Comportamiento de elección frente al riesgo. Una comparación entre los modelos Prospect Valence Learning y Expectancy Valence
ilustraciones a color, tablas
- Autores:
-
Váquiro Cuéllar, Karen Liseth
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/79415
- Palabra clave:
- 650 - Gerencia y servicios auxiliares::658 - Gerencia general
Comportamiento de elección
Riesgo
Probabilidad
Prospect Valence Learning Model
Expectancy Valence Learning Model
Iowa Gambling Task
Choice behavior
Decision making
Risk
Probability
Gestión de riesgos
Risk management
Toma de decisiones
Decision making
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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Comportamiento de elección frente al riesgo. Una comparación entre los modelos Prospect Valence Learning y Expectancy Valence |
| dc.title.translated.eng.fl_str_mv |
Decision making under risk. A comparison between the Prospect Valence Learning and Expectancy Valence models |
| title |
Comportamiento de elección frente al riesgo. Una comparación entre los modelos Prospect Valence Learning y Expectancy Valence |
| spellingShingle |
Comportamiento de elección frente al riesgo. Una comparación entre los modelos Prospect Valence Learning y Expectancy Valence 650 - Gerencia y servicios auxiliares::658 - Gerencia general Comportamiento de elección Riesgo Probabilidad Prospect Valence Learning Model Expectancy Valence Learning Model Iowa Gambling Task Choice behavior Decision making Risk Probability Gestión de riesgos Risk management Toma de decisiones Decision making |
| title_short |
Comportamiento de elección frente al riesgo. Una comparación entre los modelos Prospect Valence Learning y Expectancy Valence |
| title_full |
Comportamiento de elección frente al riesgo. Una comparación entre los modelos Prospect Valence Learning y Expectancy Valence |
| title_fullStr |
Comportamiento de elección frente al riesgo. Una comparación entre los modelos Prospect Valence Learning y Expectancy Valence |
| title_full_unstemmed |
Comportamiento de elección frente al riesgo. Una comparación entre los modelos Prospect Valence Learning y Expectancy Valence |
| title_sort |
Comportamiento de elección frente al riesgo. Una comparación entre los modelos Prospect Valence Learning y Expectancy Valence |
| dc.creator.fl_str_mv |
Váquiro Cuéllar, Karen Liseth |
| dc.contributor.advisor.none.fl_str_mv |
García Molina, Mario |
| dc.contributor.author.none.fl_str_mv |
Váquiro Cuéllar, Karen Liseth |
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Grupo Interdisciplinario en Teoría e Investigación Aplicada en Ciencias Económicas |
| dc.subject.ddc.spa.fl_str_mv |
650 - Gerencia y servicios auxiliares::658 - Gerencia general |
| topic |
650 - Gerencia y servicios auxiliares::658 - Gerencia general Comportamiento de elección Riesgo Probabilidad Prospect Valence Learning Model Expectancy Valence Learning Model Iowa Gambling Task Choice behavior Decision making Risk Probability Gestión de riesgos Risk management Toma de decisiones Decision making |
| dc.subject.proposal.spa.fl_str_mv |
Comportamiento de elección Riesgo Probabilidad Prospect Valence Learning Model Expectancy Valence Learning Model Iowa Gambling Task |
| dc.subject.proposal.eng.fl_str_mv |
Choice behavior Decision making Risk Probability |
| dc.subject.unesco.none.fl_str_mv |
Gestión de riesgos Risk management Toma de decisiones Decision making |
| description |
ilustraciones a color, tablas |
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2020 |
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2020 |
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2021-04-26T20:44:38Z |
