Technological change in markets with network externalities

Technological Change;Externalities

The Theory of Implementation of Social Choice Rules

Suppose that the goals of a society can be summarized in a social choice rule, i.e., a mapping from relevant underlying parameters to final outcomes. Typically, the underlying parameters (e.g., individual preferences) are private information to the agents in society. The implementation problem is then formulated: under what circumstances can one design a mechanism so that the private information is truthfully elicited and the social optimum ends up being implemented? In designing such a mechanism, appropriate incentives will have to be given to the agents so that they do not wish to misrepresent their information. The theory of implementation or mechanism design formalizes this “social engineering” problem and provides answers to the question just posed. I survey the theory of implementation in this article, emphasizing the results based on two behavioral assumptions for the agents (dominant strategies and Nash equilibrium). Examples discussed include voting, and the allocation of private and public goods under complete and incomplete information.Implementation Theory, Mechanism Design, Asymmetric Information, Decentralization, Game Theory, Dominance, Nash Equilibrium, Monotonicity

Towards an Uncertainty-Aware Adaptive Decision Engine for Self-Protecting Software: an POMDP-based Approach

The threats posed by evolving cyberattacks have led to increased research related to software systems that can self-protect. One topic in this domain is Moving Target Defense (MTD), which changes software characteristics in the protected system to make it harder for attackers to exploit vulnerabilities. However, MTD implementation and deployment are often impacted by run-time uncertainties, and existing MTD decision-making solutions have neglected uncertainty in model parameters and lack self-adaptation. This paper aims to address this gap by proposing an approach for an uncertainty-aware and self-adaptive MTD decision engine based on Partially Observable Markov Decision Process and Bayesian Learning techniques. The proposed approach considers uncertainty in both state and model parameters; thus, it has the potential to better capture environmental variability and improve defense strategies. A preliminary study is presented to highlight the potential effectiveness and challenges of the proposed approach

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Unraveling Coordination Problems

Strategic uncertainty complicates policy design in coordination games. To rein in strategic uncertainty, the planner in this paper connects the problem of policy design to that of equilibrium selection using a global games approach. We characterize the subsidy scheme that induces coordination on a given outcome of the game as its unique equilibrium. Optimal subsidies are symmetric for identical players, continuous functions of model parameters, and do not make the targeted strategies strictly dominant for any of the players; these properties differ starkly from canonical results in the literature. Uncertainty about payoffs impels policy moderation as aggressive intervention might itself induce coordination failure. JEL codes: D81, D82, D83, D86, H20. Keywords: mechanism design, global games, contracting with externalities, unique implementation