Abstract
Sometimes, the conscious act of decision-making in humans is dramatically interrupted by situations that warrant an immediate response (e.g. when there is an imminent risk). The human body somatizes this interruption such that an action could be taken without a rational analysis. The above is known as a somatic marker. According to the somatic marker hypothesis, somatic markers could directly influence several ambits of decision-making. This research work presents the incorporation of artificial somatic reactions into affective autonomous agents who implement decision-making in the stock market. This implies the design of a general decision architecture for stock markets considering artificial somatic reactions and the definition of a set of decision-making algorithms for supporting investment decisions performed by affective autonomous agents (considering artificial somatic reactions). Test scenarios were defined using official data from Standard & Poor's 500 and Dow Jones. The experimental results are promising and indicated that affective autonomous agents are able to experience artificial somatic reactions and achieve effectiveness and efficiency in their decision-making.
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Data availability
The datasets used and analyzed during the current study correspond to S&P500 Index and Dow Jones Index, which are available in https://finance.yahoo.com/.
References
Acay DL, Sonenberg L, Tidhar G (2019) Formalizing tool use in intelligent environments. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0755-x
Aguado G, Julian V, Garcia-Fornes A, Espinosa A (2020) A Multi-Agent System for guiding users in on-line social environments. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.103740
Arias JA, Williams C, Raghvani R et al (2020) The neuroscience of sadness: a multidisciplinary synthesis and collaborative review. Neurosci Biobehav Rev. https://doi.org/10.1016/j.neubiorev.2020.01.006
Arokiasami WA, Vadakkepat P, Tan KC, Srinivasan D (2016) Interoperable multi-agent framework for unmanned aerial/ground vehicles: towards robot autonomy. Complex Intell Syst. https://doi.org/10.1007/s40747-016-0014-8
Belhadi A, Djenouri Y, Nørvåg K et al (2020) Space–time series clustering: algorithms, taxonomy, and case study on urban smart cities. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.103857
Bouanan Y, Zacharewicz G, Vallespir B (2016) DEVS modelling and simulation of human social interaction and influence. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2016.01.002
Buche C, Le Bigot N, Polceanu M (2016) Simulation within simulation for agent decision-making: theoretical foundations from cognitive science to operational computer model. Cogn Syst Res. https://doi.org/10.1016/j.cogsys.2016.03.001
Cabrera D, Cubillos C (2008) Multi-agent framework for a virtual enterprise of demand-responsive transportation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-540-68825-9_7
Cabrera D, Araya N, Jaime H et al (2015) Defining an affective algorithm for purchasing decisions in e-commerce environments. IEEE Lat Am Trans. https://doi.org/10.1109/TLA.2015.7273796
Cabrera D, Cubillos C, Cubillos A et al (2018) Affective algorithm for controlling emotional fluctuation of artificial investors in stock markets. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2802781
Cabrera D, Rubilar R, Cubillos C (2019) Resilience in the decision-making of an artificial autonomous system on the stock market. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2945471
Cabrera D, Cubillos C, Urra E, Mellado R (2020) Framework for incorporating artificial somatic markers in the decision-making of autonomous agents. Appl Sci. https://doi.org/10.3390/app10207361
Cabrera-Paniagua D, Rubilar-Torrealba R (2021) A novel artificial autonomous system for supporting investment decisions using a Big Five model approach. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.104107
Cabrera-Paniagua D, Herrera G, Cubillos C, Donoso M (2011) Towards a model for dynamic formation and operation of virtual organizations for transportation. Stud Informs Control. https://doi.org/10.24846/v20i3y201106
Cabrera-Paniagua D, Primo TT, Cubillos C (2014) Distributed stock exchange scenario using artificial emotional knowledge. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). https://doi.org/10.1007/978-3-319-12027-0_52
Cabrera-Paniagua D, Cubillos C, Vicari R, Urra E (2015) Decision-making system for stock exchange market using artificial emotions. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2015.05.004
Casadei R, Viroli M, Audrito G et al (2021) Engineering collective intelligence at the edge with aggregate processes. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.104081
Chandiok A, Chaturvedi DK (2018) CIT: Integrated cognitive computing and cognitive agent technologies based cognitive architecture for human-like functionality in artificial systems. Biol Inspired Cogn Archit. https://doi.org/10.1016/j.bica.2018.07.020
Cominelli L, Mazzei D, Pieroni M et al (2015) Damasio’s somatic marker for social robotics: Preliminary implementation and test. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-22979-9_31