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2021-04-26T20:44:38Z |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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https://repositorio.unal.edu.co/handle/unal/79415 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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Ahn, W., Busemeyer, J., Wagenmakers, E., & Stout, J. (2008). Comparison of Decision Learning Models Using the Generalization Criterion Method. Cognitive Science, 32, 1376 – 1402. https://doi.org/10.1080/03640210802352992 Bagneux, V., Font, H., & Bollon, T. (2013). Incidental emotions associated with uncertainty appraisals impair decisions in the Iowa Gambling Task. Motivation and Emotion, 37, 818 – 827. Barren, G., & Erev, I. (2003). Small feedback – based decisions and their limited correspondence to description – based decisions. Journal of Behavioral Decision Making, 16, 215 – 233. 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. https://doi.org/10.1016/0010-0277(94)90018-3 Busemeyer, J. R., & Myung, I. J. (1992). An adaptive approach to human decision making: Learning theory, decision theory, and human performance. Journal of Experimental Psychology: General, 121(2), 177–194. https://doi.org/10.1037/0096- 3445.121.2.177 Busemeyer, J. R., & Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara Gambling Task. Psychological Assessment, (14)3 253 – 262. Campos – Vazquez, R. M., & Cuilty, E. (2014). The role of emotions on risk aversion: A prospect theory experiment. Journal of Behavioral and Experimental Economics, 50, 1 – 9. https://doi.org/10.1016/j.socec.2014.01.001 Carvalho, J. C. N., Schneider-Bakos, D., Cotrena, C., Kristensen, H. C. & Fonseca, R. P. (2012). Tomada de decisão no Iowa Gambling Task: comparação quanto à variável escolaridade. RIDEP, 32(2), 171 – 186. Denburg, N., Recknor, E., Bechara, A., & Tranel, D. (2006). Psychophysiological anticipation of positive outcomes promotes advantageous decision – making in normal older persons. International Journal of Psychophysiology, 61, 19 – 25. Erev, I., & Barron, G. (2005). On adaptation, maximization, and reinforcement learning among cognitive strategies. Psychological Review, 112(4), 912 – 931. Erev, I., & Roth, A. (1998). Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria. American Economic Review 88(4), 848–881. Fatás, E., & Roig, J. (2004). Una introducción a la metodología experimental en economía. Cuadernos de Economía, 27, 7–36. Fischbacher, U. (2007). z-Tree: Zurich toolbox for ready – made economic experiments. Experimental Economics, 10(2), 171 – 178. https://doi.org/10.1007/s10683-006-9159-4 Franken, I. H. A., & Muris, P. (2005). Individual differences indecision – making. Personality and Individual Differences, 39(5), 991–998. Fridberg, D., Queller, S., Ahn, W-Y., Kim, W., Bishara, A., Busemeyer, J. R., Porrino, L., & Stout, J. (2010). Cognitive mechanisms underlying risky decision-making in chronic cannabis users. Journal of Mathematical Psychology, 54(1), 28 – 38. https://doi.org/10.1016/j.jmp.2009.10.002 Hertwig, R., Barren, G., Weber, E. U., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice Decisions from experience and the effect of rare events in risky choice. Psychological Science, 15, 534 – 539. Hinson, J.M., Jameson, T.L. & Whitney, P. (2002). Somatic markers, working memory, and decision making. Cognitive, Affective, & Behavioral Neuroscience, 2, 341–353. https://doi.org/10.3758/CABN.2.4.341 Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement Learning: A Survey Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237 – 285. Keynes, J. M. (1921). A Treatise on Probability Knight, F. (1921). Risk, Uncertainty, and Profit. Boston MA: Hart, Schaffner and Marx; Houghton Mifflin. https://oll.libertyfund.org/titles/306 Kahneman, D., & Tversky, A. (1987). Teoría prospective: un análisis de la decision bajo riesgo. Infancia y Aprendizaje, 30, 95 – 124. Luce, R. D. (1959). Individual choice behavior Individual choice behavior. New York: Wiley. Maia, T. V., & McClelland, J. L. (2004). A reexamination of the evidence for the somatic marker hypothesis: what participants really know in the Iowa gambling task. Proceedings of the National Academy of Sciences of the United States of America, 101(45), 16075 – 16080. Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7, 308 – 313. Schneider – Bakos, D., Denburg, N., Fonseca, R. P. & Parente, M. A. P. (2010). A cultural study on decision making: performance differenceson the Iowa gambling task between selected groups of Brazilians and Americans. Psychology y Neuroscience, 3(1), 101 – 107. Schwartz, G. (1978). Estimating the dimension of a model Estimating the dimension of a model. Annals of Statistics, 5, 461 – 464. Smith, V. (1976). Experimental economics: Induced value theory. The American Economic Review, 66(2), 274 – 279. Smith, V. (1982). Microeconomic systems as an experimental science. The American Economic Review, 72(5), 923 – 955. Steingroever, H., Wetzels, R., & Wagenmakers, E. (2013). Validating the PVL – Delta model for the Iowa gambling task. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00898 Stout, J., Busemeyer, J., Lin, A., Grant, S., & Bonson, K. (2004). Cognitive modeling analysis of decision – making processes in cocaine abusers. Psychonomic Bulletin y Review, 11, 742 – 47. http://dx.doi.org/10.3758/BF03196629 Tversky, A., & Kahneman, D. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263 – 292. Tversky, A., & Kahneman, D. (1986). Rational choice and the framing of decisions. The Journal of Business, 59(4), 251 – 278. Tversky, A., & Kahneman, D. (1992). Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty, 5, 297 – 323. Wagar, B. M., & Dixon, M. (2006). Affective guidance in the Iowa gambling task. Cognitive, Affective & Behavioral Neuroscience, 6(4), 277–290. https://doi.org/10.3758/CABN.6.4.277 Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., & Wagenmakers, E. J. (2011). Statistical Evidence in Experimental Psychology: An Empirical Comparison Using 855 t Tests. Perspectives on psychological science : a journal of the Association for Psychological Science, 6(3), 291 – 298. https://doi.org/10.1177/1745691611406923. Wood, S., Busemeyer, J., Koling, A., Cox, C. R., & Davis, H. (2005). Older adults as adaptive decision makers: Evidence from the Iowa gambling task. Psychology and Aging, 20, 220 – 25. http://dx.doi.org/10.1037/0882-7974.20.2.220 Worthy, D., Hawthorne, M. & Otto, A. (2013). “Heterogeneity of strategy use in the Iowa gambling task: A comparison of win-stay/lose-shift and reinforcement learning models”. Psychonomic bulletin y review, 20(2), 364 – 371. Yechiam, E., Stout, J.C., Busemeyer, J.R., Rock, S.L., & Finn, P.R. (2005). Individual differences in the response to forgone payoffs: An examination of high functioning drug abusers. Journal of Behavioral Decision Making, 18, 97–110. Yechiam, E., & Busemeyer, J.R. (2008). Evaluating generalizability and parameter consistency in learning models. Games and Economic Behavior, (63)1, 370 – 394. https://doi.org/10.1016/j.geb.2007.08.011. Yechiam, E., & Ert, E. (2007). Evaluating the reliance on past choices in adaptive learning models. Journal of Mathematical Psychology, 51, 75 – 84. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2García Molina, Mario4b102bef744ebf3190f7d0898acd3dc0Váquiro Cuéllar, Karen Lisetha1f6ea83c528d161082f08509d0851bfGrupo Interdisciplinario en Teoría e Investigación Aplicada en Ciencias Económicas2021-04-26T20:44:38Z2021-04-26T20:44:38Z2020https://repositorio.unal.edu.co/handle/unal/79415Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones a color, tablasLa tarea basada en el juego de cartas, Iowa Gambling Task modela la toma de decisiones bajo riesgo. El propósito del presente estudio es proveer evidencia acerca de los procesos inherentes al comportamiento de elección bajo riesgo a partir de esta tarea, en términos de utilidad, actualización de expectativas y mecanismo de elección de un grupo de (15) hombres y (15) mujeres. Además de verificar si existe diferencia en el comportamiento de elección frente al riesgo entre hombres y mujeres. Así, se compararon y evaluaron los modelos de decisión Expectancy Valence y Prospect Valence, a través de la estimación de máxima verosimilitud y de los criterios de diferencias logarítmicas, de bondad de ajuste, G2 y de Información Bayesiano. Los resultados proveen evidencia que los procesos inherentes al comportamiento de elección bajo riesgo en mujeres, están relacionados con la función de utilidad prospectiva (PU), la regla de actualización de refuerzo (DRI) y el mecanismo de elección independiente del ensayo (TIC) – modelo Prospect Valence; mientras que los procesos inherentes al comportamiento de elección bajo riesgo en hombres, están relacionados con la función de utilidad de expectativa (EU), la regla de actualización de refuerzo (DRI) y el mecanismo de elección independiente del ensayo (TIC) – modelo Expectancy – Prospect Valence.Task-based card game, Iowa Gambling Task models low-risk decision making. The