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Статті в журналах з теми "Dynamics of learning"

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Автор: Grafiati

Опубліковано: 23 вересня 2022

Оновлено: 28 вересня 2022

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1

Kovacs, Alexander, Johann Fischbacher, Harald Oezelt, Markus Gusenbauer, Lukas Exl, Florian Bruckner, Dieter Suess, and Thomas Schrefl. "Learning magnetization dynamics." Journal of Magnetism and Magnetic Materials 491 (December 2019): 165548. http://dx.doi.org/10.1016/j.jmmm.2019.165548.

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2

Bergin, James, and Dan Bernhardt. "Comparative Learning Dynamics*." International Economic Review 45, no. 2 (May 2004): 431–65. http://dx.doi.org/10.1111/j.1468-2354.2004.00132.x.

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3

Gilbert, Charles D. "Learning: Neuronal dynamics and perceptual learning." Current Biology 4, no. 7 (July 1994): 627–29. http://dx.doi.org/10.1016/s0960-9822(00)00138-x.

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4

TRIFANESCU, Letitia. "Learning Dynamics in Feminine Precarious Migration. A Qualitative Perspective." Revista Romaneasca pentru Educatie Multidimensionala 5, no. 2 (December 31, 2013): 85–100. http://dx.doi.org/10.18662/rrem/2013.0502.08.

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5

Hou, Huaidian, and Lingxiao Wang. "Measuring Dynamics in Evacuation Behaviour with Deep Learning." Entropy 24, no. 2 (January 27, 2022): 198. http://dx.doi.org/10.3390/e24020198.

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Анотація:

Bounded rationality is one crucial component in human behaviours. It plays a key role in the typical collective behaviour of evacuation, in which heterogeneous information can lead to deviations from optimal choices. In this study, we propose a framework of deep learning to extract a key dynamical parameter that drives crowd evacuation behaviour in a cellular automaton (CA) model. On simulation data sets of a replica dynamic CA model, trained deep convolution neural networks (CNNs) can accurately predict dynamics from multiple frames of images. The dynamical parameter could be regarded as a factor describing the optimality of path-choosing decisions in evacuation behaviour. In addition, it should be noted that the performance of this method is robust to incomplete images, in which the information loss caused by cutting images does not hinder the feasibility of the method. Moreover, this framework provides us with a platform to quantitatively measure the optimal strategy in evacuation, and this approach can be extended to other well-designed crowd behaviour experiments.

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6

Fang, Aili, Kehua Yuan, Jinhua Geng, and Xinjiang Wei. "Opinion Dynamics with Bayesian Learning." Complexity 2020 (February 22, 2020): 1–5. http://dx.doi.org/10.1155/2020/8261392.

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Bayesian learning is a rational and effective strategy in the opinion dynamic process. In this paper, we theoretically prove that individual Bayesian learning can realize asymptotic learning and we test it by simulations on the Zachary network. Then, we propose a Bayesian social learning model with signal update strategy and apply the model on the Zachary network to observe opinion dynamics. Finally, we contrast the two learning strategies and find that Bayesian social learning can lead to asymptotic learning more faster than individual Bayesian learning.

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7

Rupčić, Nataša. "Learning-forgetting-unlearning-relearning – the learning organization’s learning dynamics." Learning Organization 26, no. 5 (July 8, 2019): 542–48. http://dx.doi.org/10.1108/tlo-07-2019-237.

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8

JOHANNES, MICHAEL, LARS A. LOCHSTOER, and YIQUN MOU. "Learning about Consumption Dynamics." Journal of Finance 71, no. 2 (March 18, 2016): 551–600. http://dx.doi.org/10.1111/jofi.12246.

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9

Häse, Florian, Stéphanie Valleau, Edward Pyzer-Knapp, and Alán Aspuru-Guzik. "Machine learning exciton dynamics." Chemical Science 7, no. 8 (2016): 5139–47. http://dx.doi.org/10.1039/c5sc04786b.

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10

Farber, H. S., and R. Gibbons. "Learning and Wage Dynamics." Quarterly Journal of Economics 111, no. 4 (November 1, 1996): 1007–47. http://dx.doi.org/10.2307/2946706.

