pág. 8906
A DRIFT DIFFUSION MODEL APPROACH TO
MORAL DECISION-MAKING: TOWARD A
COMPUTATIONAL FRAMEWORK FOR ETHICAL
DILEMMAS
UN ENFOQUE DE MODELO DE DIFUSIÓN DE DERIVA PARA LA
TOMA DE DECISIONES MORALES: HACIA UN MARCO
COMPUTACIONAL PARA DILEMAS ÉTICOS
Carlos Ledesma – Alonso
Student, Faculty of Bioethics, Universidad Anáhuac Campus Norte, Estado de México
pág. 8907
DOI: https://doi.org/10.37811/cl_rcm.v9i3.18524
A Drift Diffusion Model Approach to Moral Decision-Making: Toward a
Computational Framework for Ethical Dilemmas
Carlos Ledesma – Alonso1
carlos_ledesma@anahuac.mx
https://orcid.org/0009-0005-3744-6422
Student, Faculty of Bioethics, Universidad Anáhuac Campus Norte, Estado de México
Huixquilucan, , Mexico
ABSTRACT
Can ethical dilemmas be resolved through computational models? In this paper I propose a novel
integration of the Drift Diffusion Model (DDM) with a value-based framework for moral reasoning.
Based on insights from neuroethics and cognitive neuroscience, I argue that moral decisions —
especially under conditions of uncertainty, irreversibility, and emotional interference— can be modeled
as processes of noisy evidence accumulation. I introduce the concept of an ethical balance mainly
composed of three evaluative variables: sentience, intentionality, and innocence. These inputs are
mapped to DDM parameters such as drift rate, decision threshold, and starting point bias. Through
illustrative moral scenarios, I show how this framework can both predict and simulate moral judgments.
The goal is not to replace normative ethics, but to demonstrate how scientific modeling can enhance our
understanding of how moral reasoning unfolds in the brain. This maybe opens the path to a
computational ethics grounded in real cognitive processes.
Keywords: drift diffusion model, neuroethics, decision – making, ethical balance, moral dilemmas
1 Autor principal
Correspondencia: carlos_ledesma@anahuac.mx
DOI: https://doi.org/10.37811/cl_rcm.v9i3.18524
A Drift Diffusion Model Approach to Moral Decision-Making: Toward a
Computational Framework for Ethical Dilemmas
Carlos Ledesma – Alonso1
carlos_ledesma@anahuac.mx
https://orcid.org/0009-0005-3744-6422
Student, Faculty of Bioethics, Universidad Anáhuac Campus Norte, Estado de México
Huixquilucan, , Mexico
ABSTRACT
Can ethical dilemmas be resolved through computational models? In this paper I propose a novel
integration of the Drift Diffusion Model (DDM) with a value-based framework for moral reasoning.
Based on insights from neuroethics and cognitive neuroscience, I argue that moral decisions —
especially under conditions of uncertainty, irreversibility, and emotional interference— can be modeled
as processes of noisy evidence accumulation. I introduce the concept of an ethical balance mainly
composed of three evaluative variables: sentience, intentionality, and innocence. These inputs are
mapped to DDM parameters such as drift rate, decision threshold, and starting point bias. Through
illustrative moral scenarios, I show how this framework can both predict and simulate moral judgments.
The goal is not to replace normative ethics, but to demonstrate how scientific modeling can enhance our
understanding of how moral reasoning unfolds in the brain. This maybe opens the path to a
computational ethics grounded in real cognitive processes.
Keywords: drift diffusion model, neuroethics, decision – making, ethical balance, moral dilemmas
1 Autor principal
Correspondencia: carlos_ledesma@anahuac.mx
pág. 8908
Un Enfoque de Modelo de Difusión de Deriva para la Toma de Decisiones
Morales: Hacia un Marco Computacional para Dilemas Éticos
RESUMEN
¿Se pueden resolver los dilemas éticos a través de modelos computacionales? En este artículo propongo
una integración novedosa del Modelo de Difusión de la Deriva (DDM) con un marco basado en valores
para el razonamiento moral. Con base en los conocimientos de la neuroética y la neurociencia cognitiva,
sostengo que las decisiones morales —especialmente en condiciones de incertidumbre, irreversibilidad
e interferencia emocional— pueden modelarse como procesos de acumulación de evidencia ruidosa.
Introduzco el concepto de un equilibrio ético compuesto principalmente por tres variables evaluativas:
sensibilidad, intencionalidad e inocencia. Estas entradas se asignan a los parámetros de DDM, como la
velocidad de deriva, el umbral de decisión y el sesgo del punto de partida. A través de escenarios morales
ilustrativos, muestro cómo este marco puede predecir y simular juicios morales. El objetivo no es
reemplazar la ética normativa, sino demostrar cómo el modelado científico puede mejorar nuestra
comprensión de cómo se despliega el razonamiento moral en el cerebro. Esto tal vez abra el camino a
una ética computacional basada en procesos cognitivos reales.
Palabras clave: modelo de difusión de deriva, neuroética, toma de decisiones, balanza ética, dilemas
morales.
Artículo recibido 11 junio 2025
Aceptado para publicación: 30 junio 2025
Un Enfoque de Modelo de Difusión de Deriva para la Toma de Decisiones
Morales: Hacia un Marco Computacional para Dilemas Éticos
RESUMEN
¿Se pueden resolver los dilemas éticos a través de modelos computacionales? En este artículo propongo
una integración novedosa del Modelo de Difusión de la Deriva (DDM) con un marco basado en valores
para el razonamiento moral. Con base en los conocimientos de la neuroética y la neurociencia cognitiva,
sostengo que las decisiones morales —especialmente en condiciones de incertidumbre, irreversibilidad
e interferencia emocional— pueden modelarse como procesos de acumulación de evidencia ruidosa.
Introduzco el concepto de un equilibrio ético compuesto principalmente por tres variables evaluativas:
sensibilidad, intencionalidad e inocencia. Estas entradas se asignan a los parámetros de DDM, como la
velocidad de deriva, el umbral de decisión y el sesgo del punto de partida. A través de escenarios morales
ilustrativos, muestro cómo este marco puede predecir y simular juicios morales. El objetivo no es
reemplazar la ética normativa, sino demostrar cómo el modelado científico puede mejorar nuestra
comprensión de cómo se despliega el razonamiento moral en el cerebro. Esto tal vez abra el camino a
una ética computacional basada en procesos cognitivos reales.
Palabras clave: modelo de difusión de deriva, neuroética, toma de decisiones, balanza ética, dilemas
morales.
Artículo recibido 11 junio 2025
Aceptado para publicación: 30 junio 2025
pág. 8909
INTRODUCTION
Science has explained most natural phenomena — from planetary motion to subatomic interactions —
with remarkable precision. From Newton’s laws and Maxwell’s equations, to Einstein’s relativity and
Dirac’s quantum predictions, physics offers deep explanatory power. Other fields such as medicine and
biology have also solved foundational questions, including the origin of species through Darwinian
evolution. This broad explanatory capacity suggests that science may also illuminate the nature of
morality.
In this article, I analyze and discuss how the scope of science reaches areas where it was thought that
only ethics and morality —both philosophical currents— had interference. In addition, I argue that, by
understanding the neurobiological mechanisms that underlie phenomena such as altruism or
compassion, both components of an ethical dilemma, neuroethics (a relatively recent emerging science)
can help us unveil the phenomenon behind morality and even converge in decision-making with ethical
implications.
Concept of factual science and its historical context
To understand how ethics should be added as just another science, we must have a complete definition
of science. To consider ethics as a science, we must adopt a rigorous definition. According to Mario
Bunge (2013), a factual science is defined by a real domain, a community of researchers, a realistic
epistemology, and verifiable methods and theories. Ethics meets these conditions when grounded in
cognitive neuroscience, thereby justifying neuroethics as a scientific discipline (Bunge, 2013).
Science, contextualized from a historical perspective, according to Thomas Kuhn, goes through different
transitions: pre-scientific, normal, and revolutionary. The transition from one transition to another is
carried out by the change of scientific paradigms and in this way, science advances (Kuhn, 1994) .
However, Kuhn's thesis and his paradigms do not seem to be entirely clear mainly due to the fact what
that Kun’s consider revolutions are just great discoveries not revolutions by itself.
Despite the great scientific advances, from Archimedes to Einstein, epistemologically speaking it has
been affirmed that science has its limits, or at least that is what a few philosophers of science argue.
The most recognized is probably the philosopher Paul K. Feyerabend. In one of his works, he stated that
Galileo Galilei could not have made the lunar observations and drawn his conclusions, if he had carried
INTRODUCTION
Science has explained most natural phenomena — from planetary motion to subatomic interactions —
with remarkable precision. From Newton’s laws and Maxwell’s equations, to Einstein’s relativity and
Dirac’s quantum predictions, physics offers deep explanatory power. Other fields such as medicine and
biology have also solved foundational questions, including the origin of species through Darwinian
evolution. This broad explanatory capacity suggests that science may also illuminate the nature of
morality.
In this article, I analyze and discuss how the scope of science reaches areas where it was thought that
only ethics and morality —both philosophical currents— had interference. In addition, I argue that, by
understanding the neurobiological mechanisms that underlie phenomena such as altruism or
compassion, both components of an ethical dilemma, neuroethics (a relatively recent emerging science)
can help us unveil the phenomenon behind morality and even converge in decision-making with ethical
implications.
Concept of factual science and its historical context
To understand how ethics should be added as just another science, we must have a complete definition
of science. To consider ethics as a science, we must adopt a rigorous definition. According to Mario
Bunge (2013), a factual science is defined by a real domain, a community of researchers, a realistic
epistemology, and verifiable methods and theories. Ethics meets these conditions when grounded in
cognitive neuroscience, thereby justifying neuroethics as a scientific discipline (Bunge, 2013).
Science, contextualized from a historical perspective, according to Thomas Kuhn, goes through different
transitions: pre-scientific, normal, and revolutionary. The transition from one transition to another is
carried out by the change of scientific paradigms and in this way, science advances (Kuhn, 1994) .
However, Kuhn's thesis and his paradigms do not seem to be entirely clear mainly due to the fact what
that Kun’s consider revolutions are just great discoveries not revolutions by itself.
Despite the great scientific advances, from Archimedes to Einstein, epistemologically speaking it has
been affirmed that science has its limits, or at least that is what a few philosophers of science argue.
