A wide-ranging discussion of the evolution of pricing in early transportation industries, such as lighthouses, canals, and turnpikes, is presented. It shows that price discrimination was an important factor in the development of those industries, and tended to intensify with time. In order to make differential tariffs effective, service providers had the right of detailed inspection of the cargo. These historical precedents help explain the drive by large sectors of the telecommunications industry to gain greater control over what is transmitted over the Internet. The implications for the evolution of the Internet are briefly explored. 1

Abstract. Technology appears to be making fine-scale charging (as in tolls on roads that depend on time of day or even on current and anticipated levels of congestion) increasingly feasible. And such charging appears to be increasingly desirable, as traffic on roads continues to grow, and costs and public opposition limit new construction. Similar incentives towards fine-scale charging also appear to be operating in communications and other areas, such as electricity usage. Standard economic theory supports such measures, and technology is being developed and deployed to implement them. But their spread is not very rapid, and prospects for the future are uncertain. This paper presents a collection of sketches, some from ancient history, some from current developments, that illustrate the costs that charging imposes. Some of those costs are explicit (in terms of the monetary costs to users, and the costs of implementing the charging mechanisms). Others are implicit, such as the time or the mental processing costs of users. These argue that the case for fine-scale charging is not unambiguous, and that in many cases may be inappropriate. 1

Historical precedents suggest that the basic issues underlying the debate about network neutrality, dealing with the balance between efficiency and fairness in markets, will never be resolved. Should net neutrality dominate, attention would likely turn to other parts of the economy that might be perceived as choke points for economic activities, such as Net search. Traditionally, the balance between efficiency and fairness that was struck by policy makers depended heavily on cost considerations. When a service was expensive to provide, fairness was deemphasized. In the current discussion of network neutrality, this issue appears to be unduly neglected. 1

Abstract — To discover an efficient fair sending rate for a flow, Transmission Control Protocol (TCP) saturates the bottleneck link and its buffer until the router discards a packet. Such TCP-caused queuing is detrimental for interactive and other delay-sensitive applications. In this paper, we present Multimodal Control Protocol (MCP) which strives to maintain low queues and avoid congestion losses at network links. The multimodal MCP engages routers and hosts in limited explicit communication. A distinguishing property of MCP is stable transmission after converging to efficient fair states. To ensure convergence to fairness, MCP incorporates an innovative mechanism that enables a flow to urge all flows sharing its bottleneck links to operate in a fairing mode, dedicated to fairness improvement. To make the stable fair rates independent of round-trip times and packet sizes, MCP employs rate-based control and uniform timing of adjustments. I.

The problem of interest is how to dynamically allocate wireless access services in a competitive market which implements a take-it-or-leave-it allocation mechanism. In this paper we focus on the subproblem of preference elicitation, given a mechanism. The user, due to a number of cognitive and technical reasons, is assumed to be initially uninformed over their preferences in the wireless domain. The solution we have developed is a closed-loop user-agent system that assists the user in application, task and context dependent service provisioning by adaptively and interactively learning to select the best wireless data service. The agent learns an incrementally revealed user preference model given explicit or implicit feedback on its decisions by the user. We model this closed-loop system as a Markov Decision Process, where the agent actions are rewarded by the user, and show how a reinforcement learning algorithm can be used to learn a model of the user's preferences on-line in the given allocation mechanism. We evaluate the performance and value of the agent in a series of preliminary empirical user studies.

Abstract. The measurement of the volume of information is fraught with difficulties. However, trends in availability and usage can be very revealing, as is shown by some examples. This paper argues that considerations of the volume of information should not be divorced from those of the value of information. In communications, informative comparisons appear to be possible by classifying technologies in just a few dimensions, associated with the features of cost, speed, availability, and usability. An argument is also made that as a rough approximation, the value of information in terms of its volume is best thought of on a logarithmic scale. This approach provides a rough quantitative guide to the diminishing marginal utility delivered by the rapid progress in computing, storage, and communication technologies. It offers a partial explanation for slow uptake of ultra-high speed broadband and related phenomena. This paper will appear in a special section of the International Journal of Communication: “How to measure how much information is out there? Methodological and statistical challenges for the social sciences.”

Network pricing has important implications in the revenue generation, resource management, system optimization and congestion control of computer networks. We depart from the prevalent idea of marginal cost pricing and provide a holistic, bi-level optimiza-tion framework to model the interaction between network entities in a connection oriented network. Users are treated as utility maximizing entities who allocate the available band-width among themselves by playing a distributed, noncooperative rate game. The ensuing Nash equilibrium is analyzed for the single link Erlang network and the multi-link prod-uct form networks. Variants based on the upper bound of the blocking are also studied owing to their role in reducing computational complexity. Theoretical results are then val-idated using numerical simulation for varying network scenarios. An extension of the rate adaptation game based on Recursive Least Squares is proposed for dealing with the imper-fect information scenario. These exhibited favorable convergence, accuracy and scalability properties. Gradient-free schemes are then developed for revenue maximization. These are based on novel stochastic approximation techniques such as Finite Difference Stochastic Approximation (FDSA) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is observed that the network employed price discrimination for optimizing its objective function and partitioning its available capacity among competing users.