Robert C. Hampshire

Sending mixed signals: multilevel reputation effects in peer-to-peer lending markets

Online peer-to-peer (P2P) lending organizations enable an individual to obtain an unsecured loan from a collection of individuals without the participation of a bank. Previous research has addressed the use of reputation systems to reduce information asymmetry based on individual history within online markets. Within the last few years one of the market leaders in P2P lending, Prosper.com, has sought to replace the information vetting and monitoring typically done by the bank with a community of users free to select its community members based on any criteria it chooses. By embedding individual reputations within a community reputation, incentives become aligned for peers to select highly qualified borrowers and produce more costly information signals to reduce the adverse selection and moral hazard risk typical of any principle-agent relationship. This study draws on theory from the Principle-Agent perspective to empirically examine the signals that enhance community reputation.

Peer-to-Peer Carsharing: Market Analysis and Potential Growth

Many studies show that carsharing reduces transportation costs for a large segment of the population. Carsharing also reduces the number of private vehicles on the road because carshare members do not purchase their own cars. However, the traditional carsharing business model is difficult to scale geographically to neighborhoods with lower population densities because the operator must bear the upfront fixed cost of purchasing or leasing the vehicles in the fleet. In contrast to traditional carsharing, peer-to-peer (P2P) carsharing allows car owners to convert their personal vehicles into shared cars that can be rented to other drivers on a short-term basis. This model helps to improve the situation in which most privately owned vehicles sit idle more than 90% of the day. P2P carsharing alleviates upfront costs and thus is more economically consistent with lower-density neighborhoods than is traditional carsharing. As a result, P2P carsharing provides greater potential for car accessibility than traditional carsharing does. Several new service companies are dedicated to P2P carsharing. A methodology was developed to assess the market feasibility of P2P carsharing. The methodology was applied to develop a case study of P2P carsharing in Pittsburgh, Pennsylvania. The market for P2P carsharing was found to be economically viable. However, uncertain and fragmented public policy and car insurance regimes threatened the growth and investment in P2P carsharing.

Fluid and diffusion limits for transient sojourn times of processor sharing queues with time varying rates

We provide an approximate analysis of the transient sojourn time for a processor sharing queue with time varying arrival and service rates, where the load can vary over time, including periods of overload. Using the same asymptotic technique as uniform acceleration as demonstrated in [12] and [13], we obtain fluid and diffusion limits for the sojourn time of the Mt/Mt/1 processor-sharing queue. Our analysis is enabled by the introduction of a “virtual customer” which differs from the notion of a “tagged customer” in that the former has no effect on the processing time of the other customers in the system. Our analysis generalizes to non-exponential service and interarrival times, when the fluid and diffusion limits for the queueing process are known.

A time-varying call center design via lagrangian mechanics

We consider a multiserver delay queue with finite additional waiting spaces and time-varying arrival rates, where the customers waiting in the buffer may abandon. These are features that arise naturally from the study of service systems such as call centers. Moreover, we assume rewards for successful service completions and cost rates for service resources. Finally, we consider service-level agreements that constrain both the fractions of callers who abandon and the ones who are blocked. Applying the theory of Lagrangian mechanics to the fluid limit of a related Markovian service network model, we obtain near-profit-optimal staffing and provisioning schedules. The nature of this solution consists of three modes of operation. A key step in deriving this solution is combining the modified offered load approximation for loss systems with our fluid model. We use them to estimate effectively both our service-level agreement metrics and the profit for the original queuing model. Second-order profit improvements are achieved through a modified offered load version of the conventional square root safety rule.

Dynamic pricing to control loss systems with quality of service targets

Numerous examples of real-time services arise in the service industry that can be modeled as loss systems. These include agent staffing for call centers, provisioning bandwidth for private line services, making rooms available for hotel reservations, and congestion pricing for parking spaces. Given that arriving customers make their decision to join the system based on the current service price, the manager can use price as a mechanism to control the utilization of the system. A major objective for the manager is then to find a pricing policy that maximizes total revenue while meeting the quality of service targets desired by the customers. For systems with growing demand and service capacity, we provide a dynamic pricing algorithm. A key feature of our solution is congestion pricing. We use demand forecasts to anticipate future service congestion and set the present price accordingly.

Provisioning for bandwidth sharing and exchange

Customers of bandwidth services can be divided into two distinct groups: those customers requesting bandwidth for the future and those desiring bandwidth immediately. We develop a dynamic network provisioning methodology that minimally satisfies the QoS (blocking probability) requirements for the ’on-demand’ customers. Our method is sufficiently general and captures time varying trends in the demand for services as well as different bandwidth requests for the multiple classes of customers. This allows a network provider to be efficient in reserving excess bandwidth for forward contracts. Asymptotic results and bounds for the Erlang loss system are invoked to obtain simple approximate solutions to this bandwidth provisioning problem.

Variational optimization for call center staffing

According to Koole and Mandelbauin (2001), almost 60 to 70 percent of the total costs for operating a call center involve wage and benefit expenses for personnel. It follows that determining the optimal amount of call center agents is of great interest to call center managers. This paper addresses both the staffing of agents and the provisioning of telephone lines by introducing a revenue and penalty structure. Our goal is to develop an approximate algorithm for designing a profit optimal staffing and provisioning schedule. Our method for determining the number of agents and telephone lines arises from variational optimization methods. First, we model the call center as a multiserver queue with additional waiting spaces and abandonment. This queueing system is a special case of a natural class of queueing network models for call centers called Markovian service networks. Now we add an economic structure to our queueing model for the call center. We assume that there is a reward for every successful service completion, a penalty for every abandoned call, and a cost for the number of agents and telephone lines used. We can then express the total profit for the call center as an integral functional of the time evolution for the number of customers in the system over a fixed time interval. We call this our profit functional. We then use variational calculus methods from the theory of optimal control to derive an optimal staffing and provisioning schedule from our analysis of the fluid approximation of the profit functional.

Comment on Pierce and Shoup: Evaluating the Impacts of Performance-Based Parking

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Decision Analytics for Parking Availability in Downtown Pittsburgh

ParkPGH is a novel parking application that provides real-time and predictive information on the availability of garage parking spaces within Pittsburgh’s Cultural District. The core of the application is a module that collects real-time parking information from the garages by tapping into their gate counts. The real-time component is complemented by a module that uses historical data and an events calendar to predict parking availability. In 2011, visitors to downtown Pittsburgh used ParkPGH more than 300,000 times to determine when and where to park. The application has also been beneficial to garage operators because the information it provides on parking demand affords them greater flexibility in addressing contingencies and managing lease holders. The deployment of ParkPGH, which includes a robust evaluation component, is one piece of a broader transportation ecosystem within the Greater Pittsburgh region. The lessons we learned from the initiative, the application’s relatively low cost, its ease of ...

A simulation study of Peer-to-Peer carsharing

Many studies have shown that carsharing reduces environmental pollution and the transportation costs for a large segment of the population. Car sharing also reduces the number of private vehicles on the road because members do not purchase their own car. However, the traditional carsharing business model is difficult to scale geographically to neighborhoods with lower population densities because the operator must bear the upfront fixed cost of purchasing or leasing the vehicles in the fleet. In contrast to traditional carsharing, Peer-to-Peer (P2P) carsharing allows car owners to convert their personal vehicles into shared cars which can be rented to other drivers on a short-term basis. This model leverages the fact that most privately owned vehicles sit idle over 90% of the day. This paper presents a simulation study and a reservation control policy (RCP) to increase the revenue generated from P2P carsharing. The results show that rejecting reservations, even when the time slot is available, increases revenue when the demand is sufficiently high.