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Martin Posch

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First Name:Martin
Middle Name:
Last Name:Posch
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RePEc Short-ID:ppo7
[This author has chosen not to make the email address public]
https://data-science.meduniwien.ac.at/en/institutes/medical-statistics/

Research output

as
Jump to: Working papers Articles

Working papers

  1. Ed Hopkins & Martin Posch, 2003. "Attainability of Boundary Points under Reinforcement Learning," Levine's Working Paper Archive 506439000000000350, David K. Levine.
  2. Martin Posch, 2001. "Win Stay, Lose Shift or Imitatation – Only the Choice of Peers Counts," Vienna Economics Papers vie0109, University of Vienna, Department of Economics.
  3. M. Keilbach & M. Posch, 1998. "Network Externalities and the Dynamics of Markets," Working Papers ir98089, International Institute for Applied Systems Analysis.
  4. M. Posch & A. Pichler & K. Sigmund, 1998. "The Efficiency of Adapting Aspiration Levels," Working Papers ir98103, International Institute for Applied Systems Analysis.
  5. Martin Posch, 1997. "Win Stay---Lose Shift: An Elementary Learning Rule for Normal Form Games," Research in Economics 97-06-056e, Santa Fe Institute.
  6. Martin Posch & Werner Brannath, "undated". "Win-Stay, Lose-Shift. A General Learning Rule for Repeated Normal Form Games," Computing in Economics and Finance 1997 53, Society for Computational Economics.
    repec:vie:viennp:0109 is not listed on IDEAS

Articles

  1. Nicolás M Ballarini & Gerd K Rosenkranz & Thomas Jaki & Franz König & Martin Posch, 2018. "Subgroup identification in clinical trials via the predicted individual treatment effect," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-22, October.
  2. Ristl, Robin & Xi, Dong & Glimm, Ekkehard & Posch, Martin, 2018. "Optimal exact tests for multiple binary endpoints," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 1-17.
  3. Dominic Magirr & Thomas Jaki & Franz Koenig & Martin Posch, 2016. "Sample Size Reassessment and Hypothesis Testing in Adaptive Survival Trials," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-14, February.
  4. Thomas Ondra & Sebastian Jobjörnsson & Robert A Beckman & Carl-Fredrik Burman & Franz König & Nigel Stallard & Martin Posch, 2016. "Optimizing Trial Designs for Targeted Therapies," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-19, September.
  5. Zehetmayer Sonja & Graf Alexandra C. & Posch Martin, 2015. "Sample size reassessment for a two-stage design controlling the false discovery rate," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(5), pages 429-442, November.
  6. Malina Magdalena & Ickstadt Katja & Schwender Holger & Posch Martin & Bogdan Małgorzata, 2014. "Detection of epistatic effects with logic regression and a classical linear regression model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(1), pages 83-104, February.
  7. D. Magirr & T. Jaki & M. Posch & F. Klinglmueller, 2013. "Simultaneous confidence intervals that are compatible with closed testing in adaptive designs," Biometrika, Biometrika Trust, vol. 100(4), pages 985-996.
  8. Werner Brannath & Cyrus R. Mehta & Martin Posch, 2009. "Exact Confidence Bounds Following Adaptive Group Sequential Tests," Biometrics, The International Biometric Society, vol. 65(2), pages 539-546, June.
  9. Posch, Martin & Zehetmayer, Sonja & Bauer, Peter, 2009. "Hunting for Significance With the False Discovery Rate," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 832-840.
  10. Martin Posch & Gernot Wassmer & Werner Brannath, 2008. "A Note on repeated p-values for group sequential designs," Biometrika, Biometrika Trust, vol. 95(1), pages 253-256.
  11. Posch, Martin & Futschik, Andreas, 2008. "A Uniform Improvement of Bonferroni-Type Tests by Sequential Tests," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 299-308, March.
  12. Hopkins, Ed & Posch, Martin, 2005. "Attainability of boundary points under reinforcement learning," Games and Economic Behavior, Elsevier, vol. 53(1), pages 110-125, October.
  13. W. Brannath & P. Bauer & W. Maurer & M. Posch, 2003. "Sequential Tests for Noninferiority and Superiority," Biometrics, The International Biometric Society, vol. 59(1), pages 106-114, March.
  14. Brannath W. & Posch M. & Bauer P., 2002. "Recursive Combination Tests," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 236-244, March.
  15. Martin Posch & Peter Bauer, 2000. "Interim Analysis and Sample Size Reassessment," Biometrics, The International Biometric Society, vol. 56(4), pages 1170-1176, December.
  16. Martin Posch, 1997. "Cycling in a stochastic learning algorithm for normal form games," Journal of Evolutionary Economics, Springer, vol. 7(2), pages 193-207.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Ed Hopkins & Martin Posch, 2003. "Attainability of Boundary Points under Reinforcement Learning," Levine's Working Paper Archive 506439000000000350, David K. Levine.

