Free Will and Consciousness as functional phenomena

The ongoing debate about free will and consciousness within a deterministic framework raises profound questions: In a world governed by deterministic laws, do humans have genuine choice? Can any entity embody these attributes? This essay suggests that these questions are inherently flawed. They originate from a futile attempt to reconcile irreconcilable domains — our subjective experiences, and objective, deterministic reality. Without this reconciliation, strictly speaking, we must acknowledge that all phenomena are figments of our imagination, hindering our ability to navigate the “real world.” Yet, this reconciliation, while necessary for our daily functioning, undermines our ability to systematically analyze phenomena, especially those involving nondeterministic concepts like free will and consciousness.

The essay proposes a novel approach, shifting the focus away from the precise nature of free will and consciousness or the hypothetical attainment of these qualities by machines. Instead, it reframes the philosophical question: rather than focusing on the absolute definitions of free will and consciousness, this essay emphasizes their nature as perceived phenomena. Instead of questioning whether entities possess these qualities, it explores the traits necessary for us to perceive them as such.

Consider the analogy of an automobile. We use the term “car” without delving into its true philosophical essence. Even if the manufacturer replaces the engine with leprechauns, as long as it retains its transporting function, we can still refer to it as a car. Similarly, questioning whether entities like computers possess consciousness or free will can be a distraction. Instead, we should focus on understanding the purpose served by these concepts. Once we grasp this, we can shift our inquiry from “When might a computer acquire consciousness or free will?” to the more meaningful question: “At what point would attributing free will and consciousness to a computer offer a more useful description of its capabilities?

Defining “Free Will” and “Consciousness”

To grasp the phenomena of “free will” and “consciousness,” it is crucial to understand the concept of phenomena and its opposing concept, “noumenon.” Phenomena refer to our subjective experiences, including everything we perceive through our senses and thoughts. Conversely, noumena represents the “thing-in-itself,” the objective reality of the world independent of our perception. This distinction allows us to understand the difference between our subjective experiences and the underlying reality. To illustrate this contrast between phenomenon and noumenon, let’s revisit the previous example of a car. As a perceived phenomenon, a car is the tangible object used for transportation, racing, and as a status symbol. However, as a noumenon, the car represents the “thing-in-itself,” embodying the sum of its infinite physical interactions, which by definition, due to our finite brain capacity, elude full conceptualization.

The Function of Phenomena

The inherent limited capacity of our brains to perceive creates an infinite disparity between the finite realm of phenomena and the infinite universe of noumena. This gap between the two realms offers several insights:

1. Functional Necessity: Due to our brain’s limited capacity to store phenomena, each phenomenon must serve a discernible function to stand out and to be picked among countless other unperceived noumena.
2. Predictability and Reliability: For a function to be effective, it must be consistent and reliable. Therefore, the function must be embedded within an entity that serves a secondary function - to be the identifier and a carrier of integrity for that primary function.
3. One-to-One Correspondence: The relationship between the primary function and its identifier (the secondary function) is a one-to-one correspondence. This means that a specific entity will consistently yield the same function, and if one aspect is compromised, so is the other.

To illustrate these insights, let’s consider two examples: computer code, and a more tangible tool like a hammer.

• Computer Code: In programming, due to limited resources, every piece of code must serve a specific function. It must be reliable (perform consistently) and possess a ‘function name’ for identification and utilization.
• Hammer: A hammer’s primary function is obvious — hammering nails. Yet, most of the time, it remains idle. During this period, its secondary function is crucial: to be consistently recognizable as the tool capable of fulfilling its primary purpose when needed.

Objective of the Essay

At this point, equipped with new insights, I would like to rearticulate the objective of this essay.
The essay aims to explore free will and consciousness as inherent phenomena in our experience. It argues that these concepts are often mistakenly viewed as objective realities, leading to debates about determinism. The essay proposes that by acknowledging their subjective nature, we can move beyond attempts to reconcile them with determinism and instead focus on understanding their purpose within the functional framework of phenomena.

Defining Free Will and Consciousness: A Systematic Approach

Having established a clear grasp of phenomena, let’s turn our attention back to the task of defining free will and consciousness as a functional phenomena. To achieve this, we will employ the genus-differentia method. This method involves identifying the broader category (genus) to which a term belongs and the specific characteristic (differentia) that distinguishes it from other members of that category. For instance, a car (genus) can be defined as a vehicle with the unique feature of having four wheels (differentia).

