A Perspective on Incentive Design: Challenges and Opportunities

Ratliff, Lillian J.; Dong, Roy; Sekar, Shreyas; Fiez, Tanner

doi:10.1146/annurev-control-053018-023634

Cited by 28 publications

(19 citation statements)

References 96 publications

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“…On the other hand, on the premise of ensuring a sufficiently good outcome, how to optimize the cost of providing incentives is another important issue of incentive design ( Han and Tran-Thanh, 2018 ). Hence, it is meaningful to investigate the optimal incentive strategy in this scenario by means of optimal control theory or reinforcement learning approach ( Wang et al., 2019 ; Ratliff et al., 2019 ). In this work, we have considered a well-mixed interaction where individuals perform random interactions.…”

Section: Discussionmentioning

confidence: 99%

Combination of institutional incentives for cooperative governance of risky commons

et al. 2021

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Summary Finding appropriate incentives to enforce collaborative efforts for governing the commons in risky situations is a long-lasting challenge. Previous works have demonstrated that both punishing free-riders and rewarding cooperators could be potential tools to reach this goal. Despite weak theoretical foundations, policy makers frequently impose a punishment-reward combination. Here, we consider the emergence of positive and negative incentives and analyze their simultaneous impact on sustaining risky commons. Importantly, we consider institutions with fixed and flexible incentives. We find that a local sanctioning scheme with pure reward is the optimal incentive strategy. It can drive the entire population toward a highly cooperative state in a broad range of parameters, independently of the type of institutions. We show that our finding is also valid for flexible incentives in the global sanctioning scheme, although the local arrangement works more effectively.

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Section: Discussionmentioning

confidence: 99%

Combination of institutional incentives for cooperative governance of risky commons

et al. 2021

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“…To minimize the secondary system-level cost function, the principal needs to determine the r which minimizes the system-level cost function J x, r . Framed in an incentive design setting, the problem can be formulated as a principal-agent problem, commonly known as a Stackelberg game in the optimization community [41], that we write as a bilevel programming problem (BLPP) [10]:…”

Section: Relying On Normalized Nash Equilibrium To Design Incentivesmentioning

confidence: 99%

Parametrized Inexact-ADMM based coordination games: A normalized Nash equilibrium approach

Cadre

Mou

Höschle

2022

European Journal of Operational Research

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Generalized Nash equilibrium problems are single-shot Nash equilibrium problems, whereby the decisions of all agents are coupled through a shared constraint. Such games are generally challenging to solve as they might give rise to a very large number of solutions. In this context, spanning many equilibria can be interesting to provide meaningful interpretations. In the literature, to compute equilibria, equilibrium problems are classically reformulated as optimization problems, potential games, relaxed and extended games. Applications of these reformulations to an economic dispatch problem under perfect and imperfect competition are provided. Unfortunately, these approaches only enable to describe a very limited part of the equilibrium set. To fill that gap, relying on normalized Nash equilibrium as solution concept, we provide a parametrized decomposition algorithm inspired by the Inexact-ADMM to span many more equilibrium points. Complexifying the setting, we consider an information structure in which the agents can withhold some local information from sensitive data, resulting in private coupling constraints. The convergence of the algorithm and deviations in the players' strategies at equilibrium are formally analyzed. In addition, the algorithm can be used to coordinate the agents on one specific equilibrium with desirable properties at the system level. The coordination game is formulated as a principal-agent problem, and a procedure is detailed to compute the equilibrium that minimizes a secondary cost function capturing systemlevel properties. Finally, the Inexact-ADMM is applied to a cellular resource allocation problem, exhibiting better convergence rate than vanilla ADMM, and to compute equilibria that achieve both system-level efficiency and maximum fairness.

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“…To compare (13) with system optimal, consider Figure 4; there, the solid (blue) trace corresponds to the constrained team scheduling in (13), and the dashed (green) trace corresponds to system optimal scheduling. Note that Theorem 3.3 holds for the case that the machine schedulers behave in an uncoordinated manner, each attempting to optimize global performance given the choices of others.…”

Section: Loss Of Security Due To Scheduling Constraintsmentioning

confidence: 99%

“…The defending autonomous agents' approach is similarly twofold: first, agents should be designed to directly avoid and mitigate the effects of the attacker's actions; second, the autonomous agents should be designed to influence the behavior of ordinary system users so that they avoid and mitigate the effects of the attacker's actions [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Designing for Emergent Security in Heterogeneous Human-Machine teams

Brown

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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This work seeks to design decisionmaking rules for autonomous agents to jointly influence and optimize the behavior of teamed human decisionmakers in the presence of an adversary. We study a situation in which computational jobs are scheduled on servers by a collection of autonomous machines in concert with self-interested human decisionmakers, and the human and machine schedulers must react to an adversary's attack on one of the servers. We show a simple machine scheduling policy such that if all schedulers have permission to schedule jobs on all servers, increasing the penetration of machine schedulers always increases the level of security in the system, even when the machine schedulers have no explicit coordination or communication amongst themselves. However, we show a companion result in which simple constraints on server availability can nullify the machine schedulers ability to effectively influence human schedulers; here, even if machine schedulers control an overwhelming majority of jobs, are socially-aware, and fully coordinated amongst themselves, they are incapable of influencing human decisionmakers to mitigate the harm of an attack. P. N. Brown is an Assistant Professor with the

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A Perspective on Incentive Design: Challenges and Opportunities

Cited by 28 publications

References 96 publications

Combination of institutional incentives for cooperative governance of risky commons

Combination of institutional incentives for cooperative governance of risky commons

Parametrized Inexact-ADMM based coordination games: A normalized Nash equilibrium approach

Designing for Emergent Security in Heterogeneous Human-Machine teams

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