    Cited by:

    1. Alan Beggs, 2015. "Reference Points and Learning," Economics Series Working Papers 767, University of Oxford, Department of Economics.
    2. Josephson, Jens, 2001. "A Numerical Analysis of the Evolutionary Stability of Learning Rules," SSE/EFI Working Paper Series in Economics and Finance 474, Stockholm School of Economics.
    3. Mario Bravo & Mathieu Faure, 2015. "Reinforcement Learning with Restrictions on the Action Set," Post-Print hal-01457301, HAL.
    4. Jacques Durieu & Philippe Solal, 2012. "Models of adaptive learning in game theory," Post-Print halshs-00667674, HAL.
    5. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Gotts, Nicholas M. & Polhill, J. Gary, 2007. "Transient and asymptotic dynamics of reinforcement learning in games," Games and Economic Behavior, Elsevier, vol. 61(2), pages 259-276, November.
    6. Takahashi, Satoru & Fudenberg, Drew, 2011. "Heterogeneous beliefs and local information in stochastic fictitious play," Scholarly Articles 27755310, Harvard University Department of Economics.
    7. Schuster, Stephan, 2010. "Network Formation with Adaptive Agents," MPRA Paper 27388, University Library of Munich, Germany.
    8. Ed Hopkins, 2006. "Adaptive Learning Models of Consumer Behaviour," Levine's Bibliography 122247000000000658, UCLA Department of Economics.
    9. Han, Jungsuk & Sangiorgi, Francesco, 2015. "Searching for Information," Working Paper Series 300, Sveriges Riksbank (Central Bank of Sweden).
    10. Antonio Morales, 2005. "On the Role of the Group Composition for Achieving Optimality," Annals of Operations Research, Springer, vol. 137(1), pages 387-397, July.
    11. Ianni, Antonella, 2014. "Learning strict Nash equilibria through reinforcement," Journal of Mathematical Economics, Elsevier, vol. 50(C), pages 148-155.
    12. Conor Mayo-Wilson & Kevin Zollman & David Danks, 2013. "Wisdom of crowds versus groupthink: learning in groups and in isolation," International Journal of Game Theory, Springer;Game Theory Society, vol. 42(3), pages 695-723, August.
    13. Oyarzun, Carlos & Ruf, Johannes, 2014. "Convergence in models with bounded expected relative hazard rates," Journal of Economic Theory, Elsevier, vol. 154(C), pages 229-244.
    14. Naoki Funai, 2019. "Convergence results on stochastic adaptive learning," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 68(4), pages 907-934, November.
    15. John Duffy & Ed Hopkins, 2001. "Learning, Information and Sorting in Market Entry Games: Theory and Evidence," Edinburgh School of Economics Discussion Paper Series 78, Edinburgh School of Economics, University of Edinburgh.
    16. Oyarzun, Carlos & Sarin, Rajiv, 2013. "Learning and risk aversion," Journal of Economic Theory, Elsevier, vol. 148(1), pages 196-225.
    17. Martino Banchio & Giacomo Mantegazza, 2022. "Artificial Intelligence and Spontaneous Collusion," Papers 2202.05946, arXiv.org, revised Sep 2023.
    18. Funai, Naoki, 2022. "Reinforcement learning with foregone payoff information in normal form games," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 638-660.
    19. Georgios Chasparis & Jeff Shamma & Anders Rantzer, 2015. "Nonconvergence to saddle boundary points under perturbed reinforcement learning," International Journal of Game Theory, Springer;Game Theory Society, vol. 44(3), pages 667-699, August.
    20. Erik Mohlin & Robert Ostling & Joseph Tao-yi Wang, 2014. "Learning by Imitation in Games: Theory, Field, and Laboratory," Economics Series Working Papers 734, University of Oxford, Department of Economics.
    21. Leslie, David S. & Collins, E.J., 2006. "Generalised weakened fictitious play," Games and Economic Behavior, Elsevier, vol. 56(2), pages 285-298, August.
    22. Georgios Chasparis & Jeff Shamma, 2012. "Distributed Dynamic Reinforcement of Efficient Outcomes in Multiagent Coordination and Network Formation," Dynamic Games and Applications, Springer, vol. 2(1), pages 18-50, March.
    23. Schuster, Stephan, 2012. "Applications in Agent-Based Computational Economics," MPRA Paper 47201, University Library of Munich, Germany.

