Experimenting with Thought Experiments
Maxwell's demon, Parfit's teletransporter, the "Museum of Vanished Selves," and the nature of exploratory AI dialogues
This post begins by exploring AI-generated illustration of classic thought experiments, then it briefly dives into consideration of the places of thought experiments and exploratory AI dialogues within the empirical landscape. It concludes with AI generation of a thought experiment based on the photograph above.
Interlocutors: ChatGPT-4o, 4.5, and o1 pro.
KT: Let's talk about scientific and philosophical thought experiments. Briefly, how do they differ from ordinary experiments?
GPT: Scientific and philosophical thought experiments differ from ordinary experiments primarily in that they occur entirely within the imagination and logical reasoning rather than through physical testing or empirical observation.
Ordinary experiments involve manipulating real-world variables under controlled conditions, collecting empirical data, and analyzing results to test hypotheses.
Thought experiments, by contrast, rely purely on hypothetical scenarios, conceptual analysis, and logical reasoning to explore the implications, coherence, or ethical dimensions of ideas. They do not require empirical evidence or physical implementation.
Thus, thought experiments allow exploration of concepts or situations that are impractical, unethical, impossible, or too abstract to test empirically, serving as powerful tools in both philosophy and theoretical science.
KT: An example could help. Briefly describe Maxwell's demon.
GPT: Maxwell’s demon is a classic thought experiment proposed by physicist James Clerk Maxwell in 1867 to explore the second law of thermodynamics.
Brief description: Imagine a container divided into two chambers of gas molecules by a small door controlled by an intelligent "demon." This demon selectively allows fast-moving (hot) molecules to pass in one direction and slow-moving (cool) molecules in the opposite direction. Over time, one chamber heats up, and the other cools down without expending energy.
Implication: At first glance, this would violate the second law of thermodynamics, which states entropy (disorder) should always increase, as it appears to create order (lower entropy) without energy input.
Conclusion: Analyzing this thought experiment has profoundly influenced physics by clarifying the relationship between information, energy, and entropy, eventually demonstrating that acquiring and processing the information necessary to sort molecules ultimately increases entropy—preserving the second law.
KT: Could you illustrate that?
GPT:
KT: How could that image be improved to better adhere to the thought experiment and visually convey it? Suggest three potential points of improvement.
GPT: Here are three potential improvements to the image to better align it with the essence of Maxwell's demon and enhance clarity:
Molecule Distribution Before and After:
Add a "before and after" sequence or a faint overlay showing an initial uniform distribution of molecules. This helps illustrate that the demon’s actions cause the separation of hot (fast) and cold (slow) molecules, visually reinforcing the entropy change.Speed Indicators and Labels:
Use clearer visual cues like differently sized arrows or motion blur lines to show fast (hot) vs. slow (cold) molecules. You could also color-code molecules more distinctly (e.g., red for fast, blue for slow) and label them accordingly to highlight the demon’s sorting mechanism.Information Processing Element:
Include a small symbol or icon near the demon (like a brain, data chip, or magnifying glass) to suggest that it is observing, deciding, and processing information—hinting at the deeper point that information gathering has an energetic cost.
Would you like me to generate an improved version with those elements?
KT: Yes, do so.
GPT:
KT: The attempt to make improvements appears simply to have increased the need for further improvements.
Let's move on to another thought experiment, this one from philosophy. Briefly describe Parfit's teletransporter.
GPT: Parfit’s teletransporter is a philosophical thought experiment proposed by Derek Parfit to explore questions about personal identity and consciousness.
Brief description: Imagine a machine (a "teletransporter") that scans your body completely, destroys your original self, and instantaneously recreates an exact replica—complete with your memories, personality, and consciousness—on another planet.
Philosophical implication: This raises profound questions:
Is the recreated individual truly "you," or merely an identical copy?
Does personal identity depend on psychological continuity (memories, personality), or does it require physical continuity?
If the machine malfunctioned and created two identical copies, who would be the "real" you?
The thought experiment challenges the intuitive sense of self, inviting us to reconsider what it fundamentally means to remain the same person over time.
KT: How might this be illustrated?