The most recognized is probably the philosopher Paul K. Feyerabend. In one of his works, he stated that
Galileo Galilei could not have made the lunar observations and drawn his conclusions, if he had carried
pág. 8910
out the steps of the scientific method as such (Feyerabend, 1987). In addition, Feyerabend erroneously
asserts that some pseudosciences and pseudotechniques such as homeopathy, psychoanalysis and
astrology, should be considered sciences, under the assumption that the scientific method does not exist
as such, calling it a gnoseological anarchism or anything goes. It is clear that Feyerabend's vision was
compatible with pseudoscience, making his thesis redundant, unclear and irresponsible.
Despite Feyerabend's failed attempts to categorize pseudosciences and pseudotechnologies as sciences,
science has always been successful in reaching a result that works, even if it is not carried out
experimentally. An example of this was the achievement of physicist Peter Higgs who theoretically
hypothesized a boson in the 1960s in a mathematical way, and it was confirmed experimentally in 2012
by the powerful hadron collider in Bern, Switzerland. In addition, the objective of science is the
continuous improvement of its main products (theories) and means (techniques), as well as the
subjection of ever-increasing territories to their power (Bunge, 2000).
In this regard, Bunge himself argues that there are two types of sciences: formal and factual (Bunge,
2018). These are formal sciences, such as logic or mathematics, which provide us with verifiable and
complete information about reality, but do not deal with facts, and factual science, where their statements
need to be verifiable through experience.
Adjusting our central argument to Bunge's concept, the formal sciences; demonstrate, and the factual;
confirm. While the demonstration is complete, the verification is incomplete (On. Cit. Bunge M, 2005).
In this sense, I emphasize again that science can satisfactorily explain the phenomenon of ethics.
Digression: A poorly posed question gives the wrong answer
One of the most inadequate questions is: Can science resolve moral dilemmas? Does not have an answer
of a dichotomous nature, since, if so, the answer would be yes and no, which is unsatisfactory.
Consequently, it is a poorly posed question that has implications that do not hold water. The issue is not
that it is more complex, it is just different. To ask whether science can solve ethical dilemmas would be
to reduce science to its method. To put this in perspective, let's look at it this way: mathematics is to
physics what the scientific method—or its variants—is to science. But mathematics is not physics; just
as the scientific method is not science. That said, to answer the question, it must first be posed properly.
out the steps of the scientific method as such (Feyerabend, 1987). In addition, Feyerabend erroneously
asserts that some pseudosciences and pseudotechniques such as homeopathy, psychoanalysis and
astrology, should be considered sciences, under the assumption that the scientific method does not exist
as such, calling it a gnoseological anarchism or anything goes. It is clear that Feyerabend's vision was
compatible with pseudoscience, making his thesis redundant, unclear and irresponsible.
Despite Feyerabend's failed attempts to categorize pseudosciences and pseudotechnologies as sciences,
science has always been successful in reaching a result that works, even if it is not carried out
experimentally. An example of this was the achievement of physicist Peter Higgs who theoretically
hypothesized a boson in the 1960s in a mathematical way, and it was confirmed experimentally in 2012
by the powerful hadron collider in Bern, Switzerland. In addition, the objective of science is the
continuous improvement of its main products (theories) and means (techniques), as well as the
subjection of ever-increasing territories to their power (Bunge, 2000).
In this regard, Bunge himself argues that there are two types of sciences: formal and factual (Bunge,
2018). These are formal sciences, such as logic or mathematics, which provide us with verifiable and
complete information about reality, but do not deal with facts, and factual science, where their statements
need to be verifiable through experience.
Adjusting our central argument to Bunge's concept, the formal sciences; demonstrate, and the factual;
confirm. While the demonstration is complete, the verification is incomplete (On. Cit. Bunge M, 2005).
In this sense, I emphasize again that science can satisfactorily explain the phenomenon of ethics.
Digression: A poorly posed question gives the wrong answer
One of the most inadequate questions is: Can science resolve moral dilemmas? Does not have an answer
of a dichotomous nature, since, if so, the answer would be yes and no, which is unsatisfactory.
Consequently, it is a poorly posed question that has implications that do not hold water. The issue is not
that it is more complex, it is just different. To ask whether science can solve ethical dilemmas would be
to reduce science to its method. To put this in perspective, let's look at it this way: mathematics is to
physics what the scientific method—or its variants—is to science. But mathematics is not physics; just
as the scientific method is not science. That said, to answer the question, it must first be posed properly.
pág. 8911
Given that science, in particular neuroscience and its branches, can study phenomena such as empathy,
reason, guilt and decision-making, then it can be deduced that it helps to unveil the ethical phenomenon,
that is, from a purely scientific perspective. Considering that empathy, reason, guilt and decision-making
are neurophysiological processes that are used to solve ethical dilemmas, we can argue with a certain
degree of certainty that science can deal with the explanation of the origin and neurobiological
mechanisms that underlie morality, but it is not in charge of acting as a judge and ruling what should be
done in this or that situation.
It should be added that if we wish to categorize ethics as a scientific phenomenon, it must materialize,
therefore dualism must be replaced. The psychoneural dualist view is incompatible with the ontological
reduction that asserts that all that is mental is the same as the neural. Let us remember the most forceful
thing that Bunge tells us about dualism (Bunge, 2010). First, dualism is conceptually imprecise, that is,
the very concept of action is only defined by material things, hence, to say that mind-body interaction
is untenable, because being immaterial mind is insensitive to physical stimuli. Second, dualism is
experimentally irrefutable, since something immaterial cannot be manipulated and, consequently,
experimented with. Third, dualism is incompatible with cognitive ethology, because the solid evidence
comes from anatomical and physiological studies and whose relationship brings us closer to nonhuman
animals, especially primates, and does not distance us from them. Fourth, dualism violates a
fundamental physical law, namely the law of conservation of energy. Fifth, it confuses scientists, since
when talking about "correlates that underlie x phenomenon" it is as much as saying that the legs
implement walking or that the stomach instantiates digestion, remember that there is no function without
an organ and no organ without functions. In other words, psychoneural dualism is scientifically and
philosophically untenable.
With this it is more than clear that the faculties of the brain such as thoughts, learning, morality or free
will are highly complex biophysical processes that are explained by studying the emergent properties of
neurons and other brain components (glia, neurotransmitters, etc.), their agglomerations, their functions
and their interactions through the sum of one or more scientific disciplines and not through the
invocation of immaterial entities.
Given that science, in particular neuroscience and its branches, can study phenomena such as empathy,
reason, guilt and decision-making, then it can be deduced that it helps to unveil the ethical phenomenon,
that is, from a purely scientific perspective. Considering that empathy, reason, guilt and decision-making
are neurophysiological processes that are used to solve ethical dilemmas, we can argue with a certain
degree of certainty that science can deal with the explanation of the origin and neurobiological
mechanisms that underlie morality, but it is not in charge of acting as a judge and ruling what should be
done in this or that situation.
It should be added that if we wish to categorize ethics as a scientific phenomenon, it must materialize,
therefore dualism must be replaced. The psychoneural dualist view is incompatible with the ontological
reduction that asserts that all that is mental is the same as the neural. Let us remember the most forceful
thing that Bunge tells us about dualism (Bunge, 2010). First, dualism is conceptually imprecise, that is,
the very concept of action is only defined by material things, hence, to say that mind-body interaction
is untenable, because being immaterial mind is insensitive to physical stimuli. Second, dualism is
experimentally irrefutable, since something immaterial cannot be manipulated and, consequently,
experimented with. Third, dualism is incompatible with cognitive ethology, because the solid evidence
comes from anatomical and physiological studies and whose relationship brings us closer to nonhuman
animals, especially primates, and does not distance us from them. Fourth, dualism violates a
fundamental physical law, namely the law of conservation of energy. Fifth, it confuses scientists, since
when talking about "correlates that underlie x phenomenon" it is as much as saying that the legs
implement walking or that the stomach instantiates digestion, remember that there is no function without
an organ and no organ without functions. In other words, psychoneural dualism is scientifically and
philosophically untenable.
With this it is more than clear that the faculties of the brain such as thoughts, learning, morality or free
will are highly complex biophysical processes that are explained by studying the emergent properties of
neurons and other brain components (glia, neurotransmitters, etc.), their agglomerations, their functions
and their interactions through the sum of one or more scientific disciplines and not through the
invocation of immaterial entities.
pág. 8912
A proposal for a mathematical model as a tool to elucidate ethical problems
A pitfall-free and more appropriate question would be whether science can explain and substantiate
ethical phenomena, not solve them. Ethical problems are not solved as in the formal sciences (e.g.,
mathematics), but they are reasoned and deduced based on accumulated evidence. The answer to the
question is yes, from a neuroscientific perspective, as mentioned above. Jumping from this explanation
to the question of whether science can solve ethical problems would be like trying to explain
consciousness, only by studying the anatomical-functional unit that generates it, the neuron. The
phenomenon is much broader, since it is emerging. However, with a certain process that involves
reasoning, we could try to elucidate and thus try to conclude a simple ethical conflict – in the medium
to long term – using a mathematical model.
For this, I imagined a very simple panorama, consisting of four components, in order to subtract the
fundamental parts that make up an ethical problem. Let's imagine a scenario where there are four
participants: a pig, an apple tree and two human beings. There are only a couple of rules, and they are
as follows:
1. Each individual can only eat one of the others to survive.
2. The most ethical scenario will be the one that has generated the least damage.
Scenario I
One human kills the pig, the other eats the apple tree, consequently, both humans are left alive.
Scenario II
One human kills the other human to feed the pig, then he eats the apple tree. The pig and the human are
still alive.
Scenario III
One human wish to kill the pig, the other human intercepts and kills it. The pig eats the dead human and
the remaining human the apple tree. The pig and the human are still alive.
Scenario IV
One human gives life to feed the pig, the other human eats the apple tree. The pig and the human are
still alive.
A proposal for a mathematical model as a tool to elucidate ethical problems
A pitfall-free and more appropriate question would be whether science can explain and substantiate
ethical phenomena, not solve them. Ethical problems are not solved as in the formal sciences (e.g.,
mathematics), but they are reasoned and deduced based on accumulated evidence. The answer to the
question is yes, from a neuroscientific perspective, as mentioned above. Jumping from this explanation
to the question of whether science can solve ethical problems would be like trying to explain
consciousness, only by studying the anatomical-functional unit that generates it, the neuron. The
phenomenon is much broader, since it is emerging. However, with a certain process that involves
reasoning, we could try to elucidate and thus try to conclude a simple ethical conflict – in the medium
to long term – using a mathematical model.