  2. M. Keilbach & M. Posch, 1998. "Network Externalities and the Dynamics of Markets," Working Papers ir98089, International Institute for Applied Systems Analysis.

    Cited by:

    1. Jose Luis Arroyo-Barrigüete & Ricardo Ernst & Jose Ignacio López-Sánchez & Alejandro Orero-Giménez, 2008. "On the identification of critical mass in Internet-based services subject to network effects," The Service Industries Journal, Taylor & Francis Journals, vol. 30(5), pages 643-654, May.
    2. João Leão & Vasco Santos, 2008. "New Network Goods," Working Papers Series 1 ercwp2308, ISCTE-IUL, Business Research Unit (BRU-IUL).
    3. Max Keilbach, 1999. "Network Externalities and the Path Dependence of Markets: Will Bill Gates Make It?," Computing in Economics and Finance 1999 711, Society for Computational Economics.

  3. M. Posch & A. Pichler & K. Sigmund, 1998. "The Efficiency of Adapting Aspiration Levels," Working Papers ir98103, International Institute for Applied Systems Analysis.

    Cited by:

    1. Oechssler, Jörg, 2001. "Cooperation as a Result of Learning with Aspiration Levels," Bonn Econ Discussion Papers 8/2001, University of Bonn, Bonn Graduate School of Economics (BGSE).
    2. Steffen Huck & Kai A. Konrad & Wieland Müller & Hans-Theo Normann, 2007. "The Merger Paradox and why Aspiration Levels Let it Fail in the Laboratory," Economic Journal, Royal Economic Society, vol. 117(522), pages 1073-1095, July.
    3. Walter Gutjahr, 2006. "Interaction dynamics of two reinforcement learners," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 14(1), pages 59-86, February.
    4. Anton M Unakafov & Thomas Schultze & Alexander Gail & Sebastian Moeller & Igor Kagan & Stephan Eule & Fred Wolf, 2020. "Emergence and suppression of cooperation by action visibility in transparent games," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-32, January.
    5. Cui Zhiwei & Zhai Jian & Liu Xuan, 2009. "The Efficiency of Observability and Mutual Linkage," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 9(1), pages 1-36, July.
    6. Tanimoto, Jun, 2010. "The effect of assortativity by degree on emerging cooperation in a 2×2 dilemma game played on an evolutionary network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3325-3335.
    7. Cho, In-Koo & Matsui, Akihiko, 2005. "Learning aspiration in repeated games," Journal of Economic Theory, Elsevier, vol. 124(2), pages 171-201, October.
    8. Marco Novarese & Salvatore Rizzello, 2003. "Satisfaction and Learning: an experimental game to measure happiness," Microeconomics 0306004, University Library of Munich, Germany.
    9. Knudsen, Thorbjørn, 2008. "Reference groups and variable risk strategies," Journal of Economic Behavior & Organization, Elsevier, vol. 66(1), pages 22-36, April.

