Efficiency in Multi-objective Games

In a multi-objective game, each agent individually evaluates each overall action-profile on multiple objectives. I generalize the price of anarchy to multi-objective games and provide a polynomial-time algorithm to assess it. This work asserts that policies on tobacco promote a higher economic efficiency

HandyBroker - An intelligent product-brokering agent for M-commerce applications with user preference tracking

One of the potential applications for agent-based systems is m-commerce. A lot of research has been done on making such systems intelligent to personalize their services for users. In most systems, user-supplied keywords are generally used to help generate profiles for users. In this paper, an evolutionary ontology-based product-brokering agent has been designed for m-commerce applications. It uses an evaluation function to represent a user’s preference instead of the usual keyword-based profile. By using genetic algorithms, the agent tracks the user’s preferences for a particular product by tuning some parameters inside its evaluation function. A prototype called “Handy Broker” has been implemented in Java and the results obtained from our experiments looks promising for m-commerce use

Three Principles of Competitive Nonlinear Pricing.

We make three contributions to the theory of contracting under asymmetric information. First, we establish a competitive revelation principle for contracting games in which several principals compete for one privately informed agent. In particular, we show that given any profile of incentive compatible indirect contracting mechanisms, there exists an incentive compatible direct contracting mechanism which in all circumstances generates the same contract selection as the profile of indirect mechanisms. Second, we establish a competitive taxation principle.INFORMATION ; GAMES ; TAXATION

Reinforcement Learning for Nash Equilibrium Generation

Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.We propose a new conceptual multi-agent framework which, given a game with an undesirable Nash equilibrium, will almost surely generate a new Nash equilibrium at some predetennined, more desirable pure action profile. The agent(s) targeted for reinforcement learn independently according to a standard model-free algorithm, using internally-generated states corresponding to high-level preference rankings over outcomes. We focus in particular on the case in which the additional reward can be considered as resulting from an internal (re-)appraisal, such that the new equilibrium is stable independent of the continued application of the procedure

On Partial Honesty Nash Implementation

An agent is said to be partially honest if he or she weakly prefers an outcome at a strategy profile with his truthful strategy than an outcome at a strategy profile with his false strategy, then this player must prefer strictly the \true" strategy profille to the \false" strategy profile. In this paper we consider an exchange economy with single peaked preferences. With many agents (n ≥3), if there exists at least one partially honest agent, we prove that any solution of the problem of fair division satisfying unanimity is Nash implementable.Nash implementation; Partial honesty; Single-peaked preferences