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This paper studies the distributed mode consensus problem in a multi-agent system, in which the agents each possess a certain attribute and they aim to agree upon the mode (the most frequent attribute owned by the agents) via distributed…

Systems and Control · Electrical Eng. & Systems 2024-03-04 Chao Huang , Hyungbo Shim , Siliang Yu , Brian D. O. Anderson

We present a simple game model where agents with different memory lengths compete for finite resources. We show by simulation and analytically that an instability exists at a critical memory length, and as a result, different memory lengths…

Adaptation and Self-Organizing Systems · Physics 2015-05-12 James Burridge , Yu Gao , Yong Mao

We consider a simple decision model in which a set of agents randomly choose one of two competing shops selling the same perishable products (typically food). The satisfaction of agents with respect to a given store is related to the…

Physics and Society · Physics 2011-06-21 Gaultier Lambert , Guillaume Chevereau , Eric Bertin

How much should you receive in a week to be indifferent to \$ 100 in six months? Note that the indifference requires a rule to ensure the similarity between early and late payments. Assuming that rational individuals have low accuracy, then…

General Economics · Economics 2020-03-02 José Cláudio do Nascimento

Any agent that is part of the environment it interacts with and has versatile actuators (such as arms and fingers), will in principle have the ability to self-modify -- for example by changing its own source code. As we continue to create…

Artificial Intelligence · Computer Science 2016-05-11 Tom Everitt , Daniel Filan , Mayank Daswani , Marcus Hutter

Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the single-agent decision-theoretic setting, inverse optimal control techniques assume that observed behavior…

Computer Science and Game Theory · Computer Science 2013-08-19 Kevin Waugh , Brian D. Ziebart , J. Andrew Bagnell

We study the effect of impairment on stochastic multi-armed bandits and develop new ways to mitigate it. Impairment effect is the phenomena where an agent only accrues reward for an action if they have played it at least a few times in the…

Machine Learning · Computer Science 2020-06-23 Priyank Agrawal , Theja Tulabandhula

We propose a payoff function extending Minority Games (MG) that captures the competition between agents to make money. In constrast with previous MG, the best strategies are not always targeting the minority but are shifting…

Condensed Matter · Physics 2009-11-07 Jorgen Vitting Andersen , Didier Sornette

Consider an agent interacting with an environment in cycles. In every interaction cycle the agent is rewarded for its performance. We compare the average reward U from cycle 1 to m (average value) with the future discounted reward V from…

Machine Learning · Computer Science 2007-05-23 Marcus Hutter

In a continuous time stochastic economy, this paper considers the problem of consumption and investment in a financial market in which the representative investor exhibits a change in the discount rate. The investment opportunities are a…

Optimization and Control · Mathematics 2011-07-12 Traian A. Pirvu , Huayue Zhang

Sequential allocation is a simple and widely studied mechanism to allocate indivisible items in turns to agents according to a pre-specified picking sequence of agents. At each turn, the current agent in the picking sequence picks its most…

Data Structures and Algorithms · Computer Science 2019-09-17 Mingyu Xiao , Jiaxing Ling

We study the problem of designing an optimal sequence of incentives that a principal should offer to an agent so that the agent's optimal behavior under the incentives realizes the principal's objective expressed as a temporal logic…

Optimization and Control · Mathematics 2019-03-20 Yagiz Savas , Vijay Gupta , Melkior Ornik , Lillian J. Ratliff , Ufuk Topcu

We study portfolio selection in a complete continuous-time market where the preference is dictated by the rank-dependent utility. As such a model is inherently time inconsistent due to the underlying probability weighting, we study the…

Mathematical Finance · Quantitative Finance 2020-06-04 Ying Hu , Hanqing Jin , Xun Yu Zhou

In this paper, we consider the revealed preferences problem from a learning perspective. Every day, a price vector and a budget is drawn from an unknown distribution, and a rational agent buys his most preferred bundle according to some…

Computer Science and Game Theory · Computer Science 2012-11-20 Morteza Zadimoghaddam , Aaron Roth

Merton portfolio management problem is studied in this paper within a stochastic volatility, non constant time discount rate, and power utility framework. This problem is time inconsistent and the way out of this predicament is to consider…

Portfolio Management · Quantitative Finance 2024-02-09 Oumar Mbodji , Traian A. Pirvu

In a dynamic matching market, such as a marriage or job market, how should agents balance accepting a proposed match with the cost of continuing their search? We consider this problem in a discrete setting, in which agents have cardinal…

Computer Science and Game Theory · Computer Science 2021-06-16 Ishan Agarwal , Richard Cole , Yixin Tao

A prominent theme in behavioural contract theory is the study of present-biased agents represented through quasi-hyperbolic discounting. In a model of competitive credit provision, we study an alternative to this framework in which the…

Theoretical Economics · Economics 2026-02-11 Siddharth Chatterjee , Daniel F. Garrett

This paper describes a study of agent bidding strategies, assuming combinatorial valuations for complementary and substitutable goods, in three auction environments: sequential auctions, simultaneous auctions, and the Trading Agent…

Computer Science and Game Theory · Computer Science 2012-07-19 Amy Greenwald , Justin Boyan

The modernization of the power system introduces technologies that may improve the system's efficiency by enhancing the capabilities of users. Despite their potential benefits, such technologies can have a negative impact. This subject has…

Computer Science and Game Theory · Computer Science 2019-06-28 Carlos Barreto , Eduardo Mojica-Nava , Nicanor Quijano

The main challenge of multiagent reinforcement learning is the difficulty of learning useful policies in the presence of other simultaneously learning agents whose changing behaviors jointly affect the environment's transition and reward…

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