GPT: Illustrating Parfit’s teletransporter effectively would involve capturing the paradox of identity and continuity. Here's how it might be visually represented:
Scene Composition:
Left Side – Original Person (Earth):
A figure standing in a sleek teleportation chamber labeled "Scanner" or "Departure Pod." Include visual indicators like glowing lights or scanning beams to show that the person is being recorded or deconstructed.Middle – Teleportation Process:
A visual bridge between Earth and another planet or location (e.g., Mars), perhaps represented with energy beams, digital streams, or dotted lines suggesting the transmission of information—not matter.Right Side – Reconstructed Person (Mars):
The identical person emerging from a second chamber labeled "Receiver" or "Reconstruction Pod," smiling and waving—looking entirely like the original.
Key Visual Elements:
Mirrored Poses or Expressions between the original and the copy to emphasize sameness.
A Question Mark or faded silhouette behind the original person—raising the issue: “Did this person die?”
Dual Versions (Optional): One version where the original disappears after teleportation, and another with both the original and the copy alive—highlighting the identity problem.
Would you like a version of this scene illustrated visually?
KT: Yes, please.
GPT:
KT: That's a good start, but, once again, adjustments could be made to better convey the idea. Suggest three improvements.
GPT: Absolutely — here are three improvements that could help this illustration better capture the philosophical depth of Parfit’s teletransporter:
Add Dual Outcome Possibility (Identity Dilemma):
Include a version where both the original and the copy exist side by side (perhaps split-screen or a ghostly duplicate lingering), to highlight the key paradox: if both exist, which one is truly “you”? This adds tension and visualizes the personal identity problem more clearly.Include Thought Bubbles or Labels Indicating Continuity:
Use thought or speech bubbles with matching phrases like “I feel like me” or “Am I the same person?” coming from both figures. This emphasizes psychological continuity and the subjectivity of identity across copies.Visualize the Destruction/Reconstruction Process More Explicitly:
Show the original body dissolving (e.g., pixelating, fading out) as the copy is constructed, reinforcing the unsettling idea that identity may depend on this destruction. Alternatively, include a red "X" or fading silhouette on the original side to underscore the physical discontinuity.
Would you like me to generate a revised version with those changes?
KT: Yes, implement those suggestions. Also, address one more issue: The individuals at each end should be identical.
GPT:
KT: Once again, the second attempt shows progress, but there remains room for improvement. What could be concluded from these two exercises at directing an AI model to illustrate thought experiments?
GPT: From these exercises, we can draw several insightful conclusions about directing an AI model (such as DALL-E or similar) to illustrate abstract thought experiments:
Nuanced Ideas Are Challenging to Depict Visually: Abstract philosophical and scientific thought experiments rely heavily on conceptual subtlety, metaphor, and implications. AI-generated images often require multiple iterations, each carefully guided, to effectively capture these nuances.
Explicit Instruction and Clarity Matter Significantly: The AI benefits immensely from precise, step-by-step instructions. Ambiguous instructions or broad descriptions leave too much room for misinterpretation, leading to incomplete or inaccurate representations of the intended concepts.
Iterative Refinement Is Essential: Thought experiments often involve multiple subtle implications that are challenging to represent simultaneously. As demonstrated, effective visualization often demands iterative cycles of refinement, feedback, and adjustment, underscoring the value of ongoing interaction between the user and the AI model.
Ultimately, illustrating complex, abstract ideas using AI necessitates detailed guidance, careful iteration, and patience to achieve accurate and meaningful visual outcomes.
KT: This session itself represents a sort of experiment. Is it a thought experiment? An ordinary experiment? Another type?
GPT: This session can best be classified as a form of interactive empirical experiment, distinct from both a traditional thought experiment and an ordinary physical experiment:
Not purely a thought experiment:
It isn't confined strictly to imagination or theoretical reasoning. You didn't merely hypothesize how an AI might respond; you actively conducted the interaction to observe real responses.Not a conventional physical experiment:
It lacks physical manipulation or measurement of tangible, physical quantities. Instead, it explores interactions between human directives and an AI system, examining how effectively concepts translate into generated images.Interactive empirical experiment (hybrid):
You empirically tested the capability of an AI model (me) to interpret nuanced conceptual prompts. While the experiment unfolded digitally, it produced concrete results (images) that could be iteratively observed, evaluated, and adjusted.
