/pdf/the-theory-of-industrial-organization-19bejph0ej.pdf

The Theory of Industrial Organization

We consider the problem of routing traffic to optimize the performance of a congested network. We are given a network, a rate of traffic between each pair of nodes, and a latency function for each edge specifying the time needed to traverse the edge given its congestion; the objective is to route traffic such that the sum of all travel times---the total latency---is minimized.In many settings, it may be expensive or impossible to regulate network traffic so as to implement an optimal assignment of routes. In the absence of regulation by some central authority, we assume that each network user routes its traffic on the minimum-latency path available to it, given the network congestion caused by the other users. In general such a "selfishly motivated" assignment of traffic to paths will not minimize the total latency; hence, this lack of regulation carries the cost of decreased network performance.In this article, we quantify the degradation in network performance due to unregulated traffic. We prove that if the latency of each edge is a linear function of its congestion, then the total latency of the routes chosen by selfish network users is at most 4/3 times the minimum possible total latency (subject to the condition that all traffic must be routed). We also consider the more general setting in which edge latency functions are assumed only to be continuous and nondecreasing in the edge congestion. Here, the total latency of the routes chosen by unregulated selfish network users may be arbitrarily larger than the minimum possible total latency; however, we prove that it is no more than the total latency incurred by optimally routing twice as much traffic.

/pdf/how-bad-is-selfish-routing-g644vu88rg.pdf

How bad is selfish routing

Information, trade and common knowledge

A major challenge in the operation of wireless communications systems is the efficient use of radio resources. One important component of radio resource management is power control, which has been studied extensively in the context of voice communications. With the increasing demand for wireless data services, it is necessary to establish power control algorithms for information sources other than voice. We present a power control solution for wireless data in the analytical setting of a game theoretic framework. In this context, the quality of service (QoS) a wireless terminal receives is referred to as the utility and distributed power control is a noncooperative power control game where users maximize their utility. The outcome of the game results in a Nash (1951) equilibrium that is inefficient. We introduce pricing of transmit powers in order to obtain Pareto improvement of the noncooperative power control game, i.e., to obtain improvements in user utilities relative to the case with no pricing. Specifically, we consider a pricing function that is a linear function of the transmit power. The simplicity of the pricing function allows a distributed implementation where the price can be broadcast by the base station to all the terminals. We see that pricing is especially helpful in a heavily loaded system.

/pdf/efficient-power-control-via-pricing-in-wireless-data-34gk0jwxel.pdf

Efficient power control via pricing in wireless data networks

The History of Economic Analysis

We show that if students care primarily about their status (relative rank) in class, they are best motivated to work not by revealing their exact numerical exam scores (100,99,...,1), but instead by clumping them in broad categories (A,B,C). If their abilities are disparate, the optimal grading scheme awards fewer A's than there are alpha-quality students, creating small elites. If their abilities are common knowledge, then it is better to grade them on an absolute scale (100 to 90 is an A, etc.) rather than on a curve (top 15% is an A, etc.). We develop criteria for optimal grading schemes in terms of the stochastic dominance between student performances.

/pdf/grading-exams-100-99-1-or-a-b-c-incentives-in-games-of-53bj3gramy.pdf

Grading Exams: 100, 99, ..., 1 or A, B, C? Incentives in Games of Status

We derive the existence of a Walras equilibrium directly from Nash's theorem on noncooperative games. No price player is involved, nor are generalized games. Instead we use a variant of the Shapley-Shubik trading-post game.

From Nash to Walras via Shapley-Shubik

Price formation and trade in a large exchange economy is modeled as a non-atomic noncooperative game in two contrasting ways: (1) with fiat money, with borrowing and bankruptcy permitted, and (2) with a commodity money and no borrowing.

Noncooperative exchange with a continuum of traders

There are many situations in which a customer's proclivity to buy the product of any firm depends not only on the classical attributes of the product such as its price and quality, but also on who else is buying the same product. We model these situations as games in which firms compete for customers located in a “social network”. Nash Equilibrium (NE) in pure strategies exist and are unique. Indeed there are closed-form formulae for the NE in terms of the exogenous parameters of the model, which enables us to compute NE in polynomial time. An important structural feature of NE is that, if there are no a priori biases between customers and firms, then there is a cut-off level above which high cost firms are blockaded at an NE, while the rest compete uniformly throughout the network. We finally explore the relation between the connectivity of a customer and the money firms spend on him. This relation becomes particularly transparent when externalities are dominant: NE can be characterized in terms of the invariant measures on the recurrent classes of the Markov chain underlying the social network.

Pradeep Dubey

Papers

Grading Exams: 100, 99, ..., 1 or A, B, C? Incentives in Games of Status

From Nash to Walras via Shapley-Shubik

Noncooperative exchange with a continuum of traders

Competing for Customers in a Social Network: The Quasi-linear Case

Nash equilibria of market games: Finiteness and inefficiency