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11

Moioli, Renan C., and Phil Husbands. "Neuronal Assembly Dynamics in Supervised and Unsupervised Learning Scenarios." Neural Computation 25, no. 11 (November 2013): 2934–75. http://dx.doi.org/10.1162/neco_a_00502.

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Анотація:

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system's variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions.

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12

Bloembergen, Daan, Karl Tuyls, Daniel Hennes, and Michael Kaisers. "Evolutionary Dynamics of Multi-Agent Learning: A Survey." Journal of Artificial Intelligence Research 53 (August 17, 2015): 659–97. http://dx.doi.org/10.1613/jair.4818.

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The interaction of multiple autonomous agents gives rise to highly dynamic and nondeterministic environments, contributing to the complexity in applications such as automated financial markets, smart grids, or robotics. Due to the sheer number of situations that may arise, it is not possible to foresee and program the optimal behaviour for all agents beforehand. Consequently, it becomes essential for the success of the system that the agents can learn their optimal behaviour and adapt to new situations or circumstances. The past two decades have seen the emergence of reinforcement learning, both in single and multi-agent settings, as a strong, robust and adaptive learning paradigm. Progress has been substantial, and a wide range of algorithms are now available. An important challenge in the domain of multi-agent learning is to gain qualitative insights into the resulting system dynamics. In the past decade, tools and methods from evolutionary game theory have been successfully employed to study multi-agent learning dynamics formally in strategic interactions. This article surveys the dynamical models that have been derived for various multi-agent reinforcement learning algorithms, making it possible to study and compare them qualitatively. Furthermore, new learning algorithms that have been introduced using these evolutionary game theoretic tools are reviewed. The evolutionary models can be used to study complex strategic interactions. Examples of such analysis are given for the domains of automated trading in stock markets and collision avoidance in multi-robot systems. The paper provides a roadmap on the progress that has been achieved in analysing the evolutionary dynamics of multi-agent learning by highlighting the main results and accomplishments.

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13

Yamaguchi, Akihiko, Jun Takamatsu, and Tsukasa Ogasawara. "1A1-O03 Utilizing Dynamics and Reward Models in Learning Strategy Fusion(Evolution and Learning for Robotics)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2011 (2011): _1A1—O03_1—_1A1—O03_4. http://dx.doi.org/10.1299/jsmermd.2011._1a1-o03_1.

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14

Herzberg, Heidrun. "Learning habitus and the dynamics of lifelong learning." Studies in the Education of Adults 38, no. 1 (March 2006): 37–47. http://dx.doi.org/10.1080/02660830.2006.11661523.

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15

Zhu, Guangxiang, Jianhao Wang, Zhizhou Ren, Zichuan Lin, and Chongjie Zhang. "Object-Oriented Dynamics Learning through Multi-Level Abstraction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6989–98. http://dx.doi.org/10.1609/aaai.v34i04.6183.

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Анотація:

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability. Our results show that MAOP significantly outperforms previous methods in terms of sample efficiency and generalization over novel environments for learning environment models. We also demonstrate that learned dynamics models enable efficient planning in unseen environments, comparable to true environment models. In addition, MAOP learns semantically and visually interpretable disentangled representations.

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16

Bastos, Paulo, Daniel A. Dias, and Olga A. Timoshenko. "Learning, Prices, and Firm Dynamics." International Finance Discussion Paper 2017, no. 1193 (February 2017): 1–53. http://dx.doi.org/10.17016/ifdp.2017.1193.

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17

Konovalov, Arkady, and Ian Krajbich. "Neurocomputational Dynamics of Sequence Learning." Neuron 98, no. 6 (June 2018): 1282–93. http://dx.doi.org/10.1016/j.neuron.2018.05.013.

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18

Geleilate, Jose Mauricio Galli, Francisco Polidoro, and Ronaldo C. Parente. "Learning Dynamics in Vertical Relationships." Academy of Management Proceedings 2019, no. 1 (August 1, 2019): 18380. http://dx.doi.org/10.5465/ambpp.2019.18380abstract.

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19

Murata, Noboru, and Shun-ichi Amari. "Statistical analysis of learning dynamics." Signal Processing 74, no. 1 (April 1999): 3–28. http://dx.doi.org/10.1016/s0165-1684(98)00206-0.

