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Anirban Dasgupta studies algorithms for large datasets, networks and related learning problems. His IIT Gandhinagar profile identifies large-scale data algorithms and social networks among his research interests.^[ref]

He coauthored The Price of Stability for Network Design with Fair Cost Allocation by Elliot Anshelevich and others and Near-Optimal Network Design with Selfish Agents by Elliot Anshelevich and others. These works connect network optimization with individual route choices. A centrally cheap collection of paths may fail to persist if users can reduce their own payments by switching; conversely, a stable collection need not minimize the total cost.^[ref]

The algorithmic challenge is to control both quantities at once: computational work required to find a network and inefficiency introduced by decentralized decisions. That is a concrete bridge between approximation guarantees and strategic behavior.

Elliot Anshelevich studies algorithms for networks with decentralized or strategic decision makers. His institutional research description includes network formation, algorithmic game theory, approximation algorithms and computational questions about collective choice.^[ref]

He is a coauthor of The Price of Stability for Network Design with Fair Cost Allocation by Elliot Anshelevich and others. The paper asks how good the best stable network can be when each user can individually change routes and shared edge costs are divided among their users. This separates the best equilibrium's cost from the worst equilibrium's cost, formalized by price of stability and price of anarchy.^[ref]

The question is useful for a designer who can recommend a network but cannot prevent unilateral departures. A low-cost recommendation is sustainable only if no user can improve by leaving it; computing a cheap network and understanding its incentives are therefore different parts of the problem.

For a cost-minimization game with positive social optimum and a nonempty set $\mathcal{E}$ of the specified Nash equilibria, the price of stability isIt measures the cost of requiring stability even when the best equilibrium can be selected. The equilibrium class, such as pure or mixed, and social-cost convention must be stated.

In a symmetric two-player game with actions $A,B$, let each player's cost be one at $(A,A)$, two at $(B,B)$ and three when the actions differ. Both coordinated choices are Nash equilibria; the best equilibrium has total cost two, matching the optimum, so the price of stability is one. The worst equilibrium instead costs four, giving price of anarchy two. The distinction matters when a designer can recommend an outcome but cannot force anyone to remain at an unstable one.^[ref]

Tom Wexler works on approximation algorithms, game theory and networks. The author information accompanying Near-Optimal Network Design with Selfish Agents by Elliot Anshelevich and others identifies these research interests and his association with Denison University. It also verifies that this is the coauthor of the strategic network-design work.^[ref]

A simple shared-cost example illustrates the kind of incentive question involved. Suppose each of two users can buy a private link costing four or use a common link costing six, split equally if both use it. Both choosing the common link is stable and costs six overall. Both choosing private links is also stable: switching alone to the common link would cost six rather than four. The latter arrangement costs eight despite having no profitable unilateral move.

The contrast motivates separate analysis of the best and worst Nash equilibria. Network-design work asks how cost-allocation rules and algorithms can find efficient stable outcomes, rather than assuming every stable outcome is socially cheapest.^[ref]