Free Will and Consciousness as Phenomena

Since free will and consciousness are phenomena, they can be viewed as members of a larger set — the phenomena set. Our task now is to identify the differentia — the specific characteristics that set free will and consciousness apart from other phenomena within this set.

Exploring Phenomena through Input-Outcome Relationships

Phenomena can be characterized by their primary function (the reason we conceptualize them) and secondary function (to be the identifier and a carrier for the primary function). Functions are defined by their inputs and outcomes. By examining all possible combinations of inputs and outcomes, we can describe the full phenomena set. This approach can be described by the entity-relationship model:

• One-to-One: A specific input consistently produces a single outcome (e.g., oranges always yield orange juice).
• One-to-Many: A single input can result in multiple outcomes (e.g., oranges may sometimes produce apples or mango juice).
• Many-to-One: Diverse inputs can lead to the same outcome (e.g., both oranges and apples can produce mango juice).
• Many-to-Many: Multiple input types can produce diverse outcomes, and the same outcome can originate from different inputs (e.g., various fruits can yield different fruit juices).

Introducing the “Black Box” analogy

Since free will and consciousness are concepts associated with entities (e.g., humans or computers), the term “black box” provides a more tangible alternative to the abstract concept of a function identifier (the secondary function). In engineering, a black box represents a system whose internal mechanism is irrelevant and it is fully defined by the input it consumes and the outcome it generates ( just as functions). The following section analyzes the four subsets of the entity-relationship model and establishes their connections to free will, consciousness, and determinism.

Subset 1: One-to-One Relationships

The one-to-one relationship phenomenon, where a black box produces only one type of outcome for each specific type of input, is considered deterministic. For example, a dedicated juice machine will always produce orange juice from oranges and banana juice from bananas . Any other machine, when provided with oranges or bananas, would produce a different, non-fruit-juice outcome. This predictability is maintained by the black box structure.

The “one-to-one” phenomenon, where a single input yields a consistent output, aligns with determinism. This framework lets us attribute a black box’s behavior to its structure (secondary function). The structure’s function is to provide a unique identifier for its behavior. Importantly, the internal workings of this structure are irrelevant; it functions solely as an identifier and carrier of integrity. As long as the structure remains stable, determinism assures us of behavioral persistence, enabling reliable predictions.

For example, consider a car that consistently consumes 10L of gasoline per 100km. Labeling this behavior as ‘deterministic’ signifies that it has a consistent relationship with the car’s structure. Therefore, even without understanding the internals, as long as the structure is unchanged, the fuel consumption pattern will continue, allowing us to act accordingly and plan fueling stops confidently.

Subset 1: One-to-One — Gist

In summary, systems with a ‘one-to-one’ input-output relationship aligns with the concept of determinism. The term ‘determinism’ signifies that a system’s behavior has a consistent relationship with its underlying structure, allowing for reliable predictions and subsequent actions.

Subset 2: One-to-Many Relationships

Within subset 2 of one-to-many relationships (where one input yields multiple outputs), a further distinction can be made:

1. Same Instance: The exact same instance produces different outcomes each time. For example, a car might have varying gasoline consumption on multiple trips between the same two points.
2. Different Instances: Here, different instances of the same kind (e.g., different cars of the same model) produce distinct outcomes, yet each remains predictable. For instance, each car might have its own unique fuel efficiency but each efficiency remains consistent.

Subset 2: One-to-Many - Same Instance

The one-to-many relationship phenomenon, where the same black box instance will produce a different type of outcome each time for the same type of input, is considered crazy or unpredictable. For example, the same exact juice machine will sometimes produce orange juice and sometimes banana juice from the same type of input — oranges. This unpredictability is maintained and attributed to the black box’s compromised, yet consistent, structure.

This subset explores phenomena where the same black box instance, presented with the exact same input, consistently produces different outcomes. This inconsistency, where a fixed input and structure yield variable outcomes, contradicts determinism. Therefore, a more fitting term is needed to describe this unpredictable behavior.

