Efficiency in Games With Markovian Private Information

We study repeated Bayesian games with communication and observable actions in which the players' privately known payoffs evolve according to an irreducible Markov chain whose transitions are independent across players. Our main result implies that, generically, any Pareto-efficient payoff vector above a stationary minmax value can be approximated arbitrarily closely in a perfect Bayesian equilibrium as the discount factor goes to 1. As an intermediate step, we construct an approximately efficient dynamic mechanism for long finite horizons without assuming transferable utility

Dynamic Game-Theoretic Models to Determine the Value of Intrusion Detection Systems in the Face of Uncertainty

Firms lose millions of dollars every year to cyber-attacks and the risk to these companies is growing exponentially. The threat to monetary and intellectual property has made Information Technology (IT) security management a critical challenge to firms. Security devices, including Intrusion Detections Systems (IDS), are commonly used to help protect these firms from malicious users by identifying the presence of malicious network traffic. However, the actual value of these devices remains uncertain among the IT security community because of the costs associated with the implementation of different monitoring strategies that determine when to inspect potentially malicious traffic and the costs associated with false positive and negative errors. Game theoretic models have proven effective for determining the value of these devices under several conditions where firms and users are modeled as players. However, these models assume that both the firm and attacker have complete information about their opponent and lack the ability to account for more realistic situations where players have incomplete information regarding their opponent\u27s payoffs. The proposed research develops an enhanced model that can be used for strategic decision making in IT security management where the firm is uncertain about the user\u27s utility of intrusion. By using Harsanyi Transformation Analysis, the model provides the IT security research community with valuable insight into the value of IDS when the firm is uncertain of the incentives and payoffs available to users choosing to hack. Specifically, this dissertation considers two possible types of users with different utility for intrusion to gain further insights about the players\u27 strategies. The firm\u27s optimal strategy is to start the game with the expected value of the user\u27s utility as an estimate. Under this strategy, the firm can determine the user\u27s utility with certainty within one iteration of the game. After the first iteration, the game may be analyzed as a game of perfect information

Political Predation and Economic Development

We analyze a game between citizens and governments, whose type (benevolent or predatory) is unknown to the public. Opportunistic governments mix between predation and restraint. As long as restraint is observed, political expectations improve, people enter the modern sector, and the economy grows. Once there is predation, the reputation of the government is ruined and the economy collapses. If citizens are unable to overthrow this government, the collapse is durable. Otherwise, a new government is drawn and the economy can rebound. Consistent with stylized facts, equilibrium political and economic histories are random, unstable, and exhibit long-term divergence.

Hidden Limit Orders and Liquidity in Order Driven Markets

This paper analyzes the rationale for the submission of hidden limit orders, and compares opaque and transparent limit order books. In my sequential model, the limit order trader may be informed with some probability. Both informed and large uninformed liquidity suppliers submit hidden orders in order to decrease the informational impact of their large orders, while ensuring a large trading volume. As they cannot adopt such a strategy in the transparent market, I find that pre-trade opacity improves market liquidity, and the welfare of the participants. My model further yields empirical predictions on the use and revelation of hidden orders in opaque markets.

UNCERTAINTY AND DECISION-MAKING IN THE HUMAN BRAIN

Dissertation presented to obtain the Ph.D degree in Biology, NeuroscienceUncertainty pervades most of the events and decisions that we face every day. Uncertainty exists in previously acquired knowledge (prior) and on what our senses currently tell us (likelihood). Moreover, uncertainty exists in both non-social and social settings (social uncertainty). Understanding how the brain responds to and uses information about uncertainty thus seems crucial if we want to understand decision-making.(...

Modeling Settlement Bargaining with Algorithmic Game Theory

Past computational models of settlement bargaining have lacked explicit game theoretic foundations. Algorithmic game theory, however, offers techniques that can find perfect Bayesian equilibria even where closed-form mathematical solutions may be intractable. Some recent mathematical models tackle two-sided asymmetric information, including evidentiary signals models, in which the judgment is a sum of both shared and independent private information, and correlated signals models, in which both parties receive noisy signals about the same information. To relax assumptions inherent in these models, this paper employs several progressively more complicated techniques, including iterative elimination of dominated alternatives, no regret learning, and counterfactual regret minimization. Although these algorithms are not guaranteed to produce Nash equilibria in general-sum games like litigation, they nonetheless succeed in producing either exact or close approximate equilibria on discrete versions of the corresponding mathematical models. A single algorithmic game theory model can incorporate a number of features that state-of-the-art mathematical models cannot handle simultaneously, such as two-sided correlated signals of both liability and damages, risk aversion, and options to concede

Competition in digital markets

This thesis contains three essays on different topics related to competition in digital markets. The first essay analyzes the effects of interoperability on price competition in a market with network effects. It looks at a market in an early stage of development before the monopoly is entrenched. The second essay studies the roles of complementarity and economies of scope in conglomerate mergers involving a digital ecosystem. It shows that these two characteristics lead to foreclosure in the long run. The last essay investigates the effect of improved information, in the form of more accurate product recommendations, on price competition

Hidden Limit Orders and Liquidity in Order Driven Markets

This paper analyzes the rationale for the submission of hidden limit orders, and compares opaque and transparent limit order books. In my sequential model, the limit order trader may be informed with some probability. Both informed and large uninformed liquidity suppliers submit hidden orders in order to decrease the informational impact of their large orders, while ensuring a large trading volume. As they cannot adopt such a strategy in the transparent market, I find that pre-trade opacity improves market liquidity, and the welfare of the participants. My model further yields empirical predictions on the use and revelation of hidden orders in opaque markets