On the Provision of Public Goods on Networks: Incentives, Exit Equilibrium, and Applications to Cyber .

Attempts to improve the state of cyber-security have been on the rise over the past years. The importance of incentivizing better security decisions by users in the current landscape is two-fold: it not only helps users protect themselves against attacks, but also provides positive externalities to others interacting with them, as a protected user is less likely to become compromised and be used to propagate attacks against other entities. Therefore, security can be viewed as a public good. This thesis takes a game-theoretic approach to understanding the theoretical underpinnings of users' incentives in the provision of public goods, and in particular, cyber-security. We analyze the strategic interactions of users in the provision of security as a non-excludable public good. We propose the notion of exit equilibrium to describe users' outside options from mechanisms for incentivizing the adoption of better security decisions, and use it to highlight the crucial effect of outside options on the design of incentive mechanisms for improving the state of cyber-security. We further focus on the general problem of public good provision games on networks. We identify necessary and sufficient conditions on the structure of the network for the existence and uniqueness of the Nash equilibrium in these games. We show that previous results in the literature can be recovered as special cases of our result. We provide a graph-theoretical interpretation of users' efforts at the Nash equilibria, Pareto efficient outcomes, and semi-cooperative equilibria of these games, by linking users' effort decisions to their centralities in the interaction network. Using this characterization, we separate the effects of users' dependencies and influences (outgoing and incoming edges, respectively) on their effort levels, and uncover an alternating effect over walks of different length in the network. We also propose the design of inter-temporal incentives in a particular type of security games, namely, security information sharing agreement. We show that either public or private assessments can be used in designing incentives for participants to disclose their information in these agreements. Finally, we present a method for crowdsourcing reputation that can be useful in attaining assessments of users' efforts in security games.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133328/1/naghizad_1.pd

Social Bias Meets Data Bias: The Impacts of Labeling and Measurement Errors on Fairness Criteria

Although many fairness criteria have been proposed to ensure that machine learning algorithms do not exhibit or amplify our existing social biases, these algorithms are trained on datasets that can themselves be statistically biased. In this paper, we investigate the robustness of a number of existing (demographic) fairness criteria when the algorithm is trained on biased data. We consider two forms of dataset bias: errors by prior decision makers in the labeling process, and errors in measurement of the features of disadvantaged individuals. We analytically show that some constraints (such as Demographic Parity) can remain robust when facing certain statistical biases, while others (such as Equalized Odds) are significantly violated if trained on biased data. We also analyze the sensitivity of these criteria and the decision maker's utility to biases. We provide numerical experiments based on three real-world datasets (the FICO, Adult, and German credit score datasets) supporting our analytical findings. Our findings present an additional guideline for choosing among existing fairness criteria, or for proposing new criteria, when available datasets may be biased

An advantage based policy transfer algorithm for reinforcement learning with metrics of transferability

Reinforcement learning (RL) can enable sequential decision-making in complex and high-dimensional environments if the acquisition of a new state-action pair is efficient, i.e., when interaction with the environment is inexpensive. However, there are a myriad of real-world applications in which a high number of interactions are infeasible. In these environments, transfer RL algorithms, which can be used for the transfer of knowledge from one or multiple source environments to a target environment, have been shown to increase learning speed and improve initial and asymptotic performance. However, most existing transfer RL algorithms are on-policy and sample inefficient, and often require heuristic choices in algorithm design. This paper proposes an off-policy Advantage-based Policy Transfer algorithm, APT-RL, for fixed domain environments. Its novelty is in using the popular notion of ``advantage'' as a regularizer, to weigh the knowledge that should be transferred from the source, relative to new knowledge learned in the target, removing the need for heuristic choices. Further, we propose a new transfer performance metric to evaluate the performance of our algorithm and unify existing transfer RL frameworks. Finally, we present a scalable, theoretically-backed task similarity measurement algorithm to illustrate the alignments between our proposed transferability metric and similarities between source and target environments. Numerical experiments on three continuous control benchmark tasks demonstrate that APT-RL outperforms existing transfer RL algorithms on most tasks, and is $10\%$ to $75\%$ more sample efficient than learning from scratch