Segismundo Samuel Izquierdo

Personal Details

First Name:	Segismundo
Middle Name:	Samuel
Last Name:	Izquierdo
Suffix:
RePEc Short-ID:	piz2
	http://www.segis.izqui.org

Affiliation

(50%) Universidad de Valladolid (University of Valladolid)

http://www.uva.es
Spain, Valladolid

(50%) Grupo de Ingeniería de los Sistemas Sociales (INSISOC)
Universidad de Valladolid

Valladolid, Spain
http://www.insisoc.org/
RePEc:edi:givldes (more details at EDIRC)

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. Izquierdo, Segismundo S. & Hernández, Cesáreo & del Hoyo, Juan, 2006. "Forecasting VARMA processes using VAR models and subspace-based state space models," MPRA Paper 4235, University Library of Munich, Germany.

   Cited by:

   1. Jean-Marie Dufour & Tarek Jouini, 2011. "Asymptotic Distributions for Some Quasi-Efficient Estimators in Echelon VARMA Models," CIRANO Working Papers 2011s-25, CIRANO.

Articles

1. Izquierdo, Segismundo S. & Izquierdo, Luis R., 2022. "Stability of strict equilibria in best experienced payoff dynamics: Simple formulas and applications," Journal of Economic Theory, Elsevier, vol. 206(C).

   Cited by:

   1. Izquierdo, Segismundo S. & Izquierdo, Luis R., 2023. "Strategy sets closed under payoff sampling," Games and Economic Behavior, Elsevier, vol. 138(C), pages 126-142.

2. Sandholm, William H. & Izquierdo, Segismundo S. & Izquierdo, Luis R., 2020. "Stability for best experienced payoff dynamics," Journal of Economic Theory, Elsevier, vol. 185(C).

   Cited by:

   1. Srinivas Arigapudi & Omer Edhan & Yuval Heller & Ziv Hellman, 2022. "Mentors and Recombinators: Multi-Dimensional Social Learning," Papers 2205.00278, arXiv.org, revised Nov 2023.
   2. Sawa, Ryoji & Wu, Jiabin, 2023. "Statistical inference in evolutionary dynamics," Games and Economic Behavior, Elsevier, vol. 137(C), pages 294-316.
   3. Izquierdo, Segismundo S. & Izquierdo, Luis R., 2023. "Strategy sets closed under payoff sampling," Games and Economic Behavior, Elsevier, vol. 138(C), pages 126-142.
   4. Arigapudi, Srinivas & Heller, Yuval & Milchtaich, Igal, 2020. "Instability of Defection in the Prisoner’s Dilemma: Best Experienced Payoff Dynamics Analysis," MPRA Paper 99594, University Library of Munich, Germany.
   5. Sandholm, William H. & Izquierdo, Segismundo S. & Izquierdo, Luis R., 2019. "Best experienced payoff dynamics and cooperation in the Centipede game," Theoretical Economics, Econometric Society, vol. 14(4), November.
   6. Arigapudi, Srinivas & Heller, Yuval & Milchtaich, Igal, 2021. "Instability of defection in the prisoner's dilemma under best experienced payoff dynamics," Journal of Economic Theory, Elsevier, vol. 197(C).
   7. Ryoji Sawa, 2022. "Statistical Inference in Evolutionary Dynamics," Working Papers e170, Tokyo Center for Economic Research.
   8. Srinivas Arigapudi & Yuval Heller & Amnon Schreiber, 2021. "Sampling dynamics and stable mixing in hawk-dove games," Papers 2107.08423, arXiv.org, revised Jun 2022.
   9. Sethi, Rajiv, 2021. "Stable sampling in repeated games," Journal of Economic Theory, Elsevier, vol. 197(C).
   10. Izquierdo, Segismundo S. & Izquierdo, Luis R., 2022. "Stability of strict equilibria in best experienced payoff dynamics: Simple formulas and applications," Journal of Economic Theory, Elsevier, vol. 206(C).
   11. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Sandholm, William H., 2019. "An introduction to ABED: Agent-based simulation of evolutionary game dynamics," Games and Economic Behavior, Elsevier, vol. 118(C), pages 434-462.
   12. Arigapudi, Srinivas & Heller, Yuval & Schreiber, Amnon, 2021. "Sampling Dynamics and Stable Mixing in Hawk–Dove Games," MPRA Paper 108819, University Library of Munich, Germany.
   13. Srinivas Arigapudi & Yuval Heller & Amnon Schreiber, 2023. "Heterogeneous Noise and Stable Miscoordination," Papers 2305.10301, arXiv.org.
   14. Srinivas Arigapudi & Yuval Heller & Igal Milchtaich, 2020. "Instability of Defection in the Prisoner's Dilemma Under Best Experienced Payoff Dynamics," Papers 2005.05779, arXiv.org, revised Jan 2021.

3. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Sandholm, William H., 2019. "An introduction to ABED: Agent-based simulation of evolutionary game dynamics," Games and Economic Behavior, Elsevier, vol. 118(C), pages 434-462.

   Cited by:

   1. Sandholm, William H. & Izquierdo, Segismundo S. & Izquierdo, Luis R., 2020. "Stability for best experienced payoff dynamics," Journal of Economic Theory, Elsevier, vol. 185(C).
   2. Wang Zhijian, 2022. "Game Dynamics Structure Control by Design: an Example from Experimental Economics," Papers 2203.06088, arXiv.org.
   3. Sandholm, William H. & Izquierdo, Segismundo S. & Izquierdo, Luis R., 2019. "Best experienced payoff dynamics and cooperation in the Centipede game," Theoretical Economics, Econometric Society, vol. 14(4), November.
   4. Runtian Zhang & Jinye Li, 2020. "Impact of incentive and selection strength on green technology innovation in Moran process," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-15, June.
   5. Mertikopoulos, Panayotis & Sandholm, William H., 2018. "Riemannian game dynamics," Journal of Economic Theory, Elsevier, vol. 177(C), pages 315-364.
   6. Jiabin Wu & Hanzhe Zhang, 2022. "Polarization, antipathy, and political activism," Economic Inquiry, Western Economic Association International, vol. 60(3), pages 1005-1017, July.
      • Zhang, Hanzhe & Wu, Jiabin, 2020. "Polarization, Antipathy, and Political Activism," Working Papers 2020-11, Michigan State University, Department of Economics.
   7. Yanping Xu & Lilong Zhu, 2022. "Pharmaceutical Enterprises’ R&D Innovation Cooperation Moran Strategy When Considering Tax Incentives," IJERPH, MDPI, vol. 19(22), pages 1-13, November.
   8. Chen, Shangrong & Bravo-Melgarejo, Sai & Mongeau, Romain & Malavolti, Estelle, 2023. "Adopting and diffusing hydrogen technology in air transport: An evolutionary game theory approach," Energy Economics, Elsevier, vol. 125(C).
   9. Mantas Radzvilas & Francesco De Pretis & William Peden & Daniele Tortoli & Barbara Osimani, 2020. "Double blind vs. open review: an evolutionary game logit-simulating the behavior of authors and reviewers," Papers 2011.07797, arXiv.org.
   10. Srinivas Arigapudi & Yuval Heller & Amnon Schreiber, 2021. "Sampling dynamics and stable mixing in hawk-dove games," Papers 2107.08423, arXiv.org, revised Jun 2022.
   11. Roland Mühlenbernd & Sławomir Wacewicz & Przemysław Żywiczyński, 2022. "The Evolution of Ambiguity in Sender—Receiver Signaling Games," Games, MDPI, vol. 13(2), pages 1-19, February.
   12. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
   13. Mantas Radzvilas & Francesco De Pretis & William Peden & Daniele Tortoli & Barbara Osimani, 2023. "Incentives for Research Effort: An Evolutionary Model of Publication Markets with Double-Blind and Open Review," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1433-1476, April.
   14. Arigapudi, Srinivas & Heller, Yuval & Schreiber, Amnon, 2021. "Sampling Dynamics and Stable Mixing in Hawk–Dove Games," MPRA Paper 108819, University Library of Munich, Germany.
   15. Wang Zhijian, 2023. "Nash equilibrium selection by eigenvalue control," Papers 2302.09131, arXiv.org.
   16. Zhijian Wang & Shujie Zhou & Qinmei Yao & Yijia Wang, 2022. "Dynamic Structure in Four-strategy Game: Theory and Experiment," Papers 2203.14669, arXiv.org.

4. Sandholm, William H. & Izquierdo, Segismundo S. & Izquierdo, Luis R., 2019. "Best experienced payoff dynamics and cooperation in the Centipede game," Theoretical Economics, Econometric Society, vol. 14(4), November.