At first glance, this non-deterministic behavior might resemble the philosophical concept of “free will” — a magical spark that allows the system to defy determinism and choose autonomically the outcomes. However, in reality, we categorize this behavior (primary function) as ‘craziness’ or ‘instability’ (secondary function) and consider it the result of some defect in its structure, even if the defect remains unseen. In this case, a structural defect “carries” the unpredictable behavior. Acknowledging this pattern allows us to adjust our actions accordingly.

Imagine a light switch — sometimes it turns on the light (outcome) with each flip (input), while other times it inexplicably leaves it dark. We wouldn’t say the light switch has free will, we would acknowledge its dysfunction and describe it as “acting crazy” or “broken.” As the structural flaw persists over time, we would keep a flashlight on hand to compensate for its unreliability.

Another example: Consider three people who react differently to the same anger-inducing situation. The first overreacts (within acceptable norms), the second underreacts (also within norms), while the third randomly overreacts or under reacts but with the same intensity as the first two. Even though all reactions fall within the norm, the third person’s unpredictable behavior wouldn’t be attributed to ‘free will.’ Instead, it would be perceived as a sign of consistent instability, prompting us to plan our interactions with that person accordingly.

Reconciliation of Random Behavior with Determinism
This subset explores phenomena where the same input to a black box can produce different outputs. While this appears random from our perspective, it’s rooted in some underlying deterministic reality.

Nonlinear systems provide a possible explanation for this behavior. These are systems where even small, imperceptible changes in input can have surprisingly large effects on the outcome. Meaning, since these changes are too small to detect, the inputs are perceived as identical. However, this tiny difference triggers a chain of deterministic events that leads to drastically different outcomes.

The famous “Butterfly Effect” is a perfect example. In a nonlinear deterministic system, an imperceptible butterfly flapping its wings in one location can set off a tornado elsewhere. This highlights how a deterministic system, even with tiny variations in input, can produce outcomes that appear completely random and unpredictable.

Subset 2: One-to-Many — Same Instance — Gist
This type of non-deterministic phenomenon, where the same black box consistently produces different outcomes for the same input, exemplifies a deterministic nonlinear phenomenon. This seemingly inconsistent behavior leads us to categorize it with labels like “crazy” or “unstable.” These labels, however, implicitly acknowledge an underlying malfunction within the system’s structure that causes the unpredictable behavior. Since this malfunction is consistent over time, the observed pattern of unpredictable behavior will persist. This understanding, reflected in our choice of labels, allows us to adjust our actions accordingly.

Subset 2: One-to-Many: Different Instances

The one-to-many relationships for different instances phenomenon describes how different instances of the same type (e.g., juice machines) can consistently produce distinct outcomes from identical inputs (e.g., oranges). One machine might always produce pulpier juice, while the other creates a smoother texture. Each instance’s unique and predictable behavior is attributed to its own form of ‘free will’ .

This subset explores phenomena where different instances of the same type consistently produce distinct outcomes for the same input while each instance remains internally consistent and predictable. This behavior contradicts determinism, as identical instances should yield identical results. Furthermore, the consistent patterns within each instance mean the terms “crazy” or “unstable” are inaccurate. We need a new term to encapsulate the predictable, yet distinct, behavior of each instance.

We use the term “free will” to describe seemingly autonomous behavior that can’t be attributed to a system’s shared structure. In everyday use, “free will” doesn’t carry deep philosophical meaning. Rather, it serves as a “placeholder”, attributed to his own form of ‘free will’ , helping us identify the unique and consistent behavior of each specific instance ( secondary function ). This allows us to distinguish it from other identical instances and adjust our actions accordingly.

Gift selection highlights our everyday use of the concept of ‘free will.’ We base choices on a recipient’s perceived preferences, assuming those preferences stem from his consistent will. This expectation of consistency is prerequisite to any gift choice. In contrast, inconsistent reactions to similar gifts wouldn’t be attributed to the philosophical concept of ‘free will’ (as defined in Subset 2: The Same Instance Subset). Instead, we’d likely perceive such behavior as ‘crazy’ or ‘unstable’.

The concept of personality further illustrates that ‘free will’ implies consistency in choices rather than randomness. Each unique personality reflects an individual’s consistent behavior, determined by their aggregate free choices. Again, this everyday notion of ‘free will’ is associated with consistent behavior, and not with a philosophical concept of free will.

