14 research outputs found
Small-Scale Markets for Bilateral Resource Trading in the Sharing Economy
We consider a general small-scale market for agent-to-agent resource sharing,
in which each agent could either be a server (seller) or a client (buyer) in
each time period. In every time period, a server has a certain amount of
resources that any client could consume, and randomly gets matched with a
client. Our target is to maximize the resource utilization in such an
agent-to-agent market, where the agents are strategic. During each transaction,
the server gets money and the client gets resources. Hence, trade ratio
maximization implies efficiency maximization of our system. We model the
proposed market system through a Mean Field Game approach and prove the
existence of the Mean Field Equilibrium, which can achieve an almost 100% trade
ratio. Finally, we carry out a simulation study motivated by an agent-to-agent
computing market, and a case study on a proposed photovoltaic market, and show
the designed market benefits both individuals and the system as a whole
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
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Mean field games with singular controls of bounded velocity
This thesis studies a class of mean field games (MFG) with singular controls of bounded velocity. By relaxing absolute continuity of control processes, it generalizes the MFG framework of Lasry and Lions \cite{LL2007} and Huang, Malham\'e, and Caines \cite{HMC2006}. It provides a unique solution to the MFG with singular controls of bounded velocity and its explicit optimal control policy establishes an \epsilon-Nash equilibrium of the corresponding stochastic differential N player game with singular controls. It also includes MFGs on an infinite time horizon. Our method to approach MFGs with singular controls is from bounded velocity processes, and we analyse the relationship between singular controls with finite variation processes and singular controls with bounded velocity. Finally, it illustrates particular MFGs with explicit solutions in a systemic risk model originally formulated by Carmona, Fouque, and Sun \cite{CFS2013} in a regular control setting and an optimal partially reversible investment problem with N players originally formulated by Guo and Pham \cite{GP2005} in a single player setting
Pricing and Equilibrium Analysis of Network Market Systems
Markets have been the most successful method of identifying value of goods and services.
Both large and small scale markets have gradually been moving into the Internet domain,
with increasingly large numbers of diverse participants. In this dissertation, we consider several
problems pertaining to equilibria in networked marketplaces under different application
scenarios and market sizes. We approach the question of pricing and market design from two
perspectives. On the one hand, we desire to understand how self-interested market participants would set prices and respond to prices resulting in certain allocations. On the other
hand, we wish to evaluate how best to allocate resources so as to attain efficient equilibria.
There might be a gap between these viewpoints, and characterizing this gap is desirable.
Our technical approaches follow the number of market participants, and the nature of
trades happening in the market. In our first problem, we consider a market of providing
communication services at the level of providing Internet transit. Here, the transit Internet
Service Provider (ISP) must determine billing volumes and set prices for its customers who
are _rms that are content providers, sinks, or subsidiary ISPs. Demand from these customers
is variable, and they have different impacts on the resources that the transit ISP needs to
provision. Using measured data from several networks, we design a fair and flexible billing
scheme that correctly identifies the impact of each customer on the amount of provisioning
needed.
While the customer set in the first problem is finite, many marketplaces deal with a very
large number of agents that each have ephemeral lifetimes. Here, agents arrive, participate in
the market for some time, and then vanish. We consider two such markets in such a regime.
The first is one of apps on mobile devices that compete against each other for cellular data
service, while the second is on service marketplaces wherein many providers compete with
each other for jobs that consider both prices and provider reputations while making choices
between them. Our goal is to show that a Mean Field Game can be used to accurately
approximate these systems, determine how prices are set, and characterize the nature of
equilibria in such markets.
Finally, we consider efficiency metrics in large scale resource sharing networks in which
bilateral exchange of resources is the norm. In particular, we consider peer-to-peer (P2P)
file sharing under which peers obtain chunks of a file from each other. Here, contrary to
the intuition that chunks must be shared whenever one peer has one of value to another, we
show that a measure of suppression is needed to utilize resources efficiently. In particular, we
propose a simple and stable algorithm entitled Mode suppression that attains near optimal
file sharing times by disallowing the sharing of the most frequent chunks in the system