How to cite this paper
Gupta, N., Ahirwal, M & Atulkar, M. (2022). Simulation and modeling of human decision-making process through reinforcement learning based computational model involving past experiences.Decision Science Letters , 11(4), 366-378.
Refrences
Ahn, W. Y., Busemeyer, J. R., Wagenmakers, E. J., & Stout, J. C. (2008). Comparison of decision learning models using the generalization criterion method. Cognitive science, 32(8), 1376-1402.
Awasthi, A., Chauhan, S. S., Hurteau, X., & Breuil, D. (2008). An analytical hierarchical process-based decision-making approach for selecting car-sharing stations in medium size agglomerations. International Journal of Information and Decision Sciences, 1(1), 66-97.
Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1-3), 7-15.
Luce, R. D. (1960). Individual choice behavior, a theoretical analysis. Bulletin of the American Mathematical Society, 66, 259-260.
Chandra, T. B., & Verma, K. (2020). Analysis of quantum noise-reducing filters on chest X-ray images: A review. Measurement, 153, 107426.
Chandra, T. B., Verma, K., Singh, B. K., Jain, D., & Netam, S. S. (2020). Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme. Expert Systems with Applications, 158, 113514.
Cioffi, J. (2001). A study of the use of past experiences in clinical decision making in emergency situations. International journal of nursing studies, 38(5), 591-599.
Cohen, M., Etner, J., & Jeleva, M. (2008). Dynamic decision making when risk perception depends on past experience. Theory and Decision, 64(2), 173-192.
Dai, J., Kerestes, R., Upton, D. J., Busemeyer, J. R., & Stout, J. C. (2015). An improved cognitive model of the Iowa and Soochow Gambling Tasks with regard to model fitting performance and tests of parameter consistency. Frontiers in psychology, 6, 229.
Dancy, C. L., & Ritter, F. E. (2017). IGT-Open: An open-source, computerized version of the Iowa Gambling Task. Behavior research methods, 49(3), 972-978.
Vries, M. D., Holland, R. W., & Witteman, C. L. (2008). In the winning mood: Affect in the Iowa gambling task, 3(1), 42–50.
Fazlollahtabar, H. (2008). Applying multiple-criteria decision making methods for developing information technology industry. International Journal of Information and Decision Sciences, 1(1), 115-131.
Gupta, N., Ahirwal, M. K., & Atulkar, M. (2018, October). Computational model for human decision making: A study of prospect theory. In 2018 Conference on Information and Communication Technology (CICT) (pp. 1-6). IEEE.
Gupta, N., Ahirwal, M. K., & Atulkar, M. (2022). Human decision making modelling for gambling task under uncertainty and risk. International Journal of Information and Decision Sciences, 14(1), 15-38.
Heikkinen, M. V., Matuszewski, M., & Hammainen, H. (2008). Scenario planning for emerging mobile services decision making: mobile Peer-to-Peer Session Initiation Protocol case study. International Journal of Information and Decision Sciences, 1(1), 26-43.
Hess, L. E., Haimovici, A., Muñoz, M. A., & Montoya, P. (2014). Beyond pain: modeling decision-making deficits in chronic pain. Frontiers in behavioral neuroscience, 8, 263.
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics, 65-70.
Juliusson, E. Á., Karlsson, N., & Gärling, T. (2005). Weighing the past and the future in decision making. European journal of cognitive psychology, 17(4), 561-575.
Kar, A. K. (2015). A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network. Journal of Computational Science, 6, 23-33.
Child, C. H. T., Koluman, C., & Weyde, T. (2019, July). Modelling Emotion Based Reward Valuation with Computational Reinforcement Learning. In Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 582-588).
Kumar, R., Kumar, K. J., & Benegal, V. (2019). Underlying decision making processes on Iowa Gambling Task. Asian journal of psychiatry, 39, 63-69.
Lin, C. H., Lin, Y. K., Song, T. J., Huang, J. T., & Chiu, Y. C. (2016). A simplified model of choice behavior under uncertainty. Frontiers in Psychology, 7, 1201.
Mazursky, D. (1989). Past experience and future tourism decisions. Annals of Tourism Research, 16(3), 333-344.
Pramodh, C., Ravi, V., & Nagabhushanam, T. (2008). Indian banks' productivity ranking via data envelopment analysis and fuzzy multi-attribute decision-making hybrid. International Journal of Information and Decision Sciences, 1(1), 44-65.
PsyToolkit run experiment. (n.d.). Retrieved December 28, 2020, from https://www.psytoolkit.org/experiment-library/experiment_igt.html
Rescorla, R. A. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and non reinforcement. Current research and theory, 64-99.
Sagi, A., & Friedland, N. (2007). The cost of richness: the effect of the size and diversity of decision sets on post-decision regret. Journal of personality and social psychology, 93(4), 515.
Soshi, T., Nagamine, M., Fukuda, E., & Takeuchi, A. (2019). Pre-specified anxiety predicts future decision-making performances under different temporally constrained conditions. Frontiers in psychology, 10, 1544.
Steingroever, H., Wetzels, R., & Wagenmakers, E. J. (2013). A comparison of reinforcement learning models for the Iowa Gambling Task using parameter space partitioning. The Journal of Problem Solving, 5(2), 2.