The notion of “free will” extends beyond humans, offering a way to conceptualize unique machine behaviors that can’t be solely attributed to their structure. For example, consider ten identical cars designed to consume 10 liters of gasoline per 100 kilometers. If one consistently consumes 12 liters over the same distance, we need a term to encapsulate this specific, consistent behavior in order to identify and respond to it. In practice, we often use ‘free will’ to describe this type of consistent pattern. This usage is evident in common phrases like ‘the car likes to drink gasoline,’ which suggests a choice-making ability similar to a human’s, implying the concept of free will.

Reconciliation of Free Will with Determinism

Similar to Subset 2 — ‘same instance’, Subset 2 — ‘different instances’ represents a deterministic phenomenon. This deterministic nature suggests seemingly “free will” behavior can emerge from deterministic systems. Error propagation illustrates this concept.

Error propagation occurs when small, imperceptible errors in a system’s components accumulate over time, leading to significantly different overall outcomes. Consider a cookie factory with molds designed to produce square cookies with 10 cm sides. A slight error in manufacturing means individual sides can be up to 1 cm longer or shorter. While molds appear identical, this minor error can have extreme consequences: some molds might produce cookies with an area of 81 sq. cm, while others produce cookies with 121 sq. cm. As a result, the same amount of dough will yield significantly different numbers of cookies depending on the mold used, demonstrating how instances that appear identical yet exhibit significantly different behaviors.

Subset 2: One-to-Many — Different Instances — Gist

This non deterministic phenomenon, where seemingly identical instances produce distinct outcomes for the same input while each remains predictable, aligns with the concept of deterministic error propagation. As the structure of all instances appears identical, we attribute their unique behaviors to their form of ‘will’. This label enables us to effectively communicate the consistent, yet unpredictable nature of individual instances, facilitating understanding and informed responses.

Subset 3: Many-to-One Relationships

Subset 3: Many-to-One Relationships Subtest 3, where a black box consistently produces the same type of outcome for different inputs, can be subdivided into two distinct categories:

1. ‘All Instances’: This subset describes instances of the black box yielding the same outcome type for various inputs. A universal human behavior, converting different food types into calories, exemplifies this concept.
2. ‘Groups of Instances’: Here, groups of instances consistently produce distinct outcomes from the same inputs. For example, Group A might yield outcome A from inputs A, B, and C, while Group B produces outcome B from the same inputs. This demonstrates the unique, consistent behavior within each group.

Subset 3: Many-to-One — ‘All Instances’

The many-to-one relationships for all instances describes a non-deterministic phenomenon where varied inputs fed into all the instances of the same type of black box consistently yield identical outcomes. For example, all the juice machines of the same type produce mango juice, regardless of the fruit fed into them (banana, orange, etc.). In this case, the ability to yield the same outcome for different inputs is attributed to the ‘consciousness’ of the black box, and the particular outcome is attributed to its ‘nature’.

This subset explores phenomena where all identical instances of the same type generate identical outcomes from diverse inputs. This defies determinism, as the black box structure alone doesn’t fully account for the outcome’s uniformity. We need a new term to encapsulate this particular behavior.

The term we use is “consciousness” (or awareness), while the specific outcome type is attributed to the black box’s intrinsic nature (secondary function), shared across all instances. Furthermore, the level of consciousness is proportional to the diversity of inputs successfully processed. Greater adaptability to unforeseen inputs while maintaining consistent outcomes suggests a higher level of consciousness.

Essentially, “Consciousness” represents the degree to which its nature’s behavior is effectively executed, reflecting the system’s ability to adapt to diverse inputs.

Consider an adult human crossing a street to reach their lunch destination. The human processes a multitude of unforeseen inputs: vehicle speeds and directions, traffic light states, and obstacles. Despite this diverse input, the human consistently achieves the same outcome — reaching the other side. This ability to synthesize multiple inputs into a single, successful outcome reflects a high degree of consciousness, aligning perfectly with the many-to-one relationship of Subset 3.

In contrast, consider a bouncing ball attempting to cross the same street. Here, the number of outcomes is directly proportional to the inputs. Each unique input (vehicle direction, obstacle placement, etc.) generates a distinct outcome. This one-to-one ( and not many-to-one ) behavior indicates the absence of consciousness.

