Another view:

I find the Kowloon Walled City fascinating. #architecture

"This hurts me more than it hurts you." 🤣

In this scenario is all lumped together. Plus it is a person choice of what is and is not a scam.

Chased off.

People always post about 21 million but even Hal Finney "predicted" only 20 million #BTC. This is because he was (or was part of the group that made up) Satoshi. #bitcoin

#nowreading #bookstr #china #readstr

Appendicitis

Operating room is not

Appendectomy

🔪🎃

haiku is the way

never change a thing is why

productive is not

I don't count my steps. I just take them. 👣

Yikes, I am 210 lbs but tall so back in the day people were shorter. So that makes sense.

Get sneaky sats now

Before the jig is now up

Cover it like cat box

🤣🤣🤣🤣

* This is a draft of a paper worked on over the weekend. Been thinking a lot of the economic consequences of achieving a Artificial General Intelligence (AGI). Any feedback welcome. 🤙

# Neo-Austrian Computational Economics: A Synergistic Approach in the Age of AGI

Author: nostr:npub1y3uh89v5a4vq92t8q0j6su94zhvcdxpywjn3l6hpsr5welarqtrqj7yzhd

---

**Abstract**:

In an era marked by the ascendancy of Artificial General Intelligence (AGI), there is an imperative to revisit and recalibrate our economic paradigms. This paper carves out a space for Neo-Austrian Computational Economics — an integrative framework that melds the time-honored principles of Austrian economics with the cutting-edge methodologies inherent to AGI, providing a visionary lens to navigate the complexities of a post-AGI economy.

**Keywords**: Austrian Economics, Computational Economics, AGI, Decentralization, Human Intuition, Machine Logic.

## Introduction

The dawn of the 21st century heralded not just technological marvels, but also sparked a profound reshaping of the very substrates upon which our economic systems function. The intricate ballet of supply and demand, the delicate dance of valuations and negotiations—all traditionally human-centric arenas—are now being infiltrated by the burgeoning prowess of Artificial General Intelligence (AGI). In this transformative era, the very lexicon of economics is challenged, and novel paradigms beckon.

Historically, the Austrian school has been a bastion of human-centric economics. It celebrates the primacy of individual decision-making and reveres the unpredictability and subjectivity inherent to human economic behavior. And now, as we navigate this flux, the contours of a novel economic model begin to emerge—one that harmoniously melds the hallowed principles of Austrian economics with the dynamic promise of AGI. But why this fusion? And why now?

The economic landscape, once dotted with human agents making decisions based on limited knowledge and personal motivations, is now being punctuated with AGIs—entities that can process vast arrays of data and make precise decisions at near-instantaneous speeds. The juxtaposition of human spontaneity with AGI precision offers a panorama rife with both promise and pitfalls. Can the market dynamism, celebrated by Austrian thinkers, survive and thrive in this new digital age? Can the AGIs, with their deterministic algorithms, truly understand and factor in the intangible nuances that often drive human economic decisions?

This paper, at its core, seeks to address these questions. By introducing the framework of Neo-Austrian Computational Economics, it aims to carve a middle path—a trajectory that respects the sanctity of human intuition while harnessing the computational might of AGI. Through a deep dive into this confluence, we embark on a journey to envision an economic model that is both time-honored in its principles and futuristic in its approach.

## The Austrian Legacy in Contemporary Economics

Dive deep into the annals of economic thought, and one is sure to encounter the robust and resilient Austrian school, a school characterized by its steadfast commitment to individualism and the intricacies of human behavior. For the Austrian thinkers, markets were not merely transactional venues; they were living ecosystems, pulsating with the emotions, perceptions, and desires of individual agents.

Carl Menger, one of the pioneers of the Austrian school, introduced a radical concept that reshaped economic thought—the Subjective Value Theory. For Menger, value was not a static attribute nor an intrinsic quality of goods. Instead, it was fluid, emerging from the perceptions and needs of individuals. Every economic agent, according to Menger, operated in a sphere of subjectivity, evaluating goods and services based on personal utility and desire. This notion posed a profound question: How does one quantify value in a system where value itself is so deeply personal and varied?