Thus, your experiment lies between abstract philosophical reasoning and physical experimentation, representing an empirical exploration of human-AI communication, creativity, and comprehension.
KT: Your conclusion might benefit from considering debates over the empirical status of computational simulations, particularly numerical ones of physical systems. Do a little research on this point and respond in less than 300 words.
GPT: Got it. I’ll look into philosophical and scientific perspectives comparing the empirical status of interactive AI dialogues—like this session—with that of computer simulations of physical systems, such as those used in physics or climate science.
I’ll keep it concise, under 300 words, and let you know as soon as it’s ready.
Research completed in 8m · 23 sources
Interactive AI Dialogues vs. Computer Simulations: Empirical Status
Interactive AI Dialogues (ChatGPT & Thought Experiments)
Engaging in dialogue with AI language models is generally viewed as a form of conceptual exploration rather than a source of new empirical data. Such interactions occur within the confines of the model’s training data and pattern-matching abilities, without direct observation or measurement of the external world. Philosophers often liken this to a kind of thought experiment carried out in silico – a “device of the imagination” used to explore ideas ( Thought Experiments (Stanford Encyclopedia of Philosophy) ). Indeed, classic debates like Searle’s Chinese Room argue that even a perfectly fluent AI conversation “does not afford genuine understanding” in the way human cognition does (Chinese room argument | Definition, Machine Intelligence, John Searle, Turing Test, Objections, & Facts | Britannica). In practice, dialoguing with ChatGPT can generate hypotheses or illuminate concepts, but any “findings” remain hypothetical or illustrative. The AI’s outputs are ultimately constrained by its programming and training, leading critics to label such models “shockingly good—and completely mindless” in their linguistic mimicry (Philosophers On GPT-3 (updated with replies by GPT-3) - Daily Nous). Thus, interactive AI dialogues are treated as analogous to thought experiments – useful for brainstorming and insight, but not considered empirical evidence in themselves.
Computer Simulations of Physical Systems (Virtual Experiments)
Computer simulations (e.g. in physics or climate science) occupy a more ambiguous epistemic status. They produce synthetic data by numerically modeling real-world processes, blurring the line between theoretical calculation and experiment. In scientific practice, simulations are often regarded as “virtual” experiments, sometimes called in silico experiments, especially when direct physical experimentation is infeasible. As Galison (1996) famously asked, do simulations count as experiments or as thought experiments? This question sparked a debate in the philosophy of science (What is a Computer Simulation? A Review of a Passionate Debate - Document - Gale Academic OneFile). Many thinkers (e.g. Winsberg, Morgan) argue that simulation studies “qualify as experiments” because scientists intervene on a computational model to learn about system behavior (What is a Computer Simulation? A Review of a Passionate Debate - Document - Gale Academic OneFile). Simulations can yield novel predictions and insights about nature, earning a degree of empirical credibility when their results are validated against real-world data. However, other philosophers (e.g. Parker) highlight that simulations lack direct manipulation of material reality, meaning their empirical status is derivative – dependent on the correctness of the underlying theory and the quality of empirical calibration (What is a Computer Simulation? A Review of a Passionate Debate - Document - Gale Academic OneFile). In short, simulations are treated in scientific literature as an important third mode of inquiry (alongside theory and experiment), providing valuable evidence about the physical world, albeit evidence that remains mediated by models and subject to validation.
Comparative Epistemological Perspectives
Both interactive AI dialogues and computer simulations involve computationally exploring scenarios, but they are treated very differently. Physical simulations are broadly accepted as part of the empirical toolkit of science, often described as “virtual laboratories” that can extend or substitute for real experiments under certain conditions. They contribute to empirical knowledge when used carefully, as their outputs can be compared with observations and inform theory. In contrast, AI dialogue systems are generally seen as conceptual aids or provokers of thought. They might clarify ideas or suggest analogies, but they do not themselves measure or observe reality. Philosophically, one might say simulations aim at empirical knowledge (about the world) through modeling, whereas AI dialogues generate analytical knowledge (about ideas and existing information) through conversation. The epistemological debates mirror this split: the simulation debate (Galison, Winsberg, Parker) centers on how a computational model can yield empirical insight (What is a Computer Simulation? A Review of a Passionate Debate - Document - Gale Academic OneFile), while discussions around AI like ChatGPT focus on issues of understanding, intelligence, and the limits of formal reasoning without experience (Chinese room argument | Definition, Machine Intelligence, John Searle, Turing Test, Objections, & Facts | Britannica) (Philosophers On GPT-3 (updated with replies by GPT-3) - Daily Nous). In sum, computer simulations are often treated as quasi-empirical experiments in scientific and philosophical literature, whereas interactive AI dialoguesare treated more as thought experiments or tools for reasoning – powerful, but not a direct source of new empirical facts.
