99,118 research outputs found
Endogenous Networks in Random Population Games
Population learning in dynamic economies has been traditionally studied in over-simplified settings where payoff landscapes are very smooth. Indeed, in these models, all agents play the same bilateral stage-game against any opponent and stage-game payoffs reflect very simple strategic situations (e.g. coordination). In this paper, we address a preliminary investigation of dynamic population games over `rugged' landscapes, where agents face a strong uncertainty about expected payoffs from bilateral interactions. We propose a simple model where individual payoffs from playing a binary action against everyone else are distributed as a i.i.d. U[0,1] r.v.. We call this setting a `random population game' and we study population adaptation over time when agents can update both actions and partners using deterministic, myopic, best reply rules. We assume that agents evaluate payoffs associated to networks where an agent is not linked with everyone else by using simple rules (i.e. statistics) computed on the distributions of payoffs associated to all possible action combinations performed by agents outside the interaction set. We investigate the long-run properties of the system by means of computer simulations. We show that: (i) allowing for endogenous networks implies higher average payoff as compared to "frozen" networks; (ii) the statistics employed to evaluate payoffs strongly affect the efficiency of the system, i.e. convergence to a unique (multiple) steady-state(s) or not; (iii) for some class of statistics (e.g. MIN or MAX), the likelihood of efficient population learning strongly depends on whether agents are change-averse or not in discriminating between options delivering the same expected payoff.Dynamic Population Games, Bounded Rationality, Endogenous Networks, Fitness Landscapes, Evolutionary Environments, Adaptive Expectations.
Diversity and Adaptation in Large Population Games
We consider a version of large population games whose players compete for
resources using strategies with adaptable preferences. The system efficiency is
measured by the variance of the decisions. In the regime where the system can
be plagued by the maladaptive behavior of the players, we find that diversity
among the players improves the system efficiency, though it slows the
convergence to the steady state. Diversity causes a mild spread of resources at
the transient state, but reduces the uneven distribution of resources in the
steady state.Comment: 8 pages, 3 figure
An Investigation Report on Auction Mechanism Design
Auctions are markets with strict regulations governing the information
available to traders in the market and the possible actions they can take.
Since well designed auctions achieve desirable economic outcomes, they have
been widely used in solving real-world optimization problems, and in
structuring stock or futures exchanges. Auctions also provide a very valuable
testing-ground for economic theory, and they play an important role in
computer-based control systems.
Auction mechanism design aims to manipulate the rules of an auction in order
to achieve specific goals. Economists traditionally use mathematical methods,
mainly game theory, to analyze auctions and design new auction forms. However,
due to the high complexity of auctions, the mathematical models are typically
simplified to obtain results, and this makes it difficult to apply results
derived from such models to market environments in the real world. As a result,
researchers are turning to empirical approaches.
This report aims to survey the theoretical and empirical approaches to
designing auction mechanisms and trading strategies with more weights on
empirical ones, and build the foundation for further research in the field
Mean-Field-Type Games in Engineering
A mean-field-type game is a game in which the instantaneous payoffs and/or
the state dynamics functions involve not only the state and the action profile
but also the joint distributions of state-action pairs. This article presents
some engineering applications of mean-field-type games including road traffic
networks, multi-level building evacuation, millimeter wave wireless
communications, distributed power networks, virus spread over networks, virtual
machine resource management in cloud networks, synchronization of oscillators,
energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201
Incentives-Based Mechanism for Efficient Demand Response Programs
In this work we investigate the inefficiency of the electricity system with
strategic agents. Specifically, we prove that without a proper control the
total demand of an inefficient system is at most twice the total demand of the
optimal outcome. We propose an incentives scheme that promotes optimal outcomes
in the inefficient electricity market. The economic incentives can be seen as
an indirect revelation mechanism that allocates resources using a
one-dimensional message space per resource to be allocated. The mechanism does
not request private information from users and is valid for any concave
customer's valuation function. We propose a distributed implementation of the
mechanism using population games and evaluate the performance of four popular
dynamics methods in terms of the cost to implement the mechanism. We find that
the achievement of efficiency in strategic environments might be achieved at a
cost, which is dependent on both the users' preferences and the dynamic
evolution of the system. Some simulation results illustrate the ideas presented
throughout the paper.Comment: 38 pages, 9 figures, submitted to journa
The minority game: An economics perspective
This paper gives a critical account of the minority game literature. The
minority game is a simple congestion game: players need to choose between two
options, and those who have selected the option chosen by the minority win. The
learning model proposed in this literature seems to differ markedly from the
learning models commonly used in economics. We relate the learning model from
the minority game literature to standard game-theoretic learning models, and
show that in fact it shares many features with these models. However, the
predictions of the learning model differ considerably from the predictions of
most other learning models. We discuss the main predictions of the learning
model proposed in the minority game literature, and compare these to
experimental findings on congestion games.Comment: 30 pages, 4 figure
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