Fast-forward to our digital age, where data is often dubbed the "new oil," and this question assumes even greater significance. The digital landscapes populated by AGIs are vast, often devoid of the palpable human emotions and biases. However, these AGIs are not just passive observers; they are active participants, influencing and often driving market dynamics. In such a setting, can AGI truly grasp the essence of Menger's subjective value? Can it discern the intricate tapestry of emotions, desires, and intuitions that underpin human economic decisions?

Moreover, the Austrian emphasis on decentralized knowledge and decision-making further complicates this matrix. Friedrich Hayek, another luminary of the Austrian school, often emphasized the dispersed nature of knowledge in society. For Hayek, central planning, irrespective of its computational or algorithmic might, was inherently flawed, simply because it could never account for the myriad localized knowledge and nuances that individual agents possessed. Translating this to our AGI-dominated scenarios, one is prompted to ask: Can AGIs, despite their unparalleled data-processing capabilities, ever replicate or replace the decentralized wisdom of human agents?

This section does not merely reminisce about the Austrian legacy. Instead, it seeks to actively engage with it, juxtaposing its principles against the transformative backdrop of AGI. In doing so, it sets the stage for a profound exploration, one that probes the depths of human intuition and the heights of machine logic, seeking synergies, resolutions, and, ultimately, a harmonious path forward in our increasingly complex economic landscape.

## The Deterministic Landscape of AGI

At the crossroads of technology and cognition, Artificial General Intelligence (AGI) stands as a sentinel of the new era—an embodiment of algorithmic rigor and computational brilliance. Far transcending the capabilities of conventional narrow AI, which is designed for specific tasks, AGI emerges as a versatile entity, displaying adaptability and broad cognitive capacities reminiscent of human intelligence. However, for all its advancements and potential, the architecture of AGI remains rooted in determinism, a stark contrast to the fluidity and spontaneity of human intuition.

In its purest essence, AGI operates in a realm of zeros and ones, of boolean logic and binary decisions. These decisions, while astoundingly swift and accurate, are fundamentally predicated on vast datasets and predetermined algorithms. The AGI landscape, therefore, presents an intricate tableau of patterns, predictions, and decisions, all emanating from structured data. Such deterministic prowess, especially in economic contexts, holds the promise to redefine markets, streamline inefficiencies, and envisage future trends with a degree of accuracy hitherto deemed unattainable.

Yet, this deterministic nature is not without its nuances. The emergence of self-learning AGIs, leveraging deep learning and neural networks, has introduced a semblance of 'adaptability' to their operations. These AGIs, while still rooted in algorithms, can recognize patterns, learn from new data, and even modify their behavior, mimicking the learning trajectory of human agents. This adaptive layer adds a rich dimension to AGI's operations, enabling it to interface with dynamic market conditions, fluctuating demands, and evolving consumer preferences. But herein lies a pertinent query: Can such adaptability, no matter how advanced, ever mirror the depths of human subjectivity and spontaneity?

The deterministic vista of AGI also broaches broader philosophical and ethical considerations. In a market influenced or even dominated by AGI decisions, how do we define accountability? If an AGI-driven financial model falters, leading to economic downturns, where does the responsibility lie? With the creators of the AGI? The AGI itself? Or the data upon which its decisions were based?

Furthermore, the rise of AGI challenges the very fabric of traditional market dynamics. In a landscape where AGIs can predict consumer behavior, optimize supply chains in real-time, and dynamically adjust pricing to maximize profits, what becomes of competition, innovation, and the human entrepreneurial spirit?

This section dives deep into these complex waters, exploring the deterministic universe of AGI and its ramifications for Neo-Austrian Computational Economics. Through a rigorous examination of AGI's capabilities and constraints, we embark on a quest to understand its role, influence, and potential in reshaping contemporary economic thought and practice.

## Decentralized Networks in the AGI Ecosystem

A pivotal tenet of the Austrian school of economics has always been the emphasis on decentralization, the idea that knowledge and decision-making capacities are distributed, residing in individual actors spread across the market landscape. This conceptual framework views economies not as monolithic, centrally-driven entities, but as dynamic ecosystems wherein countless individual decisions converge to create order. As we move into the age of AGI, this decentralization takes on novel forms and implications, prompting a reevaluation of networks, nodes, and the flow of information.