purpose of this study is to provide evidence about the processes inherent to low-risk choice behavior from this task, in terms of utility, updating of expectations and choice mechanism of a group of (15) men and (15) women. . In addition to verifying if there is a difference in the behavior of choice regarding risk between men and women. Thus, the Expectancy Valence and Prospect Valence decision models were compared and evaluated, through the estimation of maximum likelihood and the criteria of logarithmic differences, goodness of fit, G2 and Bayesian Information. The results provide evidence that the processes inherent to low-risk choice behavior in women are related to the prospective utility function (PU), the reinforcement update rule (DRI) and the trial independent choice mechanism (TIC) - Prospect Valence model; while the processes inherent in low-risk choice behavior in men are related to the expectation utility function (EU), the reinforcement update rule (DRI) and the trial independent choice mechanism (TIC) - Expectancy model - Prospect Valence.Maestría1 recurso en línea (99 páginas)application/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Económicas - Maestría en Ciencias EconómicasEscuela de EconomíaFacultad de Ciencias EconómicasBogotáUniversidad Nacional de Colombia - Sede Bogotá650 - Gerencia y servicios auxiliares::658 - Gerencia generalComportamiento de elecciónRiesgoProbabilidadProspect Valence Learning ModelExpectancy Valence Learning ModelIowa Gambling TaskChoice behaviorDecision makingRiskProbabilityGestión de riesgosRisk managementToma de decisionesDecision makingComportamiento de elección frente al riesgo. Una comparación entre los modelos Prospect Valence Learning y Expectancy ValenceDecision making under risk. A comparison between the Prospect Valence Learning and Expectancy Valence modelsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAhn, W., Busemeyer, J., Wagenmakers, E., & Stout, J. (2008). Comparison of Decision Learning Models Using the Generalization Criterion Method. Cognitive Science, 32, 1376 – 1402. https://doi.org/10.1080/03640210802352992Bagneux, V., Font, H., & Bollon, T. (2013). Incidental emotions associated with uncertainty appraisals impair decisions in the Iowa Gambling Task. Motivation and Emotion, 37, 818 – 827.Barren, G., & Erev, I. (2003). Small feedback – based decisions and their limited correspondence to description – based decisions. Journal of Behavioral Decision Making, 16, 215 – 233.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. https://doi.org/10.1016/0010-0277(94)90018-3Busemeyer, J. R., & Myung, I. J. (1992). An adaptive approach to human decision making: Learning theory, decision theory, and human performance. Journal of Experimental Psychology: General, 121(2), 177–194. https://doi.org/10.1037/0096- 3445.121.2.177Busemeyer, J. R., & Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara Gambling Task. Psychological Assessment, (14)3 253 – 262.Campos – Vazquez, R. M., & Cuilty, E. (2014). The role of emotions on risk aversion: A prospect theory experiment. Journal of Behavioral and Experimental Economics, 50, 1 – 9. https://doi.org/10.1016/j.socec.2014.01.001Carvalho, J. C. N., Schneider-Bakos, D., Cotrena, C., Kristensen, H. C. & Fonseca, R. P. (2012). Tomada de decisão no Iowa Gambling Task: comparação quanto à variável escolaridade. RIDEP, 32(2), 171 – 186.Denburg, N., Recknor, E., Bechara, A., & Tranel, D. (2006). Psychophysiological anticipation of positive outcomes promotes advantageous decision – making in normal older persons. International Journal of Psychophysiology, 61, 19 – 25.Erev, I., & Barron, G. (2005). On adaptation, maximization, and reinforcement learning among cognitive strategies. Psychological Review, 112(4), 912 – 931.Erev, I., & Roth, A. (1998). Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria. American Economic Review 88(4), 848–881.Fatás, E., & Roig, J. (2004). Una introducción a la metodología experimental en economía. Cuadernos de Economía, 27, 7–36.Fischbacher, U. (2007). z-Tree: Zurich toolbox for ready – made economic experiments. Experimental Economics, 10(2), 171 – 178. https://doi.org/10.1007/s10683-006-9159-4Franken, I. H. A., & Muris, P. (2005). Individual differences indecision – making. Personality and Individual Differences, 39(5), 991–998.Fridberg, D., Queller, S., Ahn, W-Y., Kim, W., Bishara, A., Busemeyer, J. R., Porrino, L., & Stout, J. (2010). Cognitive mechanisms underlying risky decision-making in chronic cannabis users. Journal of Mathematical Psychology, 54(1), 28 – 38. https://doi.org/10.1016/j.jmp.2009.10.002Hertwig, R., Barren, G., Weber, E. U., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice Decisions from experience and the effect of rare events in risky choice. Psychological Science, 15, 534 – 539.Hinson, J.M., Jameson, T.L. & Whitney, P. (2002). Somatic markers, working memory, and decision making. Cognitive, Affective, & Behavioral Neuroscience, 2, 341–353. https://doi.org/10.3758/CABN.2.4.341Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement Learning: A Survey Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237 – 285.Keynes, J. M. (1921). A Treatise on ProbabilityKnight, F. (1921). Risk, Uncertainty, and Profit. Boston MA: Hart, Schaffner and Marx; Houghton Mifflin. https://oll.libertyfund.org/titles/306Kahneman, D., & Tversky, A. (1987). Teoría prospective: un análisis de la decision bajo riesgo. Infancia y Aprendizaje, 30, 95 – 124.Luce, R. D. (1959). Individual choice behavior Individual choice behavior. New York: Wiley.Maia, T. V., & McClelland, J. L. (2004). A reexamination of the evidence for the somatic marker hypothesis: what participants really know in the Iowa gambling task. Proceedings of the National Academy of Sciences of the United States of America, 101(45), 16075 – 16080.Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7, 308 – 313.Schneider – Bakos, D., Denburg, N., Fonseca, R. P. & Parente, M. A. P. (2010). A cultural study on decision making: performance differenceson the Iowa gambling task between selected groups of Brazilians and Americans. Psychology y Neuroscience, 3(1), 101 – 107.Schwartz, G. (1978). Estimating the dimension of a model Estimating the dimension of a model. Annals of Statistics, 5, 461 – 464.Smith, V. (1976). Experimental economics: Induced value theory. The American Economic Review, 66(2), 274 – 279.Smith, V. (1982). Microeconomic systems as an experimental science. The American Economic Review, 72(5), 923 – 955.Steingroever, H., Wetzels, R., & Wagenmakers, E. (2013). Validating the PVL – Delta model for the Iowa gambling task. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00898Stout, J., Busemeyer, J., Lin, A., Grant, S., & Bonson, K. (2004). Cognitive modeling analysis of decision – making processes in cocaine abusers. Psychonomic Bulletin y Review, 11, 742 – 47. http://dx.doi.org/10.3758/BF03196629Tversky, A., & Kahneman, D. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263 – 292.Tversky, A., & Kahneman, D. (1986). Rational choice and the framing of decisions. The Journal of Business, 59(4), 251 – 278.Tversky, A., & Kahneman, D. (1992). Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty, 5, 297 – 323.Wagar, B. M., & Dixon, M. (2006). Affective guidance in the Iowa gambling task. Cognitive, Affective & Behavioral Neuroscience, 6(4), 277–290. https://doi.org/10.3758/CABN.6.4.277Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., & Wagenmakers, E. J. (2011). Statistical Evidence in Experimental Psychology: An Empirical Comparison Using 855 t Tests. Perspectives on psychological science : a journal of the Association for Psychological Science, 6(3), 291 – 298. https://doi.org/10.1177/1745691611406923.Wood, S., Busemeyer, J., Koling, A., Cox, C. R., & Davis, H. (2005). 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KAREN LISETH VAQUIRO CUELLAR. DNI 1013645052.pdfVERSIÓN FINAL. KAREN LISETH VAQUIRO CUELLAR. DNI 1013645052.pdfTesis de Maestría en Ciencias Económicasapplication/pdf1822702https://repositorio.unal.edu.co/bitstream/unal/79415/1/VERSIO%cc%81N%20FINAL.%20KAREN%20LISETH%20VAQUIRO%20CUELLAR.%20DNI%201013645052.pdfbe50a0c1bd8953dce178a20ca11fdf4bMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/79415/2/license.txtcccfe52f796b7c63423298c2d3365fc6MD52THUMBNAILVERSIÓN FINAL. KAREN LISETH VAQUIRO CUELLAR. DNI 1013645052.pdf.jpgVERSIÓN FINAL. KAREN LISETH VAQUIRO CUELLAR. 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