   Cited by:

   1. Sandholm, William H. & Izquierdo, Segismundo S. & Izquierdo, Luis R., 2020. "Stability for best experienced payoff dynamics," Journal of Economic Theory, Elsevier, vol. 185(C).
   2. Sawa, Ryoji & Wu, Jiabin, 2023. "Statistical inference in evolutionary dynamics," Games and Economic Behavior, Elsevier, vol. 137(C), pages 294-316.
   3. Izquierdo, Segismundo S. & Izquierdo, Luis R., 2023. "Strategy sets closed under payoff sampling," Games and Economic Behavior, Elsevier, vol. 138(C), pages 126-142.
   4. Arigapudi, Srinivas & Heller, Yuval & Milchtaich, Igal, 2020. "Instability of Defection in the Prisoner’s Dilemma: Best Experienced Payoff Dynamics Analysis," MPRA Paper 99594, University Library of Munich, Germany.
   5. Arigapudi, Srinivas & Heller, Yuval & Milchtaich, Igal, 2021. "Instability of defection in the prisoner's dilemma under best experienced payoff dynamics," Journal of Economic Theory, Elsevier, vol. 197(C).
   6. Ryoji Sawa, 2022. "Statistical Inference in Evolutionary Dynamics," Working Papers e170, Tokyo Center for Economic Research.
   7. Srinivas Arigapudi & Yuval Heller & Amnon Schreiber, 2021. "Sampling dynamics and stable mixing in hawk-dove games," Papers 2107.08423, arXiv.org, revised Jun 2022.
   8. Sethi, Rajiv, 2021. "Stable sampling in repeated games," Journal of Economic Theory, Elsevier, vol. 197(C).
   9. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
   10. Izquierdo, Segismundo S. & Izquierdo, Luis R., 2022. "Stability of strict equilibria in best experienced payoff dynamics: Simple formulas and applications," Journal of Economic Theory, Elsevier, vol. 206(C).
   11. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Sandholm, William H., 2019. "An introduction to ABED: Agent-based simulation of evolutionary game dynamics," Games and Economic Behavior, Elsevier, vol. 118(C), pages 434-462.
   12. Arigapudi, Srinivas & Heller, Yuval & Schreiber, Amnon, 2021. "Sampling Dynamics and Stable Mixing in Hawk–Dove Games," MPRA Paper 108819, University Library of Munich, Germany.
   13. Srinivas Arigapudi & Yuval Heller & Amnon Schreiber, 2023. "Heterogeneous Noise and Stable Miscoordination," Papers 2305.10301, arXiv.org.
   14. Srinivas Arigapudi & Yuval Heller & Igal Milchtaich, 2020. "Instability of Defection in the Prisoner's Dilemma Under Best Experienced Payoff Dynamics," Papers 2005.05779, arXiv.org, revised Jan 2021.

5. Segismundo S. Izquierdo & Luis R. Izquierdo, 2018. "Mamdani Fuzzy Systems for Modelling and Simulation: A Critical Assessment," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(3), pages 1-2.

   Cited by:

   1. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu, 2021. "Fuzzy Control System for Smart Energy Management in Residential Buildings Based on Environmental Data," Energies, MDPI, vol. 14(3), pages 1-18, February.

6. Luis R. Izquierdo & Doina Olaru & Segismundo S. Izquierdo & Sharon Purchase & Geoffrey N. Soutar, 2015. "Fuzzy Logic for Social Simulation Using NetLogo," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-1.

   Cited by:

   1. Sergio F. Góngora y Moreno & J. Octavio Gutierrez-Garcia, 2018. "Collective action in organizational structures," Computational and Mathematical Organization Theory, Springer, vol. 24(1), pages 1-33, March.

7. Segismundo S. Izquierdo & Luis R. Izquierdo, 2015. "The “Win-Continue, Lose-Reverse” Rule In Oligopolies: Robustness Of Collusive Outcomes," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 18(05n06), pages 1-23, August.

   Cited by:

   1. Timo Klein, 2018. "Autonomous Algorithmic Collusion: Q-Learning Under Sequantial Pricing," Tinbergen Institute Discussion Papers 18-056/VII, Tinbergen Institute, revised 01 Nov 2020.
   2. Axel Gautier & Ashwin Ittoo & Pieter Cleynenbreugel, 2020. "AI algorithms, price discrimination and collusion: a technological, economic and legal perspective," European Journal of Law and Economics, Springer, vol. 50(3), pages 405-435, December.

8. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Vega-Redondo, Fernando, 2014. "Leave and let leave: A sufficient condition to explain the evolutionary emergence of cooperation," Journal of Economic Dynamics and Control, Elsevier, vol. 46(C), pages 91-113.

   Cited by:

   1. Jiabin Wu, 2020. "Labelling, homophily and preference evolution," International Journal of Game Theory, Springer;Game Theory Society, vol. 49(1), pages 1-22, March.
   2. Li, Yan & Ye, Hang & Zhang, Hong, 2016. "Evolution of cooperation driven by social-welfare-based migration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 48-56.
   3. Strømland, Eirik & Tjøtta, Sigve & Torsvik, Gaute, 2016. "Reciprocity evolving: partner choice and communication in a repeated prisoner’s dilemma," Working Papers in Economics 01/16, University of Bergen, Department of Economics.
   4. Strømland, Eirik & Tjøtta, Sigve & Torsvik, Gaute, 2018. "Mutual choice of partner and communication in a repeated prisoner's dilemma," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 75(C), pages 12-23.
   5. Zeng, Weijun & Ai, Hongfeng & Zhao, Man, 2019. "Asymmetrical expectations of future interaction and cooperation in the iterated prisoner's dilemma game," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 148-164.
   6. Daniele Nosenzo & Fabio Tufano, 2017. "The Effect of Voluntary Participation on Cooperation," Discussion Papers 2017-12, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
      • Nosenzo, Daniele & Tufano, Fabio, 2017. "The effect of voluntary participation on cooperation," Journal of Economic Behavior & Organization, Elsevier, vol. 142(C), pages 307-319.
   7. Jiabin Wu, 2016. "Evolving assortativity and social conventions," Economics Bulletin, AccessEcon, vol. 36(2), pages 936-941.
   8. Premo, L.S. & Brown, Justin R., 2019. "The opportunity cost of walking away in the spatial iterated prisoner’s dilemma," Theoretical Population Biology, Elsevier, vol. 127(C), pages 40-48.
   9. Jiabin Wu, 2021. "Matching markets and cultural selection," Review of Economic Design, Springer;Society for Economic Design, vol. 25(4), pages 267-288, December.
   10. Daniele Nosenzo & Fabio Tufano, 2015. "Entry or Exit? The Effect of Voluntary Participation on Cooperation," Discussion Papers 2015-04, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
       • Daniele Nosenzo & Fabio Tufano, 2015. "Entry or Exit? The Effect of Voluntary Participation on Cooperation," Discussion Papers 2015-20, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
   11. Takako Fujiwara-Greve & Masahiro Okuno-Fujiwara & Nobue Suzuki, 2013. "Efficiency may Improve when Defectors Exist," Working Papers e060, Tokyo Center for Economic Research.
       • Takako Fujiwara-Greve & Masahiro Okuno-Fujiwara & Nobue Suzuki, 2015. "Efficiency may improve when defectors exist," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 60(3), pages 423-460, November.
   12. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
   13. Takako Fujiwara-Greve & Masahiro Okuno-Fujiwara, 2013. "Diverse Behavior Patterns in a Symmetric Society with Voluntary Partnerships," Working Papers e062, Tokyo Center for Economic Research.
   14. Kurokawa, Shun, 2022. "Evolution of trustfulness in the case where resources for cooperation are sometimes absent," Theoretical Population Biology, Elsevier, vol. 145(C), pages 63-79.
   15. Kurokawa, Shun & Zheng, Xiudeng & Tao, Yi, 2019. "Cooperation evolves more when players keep the interaction with unknown players," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 209-216.
   16. Zhang, Hong, 2015. "Moderate tolerance promotes tag-mediated cooperation in spatial Prisoner’s dilemma game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 52-61.
   17. Pin, Paolo & Rogers, Brian W., 2015. "Cooperation, punishment and immigration," Journal of Economic Theory, Elsevier, vol. 160(C), pages 72-101.
   18. Serdarevic, Nina & Strømland, Eirik & Tjøtta, Sigve, 2018. "It Pays to be Nice: The Benefits of Cooperating in Markets," Working Papers in Economics 12/18, University of Bergen, Department of Economics.
   19. Qu, Xinglong & Zhou, Changli & Cao, Zhigang & Yang, Xiaoguang, 2016. "Conditional dissociation as a punishment mechanism in the evolution of cooperation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 215-223.
   20. Jiabin Wu, 2017. "Social Hierarchy and the Evolution of Behavior," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-16, December.