In the modern digital realm, AGI doesn't exist in isolation; it manifests as part of vast interconnected networks. Think of each AGI as a node, not just processing but also disseminating information, influencing, and being influenced by a myriad of other nodes. These networks of AGIs, in essence, function similarly to decentralized market structures, with individual AGIs making autonomous decisions based on localized data and yet, collectively contributing to a broader system behavior.

However, the dynamism of these AGI networks introduces new layers of complexity. Unlike human agents, whose decisions, while influenced by others, still retain a degree of unpredictability and subjectivity, AGIs operate on deterministic logic. This determinism, when scaled across a network, can lead to emergent behaviors that are both predictable in some respects and startlingly complex in others. It's akin to observing a superorganism, where individual entities (AGIs) collaborate and compete, leading to patterns and outcomes that might not be evident when analyzing individual nodes.

But where does this leave the human agent? In these intricate webs of AGIs, it becomes vital to ensure that the human actor's role is not diminished. Austrian economics has always championed the idea that individual knowledge, experiences, and decisions are paramount. As such, integrating human agents into these AGI networks, ensuring their decisions carry weight, and that they are not merely passive observers, becomes a challenge of paramount importance.

Furthermore, the idea of trust comes to the fore. In traditional Austrian market structures, trust is built over repeated interactions and shared experiences. However, in AGI networks, trust assumes a different dimension. How do individuals trust a network where decisions are made at lightning speed, often based on opaque algorithms? The potential solution might lie at the intersection of technology and transparency. Concepts like decentralized ledgers or blockchain technology, which offer transparent, immutable records of transactions and decisions, might hold the key to building trust in this new age.

This section, therefore, takes the reader on a journey through the labyrinthine networks of AGI in the context of Austrian decentralization. It explores the challenges, opportunities, and potential pathways to ensure that these networks, while harnessing the efficiency of AGI, still reflect the ethos of individualism, trust, and decentralized wisdom that Austrian economics so passionately advocates. Through this exploration, we aim to craft a vision for a future where humans and AGIs coexist, collaborate, and together, create a harmonious economic tapestry.

## Implications and Future Directions

As the dawn of AGI reshapes our economic horizons, it beckons not only an exploration of its capabilities but also a deep introspection into its broader implications. The fusion of traditional Austrian principles with the computational prowess of AGI presents a plethora of challenges and opportunities that stretch beyond the confines of pure economic theory, extending into the domains of ethics, society, and human agency.

Firstly, there's the pertinent challenge of trust. Austrian economics, at its core, emphasizes the organic evolution of market structures, built over repeated interactions and cultivated trust between market agents. But how does one foster trust in an AGI-driven ecosystem? When market decisions, influenced or even dominated by AGI algorithms, impact livelihoods, economies, and societal structures, the transparency of these algorithms becomes paramount. Trust, in this new paradigm, necessitates a clear understanding of how AGI makes decisions, the data it relies upon, and the potential biases it might harbor.

This leads to the broader ethical conundrum: the responsibility and accountability of AGI-driven decisions. If an AGI, operating on a set algorithm, makes an economic decision that leads to a recession or exacerbates income inequalities, who is held accountable? Is it the creators of the AGI, the algorithm itself, or the data that trained it? And how does society reckon with these challenges, ensuring that accountability mechanisms are in place to address potential missteps?

Furthermore, there's the evolving role of the human agent. Austrian principles cherish the individual, placing them at the heart of economic interactions. In a future dominated by AGI, ensuring the continued relevance of human decisions becomes a complex endeavor. It necessitates frameworks that seamlessly integrate human intuition with AGI precision, ensuring that human agents remain central to the economic narrative.

Then, there's the promise of innovation. With AGIs capable of processing vast datasets and identifying patterns beyond human cognition, there's potential for groundbreaking innovations that could redefine industries. However, ensuring that such innovations align with societal needs and values is crucial. How does one strike a balance between AGI-driven innovation and societal welfare?

Lastly, the rise of AGI accentuates the need for global collaboration. Economic decisions, increasingly influenced by interconnected AGIs, will not be confined to national boundaries. They will have global ramifications, necessitating international dialogue, cooperation, and regulatory frameworks that ensure AGI's benefits are widespread and its challenges collectively addressed.

This section delves deep into these multifaceted implications, drawing from interdisciplinary insights, case studies, and forward-looking analyses. Through this exploration, the aim is not just to understand the challenges of a post-AGI world but to actively shape its trajectory, ensuring that the fusion of Austrian principles and AGI leads to a future that's inclusive, equitable, and reflective of shared human values.