For this, I imagined a very simple panorama, consisting of four components, in order to subtract the
fundamental parts that make up an ethical problem. Let's imagine a scenario where there are four
participants: a pig, an apple tree and two human beings. There are only a couple of rules, and they are
as follows:
1. Each individual can only eat one of the others to survive.
2. The most ethical scenario will be the one that has generated the least damage.
Scenario I
One human kills the pig, the other eats the apple tree, consequently, both humans are left alive.
Scenario II
One human kills the other human to feed the pig, then he eats the apple tree. The pig and the human are
still alive.
Scenario III
One human wish to kill the pig, the other human intercepts and kills it. The pig eats the dead human and
the remaining human the apple tree. The pig and the human are still alive.
Scenario IV
One human gives life to feed the pig, the other human eats the apple tree. The pig and the human are
still alive.
pág. 8913
Scenario V
The two humans are slaughtered, and the pig gets to choose who to eat. Only the pig remains alive.
From the above panorama, different variables can be subtracted to consider and dilute an ethical
dilemma. One of them is sentience. By definition, an organism is sentient if it has a nervous system
complex enough to perceive pain and process it and, consequently, generate suffering. The next is
innocence. By definition, it is considered an innocent being, if it does not seek to harm any sentient
being without any logical and convincing reason – innocent or not – or the damage it generates is
minimal and unintentional. The last is intentionality. A being is intentional, if he has the cognitive
capacity to make decisions, whether good, bad or neutral.
Therefore, this thought experiment reveals key variables for moral evaluation: sentience, innocence, and
intentionality. Assigning binary values to each (1 or 0), we can derive an “ethical valence” score. In
Scenario III, a human defends a sentient, innocent being, resulting in two high-valence survivors.
Scenario IV is similarly ethical due to altruistic sacrifice. This algorithmic structure lays the foundation
for the proposed ethical balance.
For example, a pig is a sentient, innocent and intentionally good being, it has a valence of 3, because it
does not harm. The apple tree has 1 point (innocence), because although they sense (perceive) and have
intention (survive), they are in zero degrees compared to a mammal. In humans, their valence can range
from having 1 point to having 3 points. In scenario III, the human intends to defend a being of valence
of 3 (innocent, sentient and well-intentioned), eliminating one that has bad intentions. Thus, in panorama
III, two beings of valence of 3 are still alive. However, scenario IV may be just as good as scenario III,
because a human sacrificed himself to feed a sentient, innocent, well-meaning being. I extend this better
in the next section.
This panorama can be extended in complexity, if we consider a fourth factor: irreversibility. By
definition, an event is irreversible if none of its components can be restored in a primordial way, e.g.
death. Which means that we can confer gradation on any ethical conflict in any area of life. If we want
to use this ethical scale in any ethical or moral landscape, irreversibility must be considered in order to
confer the level of importance on it. In the hypothetical case of the pig, humans and the apple tree, there
will always be death, something irreversible. This means that, in any ethical or moral conflict, there will
Scenario V
The two humans are slaughtered, and the pig gets to choose who to eat. Only the pig remains alive.
From the above panorama, different variables can be subtracted to consider and dilute an ethical
dilemma. One of them is sentience. By definition, an organism is sentient if it has a nervous system
complex enough to perceive pain and process it and, consequently, generate suffering. The next is
innocence. By definition, it is considered an innocent being, if it does not seek to harm any sentient
being without any logical and convincing reason – innocent or not – or the damage it generates is
minimal and unintentional. The last is intentionality. A being is intentional, if he has the cognitive
capacity to make decisions, whether good, bad or neutral.
Therefore, this thought experiment reveals key variables for moral evaluation: sentience, innocence, and
intentionality. Assigning binary values to each (1 or 0), we can derive an “ethical valence” score. In
Scenario III, a human defends a sentient, innocent being, resulting in two high-valence survivors.
Scenario IV is similarly ethical due to altruistic sacrifice. This algorithmic structure lays the foundation
for the proposed ethical balance.
For example, a pig is a sentient, innocent and intentionally good being, it has a valence of 3, because it
does not harm. The apple tree has 1 point (innocence), because although they sense (perceive) and have
intention (survive), they are in zero degrees compared to a mammal. In humans, their valence can range
from having 1 point to having 3 points. In scenario III, the human intends to defend a being of valence
of 3 (innocent, sentient and well-intentioned), eliminating one that has bad intentions. Thus, in panorama
III, two beings of valence of 3 are still alive. However, scenario IV may be just as good as scenario III,
because a human sacrificed himself to feed a sentient, innocent, well-meaning being. I extend this better
in the next section.
This panorama can be extended in complexity, if we consider a fourth factor: irreversibility. By
definition, an event is irreversible if none of its components can be restored in a primordial way, e.g.
death. Which means that we can confer gradation on any ethical conflict in any area of life. If we want
to use this ethical scale in any ethical or moral landscape, irreversibility must be considered in order to
confer the level of importance on it. In the hypothetical case of the pig, humans and the apple tree, there
will always be death, something irreversible. This means that, in any ethical or moral conflict, there will
pág. 8914
always be damage (last factor), if there is an irreversible factor in the background, such as death. The
other factor to add importance to an ethical conflict will be the damage it causes, and not the happiness
it causes. Hence, this method should not be considered strictly utilitarian. If there is no harm, there will
be well-being, which is not the same as happiness.
Towards ethical computation: Simulation Framework for the Ethical Drift Diffusion Model
Once the variables have been extracted, I propose the use of the mathematical model called the Drift
Diffusion Model (DDM). DDM is defined by a series of mathematical equations that contain various
parameters to which different values can be assigned, in turn, each of these parameters can be adjusted
in the model, which affects its subsequent behavior (Myers et al., 2022). For example, the higher the
value of the drift rate (μ), the faster the decision will be made. On the other hand, decision boundaries
(represented ±θ) determine the amount of evidence accumulated to make the decision. In this sense, it
has been studied the influence of the affect – as – information may determine the drift rate along with
other choice-relevant attributes (Roberts & Hutcherson, 2019). Also, this model has been used in patients
with lesions in the orbitofrontal cortex in a decision-making task (Peters & D’Esposito, 2020).
To advance the empirical applicability of the proposed ethical balance model, I´ve to summaries a
simulation-based implementation using the Drift Diffusion Model (DDM). The simulation aims to
model moral decision-making under conflict by quantitatively mapping ethical variables to DDM
parameters.
Each option in a moral dilemma is evaluated according to five key ethical variables:
always be damage (last factor), if there is an irreversible factor in the background, such as death. The
other factor to add importance to an ethical conflict will be the damage it causes, and not the happiness
it causes. Hence, this method should not be considered strictly utilitarian. If there is no harm, there will
be well-being, which is not the same as happiness.
Towards ethical computation: Simulation Framework for the Ethical Drift Diffusion Model
Once the variables have been extracted, I propose the use of the mathematical model called the Drift
Diffusion Model (DDM). DDM is defined by a series of mathematical equations that contain various
parameters to which different values can be assigned, in turn, each of these parameters can be adjusted
in the model, which affects its subsequent behavior (Myers et al., 2022). For example, the higher the
value of the drift rate (μ), the faster the decision will be made. On the other hand, decision boundaries
(represented ±θ) determine the amount of evidence accumulated to make the decision. In this sense, it
has been studied the influence of the affect – as – information may determine the drift rate along with
other choice-relevant attributes (Roberts & Hutcherson, 2019). Also, this model has been used in patients
with lesions in the orbitofrontal cortex in a decision-making task (Peters & D’Esposito, 2020).
To advance the empirical applicability of the proposed ethical balance model, I´ve to summaries a
simulation-based implementation using the Drift Diffusion Model (DDM). The simulation aims to
model moral decision-making under conflict by quantitatively mapping ethical variables to DDM
parameters.
Each option in a moral dilemma is evaluated according to five key ethical variables:
pág. 8915
Table 1. Ethical Variables as Inputs. A weighted ethical value (V) is calculated for each actor or action
as: 𝑣 = 𝛼𝑆 + 𝛽𝐼 + 𝛾𝑁 − 𝛿𝑅 − 𝜖𝐷; where α, β, γ, δ, ε are tunable weights reflecting ethical emphasis.
The values in S, R and D oscillate between 0 to 1.0 (e. g. 0.8), this is mainly based on the context of the
dilemma because it’s a conceptual estimation.
I have to make emphasis that the DDM decision process compares two options (A and B), with the
following parameter mappings:
Table 2. Mapping to DDM Parameters.
DDM Parameter Source Meaning
Drift rate (μ) 𝑉𝐴 − 𝑉𝐵 Bias toward the ethically superior
option
Decision threshold
(θ)
Function of max (R, D) Stricter criteria under high-stakes
decisions
Starting point (z) Prior bias, e.g., preference for
humans
Encodes pre-existing moral
inclinations
Let us now examine an illustrative and very simple application with steps of the ethical balance with
DDM.
1. Found a dilemma.
A: Save an animal who is in danger of die
B: Save a serial killer in the same conditions
2. Assume the following input values according with step 1:
Variable Symbol Type Scale Description
Sentience S Quantitative [0.0 – 1.0] Capacity to feel pain/suffering
Intentionality I Binary 0 or 1 Whether harm was intentional
Innocence N Binary 0 or 1 Whether the agent is morally
blameless
Irreversibility R Quantitative [0.0 – 1.0] Degree of irreversibility of the
consequence
Damage D Quantitative [0.0 – 1.0] Magnitude of harm inflicted
Table 1. Ethical Variables as Inputs. A weighted ethical value (V) is calculated for each actor or action
as: 𝑣 = 𝛼𝑆 + 𝛽𝐼 + 𝛾𝑁 − 𝛿𝑅 − 𝜖𝐷; where α, β, γ, δ, ε are tunable weights reflecting ethical emphasis.
The values in S, R and D oscillate between 0 to 1.0 (e. g. 0.8), this is mainly based on the context of the
dilemma because it’s a conceptual estimation.
I have to make emphasis that the DDM decision process compares two options (A and B), with the
following parameter mappings:
Table 2. Mapping to DDM Parameters.
DDM Parameter Source Meaning
Drift rate (μ) 𝑉𝐴 − 𝑉𝐵 Bias toward the ethically superior
option
Decision threshold
(θ)
Function of max (R, D) Stricter criteria under high-stakes
decisions
Starting point (z) Prior bias, e.g., preference for
humans
Encodes pre-existing moral
inclinations
Let us now examine an illustrative and very simple application with steps of the ethical balance with
DDM.