Yechiam, E., & Busemeyer, J. R. (2005). Comparison of basic assumptions embedded in learning models for experience-based decision making. Psychonomic bulletin & review, 12(3), 387-402.
Awasthi, A., Chauhan, S. S., Hurteau, X., & Breuil, D. (2008). An analytical hierarchical process-based decision-making approach for selecting car-sharing stations in medium size agglomerations. International Journal of Information and Decision Sciences, 1(1), 66-97.
Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1-3), 7-15.
Luce, R. D. (1960). Individual choice behavior, a theoretical analysis. Bulletin of the American Mathematical Society, 66, 259-260.
Chandra, T. B., & Verma, K. (2020). Analysis of quantum noise-reducing filters on chest X-ray images: A review. Measurement, 153, 107426.
Chandra, T. B., Verma, K., Singh, B. K., Jain, D., & Netam, S. S. (2020). Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme. Expert Systems with Applications, 158, 113514.
Cioffi, J. (2001). A study of the use of past experiences in clinical decision making in emergency situations. International journal of nursing studies, 38(5), 591-599.
Cohen, M., Etner, J., & Jeleva, M. (2008). Dynamic decision making when risk perception depends on past experience. Theory and Decision, 64(2), 173-192.
Dai, J., Kerestes, R., Upton, D. J., Busemeyer, J. R., & Stout, J. C. (2015). An improved cognitive model of the Iowa and Soochow Gambling Tasks with regard to model fitting performance and tests of parameter consistency. Frontiers in psychology, 6, 229.
Dancy, C. L., & Ritter, F. E. (2017). IGT-Open: An open-source, computerized version of the Iowa Gambling Task. Behavior research methods, 49(3), 972-978.
Vries, M. D., Holland, R. W., & Witteman, C. L. (2008). In the winning mood: Affect in the Iowa gambling task, 3(1), 42–50.
Fazlollahtabar, H. (2008). Applying multiple-criteria decision making methods for developing information technology industry. International Journal of Information and Decision Sciences, 1(1), 115-131.
Gupta, N., Ahirwal, M. K., & Atulkar, M. (2018, October). Computational model for human decision making: A study of prospect theory. In 2018 Conference on Information and Communication Technology (CICT) (pp. 1-6). IEEE.
Gupta, N., Ahirwal, M. K., & Atulkar, M. (2022). Human decision making modelling for gambling task under uncertainty and risk. International Journal of Information and Decision Sciences, 14(1), 15-38.
Heikkinen, M. V., Matuszewski, M., & Hammainen, H. (2008). Scenario planning for emerging mobile services decision making: mobile Peer-to-Peer Session Initiation Protocol case study. International Journal of Information and Decision Sciences, 1(1), 26-43.
Hess, L. E., Haimovici, A., Muñoz, M. A., & Montoya, P. (2014). Beyond pain: modeling decision-making deficits in chronic pain. Frontiers in behavioral neuroscience, 8, 263.
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics, 65-70.
Juliusson, E. Á., Karlsson, N., & Gärling, T. (2005). Weighing the past and the future in decision making. European journal of cognitive psychology, 17(4), 561-575.
Kar, A. K. (2015). A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network. Journal of Computational Science, 6, 23-33.
Child, C. H. T., Koluman, C., & Weyde, T. (2019, July). Modelling Emotion Based Reward Valuation with Computational Reinforcement Learning. In Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 582-588).
Kumar, R., Kumar, K. J., & Benegal, V. (2019). Underlying decision making processes on Iowa Gambling Task. Asian journal of psychiatry, 39, 63-69.
Lin, C. H., Lin, Y. K., Song, T. J., Huang, J. T., & Chiu, Y. C. (2016). A simplified model of choice behavior under uncertainty. Frontiers in Psychology, 7, 1201.
Mazursky, D. (1989). Past experience and future tourism decisions. Annals of Tourism Research, 16(3), 333-344.
Pramodh, C., Ravi, V., & Nagabhushanam, T. (2008). Indian banks' productivity ranking via data envelopment analysis and fuzzy multi-attribute decision-making hybrid. International Journal of Information and Decision Sciences, 1(1), 44-65.
PsyToolkit run experiment. (n.d.). Retrieved December 28, 2020, from https://www.psytoolkit.org/experiment-library/experiment_igt.html
Rescorla, R. A. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and non reinforcement. Current research and theory, 64-99.
Sagi, A., & Friedland, N. (2007). The cost of richness: the effect of the size and diversity of decision sets on post-decision regret. Journal of personality and social psychology, 93(4), 515.
Soshi, T., Nagamine, M., Fukuda, E., & Takeuchi, A. (2019). Pre-specified anxiety predicts future decision-making performances under different temporally constrained conditions. Frontiers in psychology, 10, 1544.
Steingroever, H., Wetzels, R., & Wagenmakers, E. J. (2013). A comparison of reinforcement learning models for the Iowa Gambling Task using parameter space partitioning. The Journal of Problem Solving, 5(2), 2.
Yechiam, E., & Busemeyer, J. R. (2005). Comparison of basic assumptions embedded in learning models for experience-based decision making. Psychonomic bulletin & review, 12(3), 387-402.