Allocating Stimulus Checks in Times of Crisis

Papachristou, Marios; Kleinberg, Jon

doi:10.1145/3485447.3512047

Cited by 5 publications

(17 citation statements)

References 21 publications

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Section: Related Workmentioning

confidence: 99%

“…Finally, the work closest to ours is that of [PK22], where the authors use the static Eisenberg-Noe model for studying the allocation of stimulus checks subject to financial shocks. The static nature of [PK22], however, means that it is hard to directly use their techniques and results in a dynamic setting. When the temporal dimension is introduced, the model's dynamics change significantly, since accumulated liabilities from round t − 1 affect the future states of the model; in a sense, the behavior is closer to the dynamics in queuing network models [KY14,BFL21].…”

Section: Related Workmentioning

confidence: 99%

“…A planner acts as an external force and is responsible for (optimally) allocating resources, also known as interventions (or bailouts) subject to budget constraints so that defaults are averted. Interventions can be fractionally allocated [AK19,Dem18] or discretely allocated [PK22] with the former setting being a computationally tractable problem and the latter being a computationally intractable one.…”

Section: Introductionmentioning

confidence: 99%

“…When modeling and studying such networked interactions, much of the literature assume the interactions are one-shot (i.e., static)(e.g. [GY15,PK22] etc.). However in many situations, networked interactions evolve over time, subject to an uncertain environment, and where the planner's interventions at some point in time, affect the state of the system in future times.…”

Section: Introductionmentioning

confidence: 99%

“…We initially study the design of an optimal fractional intervention policy, which is a computationally tractable problem, (assuming that the system responds optimally at every round). Next, we study the allocation of discrete resources, which is a computationally intractable problem whose static version has been studied in [PK22]; here, we design approximate discrete intervention policies that are based on the fractional intervention policy.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Interventions for Networked Contagions

Papachristou¹,

Banerjee²,

Kleinberg³

2022

Preprint

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We study the problem of designing dynamic intervention policies for minimizing networked defaults in financial networks. Formally, we consider a dynamic version of the celebrated Eisenberg-Noe model of financial network liabilities, and use this to study the design of external intervention policies. Our controller has a fixed resource budget in each round, and can use this to minimize the effect of demand/supply shocks in the network. We formulate the optimal intervention problem as a Markov Decision Process, and show how we can leverage the problem structure to efficiently compute optimal intervention policies with continuous interventions, and constant-factor approximations with discrete interventions. Going beyond financial networks, we argue that our model captures dynamic network intervention in a much broader class of dynamic demand/supply settings with networked inter-dependencies. To demonstrate this, we apply our intervention algorithms to a wide variety of application domains, including ridesharing, online transaction platforms, and financial networks with agent mobility; in each case, we study the relationship between node centrality and intervention strength, as well as fairness properties of the optimal interventions.

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Section: Related Workmentioning

confidence: 99%