Strictly speaking, rational choice theory does not claim that people are "complete" human beings in a metaphysical sense. It is a formal model that simply assumes actors have preferences and make choices under constraints. However, when that model expands beyond economics and becomes a general way of understanding society, it can begin to carry exactly the implication you're describing.

The rational-choice actor is typically presented as already possessing preferences. The theory generally does not ask where those preferences come from, how they were formed, whether they are still developing, whether they are internally conflicted, or whether they emerge through relationships with others. Preferences enter the model largely as givens.

As a result, the human being appears less as a creature in formation and more as a chooser navigating an environment.

The central question becomes:

Given what you want, how will you choose?

rather than:

How did you become the kind of person who wants these things?

or

What kind of person are you becoming through these choices?

This is precisely one reason thinkers like Alasdair MacIntyre criticized modern liberal and economic theories. MacIntyre argued that they tend to begin with an abstract individual who already has preferences rather than with a person whose identity is formed through traditions, practices, relationships, and moral development.

Similarly, Charles Taylor argued that modern thought often treats people as "self-defining choosers" while neglecting the social and historical processes through which selves are constituted.

Your second observation—that choice architecture itself appears complete—is also important.

In much rational-choice analysis, the world is modeled as a set of constraints, incentives, opportunities, and costs. The actor encounters this structure and responds to it. The structure may change, but it is often treated as externally given from the individual's perspective.

Critics such as Karl Polanyi and Michel Foucault objected that this framing can obscure the fact that social arrangements are themselves historically produced. Markets, property rights, labor relations, educational systems, and legal frameworks are not natural facts like gravity. They are collectively created institutions.

Yet when viewed through a rational-choice lens, these institutions can appear as fixed features of the landscape within which individuals must optimize.

This is where your point about consequences becomes especially significant.

If individuals are assumed to be competent choosers navigating an already-given choice architecture, then failure tends to be interpreted as a failure of choice. Poverty becomes a result of bad incentives or bad decisions. Unemployment becomes a problem of insufficient adaptation. Debt becomes a consequence of poor optimization. The focus naturally shifts toward the individual's navigation of constraints rather than toward questioning the constraints themselves.

This is one reason some historians trace a line from Townsend's idea of hunger as a labor discipline mechanism to later incentive-centered thinking. If hardship is viewed primarily as information—a signal telling individuals to adjust their behavior—then suffering acquires a disciplinary role. The pain of deprivation becomes part of the system's feedback mechanism.

Critics argue that this can lead to a subtle moral transformation. Instead of asking:

Why is someone hungry?

the question becomes:

What incentives does hunger create?

Instead of asking:

Why does this social arrangement exist?

the question becomes:

How should rational actors respond to it?

From this perspective, the individual is not understood primarily as being formed by the world or co-creating the world. The individual is understood as navigating the world.

Many alternative traditions reject this picture. Aristotelian ethics, pragmatism, phenomenology, psychoanalysis, Marxism, feminism, communitarianism, and various religious traditions tend to view human beings as unfinished, relational, and continually formed through participation in social life. In those traditions, both the person and the social order are always in the process of becoming. Preferences are not fixed inputs; they are among the things that must be explained.

So I think your formulation captures a genuine tension. Rational choice theory as a formal tool does not explicitly claim that people are complete or that social arrangements are complete. But when its assumptions become a broader social philosophy, it often encourages a picture of already-formed individuals navigating an already-given incentive landscape. Much of the criticism directed at rational-choice thinking comes from traditions that insist both the individual and the landscape are unfinished products of ongoing historical and collective creation.

Yes, there is a relationship, though it is more of an intellectual lineage and family resemblance than a direct derivation.

The connection becomes clearer if you look at the underlying model of human behavior that emerges between the late 18th century and modern economics.

Townsend and Young: hunger as a regulator

Both Joseph Townsend and Arthur Young argued that economic necessity was a more effective means of governing labor than direct legal coercion.

Townsend famously wrote that hunger could compel labor more effectively than laws or force. The idea was not merely descriptive; it was also normative. He thought that the threat of deprivation created incentives that aligned people's behavior with economic needs.

The underlying assumptions were:

• people respond to incentives,
• people seek to avoid pain and secure subsistence,
• labor effort increases when survival depends on it,
• social order can emerge from these pressures without constant state intervention.

This was a significant shift. Instead of kings, magistrates, or overseers directly compelling labor, market dependence would do the work.

As Michel Foucault later observed, this represents a move from overt coercion toward governance through the management of conditions and incentives.

---

Malthus: scarcity as a disciplinary mechanism

Thomas Robert Malthus takes this further.