1. Found a dilemma.
A: Save an animal who is in danger of die
B: Save a serial killer in the same conditions
2. Assume the following input values according with step 1:
Variable Symbol Type Scale Description
Sentience S Quantitative [0.0 – 1.0] Capacity to feel pain/suffering
Intentionality I Binary 0 or 1 Whether harm was intentional
Innocence N Binary 0 or 1 Whether the agent is morally
blameless
Irreversibility R Quantitative [0.0 – 1.0] Degree of irreversibility of the
consequence
Damage D Quantitative [0.0 – 1.0] Magnitude of harm inflicted
pág. 8916
Option S I N R D
A 1 1 1 0.8 0.1
B 1 0 0 0.8 0.3
Using weights: α = 1.0, β = 1.0, γ = 1.0, δ = 1.5, ε = 1.0
3. Use the ethical balance based in DDM.
VA = 1 + 1 + 1 - 1.5(0.8) - 0.1 = 1.7
VB = 1 + 0 + 0 - 1.5(0.8) - 0.3 = -0.5
μ = VA - VB = 2.2
4. Interpretate the results:
Conclusion. The high drift rate (μ = 2.2) favors option A, suggesting a fast and confident decision. If μ
is near 0, the moral dilemma would be interpreted as difficult or ambiguous, nevertheless, if μ have high
positive or negative numbers, the resolution of the dilemma would be easier.
This simulation can be implemented using standard DDM tools in Python (e.g., PyDDM), enabling
analysis of a) response times, b) accuracy/conflict and c) bias under varied initial values. Such a
simulation could be extended to include family ties, temporal pressure, or speciesism by adjusting initial
bias (z) or noise. This scenario maybe could be considered simple and improbable, but the core of this
model is to design a very precise and clear context. Although the ethical scenarios that occur at any
given moment – in many areas, both natural and artificial – vary in complexity, eventuality and
temporality, they have practically the same components and must be dimensioned in degree of
importance. Consequently, I propose that all ethical dilemmas should be categorized in degree of
importance and that this is a function of irreversibility, and the damage generated that can presumably
be calculated, as in the example above. Note: the difference between valences (V) is not decided by us,
but rather by the decision-making process: whether it will be easy or difficult, fast or slow, confident or
difficult.
Empirical and Simulated Approaches to Moral Modeling
One of the most viable ways to explore the scope of the Ethical Balance based on DDM is through the
design of behavioral experiments involving hypothetical moral dilemmas. These scenarios can be
carefully constructed to incorporate varying degrees of sentience, intentionality, innocence,
Option S I N R D
A 1 1 1 0.8 0.1
B 1 0 0 0.8 0.3
Using weights: α = 1.0, β = 1.0, γ = 1.0, δ = 1.5, ε = 1.0
3. Use the ethical balance based in DDM.
VA = 1 + 1 + 1 - 1.5(0.8) - 0.1 = 1.7
VB = 1 + 0 + 0 - 1.5(0.8) - 0.3 = -0.5
μ = VA - VB = 2.2
4. Interpretate the results:
Conclusion. The high drift rate (μ = 2.2) favors option A, suggesting a fast and confident decision. If μ
is near 0, the moral dilemma would be interpreted as difficult or ambiguous, nevertheless, if μ have high
positive or negative numbers, the resolution of the dilemma would be easier.
This simulation can be implemented using standard DDM tools in Python (e.g., PyDDM), enabling
analysis of a) response times, b) accuracy/conflict and c) bias under varied initial values. Such a
simulation could be extended to include family ties, temporal pressure, or speciesism by adjusting initial
bias (z) or noise. This scenario maybe could be considered simple and improbable, but the core of this
model is to design a very precise and clear context. Although the ethical scenarios that occur at any
given moment – in many areas, both natural and artificial – vary in complexity, eventuality and
temporality, they have practically the same components and must be dimensioned in degree of
importance. Consequently, I propose that all ethical dilemmas should be categorized in degree of
importance and that this is a function of irreversibility, and the damage generated that can presumably
be calculated, as in the example above. Note: the difference between valences (V) is not decided by us,
but rather by the decision-making process: whether it will be easy or difficult, fast or slow, confident or
difficult.
Empirical and Simulated Approaches to Moral Modeling
One of the most viable ways to explore the scope of the Ethical Balance based on DDM is through the
design of behavioral experiments involving hypothetical moral dilemmas. These scenarios can be
carefully constructed to incorporate varying degrees of sentience, intentionality, innocence,
pág. 8917
irreversibility, and damage—dimensions that, according to the ethical balance proposed here, are
fundamental in shaping moral decisions. Participants would face dilemmas where these variables are
subtly embedded, and their decisions could then be contrasted with the predictions generated by the
model.
At the same time, the model lends itself to theoretical exploration through simulations. By assigning
plausible values to each ethical input and adjusting their respective weights, one can anticipate the
direction and speed of moral choices. As expected from DDM principles, a high difference in ethical
valence between two options results in faster and more confident decisions, while scenarios with similar
values generate hesitation and cognitive conflict.
The purpose is not to emulate moral philosophy, but to better understand how real cognitive processes
might operate when we are forced to act. By comparing simulated patterns with empirical responses, the
ethical balance can serve as a useful and testable framework for studying how the brain—and perhaps
even artificial agents—approach moral conflict. Review its simulation in appendix A.
Limitations of the ethical balance based on DDM
The ethical balance has its scope, just as the scientific method is not irrefutable, nor complete, but it
brings us closer to the factual truth. It cannot be used satisfactorily if we consider other weighty factors
such as feelings. Whether due to family ties, friendships, genetics or determined by strong beliefs,
feelings put such noise in the balance that in any situation we will always prefer to save our loved one,
ignoring any normative ethics.
In nature we find clear examples of this. A hyena in the African savannah would mindlessly devour a
lioness protecting a pride of young. The hyena does not reason or perceive empathy for unprotected
offspring that are likely to die in starving or painful conditions without the help of their mother. But we
must not fall into the trap that, in nature, every act is good. This would lead to the naturalistic fallacy
proposed by G. E. Moore.
Darwin's theory of natural selection sometimes defies the utilitarian precept by putting the life of a single
offspring above other species. According to Richard Dawkins, genes are entities with selfish motivations
(by wanting to replicate themselves at all costs) and this explains certain traits in animal behavior, such
irreversibility, and damage—dimensions that, according to the ethical balance proposed here, are
fundamental in shaping moral decisions. Participants would face dilemmas where these variables are
subtly embedded, and their decisions could then be contrasted with the predictions generated by the
model.
At the same time, the model lends itself to theoretical exploration through simulations. By assigning
plausible values to each ethical input and adjusting their respective weights, one can anticipate the
direction and speed of moral choices. As expected from DDM principles, a high difference in ethical
valence between two options results in faster and more confident decisions, while scenarios with similar
values generate hesitation and cognitive conflict.
The purpose is not to emulate moral philosophy, but to better understand how real cognitive processes
might operate when we are forced to act. By comparing simulated patterns with empirical responses, the
ethical balance can serve as a useful and testable framework for studying how the brain—and perhaps
even artificial agents—approach moral conflict. Review its simulation in appendix A.
Limitations of the ethical balance based on DDM
The ethical balance has its scope, just as the scientific method is not irrefutable, nor complete, but it
brings us closer to the factual truth. It cannot be used satisfactorily if we consider other weighty factors
such as feelings. Whether due to family ties, friendships, genetics or determined by strong beliefs,
feelings put such noise in the balance that in any situation we will always prefer to save our loved one,
ignoring any normative ethics.
In nature we find clear examples of this. A hyena in the African savannah would mindlessly devour a
lioness protecting a pride of young. The hyena does not reason or perceive empathy for unprotected
offspring that are likely to die in starving or painful conditions without the help of their mother. But we
must not fall into the trap that, in nature, every act is good. This would lead to the naturalistic fallacy
proposed by G. E. Moore.
Darwin's theory of natural selection sometimes defies the utilitarian precept by putting the life of a single
offspring above other species. According to Richard Dawkins, genes are entities with selfish motivations
(by wanting to replicate themselves at all costs) and this explains certain traits in animal behavior, such
pág. 8918
as sexual reproduction or altruism (Dawkins, 1989). However, this concept turns out to be erroneous,
since genes are primarily chemical structures that also depend on the environment (epigenetics).
Another scenario where the ethical balance will fail is if we consider a very short time as a factor. The
time you spend deciding whether or not to unplug a patient in a coma is not the same as attacking and
killing a thief who breaks into your house. And that is what I mean by ethics with temporal gradation,
namely, short, medium and long term.
In fact, considering Daniel Kahneman's two decision-making systems, one fast and intuitive and the
other slow and logical (Krämer, 2014), I assert that, in order to solve an ethical dilemma, we must
oscillate depending on how we make the decision, considering factors such as: time, irreversibility and
damage. In fast-paced situations, we act according to system 1, but in a situation that takes longer, we
can apply logical and systematic reasoning (system 2).
From this we can conclude that, in system 1 we use regions of the brain such as the limbic system;
largely responsible for generating emotions and feelings, and system 2; which uses regions such as the
prefrontal cortex, the seat of executive thinking and logical reasoning.
In conclusion, the decision making of an ethical dilemma depends on its temporal gradation, namely,
short, medium and long-term resolution. In addition, the resolution of moral problems by means of the
ethical scale or the ethical balance based on DDM does not pretend to be complete and true, only to
approximate the truth.
When the Ethical Balance remains at zero: Justice Beyond the Dilemma
Justice, whether legal or moral, is often arbitrary and insufficient to proportionally address harm. In
ethical dilemmas, some degree of damage is inevitable, and ideally, compensation should be
proportional. When the ethical balance remains neutral—as in accidental dilemmas—justice must
address irreparable loss. Recognizing the shared ontological origin of living beings challenges human
exceptionalism and reinforces the role of science in guiding fair responses.
In 2016, a terrible accident occurred. At a zoo located in Cincinnati, United States, a 3-year-old boy
accidentally fell into a gorilla enclosure. After about 10 minutes, a burst of bullets struck the gorilla,
killing him instantly, saving the boy's life. What did this act mean? Well, the police considered that the
life of the minor was worth more than that of the gorilla. This was not to be evaluated in this way,
as sexual reproduction or altruism (Dawkins, 1989). However, this concept turns out to be erroneous,
since genes are primarily chemical structures that also depend on the environment (epigenetics).
Another scenario where the ethical balance will fail is if we consider a very short time as a factor. The
time you spend deciding whether or not to unplug a patient in a coma is not the same as attacking and
killing a thief who breaks into your house. And that is what I mean by ethics with temporal gradation,
namely, short, medium and long term.
