Nothing Special   »   [go: up one dir, main page]
Scribd Logo
Upload

Welcome to Scribd!

  • 37 views

    Syllabus: Economics 805, Part 1 Evolution and Learning in Games

    Uploaded by

    Anonymous bZTdTpL
    This document is the syllabus for Economics 805, Part 1 on Evolution and Learning in Games taught by Prof. William H. Sandholm at the University of Wisconsin in Fall 2017. The course will introduce evolutionary game theory and the theory of learning in games. It will cover population games, evolutionary dynamics, and various topics such as evolution in extensive form games and learning via calibrated forecasts. Requirements include problem sets and reading papers closely to summarize them. References are provided for general topics and individual papers.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd

    Syllabus: Economics 805, Part 1 Evolution and Learning in Games

    Uploaded by

    Anonymous bZTdTpL
    37 views3 pages
    This document is the syllabus for Economics 805, Part 1 on Evolution and Learning in Games taught by Prof. William H. Sandholm at the University of Wisconsin in Fall 2017. The course will introduce evolutionary game theory and the theory of learning in games. It will cover population games, evolutionary dynamics, and various topics such as evolution in extensive form games and learning via calibrated forecasts. Requirements include problem sets and reading papers closely to summarize them. References are provided for general topics and individual papers.

    Original Description:

    805 Syl

    Original Title

    syllabus

    Copyright

    © © All Rights Reserved

    Available Formats

    PDF, TXT or read online from Scribd

    Did you find this document useful?

    Is this content inappropriate?

    This document is the syllabus for Economics 805, Part 1 on Evolution and Learning in Games taught by Prof. William H. Sandholm at the University of Wisconsin in Fall 2017. The course will introduce evolutionary game theory and the theory of learning in games. It will cover population games, evolutionary dynamics, and various topics such as evolution in extensive form games and learning via calibrated forecasts. Requirements include problem sets and reading papers closely to summarize them. References are provided for general topics and individual papers.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    37 views3 pages

    Syllabus: Economics 805, Part 1 Evolution and Learning in Games

    Uploaded by

    Anonymous bZTdTpL
    This document is the syllabus for Economics 805, Part 1 on Evolution and Learning in Games taught by Prof. William H. Sandholm at the University of Wisconsin in Fall 2017. The course will introduce evolutionary game theory and the theory of learning in games. It will cover population games, evolutionary dynamics, and various topics such as evolution in extensive form games and learning via calibrated forecasts. Requirements include problem sets and reading papers closely to summarize them. References are provided for general topics and individual papers.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    You are on page 1of 3

    Prof. William H.

    Sandholm
    Department of Economics
    University of Wisconsin
    Fall 2017

    Syllabus: Economics 805, Part 1


    Evolution and Learning in Games

    Course Description
    The aim of this course is to introduce a variety of topics from evolutionary game
    theory ( = myopic disequilibrium dynamics in games played by large populations) and
    the theory of learning in games ( = disequilibrium dynamics in games played by small
    groups of players) We will start with an extended introduction to the theory population
    games and evolutionary dynamics using both (some) simulations and (mostly) formal
    analyses. After this we will more briefly consider a variety of topics, with candidates
    including (i) evolution in extensive form games; (ii) geometric game dynamics; (iii)
    stochastic evolution in cooperative games; (iv) learning via calibrated forecasts.
    All of the topics we study in this course require some knowledge about dynamical
    systems and Markov chains. Some topics require more advanced knowledge of
    probability theory and an assortment of other areas of mathematics. We will cover the
    essential mathematics in lecture, but we will likely sacrifice mathematical diligence in
    favor of covering more game theory models less rigorously.

    Course requirements

    In the first portion of the course (about 8 lectures), the basic reading material will be some
    combination of my chapter from the Handbook of Game Theory and my book. There will
    be a few problem sets that will be collected and graded. The aim here is to equip you
    with a basic command of evolutionary game theory and some of the relevant
    mathematics.

    The second portion of the course will focus on a few individual papers and book chapters.
    Some of the aims here are (i) to expose you to a variety of current research topics; (ii) to
    help you develop the habit of reading theory papers closely; and (iii) to acquaint you with
    the mathematics you would need to learn to pursue research in each of these areas.

    The homework for the second portion of the course will be to closely read a paper, either
    one from a list of suggestions or one that you find on your own and I approve. You will
    write a report summarizing the paper and explaining the main ideas of the proofs. Part
    of the way one learns to do theory is by reading papers closely until you have mastered
    part of the literature, and the aim here is to help you develop this habit.
    References

    General references
    W. H. Sandholm (2010). Population Games and Evolutionary Dynamics. MIT.
    W. H. Sandholm (2015). “Population games and deterministic evolutionary dynamics.”
    In H. P. Young and S. Zamir, eds., Handbook of Game Theory, Vol. 4, North Holland,
    703–775.