Labradors’ predictable behavior also exemplifies a many-to-one relationship. Despite diverse stimuli, such as a tail pull or a tempting tennis ball, they consistently maintain a calm demeanor. This consistency allows us to effectively communicate how a Labrador behaves by referring to them as very conscious animals with a calm nature.

The most intriguing aspect of consciousness lies in the processes of ‘conceptualization’ and ‘perception’. ‘Conceptualization’, as defined earlier in this essay, refers to the process of condensing infinite sensory data inputs into a single outcome — a concept. For instance, two forests, despite countless differences in details (tree types, leaves, terrain), are both recognized as the same phenomenon: a ‘forest’. This ability to transform diverse inputs into a unified outcome demonstrates a core aspect of consciousness (many-to-one relationships), as such, understanding conceptualization is crucial for comprehending the nature of consciousness itself.

Reconciliation of Consciousness with Determinism

While consciousness manifests as a ‘many-to-one’ phenomenon, it arises from deterministic ‘one-to-one’ noumenal interactions. Here, distinct inputs yield distinct outcomes — yet we phenomenally perceive identical outcomes due to conceptualization. This transition occurs when outcomes share functional components. During conceptualization, we focus solely on this shared functionality, disregarding the rest. Consequently, diverse outcomes appear identical because only the shared function is perceived; differing aspects become ‘by-products’ or ‘irrelevant’. Consider a gold miner extracting gold from various sources: despite originating differently, the final product appears the same because by-products (e.g., gravel types and quantities) are discarded.

Subset 3 ‘All Instances’ Gist

This non deterministic phenomenon, where all instances of the same black box type consistently yield the same predictable outcome despite diverse inputs, reflects a deterministic noumenon where outcomes are as diverse as the inputs themselves. However, the diverse outcomes share both similar and distinct components. During conceptualization, we disregard varying aspects as byproducts, focusing solely on the shared elements. We label this ability to consistently produce a specific outcome from varied inputs as ‘consciousness’, while the outcome type reflects the system’s inherent nature. This framework allows us to effectively communicate and understand consistent black box behavior.

Subset 3: Many-to-One — ‘Groups of Instances’

This subset describes a non-deterministic phenomenon where conscious instances of the same type form groups. Each group receives a variety of inputs and consistently produces a single type of outcome. For example, identical juice machines are divided into two groups: one group consistently producing coconut juice from different fruits, and the other group consistently producing lemonade. This ability to process varied inputs into a single outcome is attributed to their ‘consciousness’, while the distinct behavior of each group is attributed to their ‘culture’.

This subset explores a non-deterministic phenomenon where instances labeled as “consciousness,” all of the same type, form groups. Within each group, individuals encounter a variety of inputs. However, even though these groups share the same range of possible inputs, different groups consistently produce distinct outcomes.

While “consciousness” refers to the ability to produce consistent outcomes from diverse inputs, the shared nature of these instances doesn’t explain inter-group differences. To effectively communicate and understand the unique behaviors of each group, we need a specific term. The concept of a “set of values,” often manifesting in aspects like culture and religion, serves this purpose. In this case, the specific culture or religion acts as the ‘carrier’ of the distinct behavior. We can think of a ‘culture’ as an ‘instruction manual’ consisting of a specific set of values, enabling groups to act consistently.

Importantly, in the special case where only one group exists, we stop using the term “group”, and the “Groups of Instances” subset becomes functionally equivalent to the “All Instances” subset. Consequently, behavior that appears driven by values when observing multiple groups, when analyzing only a single group, would seem to stem from a shared nature.

We can illustrate how values and culture align with the “many-to-one” relationship for groups of instances by examining how individuals across religions and cultures overcome diverse obstacles (harsh weather, distance, expense) to uphold their group’s shared values. A devoted football fan braving such challenges to support their team exemplifies this concept. Their consistent behavior, driven by a core value (team loyalty), reflects a high degree of consciousness (creatively overcome difficulties). Similarly, a religious pilgrim undertaking a challenging journey to a sacred site demonstrates this principle. Once we understand a group’s core values, we can better predict its behavior and act accordingly.

