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  1. AU="Bakker, Michiel A."
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  3. AU="Hand, Marissa"
  4. AU="Guerra, Giselle"
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  1. Artikel ; Online: Scaffolding cooperation in human groups with deep reinforcement learning.

    McKee, Kevin R / Tacchetti, Andrea / Bakker, Michiel A / Balaguer, Jan / Campbell-Gillingham, Lucy / Everett, Richard / Botvinick, Matthew

    Nature human behaviour

    2023  Band 7, Heft 10, Seite(n) 1787–1796

    Abstract: Effective approaches to encouraging group cooperation are still an open challenge. Here we apply recent advances in deep learning to structure networks of human participants playing a group cooperation game. We leverage deep reinforcement learning and ... ...

    Abstract Effective approaches to encouraging group cooperation are still an open challenge. Here we apply recent advances in deep learning to structure networks of human participants playing a group cooperation game. We leverage deep reinforcement learning and simulation methods to train a 'social planner' capable of making recommendations to create or break connections between group members. The strategy that it develops succeeds at encouraging pro-sociality in networks of human participants (N = 208 participants in 13 groups) playing for real monetary stakes. Under the social planner, groups finished the game with an average cooperation rate of 77.7%, compared with 42.8% in static networks (N = 176 in 11 groups). In contrast to prior strategies that separate defectors from cooperators (tested here with N = 384 in 24 groups), the social planner learns to take a conciliatory approach to defectors, encouraging them to act pro-socially by moving them to small highly cooperative neighbourhoods.
    Mesh-Begriff(e) Humans ; Cooperative Behavior ; Game Theory ; Social Behavior ; Group Processes
    Sprache Englisch
    Erscheinungsdatum 2023-09-07
    Erscheinungsland England
    Dokumenttyp Journal Article
    ISSN 2397-3374
    ISSN (online) 2397-3374
    DOI 10.1038/s41562-023-01686-7
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas.

    Aleta, Alberto / Martín-Corral, David / Bakker, Michiel A / Pastore Y Piontti, Ana / Ajelli, Marco / Litvinova, Maria / Chinazzi, Matteo / Dean, Natalie E / Halloran, M Elizabeth / Longini, Ira M / Pentland, Alex / Vespignani, Alessandro / Moreno, Yamir / Moro, Esteban

    Proceedings of the National Academy of Sciences of the United States of America

    2022  Band 119, Heft 26, Seite(n) e2112182119

    Abstract: Detailed characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City, NY and ...

    Abstract Detailed characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City, NY and Seattle, WA metropolitan areas to build a detailed agent-based model of SARS-CoV-2 infection to estimate the where, when, and magnitude of transmission events during the pandemic's first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered superspreading events (SSEs). Although mass gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies.
    Mesh-Begriff(e) COVID-19/transmission ; Contact Tracing ; Humans ; New York City/epidemiology ; Pandemics ; Population Dynamics ; SARS-CoV-2 ; Time Factors ; Washington/epidemiology
    Sprache Englisch
    Erscheinungsdatum 2022-06-13
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, P.H.S.
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2112182119
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Buch ; Online: Fine-tuning language models to find agreement among humans with diverse preferences

    Bakker, Michiel A. / Chadwick, Martin J. / Sheahan, Hannah R. / Tessler, Michael Henry / Campbell-Gillingham, Lucy / Balaguer, Jan / McAleese, Nat / Glaese, Amelia / Aslanides, John / Botvinick, Matthew M. / Summerfield, Christopher

    2022  

    Abstract: Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static and homogeneous across individuals, so that aligning to a a single " ... ...

    Abstract Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static and homogeneous across individuals, so that aligning to a a single "generic" user will confer more general alignment. Here, we embrace the heterogeneity of human preferences to consider a different challenge: how might a machine help people with diverse views find agreement? We fine-tune a 70 billion parameter LLM to generate statements that maximize the expected approval for a group of people with potentially diverse opinions. Human participants provide written opinions on thousands of questions touching on moral and political issues (e.g., "should we raise taxes on the rich?"), and rate the LLM's generated candidate consensus statements for agreement and quality. A reward model is then trained to predict individual preferences, enabling it to quantify and rank consensus statements in terms of their appeal to the overall group, defined according to different aggregation (social welfare) functions. The model produces consensus statements that are preferred by human users over those from prompted LLMs (>70%) and significantly outperforms a tight fine-tuned baseline that lacks the final ranking step. Further, our best model's consensus statements are preferred over the best human-generated opinions (>65%). We find that when we silently constructed consensus statements from only a subset of group members, those who were excluded were more likely to dissent, revealing the sensitivity of the consensus to individual contributions. These results highlight the potential to use LLMs to help groups of humans align their values with one another.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Computation and Language
    Erscheinungsdatum 2022-11-27
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Buch ; Online: DADI

    Bakker, Michiel A. / Tu, Duy Patrick / Valdés, Humberto Riverón / Gummadi, Krishna P. / Varshney, Kush R. / Weller, Adrian / Pentland, Alex

    Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning

    2019  

    Abstract: We introduce a framework for dynamic adversarial discovery of information (DADI), motivated by a scenario where information (a feature set) is used by third parties with unknown objectives. We train a reinforcement learning agent to sequentially acquire ... ...

    Abstract We introduce a framework for dynamic adversarial discovery of information (DADI), motivated by a scenario where information (a feature set) is used by third parties with unknown objectives. We train a reinforcement learning agent to sequentially acquire a subset of the information while balancing accuracy and fairness of predictors downstream. Based on the set of already acquired features, the agent decides dynamically to either collect more information from the set of available features or to stop and predict using the information that is currently available. Building on previous work exploring adversarial representation learning, we attain group fairness (demographic parity) by rewarding the agent with the adversary's loss, computed over the final feature set. Importantly, however, the framework provides a more general starting point for fair or private dynamic information discovery. Finally, we demonstrate empirically, using two real-world datasets, that we can trade-off fairness and predictive performance

    Comment: Accepted at NeurIPS 2019 HCML Workshop
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Computers and Society ; Statistics - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2019-10-30
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  5. Buch ; Online: Statistical discrimination in learning agents

    Duéñez-Guzmán, Edgar A. / McKee, Kevin R. / Mao, Yiran / Coppin, Ben / Chiappa, Silvia / Vezhnevets, Alexander Sasha / Bakker, Michiel A. / Bachrach, Yoram / Sadedin, Suzanne / Isaac, William / Tuyls, Karl / Leibo, Joel Z.

    2021  

    Abstract: Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics. One primary example is \textit{statistical discrimination} -- selecting social ... ...

    Abstract Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics. One primary example is \textit{statistical discrimination} -- selecting social partners based not on their underlying attributes, but on readily perceptible characteristics that covary with their suitability for the task at hand. We present a theoretical model to examine how information processing influences statistical discrimination and test its predictions using multi-agent reinforcement learning with various agent architectures in a partner choice-based social dilemma. As predicted, statistical discrimination emerges in agent policies as a function of both the bias in the training population and of agent architecture. All agents showed substantial statistical discrimination, defaulting to using the readily available correlates instead of the outcome relevant features. We show that less discrimination emerges with agents that use recurrent neural networks, and when their training environment has less bias. However, all agent algorithms we tried still exhibited substantial bias after learning in biased training populations.

    Comment: 29 pages, 10 figures
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Science and Game Theory ; Computer Science - Multiagent Systems ; 68T07 (Primary) 91A26 ; 91-10 ; 93A16 (Secondary) ; I.2.11 ; I.2.0
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2021-10-21
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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