In fact, considering Daniel Kahneman's two decision-making systems, one fast and intuitive and the
other slow and logical (Krämer, 2014), I assert that, in order to solve an ethical dilemma, we must
oscillate depending on how we make the decision, considering factors such as: time, irreversibility and
damage. In fast-paced situations, we act according to system 1, but in a situation that takes longer, we
can apply logical and systematic reasoning (system 2).
From this we can conclude that, in system 1 we use regions of the brain such as the limbic system;
largely responsible for generating emotions and feelings, and system 2; which uses regions such as the
prefrontal cortex, the seat of executive thinking and logical reasoning.
In conclusion, the decision making of an ethical dilemma depends on its temporal gradation, namely,
short, medium and long-term resolution. In addition, the resolution of moral problems by means of the
ethical scale or the ethical balance based on DDM does not pretend to be complete and true, only to
approximate the truth.
When the Ethical Balance remains at zero: Justice Beyond the Dilemma
Justice, whether legal or moral, is often arbitrary and insufficient to proportionally address harm. In
ethical dilemmas, some degree of damage is inevitable, and ideally, compensation should be
proportional. When the ethical balance remains neutral—as in accidental dilemmas—justice must
address irreparable loss. Recognizing the shared ontological origin of living beings challenges human
exceptionalism and reinforces the role of science in guiding fair responses.
In 2016, a terrible accident occurred. At a zoo located in Cincinnati, United States, a 3-year-old boy
accidentally fell into a gorilla enclosure. After about 10 minutes, a burst of bullets struck the gorilla,
killing him instantly, saving the boy's life. What did this act mean? Well, the police considered that the
life of the minor was worth more than that of the gorilla. This was not to be evaluated in this way,
pág. 8919
because the gorilla had no intention of hurting the child. Two sentient beings, innocent and not ill-
intentioned, end up in a difficult situation. The ethical balance is not moving anywhere, what next? After
the decision is made, justice must be done. What kind of justice should there be if a life cannot be
recovered? Hence the premise that we could adopt is that human ontology is neither superior nor
essentially different from that of practically any living being. In fact, it is generally accepted that the
origin of every living organism lies in a common ancestor called LUCA (Last Universal Common
Ancestor) whose metabolic reconstruction includes common pathways throughout the tree of life
(Goldman & Becerra, 2024). As Darwin stated, [humans] are different in degree but not in class.
Justice must consider the above factors, namely sentience, innocence, and intentionality, and from these,
the harm caused, and the costs involved in claiming the harm. At the same time, I propose that justice
should rely heavily on science and its methods.
The Neurobiological Foundations of Moral Cognition
In the past, science was considered to be solely descriptive and ethics normative. The truth is that ethics
can take on a scientific nuance. Ethics alone can be considered as a semi-science, however, by adding
several fields of research such as neuroscience, physiological psychology, sociology and evolutionary
psychology, neuroethics emerges and should be considered as an emerging science.
The most famous case in neuroscience that gave rise to hypothesizes that our morality originates from
how our neurons are connected was the case of Phineas Gage, a miner who had an accident in 1858. As
a result of an explosion, an iron bar embedded itself from his jaw and pierced his frontal lobe, causing
irreversible damage. Colleagues and friends attest that before the accident, Gage was kind, hardworking,
and a good friend; however, after the event, Gage became bitter, angry, and lazy. It was clear that
something had happened to his brain.
A recent study showed that two adult patients with the Gage syndrome who had severe lesions in the
prefrontal cortex, caused before 16 months of age, had impaired social behaviors, insensitivity to
consequences, and suffered from defective moral reasoning (Anderson et al., 1999). Another study
carried out by Michael Gazzaniga and his research group showed that neuroimaging studies indicate
that there are lateral brain mechanisms in the temporoparietal junction that are involved in complex
because the gorilla had no intention of hurting the child. Two sentient beings, innocent and not ill-
intentioned, end up in a difficult situation. The ethical balance is not moving anywhere, what next? After
the decision is made, justice must be done. What kind of justice should there be if a life cannot be
recovered? Hence the premise that we could adopt is that human ontology is neither superior nor
essentially different from that of practically any living being. In fact, it is generally accepted that the
origin of every living organism lies in a common ancestor called LUCA (Last Universal Common
Ancestor) whose metabolic reconstruction includes common pathways throughout the tree of life
(Goldman & Becerra, 2024). As Darwin stated, [humans] are different in degree but not in class.
Justice must consider the above factors, namely sentience, innocence, and intentionality, and from these,
the harm caused, and the costs involved in claiming the harm. At the same time, I propose that justice
should rely heavily on science and its methods.
The Neurobiological Foundations of Moral Cognition
In the past, science was considered to be solely descriptive and ethics normative. The truth is that ethics
can take on a scientific nuance. Ethics alone can be considered as a semi-science, however, by adding
several fields of research such as neuroscience, physiological psychology, sociology and evolutionary
psychology, neuroethics emerges and should be considered as an emerging science.
The most famous case in neuroscience that gave rise to hypothesizes that our morality originates from
how our neurons are connected was the case of Phineas Gage, a miner who had an accident in 1858. As
a result of an explosion, an iron bar embedded itself from his jaw and pierced his frontal lobe, causing
irreversible damage. Colleagues and friends attest that before the accident, Gage was kind, hardworking,
and a good friend; however, after the event, Gage became bitter, angry, and lazy. It was clear that
something had happened to his brain.
A recent study showed that two adult patients with the Gage syndrome who had severe lesions in the
prefrontal cortex, caused before 16 months of age, had impaired social behaviors, insensitivity to
consequences, and suffered from defective moral reasoning (Anderson et al., 1999). Another study
carried out by Michael Gazzaniga and his research group showed that neuroimaging studies indicate
that there are lateral brain mechanisms in the temporoparietal junction that are involved in complex
pág. 8920
moral and social reasoning and that subjects with a previous callosotomy (surgical removal of the corpus
callosum) showed a clear alteration in their moral judgments (Miller et al., 2010).
While common sense often guides moral judgments, neuroimaging studies show that ethical reasoning
activates brain regions like the medial temporal, cingulate, and frontal gyri. These findings suggest that
moral cognition involves more than intuition—it requires theory of mind and higher-order processing
distributed across cortical and subcortical networks. (Garrigan et al., 2016).
In addition, another experiment that used functional magnetic resonance imaging, analyzed the brains
of participants while they solved personal moral dilemmas and showed that brain areas such as the
prefrontal cortex and the anterior cingulate cortex were activated; both related to abstract reasoning and
cognitive control (Greene et al., 2004). Common sense is rather intuitive, and many times moral
reasoning requires little more than pure intuition. In fact, it has been shown that there are cases where a
person uses theory of mind cognition to make a moral judgment (Knobe, 2005; Moll & de Oliveira-
Souza, 2007; Moretto et al., 2010), for this, several cortical and subcortical regions must be used.
Other natural causes that show that our morality depends to a large extent on our proper neuronal
functioning are neurodegenerative diseases (Alzheimer's or Parkinson's disease). It has been shown that
people suffering from frontotemporal dementia manifest violations of moral rules and norms, a fact that
may be due to what researchers call moral agnosia, or an inability to recognize right from wrong
(Mendez et al., 2005). In this regard, it has been proven that patients with Parkinson's disease have
alterations in moral and social aspects, that is, not only the motor aspect (Santens et al., 2018).
Likewise, neuroscience —and its branches— can satisfactorily explain the origin of the moral sense that
we animals perceive, which are mere manifestations of evolutionary impulses such as doing no harm,
justice or equity, community, authority and purity (Mendez, 2009). In this way, we can argue that the
origin of a basic moral sense arose in an evolutionary way and is shaped by the interaction between
species (same or different), the environment and self-interests (physiological and psychological).
The affective and emotional components of the brain allow us to understand the situation of another
subject (e.g., empathy). Such behaviors have been demonstrated in rats, corvids, monkeys, elephants,
and human babies. Prosocial mechanisms have their evolutionary roots, and many social norms that are
based on them help the correct and adequate interaction between species (Wu & Hong, 2022).
moral and social reasoning and that subjects with a previous callosotomy (surgical removal of the corpus
callosum) showed a clear alteration in their moral judgments (Miller et al., 2010).
While common sense often guides moral judgments, neuroimaging studies show that ethical reasoning
activates brain regions like the medial temporal, cingulate, and frontal gyri. These findings suggest that
moral cognition involves more than intuition—it requires theory of mind and higher-order processing
distributed across cortical and subcortical networks. (Garrigan et al., 2016).
In addition, another experiment that used functional magnetic resonance imaging, analyzed the brains
of participants while they solved personal moral dilemmas and showed that brain areas such as the
prefrontal cortex and the anterior cingulate cortex were activated; both related to abstract reasoning and
cognitive control (Greene et al., 2004). Common sense is rather intuitive, and many times moral
reasoning requires little more than pure intuition. In fact, it has been shown that there are cases where a
person uses theory of mind cognition to make a moral judgment (Knobe, 2005; Moll & de Oliveira-
Souza, 2007; Moretto et al., 2010), for this, several cortical and subcortical regions must be used.
Other natural causes that show that our morality depends to a large extent on our proper neuronal
functioning are neurodegenerative diseases (Alzheimer's or Parkinson's disease). It has been shown that
people suffering from frontotemporal dementia manifest violations of moral rules and norms, a fact that
may be due to what researchers call moral agnosia, or an inability to recognize right from wrong
(Mendez et al., 2005). In this regard, it has been proven that patients with Parkinson's disease have
alterations in moral and social aspects, that is, not only the motor aspect (Santens et al., 2018).
Likewise, neuroscience —and its branches— can satisfactorily explain the origin of the moral sense that
we animals perceive, which are mere manifestations of evolutionary impulses such as doing no harm,
justice or equity, community, authority and purity (Mendez, 2009). In this way, we can argue that the
origin of a basic moral sense arose in an evolutionary way and is shaped by the interaction between
species (same or different), the environment and self-interests (physiological and psychological).