9. Segismundo S. Izquierdo & Luis R. Izquierdo, 2011. "Strictly Dominated Strategies in the Replicator-Mutator Dynamics," Games, MDPI, vol. 2(3), pages 1-10, September.

   Cited by:

   1. Situngkir, Hokky & Prasetyo, Yanu Endar, 2012. "On social and economic spheres: an observation of the “gantangan” Indonesian tradition," MPRA Paper 39671, University Library of Munich, Germany.
   2. Brian Mintz & Feng Fu, 2022. "The Point of No Return: Evolution of Excess Mutation Rate Is Possible Even for Simple Mutation Models," Mathematics, MDPI, vol. 10(24), pages 1-9, December.

10. José Manuel Galán & Luis R. Izquierdo & Segismundo S. Izquierdo & José Ignacio Santos & Ricardo del Olmo & Adolfo López-Paredes & Bruce Edmonds, 2009. "Errors and Artefacts in Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-1.

    Cited by:

    1. Jacopo Baggio & Elissaios Papyrakis, 2014. "Agent-Based Simulations of Subjective Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 115(2), pages 623-635, January.
    2. Nieddu, Marcello & Bertani, Filippo & Ponta, Linda, 2021. "Sustainability transition and digital trasformation: an agent-based perspective," MPRA Paper 106943, University Library of Munich, Germany.
    3. Rixen, Martin & Weigand, Jürgen, 2014. "Agent-based simulation of policy induced diffusion of smart meters," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 153-167.
    4. Juan Manuel Larrosa, 2016. "Agentes computacionales y análisis económico," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 18(34), pages 87-113, January-J.
    5. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    6. Nancy Quinceno Cárdenas, 2014. "Modelación basada en agentes en el sistema pensional colombiano. Una aproximación desde el mercado laboral y la dinámica poblacional," Revista CIFE, Universidad Santo Tomás, September.
    7. Paola Tubaro, 2011. "Computational Economics," Chapters, in: John B. Davis & D. Wade Hands (ed.), The Elgar Companion to Recent Economic Methodology, chapter 10, Edward Elgar Publishing.
       • Paola Tubaro, 2011. "Computational economics," Post-Print hal-01372973, HAL.
    8. Sung-youn Kim, 2011. "A Model of Political Judgment: An Agent-Based Simulation of Candidate Evaluation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 14(2), pages 1-3.
    9. Bernardo Alves Furtado, 2018. "Modeling tax distribution in metropolitan regions with PolicySpace," Papers 1901.02391, arXiv.org.
    10. José M Galán & Maciej M Łatek & Seyed M Mussavi Rizi, 2011. "Axelrod's Metanorm Games on Networks," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-11, May.
    11. J. Gareth Polhill, 2015. "Extracting OWL Ontologies from Agent-Based Models: A Netlogo Extension," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-15.
    12. Marcello Nieddu & Filippo Bertani & Linda Ponta, 2022. "The sustainability transition and the digital transformation: two challenges for agent-based macroeconomic models," Review of Evolutionary Political Economy, Springer, vol. 3(1), pages 193-226, April.
    13. Barroso, Ricardo Vieira & Lima, Joaquim Ignacio Alves Vasconcellos & Lucchetti, Alexandre Henrique & Cajueiro, Daniel Oliveira, 2016. "Interbank network and regulation policies: an analysis through agent-based simulations with adaptive learning," MPRA Paper 73308, University Library of Munich, Germany.
    14. Kolkman, Daan, 2020. "The usefulness of algorithmic models in policy making," SocArXiv hpma8, Center for Open Science.
    15. Sara Lumbreras & Sonja Wogrin & Guillermo Navarro & Ilaria Bertazzi & Maria Pereda, 2019. "A Decentralized Solution for Transmission Expansion Planning: Getting Inspiration from Nature," Energies, MDPI, vol. 12(23), pages 1-17, November.
    16. Georg Holtz & Christian Schnülle & Malcolm Yadack & Jonas Friege & Thorben Jensen & Pablo Thier & Peter Viebahn & Émile J. L. Chappin, 2020. "Using Agent-Based Models to Generate Transformation Knowledge for the German Energiewende—Potentials and Challenges Derived from Four Case Studies," Energies, MDPI, vol. 13(22), pages 1-26, November.
    17. Rubén Fuentes-Fernández & Samer Hassan & Juan Pavón & José M. Galán & Adolfo López-Paredes, 2012. "Metamodels for role-driven agent-based modelling," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 91-112, March.
    18. Bernardo Alves Furtado & Gustavo Onofre Andre~ao, 2022. "Machine Learning Simulates Agent-Based Model Towards Policy," Papers 2203.02576, arXiv.org, revised Nov 2022.

