The Ideal Free Distribution: Theory and Engineering Application

Quijano, Nicanor; Passino, K.M.

doi:10.1109/tsmcb.2006.880134

Cited by 37 publications

(29 citation statements)

References 24 publications

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“…The suitability function used in IFD models (e.g. Quijano and Passino, 2007) is defined here as the expected CPUE at a particular time step (t) in this analysis. Thus, suitability for area i is defined as S i (t).…”

Section: Effort Redistribution Modelsmentioning

confidence: 99%

“…The approach employed here is based upon Ideal Free Distributions (IFDs). While IFDs were originally proposed for the ecological distribution of animal populations (Fretwell and Lucas, 1970), applications include temperature distributions in physics (Quijano and Passino, 2007) and spatial control designs in engineering (D'Andrea and Dullerud, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Fishing effort redistribution in response to area closures

Powers

Abeare

2009

Fisheries Research

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Section: Effort Redistribution Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fishing effort redistribution in response to area closures

Powers

Abeare

2009

Fisheries Research

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“…On the other hand, if (3) is taken in steady state, that isṗ i = 0, and p i > 0, the equilibrium point is an optimal point for the resource allocation related to a wide family of payoff functions [68]. This payoff function has been used in several works such as [27], [26], [69], among others.…”

Section: Population-games Approach For Dynamic Dispatch In Microgridsmentioning

confidence: 99%

The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

et al. 2017

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Recently, there has been an increasing interest in studying large-scale distributed systems in the control community. Efforts have been invested in developing several techniques wishing to address the main challenges found in this kind of problems, for instance, the amount of information to guarantee the proper operation of the system and the economic costs associated to the required communication structure. Moreover, another issue appears when there is a large amount of required data to control the system. The measuring and transmission processes, and the computation of the control inputs make closed-loop systems suffer from high computational burden.One way to overcome such problems is to consider the use of multi-agent systems framework, which may be cast in game-theoretical terms. Game theory studies interactions between self-interested agents. Particularly, this theory tackles with the problem of interaction between agents using different strategies that wish to maximize their welfares. For instance in [1], the authors provide connections between games, optimization, and learning for signal processing in networks. Other approaches in terms of learning and games can be found in [2]. In [3], distributed computation algorithms are developed based on generalized convex games that do not require full information, and where there is a dynamic change in terms of network topologies. Applications of game theory in control of optical networks and game-theoretic methods for smart grids are described in [4], [5], [6]. Another approach in game theoretical methods is to design protocols or mechanisms that possess some desirable properties [7]. This approach leads to a broad analysis of multi-agent interactions, particularly those involving negotiation and coordination problems [8]. Other game-theoretical applications to engineering are reported in [9].From a game-theoretical perspective, it can be distinguished among three types of games: matrix games, continuous games, and differential/dynamic games (see "The relationship among matricial games, full-potential population games, and resource allocation problems"). In matrix games (that generally use the normal form), individuals play simultaneously and only once, and the decision is mainly based on a static terms. In this case, players or agents are individually treated. Differently, in continuous games, players have infinitely many pure strategies [10], [11]. On the other hand, in dynamic games it is assumed that players can use some type of learning mechanism that allows them to adjust the actions taken based on their past decisions. Basically, it can be said that dynamic games are characterized by three main problems: i) how to model the environment where players interact; ii) how to model the objectives of players; and iii) how to specify the order in which the actions are taken and how much information each player possesses. In this case, it is assumed that there are interactions between large number of agents (which are usually unknown) [12].Among these dynamic game...

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“…♦ Assumption 1 is not very restrictive. Notice that this assumption implies that the fitness functions are monotonically decreasing, which is a common characteristic in several scenarios, e.g., coordination games [4], biological models [12], congestion games [13], and so forth.…”

Section: B Convergence To Epsilon-equilibriamentioning

confidence: 99%

A class of population dynamics for reaching epsilon-equilibria: Engineering applications

Obando

Barreiro-Gómez

Quijano

2016

2016 American Control Conference (ACC)

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Abstract-This document proposes a novel class of population dynamics that are parameterized by a nonnegative scalar . We show that any rest point of the proposed dynamics corresponds to an -equilibrium of the underlying population game. In order to derive this class of population dynamics, our approach is twofold. First, we use an extension of the pairwise comparison revision protocol and the classic mean dynamics for well-mixed populations. This approach requires full-information. Second, we employ the same revision protocol and a version of the mean dynamics for non-well-mixed populations that uses only local information. Furthermore, invariance properties of the set of allowed population states are analyzed, and stability of the -equilibria is formally proven. Finally, two engineering examples based on the −dynamics are presented: A control scenario in which noisy measurements should be mitigated, and a humanitarian engineering application related to wealth distribution in poor societies.

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The Ideal Free Distribution: Theory and Engineering Application

Cited by 37 publications

References 24 publications

Fishing effort redistribution in response to area closures

Fishing effort redistribution in response to area closures

The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

A class of population dynamics for reaching epsilon-equilibria: Engineering applications

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