Long-Term Fairness with Unknown Dynamics

While machine learning can myopically reinforce social inequalities, it may also be used to dynamically seek equitable outcomes. In this paper, we formalize long-term fairness in the context of online reinforcement learning. This formulation can accommodate dynamical control objectives, such as driving equity inherent in the state of a population, that cannot be incorporated into static formulations of fairness. We demonstrate that this framing allows an algorithm to adapt to unknown dynamics by sacrificing short-term incentives to drive a classifier-population system towards more desirable equilibria. For the proposed setting, we develop an algorithm that adapts recent work in online learning. We prove that this algorithm achieves simultaneous probabilistic bounds on cumulative loss and cumulative violations of fairness (as statistical regularities between demographic groups). We compare our proposed algorithm to the repeated retraining of myopic classifiers, as a baseline, and to a deep reinforcement learning algorithm that lacks safety guarantees. Our experiments model human populations according to evolutionary game theory and integrate real-world datasets

Distributional Consequences and Executive Regime Types: The Politics of Foreign Direct Investment Incentives

University of Minnesota Ph.D. dissertation. August 2015. Major: Political Science. Advisor: John Freeman. 1 computer file (PDF); xiii, 229 pages.This dissertation examines variation in the provision of foreign direct investment (FDI) incentives. If FDI is crucial for economic growth, why do some countries offer high levels of incentives to attract FDI, while other countries do not? This study identifies the political dimensions behind FDI incentives provision in democratic countries. I argue that provision of FDI incentives depends on the distributional consequences of FDI and a country's executive regime type. FDI inflows compete up wages and drive down rents, which implies that labor prefers high levels of FDI and FDI incentives, while native capital opposes FDI and FDI incentives. These preferences towards FDI incentives are moderated, however, by a country's executive regime type. Parliamentary democracies, which are more supportive of labor's interests, are expected to provide higher levels of FDI incentives as compared to presidential democracies, which are less supportive of labor. After deriving testable hypotheses using the tools of game theory, I examine the politics of FDI incentives provision by analyzing an original cross-national dataset of FDI incentives generated with machine learning techniques. I then explore the politics of FDI incentives provision by comparing case studies of Poland, a parliamentary democracy, and Romania, a presidential democracy. A final empirical chapter uses unique survey data from Poland to study individual-level attitudes towards FDI incentives

An LSPI based reinforcement learning approach to enable network cooperation in cognitive wireless sensor networks

The number of wirelessly communicating devices increases every day, along with the number of communication standards and technologies that they use to exchange data. A relatively new form of research is trying to find a way to make all these co-located devices not only capable of detecting each other's presence, but to go one step further - to make them cooperate. One recently proposed way to tackle this problem is to engage into cooperation by activating 'network services' (such as internet sharing, interference avoidance, etc.) that offer benefits for other co-located networks. This approach reduces the problem to the following research topic: how to determine which network services would be beneficial for all the cooperating networks. In this paper we analyze and propose a conceptual solution for this problem using the reinforcement learning technique known as the Least Square Policy Iteration (LSPI). The proposes solution uses a self-learning entity that negotiates between different independent and co-located networks. First, the reasoning entity uses self-learning techniques to determine which service configuration should be used to optimize the network performance of each single network. Afterwards, this performance is used as a reference point and LSPI is used to deduce if cooperating with other co-located networks can lead to even further performance improvements

Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation

A human computation system can be viewed as a distributed system in which the processors are humans, called workers. Such systems harness the cognitive power of a group of workers connected to the Internet to execute relatively simple tasks, whose solutions, once grouped, solve a problem that systems equipped with only machines could not solve satisfactorily. Examples of such systems are Amazon Mechanical Turk and the Zooniverse platform. A human computation application comprises a group of tasks, each of them can be performed by one worker. Tasks might have dependencies among each other. In this study, we propose a theoretical framework to analyze such type of application from a distributed systems point of view. Our framework is established on three dimensions that represent different perspectives in which human computation applications can be approached: quality-of-service requirements, design and management strategies, and human aspects. By using this framework, we review human computation in the perspective of programmers seeking to improve the design of human computation applications and managers seeking to increase the effectiveness of human computation infrastructures in running such applications. In doing so, besides integrating and organizing what has been done in this direction, we also put into perspective the fact that the human aspects of the workers in such systems introduce new challenges in terms of, for example, task assignment, dependency management, and fault prevention and tolerance. We discuss how they are related to distributed systems and other areas of knowledge.Comment: 3 figures, 1 tabl

Analysis of Economic Motives in the Individual Choice of Educational Paths

The authors consider the economic motivations when individuals choose an educational path. This line of research is relevant from both, the point of view of science — research of economic behavior of an individual, and the point of view of practice — allows to increase efficiency of investments in a human capital. The authors have developed the economic and mathematical model of choice of optimum educational paths by individuals. The model is realized in the software and approved on real data on more than 5,5 thousand students. For the analysis of the importance of rational economic expectations when an educational path has to be chosen, the paths chosen by students is compared and the educational paths optimum from the point of view of economic rationality are calculated. The analysis of the results has showed that mainly, the choice of educational paths happens according to the economic motivations. On the considered selection, 66 % of prospective students have chosen an optimum path from the point of view of economic preferences. The most significant factor providing development of optimum educational paths is an expectation of higher income upon completion of education — 22 % of all educational paths, and a possibility of cost-cutting of educating or state-subsidized education — 12 %. In our opinion, one of the most important practical results of the research of optimum educational path is the need to consider expectations of students and prospective student when developing a state policy of investment in human capital