Biblio

This article studies the value of information in route choice decisions when a fraction of players have access to high accuracy information about traffic incidents relative to others. To model such environments, we introduce a Bayesian congestion game, in which players have private information about incidents, and each player chooses her route on a network of parallel links. The links are prone to incidents that occur with an ex-ante known probability. The demand is comprised of two player populations: one with access to high accuracy incident information and another with low accuracy information, i.e. the populations differ only by their access to information. The common knowledge includes: (i) the demand and route cost functions, (ii) the fraction of highly-informed players, (iii) the incident probability, and (iv) the marginal type distributions induced by the information structure of the game. We present a full characterization of the Bayesian Wardrop Equilibrium of this game under the assumption that low information players receive no additional information beyond common knowledge. We also compute the cost to individual players and the social cost as a function of the fraction of highly-informed players when they receive perfectly accurate information. Our first result suggests that below a certain threshold of highly-informed players, both populations experience a reduction in individual cost, with the highly-informed players receiving a greater reduction. However, above this threshold, both populations realize the same equilibrium cost. Secondly, there exists another (lower or equal) threshold above which a further increase in the fraction of highly-informed players does not reduce the expected social costs. Thus, once a sufficiently large number of players are highly informed, wider distribution of more accurate information is ineffective at best, and otherwise socially harmful.

We study users’ incentives to become cybercriminals when network security is interdependent. We present a game-theoretic model in which each player (i.e., network user) decides his type, honest or malicious. Honest users represent law-abiding network users, while malicious users represent cybercriminals. After deciding on their types, the users make their security choices. We will follow [29], where breach probabilities for large-scale networks are obtained from a standard interdependent security (IDS) setup. In large-scale IDS networks, the breach probability of each player becomes a function of two variables: the player’s own security action and network security, which is an aggregate characteristic of the network; network security is computed from the security actions of the individual nodes that comprise the network. This allows us to quantify user security choices in networks with IDS even when users have only very limited, aggregate information about security choices of other users of the network.

The article by Amin, Schwartz, Cárdenas, and Sastry investigates energy theft in smart utility networks using techniques from game theory and detection theory. The game-theoretic model considers pricing and investment decisions by a distribution utility when it serves a population of strategic customers, and a fraction of customers are fraudulent. Each fraudulent customer chooses to steal electricity after accounting for the probability of fraud detection and the amount of fine that they pay if detected. The probabilistic rate of successful detection depends on the distributor's implementation of a diagnostic scheme and increases with level of investment made by the distributor monitoring fraud. The distributor (leader) chooses the level of investment, the price per unit quantity of billed electricity, and the fine schedule. The customers (followers) make their choices after they learn the distributor's decision. For specific assumptions on customer utilities and a distributor's profit function, this leader-follower game is used to compute equilibrium customer and distributor choices. For two environments, namely an unregulated monopoly and the case of perfect competition, the results provide an estimate of the extent of stealing for different levels of investment (high versus low). These results point toward the need for creating regulatory measures to incentivize investments in security and fraud monitoring.