The affective and emotional components of the brain allow us to understand the situation of another
subject (e.g., empathy). Such behaviors have been demonstrated in rats, corvids, monkeys, elephants,
and human babies. Prosocial mechanisms have their evolutionary roots, and many social norms that are
based on them help the correct and adequate interaction between species (Wu & Hong, 2022).
pág. 8921
Consequently, it can be said that an animal is endowed with an intentional brain, because they have both
positive (e.g. cooperating) and negative (e.g. killing for fun) volitional actions or purposes. In fact, it
has been shown that, in primates, such as chimpanzees, qualities such as empathy, fairness and loyalty
in the group have been described (Chung et al., 2021; de Waal, 2021), all components that lead to good
morals. And if analyzed in a sociocultural way, in all regions of the world, cultures and societies have
an almost universal basic distinction between what is good and bad. In other words, social, cultural,
intellectual, political, and biological changes have shaped and, in some way, (e.g., creation of
civilizations or secular education) the human moral sense, which is rather moral reasoning, although
this does not occur in all human actions (e.g., speciesism or climate change). In fact, according to the
theory of moral realism, it demands an objective distinction between right and wrong, good and bad,
based on reasoned arguments explained in the work of D. Brink (Brink, 1989).
On the other hand, a study that worked with Pavlovian-type learning mechanisms, based on the role
played by the amygdala in decision-making, showed that this can be useful, namely, invoking inherent
prosocial tendencies (e.g., cognitive and emotional empathy and various forms of altruism), as well as
prioritizing reciprocity between individuals and fostering cooperation (Seymour & Dolan, 2008). This
shows that the results of science, in particular, behavioral sciences, can help to promote prosocial
behaviors. In other words, the results that science reveals can be used in favor of moral reasoning.
Finally, as has been proven sufficiently, animals are equipped to solve a myriad of problems, even facing
our own nature. But you can also be good or ethical, learning to be so. A clear example of this is the
person who has been eating meat all his life and suddenly discovers solid arguments that force him to
stop consuming animals. That is, ideologies can determine a person's moral orientation. It is for this
reason that we must equip our brain with skepticism and good judgment in order to make the best
decisions.
Factors that distort ethical judgment
In an impartial environment (e.g., hospitals or clinics), when making a decision that has ethical
implications, there are times when you must be as clean as possible, that is, there are no family ties or
friends involved. In the professional field, feelings and emotions can manipulate our decisions with what
seem like compelling reasons, but in reality, they are not. An example of this would be the doctor who
Consequently, it can be said that an animal is endowed with an intentional brain, because they have both
positive (e.g. cooperating) and negative (e.g. killing for fun) volitional actions or purposes. In fact, it
has been shown that, in primates, such as chimpanzees, qualities such as empathy, fairness and loyalty
in the group have been described (Chung et al., 2021; de Waal, 2021), all components that lead to good
morals. And if analyzed in a sociocultural way, in all regions of the world, cultures and societies have
an almost universal basic distinction between what is good and bad. In other words, social, cultural,
intellectual, political, and biological changes have shaped and, in some way, (e.g., creation of
civilizations or secular education) the human moral sense, which is rather moral reasoning, although
this does not occur in all human actions (e.g., speciesism or climate change). In fact, according to the
theory of moral realism, it demands an objective distinction between right and wrong, good and bad,
based on reasoned arguments explained in the work of D. Brink (Brink, 1989).
On the other hand, a study that worked with Pavlovian-type learning mechanisms, based on the role
played by the amygdala in decision-making, showed that this can be useful, namely, invoking inherent
prosocial tendencies (e.g., cognitive and emotional empathy and various forms of altruism), as well as
prioritizing reciprocity between individuals and fostering cooperation (Seymour & Dolan, 2008). This
shows that the results of science, in particular, behavioral sciences, can help to promote prosocial
behaviors. In other words, the results that science reveals can be used in favor of moral reasoning.
Finally, as has been proven sufficiently, animals are equipped to solve a myriad of problems, even facing
our own nature. But you can also be good or ethical, learning to be so. A clear example of this is the
person who has been eating meat all his life and suddenly discovers solid arguments that force him to
stop consuming animals. That is, ideologies can determine a person's moral orientation. It is for this
reason that we must equip our brain with skepticism and good judgment in order to make the best
decisions.
Factors that distort ethical judgment
In an impartial environment (e.g., hospitals or clinics), when making a decision that has ethical
implications, there are times when you must be as clean as possible, that is, there are no family ties or
friends involved. In the professional field, feelings and emotions can manipulate our decisions with what
seem like compelling reasons, but in reality, they are not. An example of this would be the doctor who
pág. 8922
falls in love with one of his patients, and, consequently, will give him more importance and thus
preference than other patients. This is what I mean by saying as clean as possible.
However, others claim that the availability heuristic, and other types of biases, can be useful in medical
practice, as long as factors such as experience or personality are taken into account (Whelehan et al.,
2020). In turn, Marewski & Gigerenzer (Marewski & Gigerenzer, 2012) argue that heuristics are more
useful in medicine than is commonly believed, as they save a lot of time and are accurate. However, in
this sense, I assert that, although heuristics can work in a certain percentage, a set of powerful algorithms
such as deep learning using convolutional neural networks have incredible accuracy in diagnosing
diseases, even when compared to medical experts in the area (Richens et al., 2020). Even the ability of
deep learning to analyze images can provide support for physicians decisions and improve the accuracy
and efficiency of various diagnostic and treatment processes (Chan, Hadjiiski, et al., 2020; Chan,
Samala, et al., 2020). This emphasizes the fact that the less biased we can obtain better results in most
cases. These experiments support, at least indirectly, the use of machines for the resolution of some
moral dilemmas.
The beliefs that the brain generates as a result of external and internal stimuli, experiences, and their
subsequent processing, can be as labile and fragile as they are solid and immovable. This happens
because the brain is susceptible to the information presented to it. The belief that the earth revolves
around the sun is verifiable, not only through experience, but also through mathematics. However, the
belief in fictitious entities such as ghosts is irrational, illogical and unfounded, despite the fact that our
brain contains experiences that affirm it. This mental phenomenon is known as Cognitive bias, which is
nothing more than an erroneous interpretation of reality, derived from failure or an absence of a
systematic, rational and verifiable process. Conclusions drawn from cognitive bias give rise to fallacies
and can lead to poor decisions.
However, there are studies in which, using Bayesian-type probabilistic models, the results show that
they contribute to an empirical explanation of how human inductive learning biases can influence
cultural transmission (Thompson & Griffiths, 2021). In other words, humans rely on the learning of their
groupmates rather than learning them by environmental feedback. So, it can be said that some biases are
less harmful than others.
falls in love with one of his patients, and, consequently, will give him more importance and thus
preference than other patients. This is what I mean by saying as clean as possible.
However, others claim that the availability heuristic, and other types of biases, can be useful in medical
practice, as long as factors such as experience or personality are taken into account (Whelehan et al.,
2020). In turn, Marewski & Gigerenzer (Marewski & Gigerenzer, 2012) argue that heuristics are more
useful in medicine than is commonly believed, as they save a lot of time and are accurate. However, in
this sense, I assert that, although heuristics can work in a certain percentage, a set of powerful algorithms
such as deep learning using convolutional neural networks have incredible accuracy in diagnosing
diseases, even when compared to medical experts in the area (Richens et al., 2020). Even the ability of
deep learning to analyze images can provide support for physicians decisions and improve the accuracy
and efficiency of various diagnostic and treatment processes (Chan, Hadjiiski, et al., 2020; Chan,
Samala, et al., 2020). This emphasizes the fact that the less biased we can obtain better results in most
cases. These experiments support, at least indirectly, the use of machines for the resolution of some
moral dilemmas.
The beliefs that the brain generates as a result of external and internal stimuli, experiences, and their
subsequent processing, can be as labile and fragile as they are solid and immovable. This happens
because the brain is susceptible to the information presented to it. The belief that the earth revolves
around the sun is verifiable, not only through experience, but also through mathematics. However, the
belief in fictitious entities such as ghosts is irrational, illogical and unfounded, despite the fact that our
brain contains experiences that affirm it. This mental phenomenon is known as Cognitive bias, which is
nothing more than an erroneous interpretation of reality, derived from failure or an absence of a
systematic, rational and verifiable process. Conclusions drawn from cognitive bias give rise to fallacies
and can lead to poor decisions.
However, there are studies in which, using Bayesian-type probabilistic models, the results show that
they contribute to an empirical explanation of how human inductive learning biases can influence
cultural transmission (Thompson & Griffiths, 2021). In other words, humans rely on the learning of their
groupmates rather than learning them by environmental feedback. So, it can be said that some biases are
less harmful than others.
pág. 8923
An important factor to consider in human decisions is noise. According to Kahneman, noise in a system
is an undesired variability in a judgment (Kahneman et al., 2021). Kahneman affirms that noise is
present in every decision we make, however; There are strategies that can help us reduce noise. While
bias results in a consistent but erroneous conclusion, noise confers inconsistency in our judgment, so
our conclusion is likely to be wrong as well.
Emotional state is one of the factors that influence the most when we make a decision. There are
emotions even when we think rationally, because cognitive-emotional interactions have a high degree
of connectivity, so they cannot be separated (Pessoa, 2008). It has been demonstrated, in both non-
human and human animals, that cognition-emotion links play an important role in the generation of
emotional states and these, in turn, influence cognition, inducing different types of biases (Paul et al.,
2005). In fact, one hypothesis that supports the high degree of connectivity between structures related
to cognition and emotion is the somatic marker hypothesis. Damasio (Damasio, 1996) argues that
somatic markers arise in bioregulatory processes, emotions and feelings and all this is related to the
body-state structure, therefore, the somatic marker hypothesis by definition does not accept that
decision-making is purely rational or cognitive.
DISCUSSION
The ethical balance based on the mathematical model DDM can be modulated through the context, and
the variables involved. It’s limitations are determined by the complexity and what the human consider
damage and innocence. In a professional environment like a hospital, perhaps, it can be used, but I must
highlight that this model does not replace human decisions, only can help to secure a response.
On the other hand, science and it’s product (knowledge) can help us clarify the resolution of an ethical
dilemma, because it can explain the neurobiological mechanisms that underlie events to do good, such
as compassion and empathy, or doing evil, such as anger and revenge, and because with their findings,
we can even become aware of how we should treat our world in general. In addition, the positions
defended in this work can be adopted or debated, since they are proposals argued with evidence but not
assertions made by simple authority.
Neuroscience satisfactorily explains how emotions, feelings, beliefs, and personal interests can generate
biases that shape decision-making and cloud proper ethical judgment, so that we cannot always rely on
An important factor to consider in human decisions is noise. According to Kahneman, noise in a system
is an undesired variability in a judgment (Kahneman et al., 2021). Kahneman affirms that noise is
present in every decision we make, however; There are strategies that can help us reduce noise. While
bias results in a consistent but erroneous conclusion, noise confers inconsistency in our judgment, so
our conclusion is likely to be wrong as well.
