More Like this

1. From Monetary to Nonmonetary Mechanism Design via Artificial Currencies

   https://doi.org/10.1287/moor.2020.1098  

   Gorokh, Artur; Banerjee, Siddhartha; Iyer, Krishnamurthy (April 2021, Mathematics of Operations Research)

   null (Ed.)

   Nonmonetary mechanisms for repeated allocation and decision making are gaining widespread use in many real-world settings. Our aim in this work is to study the performance and incentive properties of simple mechanisms based on artificial currencies in such settings. To this end, we make the following contributions: For a general allocation setting, we provide two black-box approaches to convert any one-shot monetary mechanism to a dynamic nonmonetary mechanism using an artificial currency that simultaneously guarantees vanishing gains from nontruthful reporting over time and vanishing losses in performance. The two mechanisms trade off between their applicability and their computational and informational requirements. Furthermore, for settings with two agents, we show that a particular artificial currency mechanism also results in a vanishing price of anarchy. 

   more » « less
2. Incentive Mechanisms for Strategic Classification and Regression Problems

   https://doi.org/10.1145/3490486.3538300  

   Jin, Kun; Zhang, Xueru; Khalili, Mohammad Mahdi; Naghizadeh, Parinaz; Liu, Mingyan (July 2022, ACM Conference on Economics and Computation (EC))

   We study the design of a class of incentive mechanisms that can effectively prevent cheating in a strategic classification and regression problem. A conventional strategic classification or regression problem is modeled as a Stackelberg game, or a principal-agent problem between the designer of a classifier (the principal) and individuals subject to the classifier's decisions (the agents), potentially from different demographic groups. The former benefits from the accuracy of its decisions, whereas the latter may have an incentive to game the algorithm into making favorable but erroneous decisions. While prior works tend to focus on how to design an algorithm to be more robust to such strategic maneuvering, this study focuses on an alternative, which is to design incentive mechanisms to shape the utilities of the agents and induce effort that genuinely improves their skills, which in turn benefits both parties in the Stackelberg game. Specifically, the principal and the mechanism provider (which could also be the principal itself) move together in the first stage, publishing and committing to a classifier and an incentive mechanism. The agents are (simultaneous) second movers and best respond to the published classifier and incentive mechanism. When an agent's strategic action merely changes its observable features, it hurts the performance of the algorithm. However, if the action leads to improvement in the agent's true label, it not only helps the agent achieve better decision outcomes, but also preserves the performance of the algorithm. We study how a subsidy mechanism can induce improvement actions, positively impact a number of social well-being metrics, such as the overall skill levels of the agents (efficiency) and positive or true positive rate differences between different demographic groups (fairness). 

   more » « less
3. Allocating Public Goods via Dynamic Max-Min Fairness: Long-Run Behavior and Competitive Equilibria

   https://doi.org/10.1145/3711695  

   Onyeze, Chido; Banerjee, Siddhartha; Fikioris, Giannis; Tardos, Éva (March 2025, Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   Dynamic max-min fair allocation (DMMF) is a simple and popular mechanism for the repeated allocation of a shared resource among competing agents: in each round, each agent can choose to request or not for the resource, which is then allocated to the requesting agent with the least number of allocations received till then. Recent work has shown that under DMMF, a simple threshold-based request policy enjoys surprisingly strong robustness properties, wherein each agent can realize a significant fraction of her optimal utility irrespective of how other agents' behave. While this goes some way in mitigating the possibility of a 'tragedy of the commons' outcome, the robust policies require that an agent defend against arbitrary (possibly adversarial) behavior by other agents. This however may be far from optimal compared to real world settings, where other agents are selfish optimizers rather than adversaries. Therefore, robust guarantees give no insight on how agents behave in an equilibrium, and whether outcomes are improved under one. Our work aims to bridge this gap by studying the existence and properties of equilibria under DMMF. To this end, we first show that despite the strong robustness guarantees of the threshold based strategies,no Nash equilibrium existswhen agents participate in DMMF, each using some fixed threshold-based policy. On the positive side, however, we show that for the symmetric case, a simple data-driven request policy guarantees that no agent benefits from deviating to a different fixed threshold policy. In our proposed policy agents aim to match the historical allocation rate with a vanishing drift towards the rate optimizing overall welfare for all users. Furthermore, the resulting equilibrium outcome can be significantly better compared to what follows from the robustness guarantees. Our results are built on a complete characterization of the steady-state distribution under DMMF, as well as new techniques for analyzing strategic agent outcomes under dynamic allocation mechanisms; we hope these may prove of independent interest in related problems. 

   more » « less
4. Debauchery and Original Sin: The Currency Composition of Sovereign Debt

   Engel, Charles Park (June 2022, Journal of the European Economic Association)

   We present a model that accounts for the “mystery of original sin”, and the surge in local currency borrowing by emerging economies in the recent decade. We quantitatively investigate the currency composition of sovereign debt in the presence of two types of limited enforcement frictions arising from a government’s monetary and debt policy: strategic currency debasement and default on sovereign debt. Local currency debt obligations act as a better consumption hedge against income shocks than foreign currency debt because their real value can be affected by monetary policy. However, this provides a government with more temptation to deviate from disciplined monetary policy, thus restricting borrowing in local currency more than in foreign currency. Our model predicts that a country with less credible monetary policy borrows mainly in foreign currency as a substitute for monetary credibility. An important extension demonstrates that in the presence of an expectational Phillips curve, local currency debt improves the ability of monetary policymakers to commit. 

   more » « less
5. Information Elicitation from Rowdy Crowds

   https://doi.org/10.1145/3442381.3449840  

   Schoenebeck, Grant; Yu, Fang-Yi; Zhang, Yichi (April 2021, Proceedings of the Web Conference (WWW '21))

   We initiate the study of information elicitation mechanisms for a crowd containing both self-interested agents, who respond to incentives, and adversarial agents, who may collude to disrupt the system. Our mechanisms work in the peer prediction setting where ground truth need not be accessible to the mechanism or even exist. We provide a meta-mechanism that reduces the design of peer prediction mechanisms to a related robust learning problem. The resulting mechanisms are ϵ-informed truthful, which means truth-telling is the highest paid ϵ-Bayesian Nash equilibrium (up to ϵ-error) and pays strictly more than uninformative equilibria. The value of ϵ depends on the properties of robust learning algorithm, and typically limits to 0 as the number of tasks and agents increase. We show how to use our meta-mechanism to design mechanisms with provable guarantees in two important crowdsourcing settings even when some agents are self-interested and others are adversarial. 

   more » « less