scholarly journals Dynamic Multiagent Incentive Contracts: Existence, Uniqueness, and Implementation

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 19
Author(s):  
Qi Luo ◽  
Romesh Saigal
Keyword(s):  
Asymmetric Information ◽  
Institutional Design ◽  
Incentive Contracts ◽  
Equilibrium Constraints ◽  
Decentralized Decision Making ◽  
Hamilton Jacobi Bellman Equation ◽  
Existence Conditions ◽  
Hamilton Jacobi Bellman ◽  
New Applications ◽  
Cooperating Agents

Multiagent incentive contracts are advanced techniques for solving decentralized decision-making problems with asymmetric information. The principal designs contracts aiming to incentivize non-cooperating agents to act in his or her interest. Due to the asymmetric information, the principal must balance the efficiency loss and the security for keeping the agents. We prove both the existence conditions for optimality and the uniqueness conditions for computational tractability. The coupled principal-agent problems are converted to solving a Hamilton–Jacobi–Bellman equation with equilibrium constraints. Extending the incentive contract to a multiagent setting with history-dependent terminal conditions opens the door to new applications in corporate finance, institutional design, and operations research.

Interception of automated adversarial drone swarms in partially observed environments

2021 ◽  
pp. 1-14
Author(s):  
Daniel Saranovic ◽  
Martin Pavlovski ◽  
William Power ◽  
Ivan Stojkovic ◽  
Zoran Obradovic
Keyword(s):  
Bellman Equation ◽  
Pid Controllers ◽  
Partially Observed ◽  
Hamilton Jacobi Bellman Equation ◽  
Partial Feedback ◽  
Point Solution ◽  
Physical Limitations ◽  
Defensive Mechanisms ◽  
Hamilton Jacobi Bellman ◽  
New Framework

As the prevalence of drones increases, understanding and preparing for possible adversarial uses of drones and drone swarms is of paramount importance. Correspondingly, developing defensive mechanisms in which swarms can be used to protect against adversarial Unmanned Aerial Vehicles (UAVs) is a problem that requires further attention. Prior work on intercepting UAVs relies mostly on utilizing additional sensors or uses the Hamilton-Jacobi-Bellman equation, for which strong conditions need to be met to guarantee the existence of a saddle-point solution. To that end, this work proposes a novel interception method that utilizes the swarm’s onboard PID controllers for setting the drones’ states during interception. The drone’s states are constrained only by their physical limitations, and only partial feedback of the adversarial drone’s positions is assumed. The new framework is evaluated in a virtual environment under different environmental and model settings, using random simulations of more than 165,000 swarm flights. For certain environmental settings, our results indicate that the interception performance of larger swarms under partial observation is comparable to that of a one-drone swarm under full observation of the adversarial drone.

Institutional Design as a Commitment Device in Credit Markets with Asymmetric Information: Experimental Evidence

2000 ◽  
Vol 29 (2) ◽  
pp. 281-313 ◽  
Author(s):  
Daniela Di Cagno ◽  
Emanuela Sciubba
Keyword(s):  
Asymmetric Information ◽  
Experimental Evidence ◽  
Institutional Design ◽  
Credit Markets ◽  
Commitment Device
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