63-120.
• Ramasuri Narayanam and Y. Narahari. A novel, decentralized, local information based algorithm for community detection in social networks. CSI Journal of Computing. Volume 2, Number 1-2, 2013, pp. 40-50.
• Vikas Garg, Y. Narahari, M. Narasimha Murty. Novel Biobjective Clustering based on Cooperative Game Theory. IEEE Transactions on Knowledge and Data Engineering (TKDE). Volume 25, Number 5, 2013, 00. 1070-1082.
• Swapnil Dhamal and Y. Narahari. Scalable Preference Aggregation in Social Networks. Proceedings of the First AAAI Conference on Human Computation and Crowdsourcing (HCOMP-2013), Palm Springs, California, USA, November 2013.
• Swapnil Dhamal and Y. Narahari. Forming networks of strategic agents with desired topologies. Proceedings of 8th Workshop on Internet and Network Economics (WINE-2012), Liverpool, UK, December 2012.

• Crowdsourcing has emerged as a popular means of getting work done through an open call to a crowd. There are many challenges involved in setting up successful crowdsourcing platforms. Using mechanism design and machine learning, we are looking into research challenges such as learning the qualities of crowd workers, ensuring an acceptable level of quality, designing allocations and pricing mechanisms that arrest manipulations by strategic crowd workers, etc. The canvas of applications includes geosensing (for healthcare and public welfare projects), online education, crowdfunding, procurement of services, etc.

CROWDSOURCING MECHANISMS

• Praphul Chandra, Y. Narahari, Debmalya Mandal, Prasenjit Dey. Novel mechanisms for online crowdsourcing with unreliable, strategic agents, AAAI-2015.
• Arupratan Ray, Debmalya Mandal, and Y. Narahari. Profit maximizing prior free multi-unit procurement auctions with capacitated sellers, AAMAS-2015.
• Pankaj Dayama, B. Narayanaswamy, Dinesh Garg, and Y. Narahari. Truthful interval cover mechanisms for crowdsourcing applications, AAMAS-2015.
• Rohith D. Vallam, Priyanka Bhat, Debmalya Mandal, and Y. Narahari. A Stackelberg game approach for incentivizing participation in online educational forums with heterogeneous student population, AAAI-15.
• Swaprava Nath, Onno Zoeter, Y. Narahari, Chris Dance. Dynamic mechanism design with interdependent valuations. Review of Economic Design, 2015.
• Swaprava Nath, Balakrishnan Narayanaswamy. Productive output in hierarchical crowdsourcing, AAMAS 2014.
• Satyanath Bhat, Swaprava Nath, Sujit Gujar, Onno Zoeter, Y. Narahari, and Chris Dance. A Mechanism to Optimally Balance Cost and Quality of Labeling Tasks Outsourced to Strategic Agents, AAMAS-2014.
• Ratul Ray, Rohith Dwarakanath Vallam, Y. Narahari. Eliciting high quality feedback from crowdsourced tree networks using continuous scoring rules. AAMAS 2013.
• Shweta Jain, Balakrishnan Narayanaswamy, Y. Narahari, Saiful Husain, Voo Nyuk Yoong. Constrained Tatonnement for Distributed Demand Management in Smart Grids, ACM e-energy 2013
• Swaprava Nath, Pankaj Dayama, Dinesh Garg, Y. Narahari, and James Zou. Mechanism design for time critical and cost critical task execution via crowdsourcing , WINE-2012.