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20

Caminal, Ramon, and Xavier Vives. "Price Dynamics and Consumer Learning." Journal of Economics & Management Strategy 8, no. 1 (March 1, 1999): 95–131. http://dx.doi.org/10.1162/105864099567596.

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21

Bressloff, P. C. "Stochastic dynamics of reinforcement learning." Network: Computation in Neural Systems 6, no. 2 (January 1995): 289–307. http://dx.doi.org/10.1088/0954-898x_6_2_009.

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22

Darainy, Mohammad, and David J. Ostry. "Muscle cocontraction following dynamics learning." Experimental Brain Research 190, no. 2 (June 27, 2008): 153–63. http://dx.doi.org/10.1007/s00221-008-1457-y.

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23

Roberts, Patrick D. "Dynamics of temporal learning rules." Physical Review E 62, no. 3 (September 1, 2000): 4077–82. http://dx.doi.org/10.1103/physreve.62.4077.

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24

Wong, K. Y. Michael, S. Li, and Y. W. Tong. "Complex dynamics in learning systems." Physica A: Statistical Mechanics and its Applications 288, no. 1-4 (December 2000): 397–401. http://dx.doi.org/10.1016/s0378-4371(00)00436-2.

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25

Hansen, L. K., R. Pathria, and P. Salamon. "Stochastic dynamics of supervised learning." Journal of Physics A: Mathematical and General 26, no. 1 (January 7, 1993): 63–71. http://dx.doi.org/10.1088/0305-4470/26/1/011.

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26

Pazmandi, F. "Learning dynamics: a replica approach." Journal of Physics A: Mathematical and General 25, no. 16 (August 21, 1992): 4359–72. http://dx.doi.org/10.1088/0305-4470/25/16/013.

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27

JOST, JÜRGEN, and WEI LI. "LEARNING, EVOLUTION AND POPULATION DYNAMICS." Advances in Complex Systems 11, no. 06 (December 2008): 901–26. http://dx.doi.org/10.1142/s0219525908001908.

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Анотація:

We study a complementarity game as a systematic tool for the investigation of the interplay between individual optimization and population effects and for the comparison of different strategy and learning schemes. The game randomly pairs players from opposite populations. It is symmetric at the individual level, but has many equilibria that are more or less favorable to the members of the two populations. Which of these equilibria is then attained is decided by the dynamics at the population level. Players play repeatedly, but in each round with a new opponent. They can learn from their previous encounters and translate this into their actions in the present round on the basis of strategic schemes. The schemes can be quite simple, or very elaborate. We can then break the symmetry in the game and give the members of the two populations access to different strategy spaces. Typically, simpler strategy types have an advantage because they tend to go more quickly toward a favorable equilibrium which, once reached, the other population is forced to accept. Also, populations with bolder individuals that may not fare so well at the level of individual performance may obtain an advantage toward ones with more timid players. By checking the effects of parameters such as the generation length or the mutation rate, we are able to compare the relative contributions of individual learning and evolutionary adaptations.

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28

Kutz, J. Nathan. "Deep learning in fluid dynamics." Journal of Fluid Mechanics 814 (January 31, 2017): 1–4. http://dx.doi.org/10.1017/jfm.2016.803.

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It was only a matter of time before deep neural networks (DNNs) – deep learning – made their mark in turbulence modelling, or more broadly, in the general area of high-dimensional, complex dynamical systems. In the last decade, DNNs have become a dominant data mining tool for big data applications. Although neural networks have been applied previously to complex fluid flows, the article featured here (Ling et al., J. Fluid Mech., vol. 807, 2016, pp. 155–166) is the first to apply a true DNN architecture, specifically to Reynolds averaged Navier Stokes turbulence models. As one often expects with modern DNNs, performance gains are achieved over competing state-of-the-art methods, suggesting that DNNs may play a critically enabling role in the future of modelling complex flows.

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29

Williams, Noah. "Escape Dynamics in Learning Models." Review of Economic Studies 86, no. 2 (June 9, 2018): 882–912. http://dx.doi.org/10.1093/restud/rdy033.

