168 research outputs found
Congestion pricing by priority auction
This paper analyzes a communication network facing users with a continuous distribution of delay cost per unit time. Priority queueing is often used as a way to provide differential services for users with different delay sensitivities. Delay is a key dimension of network service quality, so priority is a valuable resource which is limited and should to be optimally allocated. We investigate the allocation of priority in queues via a simple bidding mechanism. In our mechanism, arriving users can decide not to enter the network at all or submit an announced delay sensitive value. User entering the network obtains priority over all users who make lower bids, and is charged by a payment function which is designed following an exclusion compensation principle. The payment function is proved to be incentive compatible, so the equilibrium bidding behavior leads to the implementation of "cµ-rule". Social warfare or revenue maximizing by appropriately setting the reserve payment is also analyzed
Self-Selecting Priority Queues with Burr Distributed Waiting Costs
Service providers, in the presence of congestion and heterogeneity of customer waiting costs, often introduce a fee-based premier option using which the customers self-segment themselves. Examples of this practice are found in health care, amusement parks, government (consular services), and transportation. Using a single-server queuing system with customer waiting costs modeled as a Burr Distribution, we perform a detailed analysis to (i) determine the conditions (fees, cost structure, etc.) under which this strategy is profitable for the service provider, (ii) quantify the benefits accrued by the premier customers; and (iii) evaluate the resulting impact on the other customers. We show that such self-selecting priority systems can be pareto-improving in the sense that they are beneficial to everyone. These benefits are larger when the variance in the customer waiting costs is high and the system utilization is high. We use income data from the poorest and richest areas (identified by zipcode) in the United States along with the countrywide income distribution to illustrate our results. Numerical results indicate that planning for a 20–40% enrollment in the high-priority option is robust in ensuring that all the stakeholders benefit from the proposed strategy
Soviet Economic Reform: The Longest Road
macroeconomics, Soviet Economic Reform, Russia
Matching Queues, Flexibility and Incentives
Motivated in part by online marketplaces such as ridesharing and freelancing
platforms, we study two-sided matching markets where agents are heterogeneous
in their compatibility with different types of jobs: flexible agents can
fulfill any job, whereas each specialized agent can only be matched to a
specific subset of jobs. When the set of jobs compatible with each agent is
known, the full-information first-best throughput (i.e. number of matches) can
be achieved by prioritizing dispatch of specialized agents as much as possible.
When agents are strategic, however, we show that such aggressive reservation of
flexible capacity incentivizes flexible agents to pretend to be specialized.
The resulting equilibrium throughput could be even lower than the outcome under
a baseline policy, which does not reserve flexible capacity, and simply
dispatches jobs to agents at random. To balance matching efficiency with
agents' strategic considerations, we introduce a novel robust capacity
reservation policy (RCR). The RCR policy retains a similar structure to the
first best policy, but offers additional and seemingly incompatible edges along
which jobs can be dispatched. We show a Braess' paradox-like result, that
offering these additional edges could sometimes lead to worse equilibrium
outcomes. Nevertheless, we prove that under any market conditions, and
regardless of agents' strategies, the proposed RCR policy always achieves
higher throughput than the baseline policy. Our work highlights the importance
of considering the interplay between strategic behavior and capacity allocation
policies in service systems
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