  4. Martin Posch, 1997. "Win Stay---Lose Shift: An Elementary Learning Rule for Normal Form Games," Research in Economics 97-06-056e, Santa Fe Institute.

    Cited by:

    1. Timothy Salmon, 2004. "Evidence for Learning to Learn Behavior in Normal Form Games," Theory and Decision, Springer, vol. 56(4), pages 367-404, April.
    2. Flávio L Pinheiro & Vítor V Vasconcelos & Francisco C Santos & Jorge M Pacheco, 2014. "Evolution of All-or-None Strategies in Repeated Public Goods Dilemmas," PLOS Computational Biology, Public Library of Science, vol. 10(11), pages 1-5, November.

Articles

  1. Nicolás M Ballarini & Gerd K Rosenkranz & Thomas Jaki & Franz König & Martin Posch, 2018. "Subgroup identification in clinical trials via the predicted individual treatment effect," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-22, October.

    Cited by:

    1. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
    2. Gerd Rippin & Nicolás Ballarini & Héctor Sanz & Joan Largent & Chantal Quinten & Francesco Pignatti, 2022. "A Review of Causal Inference for External Comparator Arm Studies," Drug Safety, Springer, vol. 45(8), pages 815-837, August.

  2. Ristl, Robin & Xi, Dong & Glimm, Ekkehard & Posch, Martin, 2018. "Optimal exact tests for multiple binary endpoints," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 1-17.

    Cited by:

    1. De Keyser, Arne & Bart, Yakov & Gu, Xian & Liu, Stephanie Q. & Robinson, Stacey G. & Kannan, P.K., 2021. "Opportunities and challenges of using biometrics for business: Developing a research agenda," Journal of Business Research, Elsevier, vol. 136(C), pages 52-62.
    2. Keskiner, Hilal & Gür, Bekir S., 2023. "Questioning merit-based scholarships at nonprofit private universities: Lessons from Turkey," International Journal of Educational Development, Elsevier, vol. 97(C).
    3. Wang, Li, 2022. "New testing procedures with k-FWER control for discrete data," Statistics & Probability Letters, Elsevier, vol. 180(C).
    4. Ruth Heller & Abba Krieger & Saharon Rosset, 2023. "Optimal multiple testing and design in clinical trials," Biometrics, The International Biometric Society, vol. 79(3), pages 1908-1919, September.

  3. Dominic Magirr & Thomas Jaki & Franz Koenig & Martin Posch, 2016. "Sample Size Reassessment and Hypothesis Testing in Adaptive Survival Trials," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-14, February.

    Cited by:

    1. Nezameddin Faghih & Ebrahim Bonyadi & Lida Sarreshtehdari, 2021. "Comparison of the entrepreneurial motivation in different economic groups," Journal of Global Entrepreneurship Research, Springer;UNESCO Chair in Entrepreneurship, vol. 11(1), pages 29-39, December.

  4. Thomas Ondra & Sebastian Jobjörnsson & Robert A Beckman & Carl-Fredrik Burman & Franz König & Nigel Stallard & Martin Posch, 2016. "Optimizing Trial Designs for Targeted Therapies," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-19, September.