Cromwell HC, Abe N, Barrett KC et al (2020) Mapping the interconnected neural systems underlying motivation and emotion: a key step toward understanding the human affectome. Neurosci Biobehav Rev. https://doi.org/10.1016/j.neubiorev.2020.02.032
Cubillos C, Donoso M, Rodríguez N et al (2010) Towards open agent systems through dynamic incorporation. Int J Comput Commun Control. https://doi.org/10.15837/ijccc.2010.5.2223
Cubillos C, Díaz R, Urra E et al (2013) An agent-based solution for the berth allocation problem. Int J Comput Commun Control. https://doi.org/10.15837/ijccc.2013.3.465
Cui X, Lai VS, Lowry PB, Lei Y (2020) The effects of bidder factors on online bidding strategies: a motivation-opportunity-ability (MOA) model. Decis Support Syst. https://doi.org/10.1016/j.dss.2020.113397
Damasio A (1994) Descartes’ error: emotion, rationality and the human brain. Putnam, New York
Dizon E, Pranggono B (2021) Smart streetlights in Smart City: a case study of Sheffield. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-02970-y
Dow Jones Index (2021) Dow Jones Index. https://www.dowjones.com/. Accessed 1 Nov 2020
Dyachenko Y, Nenkov N, Petrova M et al (2018) Approaches to cognitive architecture of autonomous intelligent agent. Biol Inspired Cogn Archit. https://doi.org/10.1016/j.bica.2018.10.004
Ehab N, Ismail H (2020) LogAG: an algebraic non-monotonic logic for reasoning with graded propositions. Ann Math Artif Intell. https://doi.org/10.1007/s10472-020-09697-0
Ekman P (1982) Emotion in the human face. Cambridge University Press
Ekman P (1992) An argument for basic emotions. Cogn Emot. https://doi.org/10.1080/02699939208411068
Ferretti E, Tamargo LH, García AJ et al (2017) An approach to decision making based on dynamic argumentation systems. Artif Intell. https://doi.org/10.1016/j.artint.2016.10.004
Gelbrich K, Hagel J, Orsingher C (2020) Emotional support from a digital assistant in technology-mediated services: Effects on customer satisfaction and behavioral persistence. Int J Res Mark. https://doi.org/10.1016/j.ijresmar.2020.06.004
Guillaume S, Jollant F, Jaussent I et al (2009) Somatic markers and explicit knowledge are both involved in decision-making. Neuropsychologia. https://doi.org/10.1016/j.neuropsychologia.2009.04.003
Gupta R, Koscik TR, Bechara A, Tranel D (2011) The amygdala and decision-making. Neuropsychologia. https://doi.org/10.1016/j.neuropsychologia.2010.09.029
Hoefinghoff J, Pauli J (2012) Decision making based on somatic markers. In: Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25
Hoefinghoff J, Steinert L, Pauli J (2012) Implementation of a decision making algorithm based on somatic markers on the Nao robot. In: Levi P et al (eds) Autonomous mobile systems 2012. Springer-Verlag, Berlin Heidelberg, pp 69–77
Höfinghoff J, Steinert L, Pauli J (2013) An easily adaptable decision making framework based on somatic markers on the Nao-Robot. Kogn Syst. https://doi.org/10.1785/duepublico/31363
Hoogendoorn M, Merk R-J, Treur J (2009) A decision making model based on Damasio’s Somatic marker hypothesis. In: Proceedings of the 9th international conference on cognitive modeling, pp 1001–1009
Hou Z, Ma K, Wang Y et al (2021) Attention-based learning of self-media data for marketing intention detection. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.104118
Huzard D, Mumby DG, Sandi C et al (2015) The effects of extrinsic stress on somatic markers and behavior are dependent on animal housing conditions. Physiol Behav. https://doi.org/10.1016/j.physbeh.2015.07.018
Ichise R (2018) A cognitive architecture consisting of human intelligence factors. Procedia Comp Sci 123:165–170
Ismail HO (2020) The good, the bad, and the rational: aspects of character in logical agents. Springer, Cham. https://doi.org/10.1007/978-3-030-15954-2_9
Jain S, Asawa K (2016) Programming an expressive autonomous agent. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2015.08.037
Janzen M, Axhausen KW (2018) Decision making in an agent-based simulation of long-distance travel demand. Procedia Comp Sci 130:830–835
Kaklauskas A, Abraham A, Dzemyda G et al (2020) Emotional, affective and biometrical states analytics of a built environment. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.103621
Kelley D, Twyman M (2020) Biasing in an independent core observer model artificial general intelligence cognitive architecture. Procedia Comp Sci 169:535–541
Liang CC, Liang WY, Tseng TL (2019) Evaluation of intelligent agents in consumer-to-business e-Commerce. Comput Stand Interfaces. https://doi.org/10.1016/j.csi.2019.03.002
Linquist S, Bartol J (2013) Two myths about somatic markers. Br J Philos Sci. https://doi.org/10.1093/bjps/axs020
Lv Y, Zhu J, Jiang Y (2020) Using EGDL to represent domain knowledge for imperfect information automated negotiations. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02274-7
Mellado Silva R, Cubillos C, Cabrera Paniagua D (2016) A constructive heuristic for solving the Job-Shop Scheduling Problem. IEEE Lat Am Trans. https://doi.org/10.1109/TLA.2016.7555250
