# Artificial Punishment Signals for Guiding the Decision-Making Process of an Autonomous System
## 1 Introduction  
From a general interpretation, a somatic marker represents an automatic activation of bodily or physiological signals in the presence of stimuli internal or external to the individual. Somatic markers are acquired and developed through the experience of the individual [1]. Examples of the manifestation of a somatic marker are increased heart rate, sweating, muscle twitching, or abdominal pain [1]. It is important to note that these bodily reactions do not occur intentionally but rather as a reflection of the emergence of an emotion in the presence of an internal or external stimulus. When an adverse situation occurs for an individual, a somatic marker can reflect through physiological changes that something is wrong and that it is necessary to update the course of action or make a decision [1, 2].  
The formation of somatic markers is a process that continues throughout a person's life, although it acquires special importance during the stages from childhood to adolescence. Likewise, the existence of rewards or punishments as a consequence of decision making impacts the profiling of each somatic marker. Both reward and somatic punishments represent bodily states with positive or negative sensations, respectively. Within a flow of uncertain decisions, these positive or negative sensations can configure signals that alert, anticipate, or suggest courses of action over others [1]. Whether due to an internal or external stimulus, an individual may experience a reward sensation (e.g., increase ina feeling of well-being) or a punishment sensation (e.g., increase feelings of displeasure, sadness, anger) [3]. These sensations are experienced in real time, that is, when the stimulus is presented and the individual faces it. Likewise, both the sensations experienced by the individual and the actions taken by itself are recorded within the experience and contribute to the formation of somatic markers.  
Several studies have been carried out on the effect of somatic punishment on decision making [4; 5; 6]. Also, to a lesser extent, some other studies have explored ways to artificially implement somatic markers within decision systems [3; 7; 8]. However, to our knowledge, no proposals have been developed that specifically address the implementation of artificial somatic signals arranged as somatic punishments.  
In this paper, we present a new mechanism for generating artificial somatic punishments in an autonomous system. Somatic responses and somatic punishments enhance risk sensitivity, conferring to the system the ability to adapt to dynamic environments. To do that, the present work addresses (1) the design of an artificial somatic punishment generation mechanism for an autonomous system, (2) the definition of a decision model incorporating such a mechanism, (3) the implementation of an evaluation scenario using official and public data from Standard & Poor's 500 (S&P500) [9] and Dow Jones Index (DJI) [10], which correspond to a group of indices that represent the market value of the companies listed in each of these indices in a time-series format with a daily time unit, and (4) the evaluation of the proposal through the execution of 10,000 independent experiments yielding promising results.  
The obtained results show a behavior close to what could be observed in real stock markets, as well as in other research works such as [7; 11]. In this sense, a relevant novelty of the current research work lies in the design, implementation, and evaluation of a novel artificial mechanism to be incorporated in autonomous systems, which address to extend the capability and functionality of autonomous systems for domains where humans can delegate their own decision making.