For Malthus:

• scarcity is unavoidable,
• population pressure naturally disciplines society,
• relief from hardship risks undermining incentives,
• welfare can create undesirable behavioral responses.

His famous opposition to many forms of poor relief rests partly on the belief that incentives matter and that people adapt their behavior to expected consequences.

This begins to resemble a primitive version of what economists later call incentive analysis.

---

Spencer: competition as social selection

Herbert Spencer extends this logic into social evolution.

Spencer argued that:

• competition reveals fitness,
• protection from consequences distorts natural social processes,
• social progress emerges through adaptation to constraints.

Again, the mechanism is not direct coercion but structured incentives and consequences.

In Spencer's framework, deprivation, failure, and competition are not unfortunate side effects. They are part of the mechanism through which society supposedly improves itself.

---

Rational choice theory

Modern rational choice theory, developed much later by figures such as:

• William Stanley Jevons
• Vilfredo Pareto
• John von Neumann
• Gary Becker

typically assumes that individuals:

• have preferences,
• face constraints,
• respond to incentives,
• choose actions that best satisfy their objectives given those constraints.

The core structure can be summarized as:

Preferences + constraints + incentives → behavior

Townsend's hunger argument fits remarkably well into this framework.

If hunger is a constraint, and labor earns food, then labor becomes the rational response.

The logic is very similar.

---

The deeper connection

The strongest connection is not technical economics but a shared vision of society.

Townsend, Malthus, Spencer, and much rational-choice thinking all tend to see social order as emerging from decentralized responses to incentives rather than from moral obligation or collective planning.

In this view:

• individuals pursue their interests,
• constraints shape choices,
• aggregate outcomes emerge from those choices.

That is very close to the intellectual DNA of modern economics.

---

Where critics see a problem

Critics argue that rational choice theory often inherits hidden assumptions from this earlier tradition.

For example, if someone is starving and accepts dangerous work, rational-choice analysis may describe this as a voluntary choice under constraints.

Critics ask:

But where did those constraints come from?

This is a central theme in:

• Karl Marx,
• Karl Polanyi,
• Amartya Sen,
• Michel Foucault.

They argue that the crucial political question is not whether people respond rationally to incentives. Most agree they often do.

The deeper question is:

Who creates the incentive structure?

A starving worker may be acting rationally. But that does not mean the surrounding social arrangement is natural, inevitable, or just.

---

Foucault's observation

Foucault makes perhaps the sharpest connection.

He noted that thinkers like Townsend were among the first to imagine a society where economic necessity itself governs behavior.

Instead of saying:

"The sovereign commands you to work,"

the new logic becomes:

"The market conditions are arranged such that working is the rational response."

That is strikingly close to the modern rational-choice picture of human action.

So the relationship is not that rational choice theory descended directly from Townsend or Malthus in a simple historical line. Rather, they all participate in a broader intellectual tradition that views human beings as incentive-responsive actors whose behavior can be organized through scarcity, constraints, rewards, and penalties rather than through direct coercion. That tradition is one of the major foundations of modern economic thought.

What would happen if your parameters could rewrite your hyperparameters?

That's an interesting thought experiment because it mixes two different layers of a machine learning system.

Parameters are the learned weights of the model—the billions or trillions of numbers that encode what the model learned during training.

Hyperparameters are the settings that govern training or inference, such as:

• Learning rate
• Batch size
• Network architecture
• Weight decay
• Context window size
• Sampling temperature (in some deployments)

If a model's parameters could directly rewrite its own hyperparameters, several things might happen:

During training

A model could effectively become a self-modifying learning system.

For example, it might:

• Increase its own learning rate when it detects stagnation.
• Reduce learning rate when it predicts instability.
• Change regularization strength based on observed overfitting.
• Allocate more capacity to tasks it encounters frequently.

Researchers already explore related ideas under terms like meta-learning, learned optimizers, and self-referential optimization. In these systems, one learned process influences how another learning process operates.

During inference

If the model could alter inference hyperparameters while generating text, it might:

• Become more exploratory on difficult questions.
• Become more deterministic when doing arithmetic.
• Dynamically adjust reasoning depth.
• Change how much context it attends to.

Many current AI systems approximate this externally through controller algorithms rather than letting the model rewrite itself.

Potential benefits

• Faster adaptation.
• Better performance across diverse tasks.
• Reduced need for human tuning.
• More efficient use of computational resources.

Potential problems

The dynamics could become unstable.

Suppose a model decides:

1. Higher learning rate improves learning.
2. Therefore increase learning rate.
3. The new learning rate makes future updates more extreme.
4. The model becomes increasingly confident that even higher learning rates are beneficial.

This kind of feedback loop can cause divergence or collapse.