Emotional state is one of the factors that influence the most when we make a decision. There are
emotions even when we think rationally, because cognitive-emotional interactions have a high degree
of connectivity, so they cannot be separated (Pessoa, 2008). It has been demonstrated, in both non-
human and human animals, that cognition-emotion links play an important role in the generation of
emotional states and these, in turn, influence cognition, inducing different types of biases (Paul et al.,
2005). In fact, one hypothesis that supports the high degree of connectivity between structures related
to cognition and emotion is the somatic marker hypothesis. Damasio (Damasio, 1996) argues that
somatic markers arise in bioregulatory processes, emotions and feelings and all this is related to the
body-state structure, therefore, the somatic marker hypothesis by definition does not accept that
decision-making is purely rational or cognitive.
DISCUSSION
The ethical balance based on the mathematical model DDM can be modulated through the context, and
the variables involved. It’s limitations are determined by the complexity and what the human consider
damage and innocence. In a professional environment like a hospital, perhaps, it can be used, but I must
highlight that this model does not replace human decisions, only can help to secure a response.
On the other hand, science and it’s product (knowledge) can help us clarify the resolution of an ethical
dilemma, because it can explain the neurobiological mechanisms that underlie events to do good, such
as compassion and empathy, or doing evil, such as anger and revenge, and because with their findings,
we can even become aware of how we should treat our world in general. In addition, the positions
defended in this work can be adopted or debated, since they are proposals argued with evidence but not
assertions made by simple authority.
Neuroscience satisfactorily explains how emotions, feelings, beliefs, and personal interests can generate
biases that shape decision-making and cloud proper ethical judgment, so that we cannot always rely on
pág. 8924
simple common sense to resolve moral dilemmas. Common sense is essentially intuitive, so a dilemma
that requires more than one way of perceiving a problem is unsatisfactory. By way of digression, in a
situation where a quick decision must be made, we cannot use any formal methodology, namely slow
methodology; only to act by intuition quickly, that is; use Daniel Kahneman's system I.
According to Damasio's hypothesis (Bechara et al., 1999), even if a person tries to be completely
rational, there will always be a degree of emotion or feeling involved. But in practice, a person is able
to affirm that he can make a rational decision, by following a concatenated series of reasoning. Perhaps
the degree or percentage of emotion is what can determine the direction of a decision.
Neuroscientist Sam Harris has made a good case regarding the involvement of science around human
values. He came very close to the question by asserting that the increase in the well-being of living
beings is explained from a scientific perspective (Harris, 2011) and with this science can explain human
values. However, there are philosophers who are detractors of this position.
Brian Earp asserts that a crucial question is how science defines well-being. In addition, he affirms that
what Harris calls science is in reality philosophy (Earp, 2015). In my book My Morals Groping
(Anonymous, 2022), I tried to refute the crucial question to which this philosopher refers, about how
science can define well-being. Neuroscience not only defines well-being, also explains it and even
demonstrates it. Although it is neurophysiologically difficult to define well-being, it can be evaluated
qualitatively. Even so, there are neural traits that can define a state of well-being.
In this article, I used a kind of algorithm based on various factors, but always considering evidence that
science has helped to clarify. More in favor of beings who cannot express in words what they feel:
animals. Such is the case of how the brain of animals is more sensitive and cognitive than we used to
believe.
In this regard, a pilot study investigated the empathy that people perceive towards animals and
determined that a more empathetic attitude is linked to knowledge of skills, cognition and sensitivity
that people have about animals (Cornish et al., 2018). Heyes (Heyes, 2018) argues that research
conducted on human animals and infants suggests that the mechanism of empathy and emotional
contagion is built through social interaction, and that, in turn, it is as versatile as it is fragile. As a result
of this, he asserted that the more we know about the brain of animals, the more ethical our attitude
simple common sense to resolve moral dilemmas. Common sense is essentially intuitive, so a dilemma
that requires more than one way of perceiving a problem is unsatisfactory. By way of digression, in a
situation where a quick decision must be made, we cannot use any formal methodology, namely slow
methodology; only to act by intuition quickly, that is; use Daniel Kahneman's system I.
According to Damasio's hypothesis (Bechara et al., 1999), even if a person tries to be completely
rational, there will always be a degree of emotion or feeling involved. But in practice, a person is able
to affirm that he can make a rational decision, by following a concatenated series of reasoning. Perhaps
the degree or percentage of emotion is what can determine the direction of a decision.
Neuroscientist Sam Harris has made a good case regarding the involvement of science around human
values. He came very close to the question by asserting that the increase in the well-being of living
beings is explained from a scientific perspective (Harris, 2011) and with this science can explain human
values. However, there are philosophers who are detractors of this position.
Brian Earp asserts that a crucial question is how science defines well-being. In addition, he affirms that
what Harris calls science is in reality philosophy (Earp, 2015). In my book My Morals Groping
(Anonymous, 2022), I tried to refute the crucial question to which this philosopher refers, about how
science can define well-being. Neuroscience not only defines well-being, also explains it and even
demonstrates it. Although it is neurophysiologically difficult to define well-being, it can be evaluated
qualitatively. Even so, there are neural traits that can define a state of well-being.
In this article, I used a kind of algorithm based on various factors, but always considering evidence that
science has helped to clarify. More in favor of beings who cannot express in words what they feel:
animals. Such is the case of how the brain of animals is more sensitive and cognitive than we used to
believe.
In this regard, a pilot study investigated the empathy that people perceive towards animals and
determined that a more empathetic attitude is linked to knowledge of skills, cognition and sensitivity
that people have about animals (Cornish et al., 2018). Heyes (Heyes, 2018) argues that research
conducted on human animals and infants suggests that the mechanism of empathy and emotional
contagion is built through social interaction, and that, in turn, it is as versatile as it is fragile. As a result
of this, he asserted that the more we know about the brain of animals, the more ethical our attitude
pág. 8925
towards them is. In this way, it can be concluded that the advance of science can have the effect of a
substantial improvement in our respect for living beings and thus make us more ethical. Thus, science
does have to do with ethical issues.
In addition, logical reasoning does not have a patent. It is not typical of philosophy, nor of science,
although both use it to reach conclusions or theories. But, unlike philosophy, science uses a methodology
to reach a result. In this sense, and only in this, philosophy serves as a tool for science.
The use of methodologies to solve bioethical dilemmas is not new (De los Ríos-Uriarte, 2017). There
are several methods that serve as a basis for the deliberation of a problem in clinical medicine, such as
the Thomasma-Pellegrino method, or the Jonsen-Siegler four-topic method (Jonsen, 1982). Both are to
some extent useful and frequently used; At the same time, they consider values, principles, and medical
findings, but organizing information does not necessarily mean making a decision. At some point in
both methodologies, deliberation has to be deliberated, and the inclusion of values certainly introduces
noise to decision-making. Instead, the use of the mathematical drift diffusion model goes to the heart of
the matter: decision-making in the brain based on evidence and certain variables.
It should be added that normative ethics can guide us to be better people with our environment, as well
as with biotic and abiotic systems, and not only with other people. That is why there are rules and norms
that set limits on certain behaviors, that is, in the legal framework. Even so, there are actions that will
never be penalized despite the fact that their nature is objectively bad. In this regard, the philosopher
and ethicist Peter Singer stated that, if an individual has the moral obligation to do something, he should
do it, otherwise, it can be considered immoral (Singer, 1972). While it may cause some discomfort to
admit that one is immoral because one has not planted trees, donated to a charity, or adopted an animal
in need, one is responsible on a small but perceptible scale. Consequently, that someone can be
considered immoral.
From this I can also suggest that, if a person has physical, intellectual, economic, or academic abilities
above the statistical average, he has a moral obligation to do something known as effective altruism, and
not only with other humans, but with all living things. A privilege obtained by fortuitous events or
generated by the combination of personal success and luck, should be shared (not with everyone) and
towards them is. In this way, it can be concluded that the advance of science can have the effect of a
substantial improvement in our respect for living beings and thus make us more ethical. Thus, science
does have to do with ethical issues.
In addition, logical reasoning does not have a patent. It is not typical of philosophy, nor of science,
although both use it to reach conclusions or theories. But, unlike philosophy, science uses a methodology
to reach a result. In this sense, and only in this, philosophy serves as a tool for science.
The use of methodologies to solve bioethical dilemmas is not new (De los Ríos-Uriarte, 2017). There
are several methods that serve as a basis for the deliberation of a problem in clinical medicine, such as
the Thomasma-Pellegrino method, or the Jonsen-Siegler four-topic method (Jonsen, 1982). Both are to
some extent useful and frequently used; At the same time, they consider values, principles, and medical
findings, but organizing information does not necessarily mean making a decision. At some point in
both methodologies, deliberation has to be deliberated, and the inclusion of values certainly introduces
noise to decision-making. Instead, the use of the mathematical drift diffusion model goes to the heart of
the matter: decision-making in the brain based on evidence and certain variables.
It should be added that normative ethics can guide us to be better people with our environment, as well
as with biotic and abiotic systems, and not only with other people. That is why there are rules and norms
that set limits on certain behaviors, that is, in the legal framework. Even so, there are actions that will
never be penalized despite the fact that their nature is objectively bad. In this regard, the philosopher
and ethicist Peter Singer stated that, if an individual has the moral obligation to do something, he should
do it, otherwise, it can be considered immoral (Singer, 1972). While it may cause some discomfort to
admit that one is immoral because one has not planted trees, donated to a charity, or adopted an animal
in need, one is responsible on a small but perceptible scale. Consequently, that someone can be
considered immoral.
From this I can also suggest that, if a person has physical, intellectual, economic, or academic abilities
above the statistical average, he has a moral obligation to do something known as effective altruism, and
not only with other humans, but with all living things. A privilege obtained by fortuitous events or
generated by the combination of personal success and luck, should be shared (not with everyone) and
pág. 8926
not limited (based on one's own well-being). This is without defocusing personal interests either, as long
as they do not border on banality.
On the other hand, the use of this type of algorithms and mathematical models such as the drift diffusion
model has good possibilities to help us solve some moral dilemmas, however, the technical difficulty
will result in the assignment of values to the parameters, as well as their calibration according to the
context. With the use of new computer technologies and more advanced algorithms such as Deep
Learning and its neural networks that self-update to learn on their own, the idea that they can help
humans solve problems that were intended only for the human brain is not as far-fetched as it seems.