11. Luis R. Izquierdo & Segismundo S. Izquierdo & José Manuel Galán & José Ignacio Santos, 2009. "Techniques to Understand Computer Simulations: Markov Chain Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-6.

    Cited by:

    1. Sven Banischa & Ricardo Lima & Tanya Araújo, 2012. "Agent based models and opinion dynamics as markov chains," Working Papers Department of Economics 2012/10, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    2. Catalano, Michele & Di Guilmi, Corrado, 2019. "Uncertainty, rationality and complexity in a multi-sectoral dynamic model: The dynamic stochastic generalized aggregation approach," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 117-144.
       • Michele Catalano & Corrado Di Guilmi, 2016. "Uncertainty, rationality and complexity in a multi sectoral dynamic model: the Dynamic Stochastic Generalized Aggregation approach," CAMA Working Papers 2016-16, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Akihisa Okada & Daisuke Inoue & Shihori Koyama & Tadayoshi Matsumori & Hiroaki Yoshida, 2022. "Dynamical cooperation model for mitigating the segregation phase in Schelling’s model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(10), pages 1-10, October.
    4. Declan Mungovan & Enda Howley & Jim Duggan, 2011. "The influence of random interactions and decision heuristics on norm evolution in social networks," Computational and Mathematical Organization Theory, Springer, vol. 17(2), pages 152-178, May.
    5. Wyburn, John & Hayward, John, 2019. "An application of an analogue of the partition function to the evolution of diglossia," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 447-463.
    6. José Manuel Galán & Luis R. Izquierdo & Segismundo S. Izquierdo & José Ignacio Santos & Ricardo del Olmo & Adolfo López-Paredes & Bruce Edmonds, 2009. "Errors and Artefacts in Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-1.
    7. Andrew W. Bausch, 2014. "Evolving intergroup cooperation," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 369-393, December.
    8. Edoardo Gaffeo & Mauro Gallegati & Umberto Gostoli, 2012. "An agent-based "proof of principle" for Walrasian macroeconomic theory," CEEL Working Papers 1202, Cognitive and Experimental Economics Laboratory, Department of Economics, University of Trento, Italia.
       • Edoardo Gaffeo & Mauro Gallegati & Umberto Gostoli, 2015. "An agent-based “proof of principle” for Walrasian macroeconomic theory," Computational and Mathematical Organization Theory, Springer, vol. 21(2), pages 150-183, June.
    9. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
       • Jakob Grazzini & Matteo Richiardi, 2014. "Estimation of Ergodic Agent-Based Models by Simulated Minimum Distance," Economics Papers 2014-W07, Economics Group, Nuffield College, University of Oxford.
    10. José M Galán & Maciej M Łatek & Seyed M Mussavi Rizi, 2011. "Axelrod's Metanorm Games on Networks," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-11, May.
    11. Di Guilmi, Corrado & Carvalho, Laura, 2017. "The dynamics of leverage in a demand-driven model with heterogeneous firms," Journal of Economic Behavior & Organization, Elsevier, vol. 140(C), pages 70-90.
        • Corrado Di Guilmi & Laura Carvalho, 2016. "The Dynamics Of Leverage In A Demand-Driven Model With Heterogeneous Firms," Anais do XLIII Encontro Nacional de Economia [Proceedings of the 43rd Brazilian Economics Meeting] 141, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    12. Severin Reissl, 2022. "Fiscal multipliers, expectations and learning in a macroeconomic agent‐based model," Economic Inquiry, Western Economic Association International, vol. 60(4), pages 1704-1729, October.
    13. Pfau, Jens & Kirley, Michael & Kashima, Yoshihisa, 2013. "The co-evolution of cultures, social network communities, and agent locations in an extension of Axelrod’s model of cultural dissemination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(2), pages 381-391.
    14. Grazzini, Jakob & Richiardi, Matteo, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201335, University of Turin.
        • Jakob Grazzini & Matteo G. Richiardi, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," LABORatorio R. Revelli Working Papers Series 130, LABORatorio R. Revelli, Centre for Employment Studies.
    15. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046.

12. Segismundo S. Izquierdo & Luis R. Izquierdo & Nicholas M. Gotts, 2008. "Reinforcement Learning Dynamics in Social Dilemmas," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(2), pages 1-1.

