Resilience in the Decision-Making of an Artificial Autonomous System on the Stock Market
This paper presents the design of a resilience mechanism for supporting investment decision-making processes performed by artificial autonomous systems. In the field of Psychology, resilience is understood as the capacity of people to overcome adversity. Resilience has been determined to be a perman...
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doaj-2dca7d222e8e4d2da9c75707760dfbdd2021-04-05T17:34:16ZengIEEEIEEE Access2169-35362019-01-01714524614525810.1109/ACCESS.2019.29454718856190Resilience in the Decision-Making of an Artificial Autonomous System on the Stock MarketDaniel Cabrera0https://orcid.org/0000-0001-5826-287XRolando Rubilar1https://orcid.org/0000-0003-3025-1946Claudio Cubillos2Escuela de Ingeniería Comercial, Universidad de Valparaíso, Viña del Mar, ChileDirección de Innovación y Transferencia Tecnológica, Universidad de Valparaíso, Viña del Mar, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileThis paper presents the design of a resilience mechanism for supporting investment decision-making processes performed by artificial autonomous systems. In the field of Psychology, resilience is understood as the capacity of people to overcome adversity. Resilience has been determined to be a permanent necessary element for the life of an individual. In addition, different levels of intelligence, analysis capacities, and degrees of autonomy have been progressively incorporated within information systems that are oriented to support decision-making processes, such as those for stock markets. Particularly, the inclusion of affective criteria or variables within decision-making systems represents a promising line of action. However, to the best of our knowledge, there are no proposals that suggest the inclusion of a psychological approach to resilience within an autonomous decision-making system for stock markets. Specifically, the incorporation of a psychological approach to resilience allows the autonomous system to face special difficult investment scenarios (e.g., an economic shock) and prevent the system from achieving a permanent negative performance. Thus, psychological resilience can enable an artificial autonomous system to adapt its decision-making processes according to uncertain investment environments. Our proposal conducts experiments using official data from the Standard & Poor's 500 Index. The results are promising and are based on a second-order autoregressive model. The test results suggest that the use of a resilience mechanism within an artificial autonomous system can contain and recover the affective dimensions of the system when it faces adverse decision scenarios.https://ieeexplore.ieee.org/document/8856190/Resilienceartificial autonomous systemstock market |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daniel Cabrera Rolando Rubilar Claudio Cubillos |
spellingShingle |
Daniel Cabrera Rolando Rubilar Claudio Cubillos Resilience in the Decision-Making of an Artificial Autonomous System on the Stock Market IEEE Access Resilience artificial autonomous system stock market |
author_facet |
Daniel Cabrera Rolando Rubilar Claudio Cubillos |
author_sort |
Daniel Cabrera |
title |
Resilience in the Decision-Making of an Artificial Autonomous System on the Stock Market |
title_short |
Resilience in the Decision-Making of an Artificial Autonomous System on the Stock Market |
title_full |
Resilience in the Decision-Making of an Artificial Autonomous System on the Stock Market |
title_fullStr |
Resilience in the Decision-Making of an Artificial Autonomous System on the Stock Market |
title_full_unstemmed |
Resilience in the Decision-Making of an Artificial Autonomous System on the Stock Market |
title_sort |
resilience in the decision-making of an artificial autonomous system on the stock market |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
This paper presents the design of a resilience mechanism for supporting investment decision-making processes performed by artificial autonomous systems. In the field of Psychology, resilience is understood as the capacity of people to overcome adversity. Resilience has been determined to be a permanent necessary element for the life of an individual. In addition, different levels of intelligence, analysis capacities, and degrees of autonomy have been progressively incorporated within information systems that are oriented to support decision-making processes, such as those for stock markets. Particularly, the inclusion of affective criteria or variables within decision-making systems represents a promising line of action. However, to the best of our knowledge, there are no proposals that suggest the inclusion of a psychological approach to resilience within an autonomous decision-making system for stock markets. Specifically, the incorporation of a psychological approach to resilience allows the autonomous system to face special difficult investment scenarios (e.g., an economic shock) and prevent the system from achieving a permanent negative performance. Thus, psychological resilience can enable an artificial autonomous system to adapt its decision-making processes according to uncertain investment environments. Our proposal conducts experiments using official data from the Standard & Poor's 500 Index. The results are promising and are based on a second-order autoregressive model. The test results suggest that the use of a resilience mechanism within an artificial autonomous system can contain and recover the affective dimensions of the system when it faces adverse decision scenarios. |
topic |
Resilience artificial autonomous system stock market |
url |
https://ieeexplore.ieee.org/document/8856190/ |
work_keys_str_mv |
AT danielcabrera resilienceinthedecisionmakingofanartificialautonomoussystemonthestockmarket AT rolandorubilar resilienceinthedecisionmakingofanartificialautonomoussystemonthestockmarket AT claudiocubillos resilienceinthedecisionmakingofanartificialautonomoussystemonthestockmarket |
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