Appendix A. Technical Simulation of the Ethical Balance
To demonstrate the internal coherence and potential applicability of the ethical balance based on Drift
Diffusion Model (DDM), a basic simulation was conducted using three moral dilemmas constructed
with hypothetical yet plausible values. Each ethical dilemma was structured around the five key
ethical dimensions proposed throughout the article: sentience (S), intentionality (I), innocence (N),
irreversibility (R), and damage (D).
Each actor within the dilemmas was assigned values ranging from 0 to 1 (or 0 or 1 for binary inputs),
and the valence of each action was computed using the ethical balance equation:
𝑣 = 𝛼𝑆 + 𝛽𝐼 + 𝛾𝑁 − 𝛿𝑅 − 𝜖𝐷
The drift rate (μ) for the simulated decision process was calculated as the difference between the
valences of Option A and Option B:
μ = VA - VB
The drift rate serves as an indicator of the expected direction and confidence of the decision: the larger
its magnitude, the more likely and rapid the selection of the favored option.
Three dilemmas were tested:
1. Innocent Dog vs. Harmful Human
2. Robot Helper vs. Sleeping Human
3. Child vs. Elderly Criminal
The simulations revealed consistent and interpretable results: scenarios with greater ethical asymmetry
produced high drift rates, favoring swift decisions; ambiguous scenarios produced drift rates near zero,
not limited (based on one's own well-being). This is without defocusing personal interests either, as long
as they do not border on banality.
On the other hand, the use of this type of algorithms and mathematical models such as the drift diffusion
model has good possibilities to help us solve some moral dilemmas, however, the technical difficulty
will result in the assignment of values to the parameters, as well as their calibration according to the
context. With the use of new computer technologies and more advanced algorithms such as Deep
Learning and its neural networks that self-update to learn on their own, the idea that they can help
humans solve problems that were intended only for the human brain is not as far-fetched as it seems.
Appendix A. Technical Simulation of the Ethical Balance
To demonstrate the internal coherence and potential applicability of the ethical balance based on Drift
Diffusion Model (DDM), a basic simulation was conducted using three moral dilemmas constructed
with hypothetical yet plausible values. Each ethical dilemma was structured around the five key
ethical dimensions proposed throughout the article: sentience (S), intentionality (I), innocence (N),
irreversibility (R), and damage (D).
Each actor within the dilemmas was assigned values ranging from 0 to 1 (or 0 or 1 for binary inputs),
and the valence of each action was computed using the ethical balance equation:
𝑣 = 𝛼𝑆 + 𝛽𝐼 + 𝛾𝑁 − 𝛿𝑅 − 𝜖𝐷
The drift rate (μ) for the simulated decision process was calculated as the difference between the
valences of Option A and Option B:
μ = VA - VB
The drift rate serves as an indicator of the expected direction and confidence of the decision: the larger
its magnitude, the more likely and rapid the selection of the favored option.
Three dilemmas were tested:
1. Innocent Dog vs. Harmful Human
2. Robot Helper vs. Sleeping Human
3. Child vs. Elderly Criminal
The simulations revealed consistent and interpretable results: scenarios with greater ethical asymmetry
produced high drift rates, favoring swift decisions; ambiguous scenarios produced drift rates near zero,
pág. 8927
indicating internal conflict or indecision. The figure below summarizes the drift rate predictions across
the simulated cases.
Figure A1. Predicted drift rates (μ) across three moral dilemmas using the Ethical Balance. Green bars
indicate a positive drift rate, meaning Option A is favored by the model's ethical balance. Red bars
indicate a negative drift rate, favoring Option B. The length of each bar represents the strength of the
decision signal: higher absolute values reflect greater ethical asymmetry and predict faster, more
confident decisions, while values near zero indicate moral ambivalence and likely hesitation.
CONCLUSION
This article has proposed a novel framework for addressing moral dilemmas by integrating philosophical
ethics with computational modeling. Specifically, it introduces an ethical decision-making algorithm
grounded in the Drift Diffusion Model (DDM), allowing moral judgments to be simulated as processes
of evidence accumulation shaped by quantifiable ethical dimensions—sentience, intentionality,
innocence, damage, and irreversibility. Rather than resolving normative debates, the model aims to
illuminate how moral cognition might operate in real-time, particularly under uncertainty or emotional
interference. The core contribution lies not in claiming algorithmic supremacy over human ethics, but
in formalizing how moral agents might weigh competing values when forced to act. By connecting
moral intuition to measurable cognitive dynamics, this framework offers a potential bridge between
neuroscience, psychology, and normative philosophy. Moreover, it opens avenues for empirical
validation through behavioral experiments and computational simulations. Ultimately, this approach
suggests that science can help us better understand—not dictate—our ethical intuitions. It offers a
generative model of moral conflict, capable of clarifying hidden assumptions, revealing cognitive biases,
indicating internal conflict or indecision. The figure below summarizes the drift rate predictions across
the simulated cases.
Figure A1. Predicted drift rates (μ) across three moral dilemmas using the Ethical Balance. Green bars
indicate a positive drift rate, meaning Option A is favored by the model's ethical balance. Red bars
indicate a negative drift rate, favoring Option B. The length of each bar represents the strength of the
decision signal: higher absolute values reflect greater ethical asymmetry and predict faster, more
confident decisions, while values near zero indicate moral ambivalence and likely hesitation.
CONCLUSION
This article has proposed a novel framework for addressing moral dilemmas by integrating philosophical
ethics with computational modeling. Specifically, it introduces an ethical decision-making algorithm
grounded in the Drift Diffusion Model (DDM), allowing moral judgments to be simulated as processes
of evidence accumulation shaped by quantifiable ethical dimensions—sentience, intentionality,
innocence, damage, and irreversibility. Rather than resolving normative debates, the model aims to
illuminate how moral cognition might operate in real-time, particularly under uncertainty or emotional
interference. The core contribution lies not in claiming algorithmic supremacy over human ethics, but
in formalizing how moral agents might weigh competing values when forced to act. By connecting
moral intuition to measurable cognitive dynamics, this framework offers a potential bridge between
neuroscience, psychology, and normative philosophy. Moreover, it opens avenues for empirical
validation through behavioral experiments and computational simulations. Ultimately, this approach
suggests that science can help us better understand—not dictate—our ethical intuitions. It offers a
generative model of moral conflict, capable of clarifying hidden assumptions, revealing cognitive biases,
pág. 8928
and informing the design of ethically sensitive artificial systems. As such, it contributes not only to the
modeling of moral cognition, but to the broader interdisciplinary dialogue on how humans—and
machines—ought to decide.
Acknowledgments
The author declares no acknowledgments.
REFERENCES
Anderson, S. W., Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1999). Impairment of social
and moral behavior related to early damage in human prefrontal cortex. Nature Neuroscience,
2(11), 1032-1037. https://doi.org/10.1038/14833
Brink, D. O. (1989). Moral Realism and the Foundations of Ethics. Cambridge University Press.
https://doi.org/10.1017/CBO9780511624612
Bunge, M. (2000). La investigación científica: Su estrategia y su filosofía. Siglo XXI.
Bunge, M. (2010). Matter and Mind: A Philosophical Inquiry. Springer Science & Business Media.
Bunge, M. (2013). Pseudociencia e ideología. Laetoli.
Bunge, M. (2018). La ciencia: Su método y su filosofía. Laetoli.
Chan, H.-P., Hadjiiski, L. M., & Samala, R. K. (2020). Computer-Aided Diagnosis in the Era of Deep
Learning. Medical physics, 47(5), e218-e227. https://doi.org/10.1002/mp.13764
Chan, H.-P., Samala, R. K., Hadjiiski, L. M., & Zhou, C. (2020). Deep Learning in Medical Image
Analysis. Advances in experimental medicine and biology, 1213, 3-21.
https://doi.org/10.1007/978-3-030-33128-3_1
Chung, H.-K., Alós-Ferrer, C., & Tobler, P. N. (2021). Conditional valuation for combinations of goods
in primates. Philosophical Transactions of the Royal Society B: Biological Sciences, 376(1819),
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Cornish, A., Wilson, B., Raubenheimer, D., & McGreevy, P. (2018). Demographics Regarding Belief
in Non-Human Animal Sentience and Emotional Empathy with Animals: A Pilot Study among
Attendees of an Animal Welfare Symposium. Animals : an Open Access Journal from MDPI,
8(10), 174. https://doi.org/10.3390/ani8100174
and informing the design of ethically sensitive artificial systems. As such, it contributes not only to the
modeling of moral cognition, but to the broader interdisciplinary dialogue on how humans—and
machines—ought to decide.
Acknowledgments
The author declares no acknowledgments.
REFERENCES
Anderson, S. W., Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1999). Impairment of social
and moral behavior related to early damage in human prefrontal cortex. Nature Neuroscience,
2(11), 1032-1037. https://doi.org/10.1038/14833
Brink, D. O. (1989). Moral Realism and the Foundations of Ethics. Cambridge University Press.
https://doi.org/10.1017/CBO9780511624612
Bunge, M. (2000). La investigación científica: Su estrategia y su filosofía. Siglo XXI.
Bunge, M. (2010). Matter and Mind: A Philosophical Inquiry. Springer Science & Business Media.
Bunge, M. (2013). Pseudociencia e ideología. Laetoli.
Bunge, M. (2018). La ciencia: Su método y su filosofía. Laetoli.
Chan, H.-P., Hadjiiski, L. M., & Samala, R. K. (2020). Computer-Aided Diagnosis in the Era of Deep
Learning. Medical physics, 47(5), e218-e227. https://doi.org/10.1002/mp.13764
Chan, H.-P., Samala, R. K., Hadjiiski, L. M., & Zhou, C. (2020). Deep Learning in Medical Image
Analysis. Advances in experimental medicine and biology, 1213, 3-21.
https://doi.org/10.1007/978-3-030-33128-3_1
Chung, H.-K., Alós-Ferrer, C., & Tobler, P. N. (2021). Conditional valuation for combinations of goods
in primates. Philosophical Transactions of the Royal Society B: Biological Sciences, 376(1819),
20190669. https://doi.org/10.1098/rstb.2019.0669
Cornish, A., Wilson, B., Raubenheimer, D., & McGreevy, P. (2018). Demographics Regarding Belief
in Non-Human Animal Sentience and Emotional Empathy with Animals: A Pilot Study among
Attendees of an Animal Welfare Symposium. Animals : an Open Access Journal from MDPI,
8(10), 174. https://doi.org/10.3390/ani8100174
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Dawkins, R. (1989). The Selfish Gene. Oxford University Press.
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