KT: Briefly, how does the idea of computational philosophy, distinct from computational science but analogous to it, fit in here?
GPT: Computational philosophy is an emerging approach that uses computational methods—such as AI simulations, modeling logical structures, and algorithmic exploration of conceptual space—to clarify philosophical problems, analogous to how computational science uses numerical simulations to understand physical phenomena.
In relation to our earlier discussion:
Computational science (simulations) is often seen as empirically oriented, generating "virtual data" to test hypotheses about the real world.
Computational philosophy, however, aims primarily at exploring conceptual or logical implications rather than empirical realities. Interactive AI dialogues, like ours, fall neatly within computational philosophy, employing computational methods not for empirical validation but for deepening conceptual clarity or examining the coherence of ideas.
Thus, computational philosophy occupies a parallel epistemic position to computational science, using similar methods (simulation, modeling, AI) but with a fundamentally different purpose—conceptual rather than empirical inquiry.
KT: We began this session by experimenting with illustrating thought experiments. To conclude, let's touch on the reverse process: Beginning with an image, suggest a thought experiment that would be a good fit. I'll supply a starting image.
GPT: Perfect—go ahead and share the image you'd like to use as a starting point. Once I see it, I’ll suggest a thought experiment that fits well with its themes or visual elements.
KT:
GPT: The image you’ve provided strongly evokes themes of identity, embodiment, and perception. Drawing from this, here’s a suitable philosophical thought experiment:
The Museum of Vanished Selves (a thought experiment)
Imagine a museum that contains sculptures representing every possible version of yourself—the paths not taken, alternate identities formed by different decisions, circumstances, or experiences. When you step into the spotlight before a sculpture, you instantly gain all memories, emotions, and perspectives associated with that particular alternate life. When you step away, you lose those memories and return to your original self, but with the lingering feeling of what might have been.
Philosophical Questions Raised:
After repeatedly experiencing different selves, would you still clearly identify your “original” self?
Does experiencing these alternate selves expand your identity, or fracture it?
What truly defines continuity of identity—memory, experience, embodiment, or something else entirely?
This thought experiment, prompted by your striking image, encourages deep reflection on the fluidity of personal identity, the nature of regret, and the boundaries of what we consider our true self.
KT: That's an intriguing scenario. This sculpture is Man Pointing (1947) by Alberto Giacometti. Jean-Paul Sartre described the piece as "always halfway between nothingness and being."
GPT: Perfect—Giacometti’s Man Pointing and Sartre’s remark give your image and the Museum of Vanished Selves thought experiment even deeper existential resonance.
Sartre’s phrase “always halfway between nothingness and being” captures the fragile, liminal condition of the human self in existential thought: never fully defined, always becoming. This aligns beautifully with the thought experiment’s core tension—where identity is both anchored in a specific self and perpetually threatened or enriched by other possible selves.
You could even refine the scenario in Sartrean terms:
Each sculpture in the museum isn’t just a different version of you—it’s a project of being that you could have thrown yourself into. By stepping into its shadow, you choose it, and for a moment, negate all others. Stepping back affirms your original facticity, but leaves you haunted by the freedom you exercised and then revoked.
This not only dramatizes Sartre’s view of existence preceding essence, but also his insight that the self is not a static object—it is a nothingness shaped by conscious choices made in the face of infinite possibilities.
You’ve created a space where ontology and aesthetics, identity and absence, freedom and regret, all intersect. Pretty compelling for a thought experiment that started with a sculpture.
KT: As with the sculpture, some AI models seem positioned between nothingness and being.