[The Electronic Oracle] ① From the Oracle of Delphi to AI: Why Do We Crave 'Absolute Answers'?

 South Korea, having recently undergone the shock of an attempted self-coup by a sitting president in the 21st century, is currently abuzz with a topic that has surfaced from the depths of our society: "Shamanism."

Why do people rely on shamanism to make critical decisions? The roots of this behavior run deep. When the ancient Greeks felt anxious about the uncertain future, they sought answers from the Oracle of Delphi, even for decisions that would determine the fate of their nation.

This blog series will explore computer models, which serve as the standard for modern decision-making, much as the Delphic Oracle did in the past.

There is a growing tendency to treat computer models as "objective" and "omniscient," akin to the ancient oracles. With the integration of Artificial Intelligence, this tendency has intensified. We often marvel at the spectacular results without scrutinizing the underlying reasoning process, effectively granting these models the authority to dictate our decisions.


The Electronic Oracle
Computer Models and Social Decisions

by D. H. Meadows and J. M. Robinson

This book offers a clear and balanced account of both the strengths and the limitations of computer models. For researchers who have System Dynamics (SD) modeling in mind, it is a treasure trove of insights and well worth careful reading.

The central message of Part I can be summarized as follows:

We tend to misunderstand computer models as tools that deliver absolute truth—modern oracles.

What the authors emphasize, however, is that computer models are not magic boxes. They are nothing more than translations of human thought—what we call mental models—into mathematical language. For this reason, readers should not feel overwhelmed by mathematical techniques. What truly matters is the story the model tells and the structure that underlies it. One of the strengths of this book is that it does not begin with equations, but with meaning.

A model, in essence, is a generalization of reality—a collection of assumptions.

Among all models, the most familiar and intuitive is the mental model operating in our own minds, one we continuously construct as we live and breathe.

At the organizational and policy level, however, the intuition of a single individual is rarely sufficient. Committees, experts, and voting procedures are therefore used to integrate multiple mental models. Yet accurately translating these mental models into words proves extremely difficult, due to the ambiguity of language and the limits of self-awareness. It is for this reason that computer models have come to occupy the position of a new oracle.

They do so because of several perceived advantages:

  • Rigor: Assumptions must be made explicit, clearly defined, and internally consistent.

  • Comprehensiveness: Models can handle far more information than the human mind alone.

  • Logic: Given a set of assumptions, a computer carries out calculations with complete internal consistency. This differs from recent concerns about AI hallucinations. Unlike human reasoning—which is often swayed by emotion or bias—a model remains computationally consistent, at least in its calculations.

  • Accessibility: Mental models are hidden, making them difficult to criticize or revise. By contrast, computer models are expressed in formal language, making their assumptions open to inspection, critique, and modification.

  • Flexibility: By adjusting parameters, one can experiment with a wide range of policies and conditions.

I believe this very clarity makes computer models a truly "democratic tool," especially when compared to personal intuition—the "hidden" mental model that is so difficult to verify. We cannot critique thoughts that remain concealed; however, once a model is made explicit, it becomes open to public debate. What do you think?

The authors argue, however, that two fundamentally different worlds are constantly in tension.

  • The World of Modelers: A world that pursues logic, quantification, and clear causality. It is an orderly domain composed of data and feedback loops.
  • The World of Policy Makers: A world dominated by clashing values, politics, and ambiguity. Here, intuition and social pressure play a decisive role.

 The real problem, however, is that despite the abundance of computer models, their actual influence on reality remains small.

  • Modelers ask: “Why don’t you use our models?”

  • Policy makers respond: “Why don’t you build models that we can actually use?”

How might we narrow this gap?

Computer models can offer insights that mental models alone cannot provide. They can be translated into a language that citizens can understand and serve as tools for democracy. At the same time, we must guard against a "technocracy" where computer experts or those wielding the sword of "efficiency" hold power without accountability—a world of black-box models, bureaucratic rule, and the political weaponization of meritocracy.

In the next post, I will introduce three questions worth asking ourselves, based on the insights from Part I.

Comments

Post a Comment

Popular posts from this blog

Thinking in Systems Guide (1) – Preface

Image
   The Beginning of Systems Thinking: A New Lens on the World Taught by a Slinky "If a factory is torn down but the rationality which produced it is left standing, then that rationality will simply produce another factory." - Robert Pirsig (Here, “rationality” can be understood simply as the reason or purpose behind it.)   Why do we always face the same problems? Have you ever had this experience? You worked hard to solve a problem at work, but before long, a similar issue appeared again in a different form. Or you may have tried to break a bad habit, succeeded for a while, and then suddenly found yourself back at square one. These kinds of repetitions happen because we focus only on the symptoms of the problem. The true cause—the underlying structure —remains untouched. The Slinky Experiment: The First Insight of Systems Thinking Donella Meadows, who taught system dynamics at MIT, often conducted a fascinating classroom experiment. She used nothing more than a Slinky—t...
Read more

The + and - Symbols: Simple Enough for Elementary Students, Yet the World's Most Confusing Concept

Image
Let me talk about the most challenging concept in the world—at least for me. It’s so simple that even elementary school students understand it. But believe me, it gets you when you least expect it. I’m talking about... + and -. And yes, there’s a twist. Actually, there’s a twist to the twist—so buckle up.  In my systems thinking lectures, I typically explain that when cause and effect move in the same direction, we use the + symbol and refer to it as a positive causal relationship. When they move in opposite directions, we use the - symbol and call it a negative causal relationship. I always emphasize firmly that "positive" and "negative" here are purely mathematical concepts—they have nothing to do with good or bad. Elementary School Level   For example, when the birth rate increases, the total population also increases. Since both birth rates and population changes move in the same direction, we write:  Births → Population (+)  Conversely, when the death rate incr...
Read more

[The Electronic Oracle] ③ Assumptions Aren’t "Guesses": A Proposal for Transparent Democracy

Image
  [The Electronic Oracle] ③ Assumptions Aren’t “Guesses”: A Proposal for Transparent Democracy When I teach System Dynamics or computer modeling, I often encounter a word that instinctively annoys both students and practitioners: "Assumption." The dictionary reveals the source of this discomfort. Assumption :  “a thing that is accepted as true or as certain to happen, without proof .” (Oxford Languages) For scientists, the phrase "without proof"  can sound like an insult. So when someone says, “That’s just your assumption,” scientists rush to defend themselves: "No! I have evidence!" In this post, I want to restore the honor of this word. Modeling assumptions are not mere tricks to patch missing data; they are influential political acts that transform secret dictatorships into transparent democracies. This article distinguishes two layers of assumptions: The Assumption as Belief (Type A)   hides in the mind and operates on authority The Model Assumption (T...
Read more