MetaQuotes (2021) MetaTrader 5. https://www.metatrader5.com/. Accessed 1 Mar 2021
Murugaveni S, Mahalakshmi K (2020) A novel approach for non-orthogonal multiple access for delay sensitive industrial IoT communications for smart autonomous factories. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02330-2
Nagoev Z, Lyutikova L, Gurtueva I (2018) Model for Automatic Speech Recognition Using Multi-Agent Recursive Cognitive Architecture. Procedia Comp Sci 145:386–392
Pajuelo-Holguera F, Gómez-Pulido JA, Ortega F (2020) Recommender systems for sensor-based ambient control in academic facilities. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.103993
Pessoa L (2019) Intelligent architectures for robotics: the merging of cognition and emotion. Phys Life Rev. https://doi.org/10.1016/j.plrev.2019.04.009
Poppa T, Bechara A (2018) The somatic marker hypothesis: revisiting the role of the ‘body-loop’ in decision-making. Curr Opin Behav Sci. https://doi.org/10.1016/j.cobeha.2017.10.007
Pudane M, Lavendelis E, Radin MA (2016) Human emotional behavior simulation in intelligent agents: processes and architecture. In: Procedia computer science. https://doi.org/10.1016/j.procs.2017.01.167
Qureshi KN, Iftikhar A, Bhatti SN et al (2020) Trust management and evaluation for edge intelligence in the Internet of Things. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.103756
Ravikumar S, Kavitha D (2021) IOT based autonomous car driver scheme based on ANFIS and black widow optimization. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02725-1
Reia SM, Amado AC, Fontanari JF (2019) Agent-based models of collective intelligence. Phys Life Rev. https://doi.org/10.1016/j.plrev.2018.10.004
Reimann M, Bechara A (2010) The somatic marker framework as a neurological theory of decision-making: review, conceptual comparisons, and future neuroeconomics research. J Econ Psychol. https://doi.org/10.1016/j.joep.2010.03.002
SoftBanks Robotics (2020) Nao-Robot. https://www.softbankrobotics.com/. Accessed 10 Jul 2020
Rosales JH, Rodríguez LF, Ramos F (2019) A general theoretical framework for the design of artificial emotion systems in autonomous agents. Cogn Syst Res. https://doi.org/10.1016/j.cogsys.2019.08.003
Saha C, Aqlan F, Lam SS, Boldrin W (2016) A decision support system for real-time order management in a heterogeneous production environment. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2016.04.035
Samsonovich AV (2020) Socially emotional brain-inspired cognitive architecture framework for artificial intelligence. Cogn Syst Res. https://doi.org/10.1016/j.cogsys.2019.12.002
Sánchez Y, Coma T, Aguelo A, Cerezo E (2019) ABC-EBDI: an affective framework for BDI agents. Cogn Syst Res. https://doi.org/10.1016/j.cogsys.2019.07.002
Sandor S, Gürvit H (2019) Development of somatic markers guiding decision-making along adolescence. Int J Psychophysiol. https://doi.org/10.1016/j.ijpsycho.2018.12.005
Standard & Poor’s 500 Index (2021) Standard & Poor’s 500 Index. https://www.standardandpoors.com/. Accessed 1 Nov 2020
Steenbergen L, Colzato LS, Maraver MJ (2020) Vagal signaling and the somatic marker hypothesis: the effect of transcutaneous vagal nerve stimulation on delay discounting is modulated by positive mood. Int J Psychophysiol. https://doi.org/10.1016/j.ijpsycho.2019.10.010
Stefanova E, Dubljević O, Herbert C et al (2020) Anticipatory feelings: neural correlates and linguistic markers. Neurosci Biobehav Rev. https://doi.org/10.1016/j.neubiorev.2020.02.015
Tom RJ, Sankaranarayanan S, Rodrigues JJPC (2020) Agent negotiation in an IoT-Fog based power distribution system for demand reduction. Sustain Energy Technol Assess. https://doi.org/10.1016/j.seta.2020.100653
Wang H, Mostafizi A, Cramer LA et al (2016) An agent-based model of a multimodal near-field tsunami evacuation: decision-making and life safety. Transp Res Part C Emerg Technol. https://doi.org/10.1016/j.trc.2015.11.010
Xtb (2021) xStation. https://www.xtb.com/int/trading-services/trading-platforms/xstation. Accessed 1 Mar 2021
Yahoo Finance (2020) Stock market live, quotes, business & finance news. In: Yahoo Financ. https://finance.yahoo.com/. Accessed 1 Nov 2020
Yan F, Iliyasu A, Hirota K (2021) Emotion space modelling for social robots. Eng Appl Artif Intell 100:104178. https://doi.org/10.1016/j.engappai.2021.104178
Zhu J, Liu W, Liu Y et al (2020) Smart city oriented optimization of residential blocks on intensive urban sensing data based on fuzzy evaluation algorithm. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02104-w
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This work was funded by ANID Chile through FONDECYT INICIACION Project No. 11190370.
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Cabrera-Paniagua, D., Rubilar-Torrealba, R. Affective autonomous agents for supporting investment decision processes using artificial somatic reactions. J Ambient Intell Human Comput 14, 677–696 (2023). https://doi.org/10.1007/s12652-021-03319-1
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DOI: https://doi.org/10.1007/s12652-021-03319-1