    Evolution in extensive form games


    W. H. Sandholm, S. S. Izquierdo, and L. R. Izquierdo (2017). “Best experienced payoff
    dynamics and cooperation in the Centipede game”. Working paper.
    M. van Veelen and J. García (2016). “In and out of equilibrium I: evolution of strategies
    in repeated games with discounting”. Journal of Economic Theory 161, 161–189.
    M. van Veelen, J. García, D. G. Rand, and M. A. Nowak (2012). “Direct reciprocity in
    structured populations”. Proceedings of the National Academy of Sciences 109, 9929–9934.
    Z. Xu (2016). “Convergence of best response dynamics in extensive form games,” Journal
    of Economic Theory 162, 21–54.

    Geometric and higher-order game dynamics


    R. Laraki and P. Mertikopoulos (2013). “Higher order learning and evolution in games.”
    Journal of Economic Theory 148, 2666–2695.
    R. Laraki and P. Mertikopoulos (2015). “Inertial game dynamics and applications to
    constrained optimization.” SIAM Journal on Control and Optimization 53, 3141–3170.
    P. Mertikopoulos and W. H. Sandholm (2016). “Learning in games via reinforcement and
    regularization”. Mathematics of Operations Research 41, 1297–1324.
    P. Mertikopoulos and W. H. Sandholm (2017). “Riemannian game dynamics”. Working
    paper.

    More deterministic game dynamics


    T. N. Cason, D. Friedman, and E. Hopkins (2014). “Cycles and instability in a Rock-Paper-
    Scissors population game: a continuous time experiment.” Review of Economic Studies
    81, 112–136.
    R. Lahkar (2011). “The dynamic instability of dispersed price equilibria.” Journal of
    Economic Theory 146, 1796–1827.
    J. Hofbauer, S. Sorin, and Y. Viossat (2011). “Time average replicator and best-reply
    dynamics.” Mathematics of Operations Research 345, 263–269.
    D. Oyama, W. H. Sandholm, and O. Tercieux (2015). “Sampling Best Response Dynamics
    and Deterministic Equilibrium Selection”. Theoretical Economics 10 (2015), 243-281.

    Stochastic evolutionary dynamics


    I. Arieli and P. Young (2016). “Stochastic learning dynamics and speed of convergence
    in population games.” Econometrica 84, 627–676.

    –2–
    D. P. Myatt and C. C. Wallace (2008). “When does one bad apple spoil the barrel? An
    evolutionary analysis of collective action.” Review of Economic Studies 75, 499–527.
    W. H. Sandholm and M. Staudigl (2016). “Large deviations and stochastic stability in the
    small noise double limit.” Theoretical Economics (2016), 279–355.
    W. H. Sandholm and M. Staudigl (2017). “Sample path large deviations for stochastic
    evolutionary game dynamics.” Working paper.

    Stochastic stability in cooperative games


    H. Nax and B. Pradelski (2015). “Evolutionary dynamics and equitable core selection in
    assignment games.” International Journal of Game Theory 44, 903–932.
    J. Newton and R. Sawa (2015). “A one-shot deviation principle for stability in matching
    problems.” Journal of Economic Theory 157, 1–27.
    R. Sawa (2014). “Coalitional stochastic stability in games, networks, and markets.” Games
    and Economic Behavior 88, 90–111.

    Learning via calibrated forecasts


    D. Blackwell (1956). “An analog of the minimax theorem for vector payoffs”. Pacific
    Journal of Mathematics 6: 1–8.
    N. Cesa-Bianchi and G. Lugosi (2006). Prediction, Learning, and Games. Cambridge
    D. P. Foster and S. Hart (2017). “Smooth calibration, leaky forecasts, finite recall, and
    Nash dynamics”. Working paper.
    S. Hart and A. Mas-Colell (2000). “A Simple Adaptive Procedure Leading to Correlated
    Equilibrium”. Econometrica 68, 1127–1150.
    W. Olszewski (2015). “Calibration and expert testing”. In H. P. Young and S. Zamir, eds.,
    Handbook of Game Theory, Vol. 4, North Holland, 703–775.
    H. P. Young (2004). Strategic Learning and Its Limits. Oxford.


    Two introductory-level math references

    J. R. Norris (1997). Markov Chains. Cambridge.


    L. Perko (2006). Differential Equations and Dynamical Systems, third edition. Springer.

    Software and guides


    W. H. Sandholm, E. Dokmaci, and F. Franchetti (2014). Dynamo: Diagrams for Evolutionary
    Game Dynamics. Software.
    F. Franchetti and W. H. Sandholm (2017). “An introduction to Dynamo: Diagrams for
    Evolutionary Game Dynamics”. Biological Theory 8, 167–178.
    L. R. Izquierdo, S. S. Izquierdo, and W. H. Sandholm (2017). Abed: Agent-Based
    Evolutionary Dynamics. Software.
    L. R. Izquierdo, S. S. Izquierdo, and W. H. Sandholm (2017). “An introduction to Abed:
    Agent-Based Evolutionary Dynamics”. Working paper.

    –3–

    You might also like