    Cited by:

    1. José Manuel Galán & Luis R. Izquierdo & Segismundo S. Izquierdo & José Ignacio Santos & Ricardo del Olmo & Adolfo López-Paredes & Bruce Edmonds, 2009. "Errors and Artefacts in Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-1.
    2. John W. Straka & Brenda C. Straka, 2020. "Reframe policymaking dysfunction through bipartisan-inclusion leadership," Policy Sciences, Springer;Society of Policy Sciences, vol. 53(4), pages 779-802, December.
    3. Cheng, Yuan & Zheng, Xiaoping, 2018. "Emergence of cooperation during an emergency evacuation," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 485-494.
    4. Luis R. Izquierdo & Segismundo S. Izquierdo & José Manuel Galán & José Ignacio Santos, 2009. "Techniques to Understand Computer Simulations: Markov Chain Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-6.
    5. Jia, Danyang & Li, Tong & Zhao, Yang & Zhang, Xiaoqin & Wang, Zhen, 2022. "Empty nodes affect conditional cooperation under reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    6. Li Zhenpeng & Tang Xijin, 2021. "Stimuli strategy and learning dynamics promote the wisdom of crowds," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(12), pages 1-8, December.
    7. Dan Miodownik & Britt Cartrite & Ravi Bhavnani, 2010. "Between Replication and Docking: "Adaptive Agents, Political Institutions, and Civic Traditions" Revisited," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(3), pages 1-1.

13. Izquierdo, Segismundo S. & Izquierdo, Luis R., 2007. "The impact of quality uncertainty without asymmetric information on market efficiency," Journal of Business Research, Elsevier, vol. 60(8), pages 858-867, August.

    Cited by:

    1. He, Zhou & Cheng, T.C.E. & Dong, Jichang & Wang, Shouyang, 2016. "Evolutionary location and pricing strategies for service merchants in competitive O2O markets," European Journal of Operational Research, Elsevier, vol. 254(2), pages 595-609.
    2. Hoang, Cong Huan & Ly, Kim Cuong & Xiao, Qin & Zhang, Xuan, 2023. "Does national culture impact trade credit provision of SMEs?," Economic Modelling, Elsevier, vol. 124(C).
    3. Yi-Chen Huang & Tak-Yu Cheng & Bin-Tzong Chie, 2022. "The Effect of Dishonest Sellers on E-commerce: An Agent-Based Modeling Approach," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 12(4), pages 1-5.
    4. José Manuel Galán & Luis R. Izquierdo & Segismundo S. Izquierdo & José Ignacio Santos & Ricardo del Olmo & Adolfo López-Paredes & Bruce Edmonds, 2009. "Errors and Artefacts in Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-1.
    5. Michał Kot, 2022. "An agent-based model of consumer choice. An evaluation of the strategy of pricing and advertising," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 73-95.
    6. Nicolau, Juan Luis & Sellers, Ricardo, 2010. "The quality of quality awards: Diminishing information asymmetries in a hotel chain," Journal of Business Research, Elsevier, vol. 63(8), pages 832-839, August.
    7. Justin Malbon, 2013. "Taking Fake Online Consumer Reviews Seriously," Journal of Consumer Policy, Springer, vol. 36(2), pages 139-157, June.
    8. Bharat Bhole & Bríd Hanna, 2015. "Word-of-Mouth Communication and Demand for Products with Different Quality Levels," Computational Economics, Springer;Society for Computational Economics, vol. 46(4), pages 627-651, December.
    9. Mariia Rochikashvili & Jan C. Bongaerts, 2018. "How Eco-Labelling Influences Environmentally Conscious Consumption of Construction Products," Sustainability, MDPI, vol. 10(2), pages 1-23, January.
    10. Gong, Cynthia M. & Lizieri, Colin & Bao, Helen X.H., 2019. "“Smarter information, smarter consumers”? Insights into the housing market," Journal of Business Research, Elsevier, vol. 97(C), pages 51-64.
    11. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.

14. 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.