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Abstract This article illustrates and characterizes how adaptive learning can lead to recurrent large fluctuations. Learning models have typically focused on the convergence of beliefs towards an equilibrium. However in stochastic environments, there may be rare but recurrent episodes where shocks cause beliefs to escape from the equilibrium, generating large movements in observed outcomes. I characterize the escape dynamics by drawing on the theory of large deviations, developing new results which make this theory directly applicable in a class of learning models. The likelihood, frequency, and most likely direction of escapes are all characterized by a deterministic control problem. I illustrate my results with two simple examples.

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30

Bastos, Paulo, Daniel A. Dias, and Olga A. Timoshenko. "Learning, prices and firm dynamics." Canadian Journal of Economics/Revue canadienne d'économique 51, no. 4 (October 8, 2018): 1257–311. http://dx.doi.org/10.1111/caje.12361.

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31

Branch, William A., and George W. Evans. "Asset Return Dynamics and Learning." Review of Financial Studies 23, no. 4 (January 5, 2010): 1651–80. http://dx.doi.org/10.1093/rfs/hhp112.

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32

Atkeson, C. G. "Learning ARM Kinematics and Dynamics." Annual Review of Neuroscience 12, no. 1 (March 1989): 157–83. http://dx.doi.org/10.1146/annurev.ne.12.030189.001105.

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33

Langley, Paul A., and Erik R. Larsen. "Edutainment, learning and system dynamics." System Dynamics Review 11, no. 4 (1995): 321–26. http://dx.doi.org/10.1002/sdr.4260110406.

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34

Liljenström, Hans. "Autonomous learning with complex dynamics." International Journal of Intelligent Systems 10, no. 1 (1995): 119–53. http://dx.doi.org/10.1002/int.4550100109.

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35

Macy, M. W., and A. Flache. "Learning dynamics in social dilemmas." Proceedings of the National Academy of Sciences 99, Supplement 3 (May 14, 2002): 7229–36. http://dx.doi.org/10.1073/pnas.092080099.

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36

Caminal, Ramon, and Xavier Vives. "Price Dynamics and Consumer Learning." Journal of Economics Management Strategy 8, no. 1 (March 1999): 95–131. http://dx.doi.org/10.1111/j.1430-9134.1999.00095.x.

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37

Endres, Dominik, and Peter Riegler. "Learning dynamics on different timescales." Journal of Physics A: Mathematical and General 32, no. 49 (November 30, 1999): 8655–63. http://dx.doi.org/10.1088/0305-4470/32/49/306.

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38

Deco, Gustavo, and Bernd Schürmann. "Neural learning of chaotic dynamics." Neural Processing Letters 2, no. 2 (March 1995): 23–26. http://dx.doi.org/10.1007/bf02312352.

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39

Biswas, Rupayan, Richa Rashmi, and Upakarasamy Lourderaj. "Machine Learning in Chemical Dynamics." Resonance 25, no. 1 (January 2020): 59–75. http://dx.doi.org/10.1007/s12045-019-0922-1.

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40

Gomes, Orlando. "Adaptive learning and complex dynamics." Chaos, Solitons & Fractals 42, no. 2 (October 30, 2009): 1206–13. http://dx.doi.org/10.1016/j.chaos.2009.03.077.

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41

Board, Simon, and Moritz Meyer-ter-Vehn. "Learning Dynamics in Social Networks." Econometrica 89, no. 6 (2021): 2601–35. http://dx.doi.org/10.3982/ecta18659.

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Анотація:

This paper proposes a tractable model of Bayesian learning on large random networks where agents choose whether to adopt an innovation. We study the impact of the network structure on learning dynamics and product diffusion. In directed networks, all direct and indirect links contribute to agents' learning. In comparison, learning and welfare are lower in undirected networks and networks with cliques. In a rich class of networks, behavior is described by a small number of differential equations, making the model useful for empirical work.

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42

Poon, Philip, Jessica C. Flack, and David C. Krakauer. "Institutional dynamics and learning networks." PLOS ONE 17, no. 5 (May 16, 2022): e0267688. http://dx.doi.org/10.1371/journal.pone.0267688.

