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  1. Book ; Online: Improving Decision Support for Infectious Disease Prevention and Control

    Manheim, David / Chamberlin, Margaret / Osoba, Osonde A / Vardavas, Raffaele / Moore, Melinda

    Aligning Models and Other Tools with Policymakers' Needs

    2016  

    Keywords Database design & theory ; Infectious & contagious diseases ; Science: general issues ; Technology ; Health Sciences ; General Science
    Language English
    Size 1 Online-Ressource
    Publisher RAND Corporation
    Document type Book ; Online
    Note English
    HBZ-ID HT030612260
    ISBN 9780833095503 ; 0833095501
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book ; Online: A Generative Machine Learning Approach to Policy Optimization in Pursuit-Evasion Games

    Navabi, Shiva / Osoba, Osonde A.

    2020  

    Abstract: We consider a pursuit-evasion game [11] played between two agents, 'Blue' (the pursuer) and 'Red' (the evader), over $T$ time steps. Red aims to attack Blue's territory. Blue's objective is to intercept Red by time $T$ and thereby limit the success of ... ...

    Abstract We consider a pursuit-evasion game [11] played between two agents, 'Blue' (the pursuer) and 'Red' (the evader), over $T$ time steps. Red aims to attack Blue's territory. Blue's objective is to intercept Red by time $T$ and thereby limit the success of Red's attack. Blue must plan its pursuit trajectory by choosing parameters that determine its course of movement (speed and angle in our setup) such that it intercepts Red by time $T$. We show that Blue's path-planning problem in pursuing Red, can be posed as a sequential decision making problem under uncertainty. Blue's unawareness of Red's action policy renders the analytic dynamic programming approach intractable for finding the optimal action policy for Blue. In this work, we are interested in exploring data-driven approaches to the policy optimization problem that Blue faces. We apply generative machine learning (ML) approaches to learn optimal action policies for Blue. This highlights the ability of generative ML model to learn the relevant implicit representations for the dynamics of simulated pursuit-evasion games. We demonstrate the effectiveness of our modeling approach via extensive statistical assessments. This work can be viewed as a preliminary step towards further adoption of generative modeling approaches for addressing policy optimization problems that arise in the context of multi-agent learning and planning [1].

    Comment: 8 pages
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Science and Game Theory ; Computer Science - Multiagent Systems ; Statistics - Machine Learning
    Subject code 629
    Publishing date 2020-10-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Book ; Online: Steps Towards Value-Aligned Systems

    Osoba, Osonde A. / Boudreaux, Benjamin / Yeung, Douglas

    2020  

    Abstract: Algorithmic (including AI/ML) decision-making artifacts are an established and growing part of our decision-making ecosystem. They are indispensable tools for managing the flood of information needed to make effective decisions in a complex world. The ... ...

    Abstract Algorithmic (including AI/ML) decision-making artifacts are an established and growing part of our decision-making ecosystem. They are indispensable tools for managing the flood of information needed to make effective decisions in a complex world. The current literature is full of examples of how individual artifacts violate societal norms and expectations (e.g. violations of fairness, privacy, or safety norms). Against this backdrop, this discussion highlights an under-emphasized perspective in the literature on assessing value misalignment in AI-equipped sociotechnical systems. The research on value misalignment has a strong focus on the behavior of individual tech artifacts. This discussion argues for a more structured systems-level approach for assessing value-alignment in sociotechnical systems. We rely primarily on the research on fairness to make our arguments more concrete. And we use the opportunity to highlight how adopting a system perspective improves our ability to explain and address value misalignments better. Our discussion ends with an exploration of priority questions that demand attention if we are to assure the value alignment of whole systems, not just individual artifacts.

    Comment: Original version appeared in Proceedings of the 2020 AAAI ACM Conference on AI, Ethics, and Society (AIES '20), February 7-8, 2020, New York, NY, USA. 5 pages, 2 figures. Corrected some typos in this version
    Keywords Computer Science - Computers and Society
    Subject code 360
    Publishing date 2020-02-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Policy-focused Agent-based Modeling using RL Behavioral Models

    Osoba, Osonde A. / Vardavas, Raffaele / Grana, Justin / Zutshi, Rushil / Jaycocks, Amber

    2020  

    Abstract: Agent-based Models (ABMs) are valuable tools for policy analysis. ABMs help analysts explore the emergent consequences of policy interventions in multi-agent decision-making settings. But the validity of inferences drawn from ABM explorations depends on ... ...

    Abstract Agent-based Models (ABMs) are valuable tools for policy analysis. ABMs help analysts explore the emergent consequences of policy interventions in multi-agent decision-making settings. But the validity of inferences drawn from ABM explorations depends on the quality of the ABM agents' behavioral models. Standard specifications of agent behavioral models rely either on heuristic decision-making rules or on regressions trained on past data. Both prior specification modes have limitations. This paper examines the value of reinforcement learning (RL) models as adaptive, high-performing, and behaviorally-valid models of agent decision-making in ABMs. We test the hypothesis that RL agents are effective as utility-maximizing agents in policy ABMs. We also address the problem of adapting RL algorithms to handle multi-agency in games by adapting and extending methods from recent literature. We evaluate the performance of such RL-based ABM agents via experiments on two policy-relevant ABMs: a minority game ABM, and an ABM of Influenza Transmission. We run some analytic experiments on our AI-equipped ABMs e.g. explorations of the effects of behavioral heterogeneity in a population and the emergence of synchronization in a population. The experiments show that RL behavioral models are effective at producing reward-seeking or reward-maximizing behaviors in ABM agents. Furthermore, RL behavioral models can learn to outperform the default adaptive behavioral models in the two ABMs examined.

    Comment: This is a more detailed version of a paper ("Modeling Agent Behaviors for Policy Analysis via Reinforcement Learning") accepted to appear in IEEE ICMLA 2020. This also corrects an error in Fig. 7 of the original arXiv submission. Fig. 7 now specifies the right ABM architecture ("flu" instead of "tax")
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Multiagent Systems
    Subject code 006
    Publishing date 2020-06-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Deep Generative Modeling in Network Science with Applications to Public Policy Research

    Hartnett, Gavin S. / Vardavas, Raffaele / Baker, Lawrence / Chaykowsky, Michael / Gibson, C. Ben / Girosi, Federico / Kennedy, David P. / Osoba, Osonde A.

    Abstract: Network data is increasingly being used in quantitative, data-driven public policy research. These are typically very rich datasets that contain complex correlations and inter-dependencies. This richness both promises to be quite useful for policy ... ...

    Abstract Network data is increasingly being used in quantitative, data-driven public policy research. These are typically very rich datasets that contain complex correlations and inter-dependencies. This richness both promises to be quite useful for policy research, while at the same time posing a challenge for the useful extraction of information from these datasets - a challenge which calls for new data analysis methods. In this report, we formulate a research agenda of key methodological problems whose solutions would enable new advances across many areas of policy research. We then review recent advances in applying deep learning to network data, and show how these methods may be used to address many of the methodological problems we identified. We particularly emphasize deep generative methods, which can be used to generate realistic synthetic networks useful for microsimulation and agent-based models capable of informing key public policy questions. We extend these recent advances by developing a new generative framework which applies to large social contact networks commonly used in epidemiological modeling. For context, we also compare and contrast these recent neural network-based approaches with the more traditional Exponential Random Graph Models. Lastly, we discuss some open problems where more progress is needed.
    Keywords covid19
    Publisher ArXiv
    Document type Article ; Online
    DOI 10.7249/wra843-1
    Database COVID19

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