To grasp the use of the term “values,” it’s important to understand how perspective shapes our perception of behavior. As impartial observers studying groups (like anthropologists), we tend to explain their actions through “values.” This trend is evident in the notable increase in the use of the phrase “social value” starting around the 1890s and continuing throughout the 20th century, with particularly significant growth from the 1960s onward, according to the Google Books Ngram Viewer (“Social value”). This aligns with the rise of various social sciences and the increasing discussion of social issues and norms during this period. However, as active members of a group, we often cease attributing our own similar behaviors to “values,” instead justifying them as simply “natural.” This tendency becomes even more apparent when we dislike a behavior exhibited by another group, often dismissing it as inherently unchangeable and rooted in their nature. Despite our shared biological foundation, we might say: “They can’t change; it’s in their blood”.

Our fluid interchange between the terms “nature” and “culture/religion” underscores a key premise of this essay: defining “free will” and “consciousness” as “what they are” is inherently futile. Instead, the focus should be on the function of these terms. The ease with which ‘culture’ — a product of our learned social environment — is substituted with its complete opposite, ‘nature’ — an intrinsic biological attribute — reveals that these concepts are merely tools to label consistent behaviors, not “true explanations” of their origins. Recognizing this highlights the flawed quest for absolute definitions of consciousness and free will.

Reconciliation of Consciousness with Determinism

Since Subset 3 ‘groups of instances’ is aligned with the Consciousness phenomenon, it can be reconciled with determinism as Subset 3 ‘all instances’.

Subset 3 ‘Groups of Instances’ Gist

This non deterministic phenomenon involves conscious instances of the same type forming groups, each consistently producing a single outcome type from the same diverse inputs. While similar to Subset 3 ‘All Instances,’ the key difference lies in the distinct outcomes produced by different groups, even when presented with these same diverse inputs. These distinct group-specific behaviors are “carried” by a unique set of values embedded within their respective cultures. Recognizing and acknowledging these cultural values enables us to interact with each group effectively and productively.

Subset 4: Many-to-Many

Since Subset 4: Many-to-many encompasses special cases of the one-to-one, one-to-many, and many-to-one relationships we’ve already discussed, it does not require further inquiry.

Conclusion

This essay reframes the traditional debate about free will and consciousness, highlighting them as perceived phenomena rather than absolute realities. Instead of seeking definitions, this approach emphasizes understanding the purpose they serve. By analyzing input-output relationships through the “black box” analogy, we identified several key phenomena that shape our perception of behavior:

Determinism: In the One-to-One subset, predictable input-output pairings demonstrate deterministic behavior. An entity’s underlying structure acts as a carrier for this consistency, allowing us to understand and anticipate its actions.

“Craziness” / Instability: The One-to-Many (Same Instance) subset reveals instances where inconsistent outputs occur despite identical inputs. This behavior is interpreted as malfunctions within the entity’s structure, which acts as a carrier for the unpredictable behavior and prompts us to adjust our interactions accordingly.

Free Will: When different instances of the same kind produce distinct yet predictable outcomes (One-to-Many, Different Instances), we perceive them as possessing unique forms of “free will.” which carries individual behaviors, helping us distinguish and interact effectively with each entity.

Consciousness: The Many-to-One subset highlights the ability of ‘conscious’ entities to adapt and produce consistent outcomes across diverse inputs. This adaptability implies a higher level of consciousness. When this behavior is consistent across all instances of the same type, we attribute it to the entity’s intrinsic nature, which carries the consistent behavior pattern. However, when instances form groups with distinct behaviors, we attribute those to a shared set of values embodied in ‘culture’, which also acts as a carrier, guiding the group’s responses. Regardless of whether the carrier is “nature” or “culture”, labeling an entity as “conscious” allows us to anticipate and navigate their behavior effectively, despite diverse and unforeseen inputs.

Embracing these concepts as functional phenomena liberates us from the constraints of determinism, opening new avenues for practical application. For instance, in the realm of autonomous vehicles, this perspective allows us to treat them as possessing consciousness for practical purposes, without the moral quandaries typically associated with such attributes. Moreover, understanding deterministic mechanisms like error propagation and nonlinearity, which underlie phenomena like free will and consciousness, could potentially enable us to engineer, by demand, these qualities in machines to replicate complex human-like behaviors.

In summary, the “phenomenal functional” approach not only broadens our intellectual horizons but also offers pragmatic tools for technological innovation and ethical clarity in handling advanced autonomous systems. It beckons a deeper exploration across disciplines to fully harness its transformative potential.