    Cited by:

    1. Sung-youn Kim, 2012. "A model of political information-processing and learning cooperation in the repeated Prisoner’s Dilemma," Journal of Theoretical Politics, , vol. 24(1), pages 46-65, January.
    2. Schuster, Stephan, 2010. "Network Formation with Adaptive Agents," MPRA Paper 27388, University Library of Munich, Germany.
    3. José Manuel Galán & Luis R. Izquierdo & Segismundo S. Izquierdo & José Ignacio Santos & Ricardo del Olmo & Adolfo López-Paredes & Bruce Edmonds, 2009. "Errors and Artefacts in Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-1.
    4. Daniel Heymann & Enrique Kawamura & Roberto Perazzo & Martin Zimmermann, 2011. "Behavioral Heuristics and Market Patterns in a Bertrand-Edgeworth Game," Working Papers 108, Universidad de San Andres, Departamento de Economia, revised Mar 2011.
       • Heymann, D. & Kawamura, E. & Perazzo, R. & Zimmermann, M.G., 2014. "Behavioral heuristics and market patterns in a Bertrand–Edgeworth game," Journal of Economic Behavior & Organization, Elsevier, vol. 105(C), pages 124-139.
    5. Yu Zhang & Jason Leezer, 2010. "Simulating human-like decisions in a memory-based agent model," Computational and Mathematical Organization Theory, Springer, vol. 16(4), pages 373-399, December.
    6. Ianni, Antonella, 2014. "Learning strict Nash equilibria through reinforcement," Journal of Mathematical Economics, Elsevier, vol. 50(C), pages 148-155.
       • Ianni, Antonella, 2011. "Learning Strict Nash Equilibria through Reinforcement," MPRA Paper 33936, University Library of Munich, Germany.
    7. Dridi, Slimane & Lehmann, Laurent, 2014. "On learning dynamics underlying the evolution of learning rules," Theoretical Population Biology, Elsevier, vol. 91(C), pages 20-36.
    8. Liangliang Chang & Zhipeng Zhang & Chengyi Xia, 2023. "Impact of Decision Feedback on Networked Evolutionary Game with Delays in Control Channel," Dynamic Games and Applications, Springer, vol. 13(3), pages 783-800, September.
    9. Wang, Xianjia & Yang, Zhipeng & Liu, Yanli & Chen, Guici, 2023. "A reinforcement learning-based strategy updating model for the cooperative evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    10. Xiaopeng Li & Zhonglin Wang & Jiuqiang Liu & Guihai Yu, 2023. "The Sense of Cooperation on Interdependent Networks Inspired by Influence-Based Self-Organization," Mathematics, MDPI, vol. 11(4), pages 1-16, February.
    11. Oyarzun, Carlos & Sarin, Rajiv, 2013. "Learning and risk aversion," Journal of Economic Theory, Elsevier, vol. 148(1), pages 196-225.
        • Carlos Oyarzun & Rajiv Sarin, 2005. "Learning and Risk Aversion," Levine's Bibliography 784828000000000482, UCLA Department of Economics.
        • Carlos Oyarzun & Rajiv Sarin, 2012. "Learning and Risk Aversion," Levine's Working Paper Archive 786969000000000572, David K. Levine.
    12. Segismundo S. Izquierdo & Luis R. Izquierdo & Nicholas M. Gotts, 2008. "Reinforcement Learning Dynamics in Social Dilemmas," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(2), pages 1-1.
    13. Jia, Danyang & Li, Tong & Zhao, Yang & Zhang, Xiaoqin & Wang, Zhen, 2022. "Empty nodes affect conditional cooperation under reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    14. Schuster, Stephan, 2012. "Applications in Agent-Based Computational Economics," MPRA Paper 47201, University Library of Munich, Germany.

Chapters

1. Segismundo S. Izquierdo & Luis R. Izquierdo, 2015. "The “Win-Continue, Lose-Reverse” Rule in Cournot Oligopolies: Robustness of Collusive Outcomes," Lecture Notes in Economics and Mathematical Systems, in: Frédéric Amblard & Francisco J. Miguel & Adrien Blanchet & Benoit Gaudou (ed.), Advances in Artificial Economics, edition 127, pages 33-44, Springer.

   Cited by:

   1. Axel Gautier & Ashwin Ittoo & Pieter Cleynenbreugel, 2020. "AI algorithms, price discrimination and collusion: a technological, economic and legal perspective," European Journal of Law and Economics, Springer, vol. 50(3), pages 405-435, December.

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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 4 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-ECM: Econometrics (2) 2005-11-09 2007-08-08
2. NEP-ETS: Econometric Time Series (2) 2005-11-09 2007-08-08
3. NEP-EVO: Evolutionary Economics (2) 2017-12-11 2018-01-15
4. NEP-FOR: Forecasting (1) 2007-08-08

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