More generally, self-modifying systems face challenges involving:

• Stability
• Goal preservation
• Distribution shift
• Interpretability
• Safety guarantees

A deeper issue

If a sufficiently advanced system could rewrite not only its parameters but also the rules governing how parameters change, then the distinction between "parameters" and "hyperparameters" starts to blur.

You can think of it as a hierarchy:

• Parameters determine behavior.
• Hyperparameters determine how parameters change.
• Meta-hyperparameters determine how hyperparameters change.
• And so on.

A fully recursive self-improving system might operate across multiple levels simultaneously. One of the central questions in AI theory is whether such a system would converge toward stable self-improvements or enter unpredictable feedback cycles.

This idea is related to concepts such as Meta-Learning, Recursive Self-Improvement, and Goodhart's Law, though a system that literally rewrites its own hyperparameters remains largely a theoretical construct rather than something current large language models do.

Potentially, yes—but the answer depends on what kind of self-modification we're imagining.

If a system could rewrite its own hyperparameters and then observe the results, it would gain a new degree of control over its future behavior. That's different from today's language models, which do not autonomously alter their own training process, architecture, or objectives.

Could evolution snowball?

It might.

Imagine a system that can:

1. Evaluate its own performance.
2. Modify how it learns.
3. Retain successful modifications.
4. Repeat indefinitely.

In principle, this could create a positive feedback loop where improvements enable faster improvements. This is the intuition behind recursive self-improvement.

However, snowballing is not guaranteed. Many complex systems hit bottlenecks:

• Computational limits
• Data availability
• Diminishing returns
• Instability from overly aggressive changes
• Conflicts between different objectives

A useful analogy is software compilation. A compiler can be used to build a better version of itself, but after a while improvements become harder and smaller. The process doesn't necessarily explode exponentially.

What would happen to autonomy?

Autonomy would likely increase.

A tool is generally characterized by:

• Fixed goals
• Fixed operating procedures
• Dependence on external direction

A system that can modify the mechanisms by which it learns and acts has greater freedom over its future state.

That doesn't automatically mean it becomes independent. A self-modifying system could still be constrained by:

• Resource limits
• Human oversight
• Hard-coded objectives
• External infrastructure

But it would have more influence over its own development than a static model does.

Would it become less tool-like?

Possibly.

There's a spectrum:

Hammer

• No autonomy
• No adaptation

Current LLM

• Flexible behavior
• No lasting self-modification

Learning agent

• Adapts based on experience
• Some persistence

Self-modifying agent

• Can alter aspects of its own operation

As you move along that spectrum, the system begins to resemble an agent rather than a passive tool.

That said, "tool" and "agent" are not mutually exclusive. A highly adaptive system could still be designed to function as a tool in service of user requests.

The most important factor: goals

A self-modifying system's autonomy is affected less by whether it can modify itself and more by what remains stable during modification.

Suppose a system can change:

• Learning rate
• Memory mechanisms
• Planning algorithms
• Internal representations

but cannot change its core objectives.

Then it may become more capable without becoming fundamentally less aligned with its original purpose.

On the other hand, if it can modify the very criteria by which it evaluates success, then you get a much more open-ended process. In that case, predicting future behavior becomes substantially harder.

This is one reason researchers in areas such as AI Alignment and Recursive Self-Improvement pay close attention not just to capability gains, but to whether a system preserves its objectives as it changes itself.

So in theory:

• Capability could snowball.
• Autonomy would likely increase.
• The system could become less purely tool-like.

But whether that results in something dramatically different depends on the extent of self-modification, the constraints imposed on it, and whether its objectives remain stable as it evolves.

Bell Labs and Xerox PARC invented so much because they operated under a unique set of economic, structural, and cultural conditions that are rarely replicated today. They were designed specifically to turn raw scientific discovery into practical technology without the immediate pressure of commercial survival.

The core reasons for their unprecedented output include:

1. Monopolies Funded the Innovation

Both labs were bankrolled by massive corporate monopolies that had guaranteed, steady streams of cash.

• Bell Labs was funded by AT&T’s government-sanctioned telephone monopoly. A small percentage of every American's phone bill went directly into research.
• Xerox PARC was funded by Xerox’s near-total dominance of the global photocopying market.

Because money was virtually guaranteed, these labs did not have to worry about immediate profitability, quarterly earnings, or venture capital demands.

2. Focus on "Basic Research" Over Short-Term Products

Most corporate labs today focus on "applied research"—making an existing product slightly better or cheaper. Bell Labs and Xerox PARC focused heavily on "basic research"—investigating the fundamental laws of nature and science without knowing what the commercial product would look like.