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Institutions have been described as ‘the humanly devised constraints that structure political, economic, and social interactions.’ This broad definition of institutions spans social norms, laws, companies, and even scientific theories. We describe a non-equilibrium, multi-scale learning framework supporting institutional quasi-stationarity, periodicity, and switching. Individuals collectively construct ledgers constituting institutions. Agents read only a part of the ledger–positive and negative opinions of an institution—its “public position” whose value biases one agent’s preferences over those of rivals. These positions encode collective perception and action relating to laws, the power of parties in political office, and advocacy for scientific theories. We consider a diversity of complex temporal phenomena in the history of social and research culture (e.g. scientific revolutions) and provide a new explanation for ubiquitous cultural resistance to change and novelty–a systemic endowment effect through hysteresis.

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43

Tewari, Anurag, and Prabhat Verma. "An Improved User Identification based on Keystroke-Dynamics and Transfer Learning." Webology 19, no. 1 (January 20, 2022): 5369–87. http://dx.doi.org/10.14704/web/v19i1/web19360.

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Анотація:

Transfer learning is one of the most appreciated classification techniques, when a small size of training dataset and limited computer capabilities exist along with the presence of noisy data. Such a scenario is encountered in keystroke dynamics whereby the typing gesticulation composed to form ‘keystroke dynamics’, is used to distinguish a human. Keystroke dynamics is persistently gaining reputation as one of the most promising and cost-effective behavioral biometrics. AlexNet and ResNet are two separate types of pre-trained convolutional neural network models, normally used to apply deep transfer learning concepts within image-based systems. Conversion of keystroke data into image data and then the formation of the artificial image data by extending available data are crucial attraction of this paper. In this paper, the pre-trained models are utilized by applying fine-tuning (of parameters in some layers) and used as an end-to-end learning and classification system by us. On the keystroke dataset, the feature extraction method (support vector machine for classification) is also applied here with pre-trained models. A relative analysis of both approaches is provided and lastly, the better one is employed in the proposed (recognition based) system. Finally, 98.57% accuracy of successful recognition is recorded.

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44

Jaiswal, Abhishek, and Yang Zhang. "Robustness of Dynamical Cluster Analysis in a Glass-Forming Metallic Liquid using an Unsupervised Machine Learning Algorithm." MRS Advances 1, no. 26 (2016): 1929–34. http://dx.doi.org/10.1557/adv.2016.52.

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Анотація:

ABSTRACTWe performed dynamical cluster analysis in a Cu-Zr-Al based glass-forming metallic liquid using an unsupervised machine learning algorithm. The size of the dynamical clusters is used to quantify the onset of cooperative dynamics as the underlying mechanism leading to the Arrhenius dynamic crossover in transport coefficients of the metallic liquid. This technique is useful to directly visualize dynamical clusters and quantify their sizes upon cooling. We demonstrate the robustness of this algorithm by performing sensitivity analysis against two key parameters: number of mobility groups and inconsistency coefficient of the hierarchical cluster tree. The results elucidate the optimized range of values for both of these parameters that capture the underlying physical picture of increasing cooperative dynamics appropriately.

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45

Dridi, Slimane, and Laurent Lehmann. "On learning dynamics underlying the evolution of learning rules." Theoretical Population Biology 91 (February 2014): 20–36. http://dx.doi.org/10.1016/j.tpb.2013.09.003.

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46

Ahmed, Akhil, Ehecatl Antonio del Rio-Chanona, and Mehmet Mercangöz. "Learning Linear Representations of Nonlinear Dynamics Using Deep Learning." IFAC-PapersOnLine 55, no. 12 (2022): 162–69. http://dx.doi.org/10.1016/j.ifacol.2022.07.305.

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47

Misra, Joyneel, Srinivas Govinda Surampudi, Manasij Venkatesh, Chirag Limbachia, Joseph Jaja, and Luiz Pessoa. "Learning brain dynamics for decoding and predicting individual differences." PLOS Computational Biology 17, no. 9 (September 3, 2021): e1008943. http://dx.doi.org/10.1371/journal.pcbi.1008943.

