# Artificial Punishment Signals for Guiding the Decision-Making Process of an Autonomous System
## 2 Literature Review  
In 1994, [PERSON] enunciated the somatic marker hypothesis [1], which states that decision making is affected by subtle homeostatic changes, originating in the body and underlying emotions, and it integrates the mind and body in this way. These changes can take the form of sweating, agitation, heart palpitations, abdominal pain, muscle tension, or others and represent signs or somatic markers in the face of situations of risk, whether real or imagined.  
Recent research incorporates the use of somatic markers in the study of decision making under stress [12], under uncertainty [13; 14; 15], concerning the difficulty in identifying and describing emotions or alexithymiaia [16], in equity profiles and selfishness in financial decisions [17], and in the perception of happiness in social interactions [18]. In the literature, it is also possible to find studies that relate somatic markers to aspects such as moral judgement and decision making [19]. For example, in [20], the effect of somatic markers and emotional intelligence on risky decision making is studied. The influence of these markers on decisions about consumer brands in e-commerce has also been evaluated in [21]. Another interesting example can be found in [22], where somatic markers' effects on adolescents' decision-making processes are evidenced. Applied examples can also be found in the field of health, relating somatic markers to decisions regarding suicide [23] or addiction problems [15].  
Reward and, particularly, punishment processes influence decision making through emotional responses [24; 25; 26; 27]. Emotional events, expressed in the body, influence decision making through a circuit or \"body loop\" of feedback afferent to the brain, whose primary channel is the vagus nerve [28]. In the case of opioid users, these emotional reactions appear abnormal [29]. Likewise, it has been seen that the medial prefrontal cortex contributes to the decision-making process that involves the risk of punishment [30]. There is evidence supporting that several neurobiological mechanisms (including the amygdala,GABA, and monoamine neurotransmitters, dopamine, and corticostriatal circuits) influence punishment [31].  
On the other hand, different aspects have been studied concerning the stock market and decision making, such as investor over-reaction [32]; optimism, volatility, and decision making [33]; climate risk and stock prices [34]; and relationships between emotions and market anomalies [35; 36], to name a few. There are several studies devoted to analyzing the human factors' influence on investment decision making, for example, personal characteristics [37] and psychological factors [38; 39]. In a complementary sense, the idea of developing systems for investment decisions that artificially incorporate affective aspects is not recent [40; 41; 42; 43; 44; 45; 46].  
In recent years, work has also been developed relating factors inherently related to human beings, such as personality or somatic markers, to model investment decisions in intelligent systems. For instance, an autonomous system based on artificial somatic markers was presented by [7]. Regarding personality, an artificial autonomous system to support investment decisions using a Big-Five modeling approach was presented in [47]. This model was extended in [11] to integrate personality traits and artificial somatic markers. However, the few known works that have suggested the implementation of artificial somatic markers in autonomous investment systems have not considered the somatic punishment approach. In this sense, the present research focuses its analysis on the implementation and evaluation of an artificial mechanism of somatic punishment, that is, the generation of a negative impact or consequence in an autonomous system when the result of its decisions deviates negatively from its objective.