Abstract
The study presents a new approach of modelling human behavior based on empirical evidence on individual differences in cognitive science and behavioral economics fields. Compared to classical studies of economics, empirical research makes use of the descriptive approach to analyze human behavior and to create models able to explain the behavior of investors and organizational traders in a more realistic way. Consistently, an economic assumption that has been strongly disputed by scientists is the concept of Homo Economicus, which is currently considered unable to capture all the details and variability that characterize human behavior (which we define, in opposition to the economic label, Homo Psychologicus). Thanks to recent empirical studies and the development of such advanced techniques as agent based models, new simulation studies are now capable of investigating a higher number of psychological variables. However, models which implement heuristics or fallacies often distribute these characteristics among all agents without distinction. The present study shows how it is possible to design multiple agents considering individual differences, which can have a different impact on organizational and economic behavior. Starting from several empirical studies, which show a negative relation between optimism and loss aversion, coefficients of the Value function of the Prospect theory have been reviewed to create agents characterized by different psychological strategies used to manage costs and risks.
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References
Arkes, H.R., Blumer, C.: The psychology of sunk cost. Organizational Behavior and Human Decision Processes 35(1), 124–140 (1985)
Benartzi, S., Thaler, R.H.: Myopic Loss Aversion and the Equity Premium Puzzle. The Quarterly Journal of Economics 110(1), 73–92 (1995)
Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the United States of America 99(suppl. 3), 7280 (2002)
Bruine de Bruin, W., Parker, A.M., Fischhoff, B.: Individual differences in adult decision-making competence. Journal of Personality and Social Psychology 92(5), 938 (2007)
Ceschi, A., Hysenbelli, D., Sartori, R., Tacconi, G.: Cooperate or Defect? How an agent based model simulation on helping behavior can be an educational tool. Paper presented at the Paper Session Presented at the Meeting of the 3rd ebuTEL, International Workshop on Evidence Based and User Centred Technology Enhanced Learning, Trento, Italy (2013)
Ceschi, A., Hysenbelli, D., Slovic, P.: When awareness of those we cannot help demotivates us from helping those we can help: An agent-based simulation study of pseudoinefficacy. In: Dickert, S. (ed.) Paper presented at the Prosocial Responses to Donation Requests: Motivators and Demotivators. Symposium Conducted at the Meeting of the 55th TeaP Conference of Experimental Psychologists, Vienna, Austria (2013)
Ceschi, A., Sartori, R., Weller, J.: The factors of reasoning: Structures of belonging of heuristics and biases. Paper presented at the Poster Presented at the Meeting of the 24th SPUDM, Subjective Probability, Utility, and Decision Making. European Association of Decision Making, Barcelona (2013)
Chapman, D.A., Polkovnichenko, V.: First-Order Risk Aversion, Heterogeneity, and Asset Market Outcomes. The Journal of Finance 64(4), 1863–1887 (2009)
de Palma, A., Ben-Akiva, M., Brownstone, D., Holt, C., Magnac, T., McFadden, D., ... Wakker, P.: Risk, uncertainty and discrete choice models. Marketing Letters 19(3-4), 269–285 (2008)
Frisch, D.: Reasons for framing effects. Organizational Behavior and Human Decision Processes 54, 399–429 (1993)
Iyer, A., Lindner, A., Kagan, I., Andersen, R.A.: Motor preparatory activity in posterior parietal cortex is modulated by subjective absolute value. PLoS Biology 8(8), e1000444 (2010)
Janssen, M.A., Ostrom, E.: Empirically based, agent-based models. Ecology and Society 11(2), 37 (2006)
Kahneman, D., Tversky, A.: Prospect theory: An analysis of decision under risk. Econometrica 47, 263–291 (1979)
LeBaron, B.: Agent-based computational finance: Suggested readings and early research. Journal of Economic Dynamics and Control 24(5), 679–702 (2000)
LeBaron, B.: Agent-based computational finance. In: Handbook of Computational Economics, vol. 2, pp. 1187–1233 (2006)
Rubaltelli, E., Dickert, S., Slovic, P.: Response mode, compatibility, and dual-processes in the evaluation of simple gambles: An eye-tracking investigation. Judgment and Decision Making 7(4), 427–440 (2012)
Sartori, R., Ceschi, A.: Biases, reasoning and personality in finance. International Journal of Psychology 47(suppl. 1), 109–151 (2012)
Shafir, E., Diamond, P., Tversky, A.: Money Illusion. The Quarterly Journal of Economics 112(2), 341–374 (1997)
Simon, H.A.: A behavioral model of rational choice. The Quarterly Journal of Economics 59, 99–118 (1955)
Simon, H.A.: Bounded rationality and organizational learning. Organization Science 2(1), 125–134 (1991)
Soman, D., Cheema, A.: The effects of windfall gains on the sunk-cost effect. Marketing Letters 12, 51–62 (2001)
Strough, J.N., Karns, T.E., Schlosnagle, L.: Decision-making heuristics and biases across the life span. Annals of the New York Academy of Sciences 1235(1), 57–74 (2011)
Thaler, R.H.: From homo economicus to homo sapiens. The Journal of Economic Perspectives 14(1), 133–141 (2000)
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Ceschi, A., Rubaltelli, E., Sartori, R. (2014). Designing a Homo Psychologicus More Psychologicus: Empirical Results on Value Perception in Support to a New Theoretical Organizational-Economic Agent Based Model. In: Omatu, S., Bersini, H., Corchado, J., RodrÃguez, S., Pawlewski, P., Bucciarelli, E. (eds) Distributed Computing and Artificial Intelligence, 11th International Conference. Advances in Intelligent Systems and Computing, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-319-07593-8_9
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DOI: https://doi.org/10.1007/978-3-319-07593-8_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07592-1
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