## Conclusion

Navigating the multifaceted realm of Neo-Austrian Computational Economics, we stand at an inflection point, witnessing the confluence of time-honored economic traditions and the transformative promise of AGI. This journey, while rife with challenges, also illuminates a horizon teeming with possibilities—a vision of an economy that harmonizes human intuition with algorithmic precision.

The Austrian school, with its deep reverence for individual agency and the spontaneous order emerging from decentralized interactions, provides a foundational lens. Yet, as we've ventured through this discourse, it's evident that the advent of AGI demands an evolution—a recalibration of these principles in light of the digital revolution. The questions posed are profound: How does subjective value, a cornerstone of Austrian thought, manifest in an AGI-dominated landscape? How do decentralized knowledge and decision-making paradigms translate when AGIs, with their vast networks, come into play? And crucially, how do we ensure that the human agent, with all their complexities and idiosyncrasies, remains central to the economic narrative?

Yet, as daunting as these questions might appear, they also underscore an exciting transformative journey. A journey where AGIs, rather than being mere tools, become partners in economic decision-making. Where human agents leverage the computational prowess of AGI, yet infuse it with the rich tapestry of human experience, emotion, and intuition. A future where markets, rather than being purely transactional arenas, evolve into collaborative ecosystems, seamlessly integrating human and AGI agents.

This paper's exploration, while extensive, is by no means exhaustive. The landscape of Neo-Austrian Computational Economics is dynamic, evolving with every technological advancement and societal shift. However, the core ethos remains: a commitment to forging a future that respects individual agency, values decentralized wisdom, and harnesses the transformative potential of AGI for the collective good.

As we conclude, the invitation is not merely to observe or analyze but to actively participate. The future of Neo-Austrian Computational Economics is not a predetermined destination but a journey—a collaborative odyssey that beckons economists, technologists, policymakers, and every curious mind. Together, through critical dialogue, innovative thinking, and shared aspirations, we can shape an economic paradigm that stands as a beacon of hope, prosperity, and human-centric innovation in the age of AGI.

## References

1. Hayek, F. A. (1945). *The Use of Knowledge in Society*. American Economic Review, 35(4), 519-530.

2. Menger, C. (1871). *Principles of Economics* (J. Dingwall & B. F. Hoselitz, Trans.). Grove City, PA: Libertarian Press. (Original work published in 1871).

3. Russell, S., & Norvig, P. (2010). *Artificial Intelligence: A Modern Approach* (3rd ed.). Upper Saddle River, NJ: Prentice Hall.

4. Kahneman, D. (2011). *Thinking, Fast and Slow*. New York, NY: Farrar, Straus and Giroux.

5. Turing, A. M. (1950). *Computing Machinery and Intelligence*. Mind, 59(236), 433-460.

6. Von Mises, L. (1949). *Human Action: A Treatise on Economics*. Yale University Press.

7. Bostrom, N. (2014). *Superintelligence: Paths, Dangers, Strategies*. Oxford: Oxford University Press.

8. Popper, K. R. (2002). *The Logic of Scientific Discovery*. London: Routledge. (Original work published in 1934).

9. Searle, J. R. (1980). *Minds, Brains, and Programs*. Behavioral and Brain Sciences, 3(3), 417-457.

10. Polanyi, M. (1966). *The Tacit Dimension*. New York, NY: Doubleday.

Membership was short lived. 🤣

Can create a character persona for LLM run privately. 🤙

https://github.com/oobabooga/text-generation-webui

This is beautiful 🤣💜

🤙

https://arxiv.org/pdf/2212.08964v1.pdf

Pretty sad when Facebook is more #privacy friendly than Discord.

https://youtu.be/uvNkdAggUGU?si=DUdC5Uyqs7eQwFz0

New book. #F1 #Formula1

Thoughts and prayers. #meme

That's EVERY big transaction. 🤣

I use to build a distro that was a precursor to BackTrack/Kali. 🤙🔒

https://flylib.com/books/en/4.375.1.123/1/

I always read "none" as "hope". 🤣

Okay relay operators, go ahead and shut down your relays. 😉

🍻

I use a cloud instance with 4 A100s and works well doing fine-tuning but at almost $7 an hour adds up.

GM!