    Cited by:

    1. Nigel Stallard, 2023. "Adaptive enrichment designs with a continuous biomarker," Biometrics, The International Biometric Society, vol. 79(1), pages 9-19, March.
    2. Daniel Gallacher & Nigel Stallard & Peter Kimani & Elvan Gökalp & Juergen Branke, 2022. "Development of a model to demonstrate the impact of National Institute of Health and Care Excellence cost‐effectiveness assessment on health utility for targeted medicines," Health Economics, John Wiley & Sons, Ltd., vol. 31(2), pages 417-430, February.

  5. Werner Brannath & Cyrus R. Mehta & Martin Posch, 2009. "Exact Confidence Bounds Following Adaptive Group Sequential Tests," Biometrics, The International Biometric Society, vol. 65(2), pages 539-546, June.

    Cited by:

    1. Gregory P. Levin & Sarah C. Emerson & Scott S. Emerson, 2014. "An evaluation of inferential procedures for adaptive clinical trial designs with pre-specified rules for modifying the sample size," Biometrics, The International Biometric Society, vol. 70(3), pages 556-567, September.

  6. Posch, Martin & Zehetmayer, Sonja & Bauer, Peter, 2009. "Hunting for Significance With the False Discovery Rate," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 832-840.

    Cited by:

    1. Zehetmayer Sonja & Graf Alexandra C. & Posch Martin, 2015. "Sample size reassessment for a two-stage design controlling the false discovery rate," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(5), pages 429-442, November.
    2. T. Tony Cai & Wenguang Sun, 2017. "Optimal screening and discovery of sparse signals with applications to multistage high throughput studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 197-223, January.
    3. Sanat K. Sarkar & Jingjing Chen & Wenge Guo, 2013. "Multiple Testing in a Two-Stage Adaptive Design With Combination Tests Controlling FDR," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1385-1401, December.

  7. Posch, Martin & Futschik, Andreas, 2008. "A Uniform Improvement of Bonferroni-Type Tests by Sequential Tests," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 299-308, March.

    Cited by:

    1. Shiwang Yu & Na Guo & Caimiao Zheng & Yu Song & Jianli Hao, 2021. "Investigating the Association between Outdoor Environment and Outdoor Activities for Seniors Living in Old Residential Communities," IJERPH, MDPI, vol. 18(14), pages 1-16, July.

  8. Hopkins, Ed & Posch, Martin, 2005. "Attainability of boundary points under reinforcement learning," Games and Economic Behavior, Elsevier, vol. 53(1), pages 110-125, October.
    See citations under working paper version above.
  9. W. Brannath & P. Bauer & W. Maurer & M. Posch, 2003. "Sequential Tests for Noninferiority and Superiority," Biometrics, The International Biometric Society, vol. 59(1), pages 106-114, March.

    Cited by:

    1. Tim Friede, 2008. "Adaptive Design Methods in Clinical Trials by S.-C. Chow and M. Chang," Biometrics, The International Biometric Society, vol. 64(1), pages 314-315, March.

  10. Brannath W. & Posch M. & Bauer P., 2002. "Recursive Combination Tests," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 236-244, March.

    Cited by:

    1. Carl-Fredrik Burman & Christian Sonesson, 2006. "Are Flexible Designs Sound?," Biometrics, The International Biometric Society, vol. 62(3), pages 664-669, September.
    2. Georg Gutjahr & Werner Brannath & Peter Bauer, 2011. "An Approach to the Conditional Error Rate Principle with Nuisance Parameters," Biometrics, The International Biometric Society, vol. 67(3), pages 1039-1046, September.
    3. Werner Brannath & Cyrus R. Mehta & Martin Posch, 2009. "Exact Confidence Bounds Following Adaptive Group Sequential Tests," Biometrics, The International Biometric Society, vol. 65(2), pages 539-546, June.
    4. Rene Schmidt & Andreas Faldum & Robert Kwiecien, 2018. "Adaptive designs for the one†sample log†rank test," Biometrics, The International Biometric Society, vol. 74(2), pages 529-537, June.
    5. Zehetmayer Sonja & Graf Alexandra C. & Posch Martin, 2015. "Sample size reassessment for a two-stage design controlling the false discovery rate," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(5), pages 429-442, November.
    6. W. Brannath & P. Bauer & W. Maurer & M. Posch, 2003. "Sequential Tests for Noninferiority and Superiority," Biometrics, The International Biometric Society, vol. 59(1), pages 106-114, March.
    7. Parsons, Nick & Friede, Tim & Todd, Susan & Marquez, Elsa Valdes & Chataway, Jeremy & Nicholas, Richard & Stallard, Nigel, 2012. "An R package for implementing simulations for seamless phase II/III clinical trials using early outcomes for treatment selection," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1150-1160.
    8. Sanat K. Sarkar & Jingjing Chen & Wenge Guo, 2013. "Multiple Testing in a Two-Stage Adaptive Design With Combination Tests Controlling FDR," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1385-1401, December.
    9. René Schmidt & Andreas Faldum & Joachim Gerß, 2015. "Adaptive designs with arbitrary dependence structure based on Fisher’s combination test," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 427-447, September.
    10. P. Bauer, 2006. "Discussions," Biometrics, The International Biometric Society, vol. 62(3), pages 676-678, September.
    11. Jingjing Chen, 2019. "A Note of Adaptive Design in Clinical Trials," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(5), pages 107-111, August.
    12. Nigel Stallard, 2023. "Adaptive enrichment designs with a continuous biomarker," Biometrics, The International Biometric Society, vol. 79(1), pages 9-19, March.
    13. Rui Tang & Xiaoye Ma & Hui Yang & Michael Wolf, 2018. "Biomarker-Defined Subgroup Selection Adaptive Design for Phase III Confirmatory Trial with Time-to-Event Data: Comparing Group Sequential and Various Adaptive Enrichment Designs," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(2), pages 371-404, August.

  11. Martin Posch & Peter Bauer, 2000. "Interim Analysis and Sample Size Reassessment," Biometrics, The International Biometric Society, vol. 56(4), pages 1170-1176, December.

    Cited by:

    1. Georg Gutjahr & Werner Brannath & Peter Bauer, 2011. "An Approach to the Conditional Error Rate Principle with Nuisance Parameters," Biometrics, The International Biometric Society, vol. 67(3), pages 1039-1046, September.
    2. Guosheng Yin & Yu Shen, 2005. "Adaptive Design and Estimation in Randomized Clinical Trials with Correlated Observations," Biometrics, The International Biometric Society, vol. 61(2), pages 362-369, June.
    3. Werner Brannath & Peter Bauer, 2004. "Optimal Conditional Error Functions for the Control of Conditional Power," Biometrics, The International Biometric Society, vol. 60(3), pages 715-723, September.

  12. Martin Posch, 1997. "Cycling in a stochastic learning algorithm for normal form games," Journal of Evolutionary Economics, Springer, vol. 7(2), pages 193-207.

    Cited by:

    1. M. Keilbach & M. Posch, 1998. "Network Externalities and the Dynamics of Markets," Working Papers ir98089, International Institute for Applied Systems Analysis.
    2. Hopkins, Ed, 1999. "A Note on Best Response Dynamics," Games and Economic Behavior, Elsevier, vol. 29(1-2), pages 138-150, October.
    3. Sebastian Bervoets & Mario Bravo & Mathieu Faure, 2020. "Learning with minimal information in continuous games," Post-Print hal-02534257, HAL.
    4. Friederike Mengel, 2007. "Learning Across Games," Working Papers. Serie AD 2007-05, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    5. Ed Hopkins, 2000. "Two Competing Models of How People Learn in Games," Edinburgh School of Economics Discussion Paper Series 51, Edinburgh School of Economics, University of Edinburgh.
    6. Mario Bravo & Mathieu Faure, 2015. "Reinforcement Learning with Restrictions on the Action Set," Post-Print hal-01457301, HAL.
    7. Laslier, Jean-Francois & Topol, Richard & Walliser, Bernard, 2001. "A Behavioral Learning Process in Games," Games and Economic Behavior, Elsevier, vol. 37(2), pages 340-366, November.
    8. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Gotts, Nicholas M. & Polhill, J. Gary, 2007. "Transient and asymptotic dynamics of reinforcement learning in games," Games and Economic Behavior, Elsevier, vol. 61(2), pages 259-276, November.
    9. Mertikopoulos, Panayotis & Sandholm, William H., 2018. "Riemannian game dynamics," Journal of Economic Theory, Elsevier, vol. 177(C), pages 315-364.
    10. Werner Güth & Hartmut Kliemt & Bezalel Peleg, 2000. "Co‐evolution of Preferences and Information in Simple Games of Trust," German Economic Review, Verein für Socialpolitik, vol. 1(1), pages 83-110, February.
    11. Alan Beggs, 2002. "On the Convergence of Reinforcement Learning," Economics Series Working Papers 96, University of Oxford, Department of Economics.
    12. Ed Hopkins & Martin Posch, 2003. "Attainability of Boundary Points under Reinforcement Learning," Levine's Working Paper Archive 506439000000000350, David K. Levine.
    13. Cominetti, Roberto & Melo, Emerson & Sorin, Sylvain, 2010. "A payoff-based learning procedure and its application to traffic games," Games and Economic Behavior, Elsevier, vol. 70(1), pages 71-83, September.
    14. Alanyali, Murat, 2010. "A note on adjusted replicator dynamics in iterated games," Journal of Mathematical Economics, Elsevier, vol. 46(1), pages 86-98, January.
    15. Ianni, Antonella, 2014. "Learning strict Nash equilibria through reinforcement," Journal of Mathematical Economics, Elsevier, vol. 50(C), pages 148-155.
    16. Giovanni Dosi & Marco Faillo & Luigi Marengo, 2018. "Beyond "Bounded Rationality": Behaviours and Learning in Complex Evolving Worlds," LEM Papers Series 2018/26, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    17. Windrum, Paul, 1999. "Simulation models of technological innovation: A Review," Research Memorandum 005, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    18. Possajennikov, A., 1997. "An Analysis of a Simple Reinforcement Dynamics : Learning to Play an "Egalitarian" Equilibrium," Other publications TiSEM d415ae0d-b06b-4a63-9dcc-e, Tilburg University, School of Economics and Management.
    19. Georgios Chasparis & Jeff Shamma & Anders Rantzer, 2015. "Nonconvergence to saddle boundary points under perturbed reinforcement learning," International Journal of Game Theory, Springer;Game Theory Society, vol. 44(3), pages 667-699, August.
    20. Darmon, Eric & Waldeck, Roger, 2005. "Convergence of reinforcement learning to Nash equilibrium: A search-market experiment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 119-130.
    21. Roger Waldeck & Eric Darmon, 2006. "Can boundedly rational sellers learn to play Nash?," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 1(2), pages 147-169, November.
    22. Max Keilbach, 1999. "Network Externalities and the Path Dependence of Markets: Will Bill Gates Make It?," Computing in Economics and Finance 1999 711, Society for Computational Economics.
    23. Norman, Thomas W.L., 2023. "Pigouvian algorithmic platform design," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 322-332.
    24. M. Posch & A. Pichler & K. Sigmund, 1998. "The Efficiency of Adapting Aspiration Levels," Working Papers ir98103, International Institute for Applied Systems Analysis.
    25. Antonio Cabrales & Walter Garcia Fontes, 2000. "Estimating learning models from experimental data," Economics Working Papers 501, Department of Economics and Business, Universitat Pompeu Fabra.

More information

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-DEV: Development (1) 2004-03-22
  2. NEP-EVO: Evolutionary Economics (1) 1999-01-11
  3. NEP-MIC: Microeconomics (1) 1999-01-11

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