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Анотація:

Insights from functional Magnetic Resonance Imaging (fMRI), as well as recordings of large numbers of neurons, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of brain signals. To decode brain dynamics, we propose an architecture based on recurrent neural networks to uncover distributed spatiotemporal signatures. We demonstrate the potential of the approach using human fMRI data during movie-watching data and a continuous experimental paradigm. The model was able to learn spatiotemporal patterns that supported 15-way movie-clip classification (∼90%) at the level of brain regions, and binary classification of experimental conditions (∼60%) at the level of voxels. The model was also able to learn individual differences in measures of fluid intelligence and verbal IQ at levels comparable to that of existing techniques. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational (i.e., classification related) properties of brain dynamics. Finally, saliency maps and lesion analysis were employed to characterize brain-region/voxel importance, and uncovered how dynamic but consistent changes in fMRI activation influenced decoding performance. When applied at the level of voxels, our framework implements a dynamic version of multivariate pattern analysis. Our approach provides a framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions.

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48

WANG, CONG, TIANRUI CHEN, GUANRONG CHEN, and DAVID J. HILL. "DETERMINISTIC LEARNING OF NONLINEAR DYNAMICAL SYSTEMS." International Journal of Bifurcation and Chaos 19, no. 04 (April 2009): 1307–28. http://dx.doi.org/10.1142/s0218127409023640.

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In this paper, we investigate the problem of identifying or modeling nonlinear dynamical systems undergoing periodic and period-like (recurrent) motions. For accurate identification of nonlinear dynamical systems, the persistent excitation condition is normally required to be satisfied. Firstly, by using localized radial basis function networks, a relationship between the recurrent trajectories and the persistence of excitation condition is established. Secondly, for a broad class of recurrent trajectories generated from nonlinear dynamical systems, a deterministic learning approach is presented which achieves locally-accurate identification of the underlying system dynamics in a local region along the recurrent trajectory. This study reveals that even for a random-like chaotic trajectory, which is extremely sensitive to initial conditions and is long-term unpredictable, the system dynamics of a nonlinear chaotic system can still be locally-accurate identified along the chaotic trajectory in a deterministic way. Numerical experiments on the Rossler system are included to demonstrate the effectiveness of the proposed approach.

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49

Salle, Isabelle, and Pascal Seppecher. "SOCIAL LEARNING ABOUT CONSUMPTION." Macroeconomic Dynamics 20, no. 7 (September 28, 2015): 1795–825. http://dx.doi.org/10.1017/s1365100515000097.

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This paper applies a social learning model to the optimal consumption rule of Allen and Carroll [Macroeconomic Dynamics5(2001), 255–271] and delivers convincing convergence dynamics toward the optimal rule. These findings constitute a significant improvement over previous results in the literature, in terms of both speed of convergence and parsimony of the learning model. The learning model exhibits several appealing features: it is frugal, is easy to apply to a various range of learning objectives, and requires few procedures and little information. Particular care is given to behavioral interpretation of the modeling assumptions in light of evidence from the fields of psychology and social science. Our results highlight the need to depart from the genetic metaphor, and account for intentional decision-making, based on agents' relative performances. By contrast, we show that convergence is strongly hindered by exact imitation processes, or random exploration mechanisms, which are usually assumed when modeling social learning behavior. Our results suggest a method for modeling bounded rationality, which could be interestingly tested in a wide range of economic models with adaptive dynamics.

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50

Chen, Danfeng, Junsheng Li, and Yuping Cai. "Topology Identification of the Hindmarsh-Rose Model via Deterministic Learning." Journal of Physics: Conference Series 2188, no. 1 (February 1, 2022): 012004. http://dx.doi.org/10.1088/1742-6596/2188/1/012004.

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Abstract In this paper, the complex dynamic behavior of the Hindmarsh-Rose (HR) model which characterizes the neuron cell is analyzed numerically. And the unknown topology of the system in dynamic environment is locally accurately identified based on the deterministic learning (DL) algorithm. Firstly, the influence of different parameters on the dynamic behavior of the HR model are investigated. Then, the nonlinear dynamics of the HR model under unknown dynamic environment is locally accurately identified. In addition, the identified system dynamics can be stored in the form of constant neural network. The achievement of this work can provide more incentives and possibilities for the application of HR model in clinic and other related researches. Simulation studies are included to demonstrate the effectiveness.

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