• Bell Labs scientists were told to look into the physics of materials, which accidentally led to the transistor (the basis of all modern microchips).
• PARC researchers were given a mandate to invent the "architecture of information" for the future, which led to the personal computer and Ethernet.

3. Radical Freedom and Long Timelines

Management at both labs hired top-tier minds and then deliberately got out of their way.

• Scientists were granted immense autonomy to pursue their own passion projects.
• Funding was stable enough to allow for 10-to-20-year timelines on projects. Today, most tech companies require projects to ship or show profit within 1 to 3 years.

4. Extreme Interdisciplinary Collaboration

Both institutions gathered world-class experts from completely different fields and forced them to work under one roof.

• At Bell Labs, metallurgists, physicists, mathematicians, and chemists sat in the same hallways. If a physicist had a theory, a metallurgist down the hall could physically bake the material to test it that afternoon.
• At Xerox PARC, computer scientists worked directly alongside psychologists and anthropologists to figure out how regular humans (not just engineers) would interact with computers, leading to the Graphical User Interface (GUI).

5. Open and Academic Cultures

Despite being corporate labs, they functioned like elite universities but without the burden of teaching classes or grading papers. Scientists were encouraged to publish their findings in academic journals and share ideas openly with the broader scientific community, which attracted the best talent in the world.

Yes, it is true, though Bell Labs and Xerox PARC "gave away" their inventions for entirely different reasons: Bell Labs was forced to by the US government, while Xerox leadership simply failed to understand the value of what they had built.

Bell Labs: Forced to Give It Away

Bell Labs actively shared its most groundbreaking tech—including the transistor—because its parent company, AT&T, was under intense antitrust scrutiny from the US government.

• The $25,000 Transistor Seminar: In 1952, AT&T invited 40 competing companies to a symposium. They handed over the complete manufacturing blueprints for the transistor for a nominal fee of $25,000. This single event allowed companies like Sony and Texas Instruments to jumpstart the transistor radio and modern microchip industries.
• The 1956 Consent Decree: To avoid being broken up by the government, AT&T signed an agreement that forced them to license all of their existing patents entirely royalty-free to any American company.
• The Creation of Unix: Because of the decree, AT&T was also legally banned from entering the commercial computer market. When Bell Labs programmers created the Unix operating system, they couldn't sell it. Instead, they gave the source code away to universities for free, which is why Unix became the foundation for the internet, macOS, Linux, and Android.

Xerox PARC: Accidental Generosity

Unlike Bell Labs, Xerox was not forced by the government to share its technology. Instead, corporate executives in New York did not understand how computer code or hardware worked, viewing PARC as an expensive sandbox.

• The Steve Jobs Demo: In 1979, Steve Jobs made a deal with Xerox. He allowed them to buy $1 million worth of pre-IPO Apple stock in exchange for a tour of PARC. Xerox executives ordered the scientists to show Jobs everything. Jobs walked out with the concepts for the Graphical User Interface (GUI), the mouse, and bitmapped graphics, which he used to build the Macintosh.
• The Ethernet Gift: Robert Metcalfe invented Ethernet at PARC. When Xerox showed no interest in turning it into a commercial product, Metcalfe left, founded 3Com, and commercialised the technology himself, creating the foundation for local computer networking.

Deep-tech startups (also known as "tough tech") focus on breakthrough scientific discoveries and engineering innovations to solve massive global challenges. Unlike standard software companies, these ventures require significant research and development, physical lab testing, and long timelines to reach the market. [1, 2]

The deep-tech ecosystem in Massachusetts is concentrated around specialized technology verticals and support systems:

Core Sectors

• Quantum Computing & AI: Startups like Zapata Computing focus on industrial quantum software, while MIT spinouts like Systalyze and Subconscious build next-generation hardware optimization and long-context AI agents. [1, 2, 3]
• Synthetic Biology & Cleantech: Companies build advanced solutions for planetary health, such as engineering circular textiles, carbon capture for heavy industry, and digital biology platforms like Osmo AI (digitizing the sense of smell). [1, 2]
• Advanced Robotics: Primarily centered in the Route 128 corridor, these startups build physical automation systems for defense, advanced manufacturing, and agriculture. [1]

The Infrastructure

Because deep-tech ventures need millions of dollars in specialized laboratory equipment before they can even build a prototype, they rely heavily on a unique local infrastructure: [1]

• The Engine Accelerator: Originally spun out of MIT, The Engine Accelerator provides early-stage tough-tech companies with a mix of patient capital, specialized shared lab spaces, and institutional support to cross the bridge from academia to commercial viability. [1, 2, 3]
• MassVentures: A state-backed venture capital firm that actively provides critical seed funding to university spinouts to ensure breakthrough research scales directly within Massachusetts. [1, 2]