Emergent Understanding
Tackling "emergence" with frameworks and simulations
The first part of this dialogue explores the notion of emergence through the lens of a recent paper that ChatGPT endeavors to explain in simple terms. The discussion then turns to the use of simulations to demonstrate emergence, touching on certain technical perils encountered in such a project. Warning: epistemic risks ahead!
For a fuller introduction to emergence, listen to the “Emergence and Layers of Reality” episode of Sean Carroll’s Mindscape podcast.
Interlocutor: ChatGPT-4o
KT: Read "What Emergence Can Possibly Mean" by Sean Carroll and Achyuth Parola and explain it in everyday, easy-to-follow language.
GPT: The paper “What Emergence Can Possibly Mean” by Sean Carroll and Achyuth Parola explores the idea of “emergence” – how larger systems exhibit behaviors and properties that aren’t obvious from the behaviors of their smaller parts. Here’s a simplified explanation:
What is Emergence?
Emergence happens when you look at a system made of many parts and notice that, as a whole, it behaves in ways you wouldn’t predict by just studying the parts individually. For instance:
Temperature is a property of many molecules moving around but doesn’t make sense for a single molecule.
Traffic patterns arise from many cars on the road but can’t be predicted by looking at one car.
Levels of Description
Scientists often describe systems at two levels:
Micro Level: This is the “small-scale” view – the particles, molecules, or individual agents.
Macro Level: This is the “big-picture” view – the table, the weather, or society.
Emergence connects these two levels. For example, even though chairs are made of atoms, you can describe “sitting” without knowing quantum physics.
Types of Emergence
Carroll and Parola propose a way to classify emergence into categories, based on how the micro and macro levels relate:
1. Type-0: Featureless Emergence
This is the simplest case. The macro system can be described as a “summary” of the micro system.
Example: Classical physics (macro) comes out of quantum physics (micro) in many situations.
No “new” properties are added; it’s just a different perspective.
2. Type-1: Local Emergence
Macro systems are built from collections of micro systems in a straightforward way.
Example: A fluid’s properties (like pressure and flow) emerge from molecules interacting locally.
Subtypes:
Type-1a (Direct): Easy to predict the macro from the micro.
Type-1b (Complex): Harder to predict, requiring a lot of data or computation.
3. Type-2: Nonlocal Emergence
Macro properties depend on widely spread-out parts of the micro system.
Example: Social trends depend on how people interact globally, not just locally.
These relationships often seem “non-local,” meaning effects aren’t limited to neighbors.
4. Type-3: Augmented (Strong) Emergence
New features appear at the macro level that don’t directly relate to the micro level.
Example: Consciousness might be a “strongly emergent” property – it isn’t just neurons firing but something more.
This is controversial because it suggests that macro systems can’t always be reduced to their parts.
Why is Emergence Useful?
Emergence explains why we can study weather, biology, or society without knowing every atom involved. It also provides a way to think about new phenomena, like consciousness or life, that are hard to break down into smaller parts.
Key Challenges
Novelty: How do we define what’s “new” in an emergent property? Is it just surprising, or truly independent of the parts?
Physics and Strong Emergence: If something truly emergent exists (like consciousness), does that mean our current physical laws are incomplete?
By breaking emergence into these types, the authors aim to provide a clear framework for understanding how different sciences connect and why macro-level phenomena can be so fascinating and complex.
KT: Briefly, and in simple terms, what's new about this classification scheme? How does it relate to "weak" and "strong" emergence?
GPT: The new thing about this classification scheme is that it organizes different types of emergence based on how macro-level properties relate to micro-level systems in terms of dynamics (how things change over time) and connections (how parts are put together). It avoids subjective ideas like whether something seems “surprising” and focuses on clear, structural differences between types.
Key Innovations:
Precise Levels: It breaks down emergence into four specific types (Type-0 to Type-3), rather than just the vague “weak” and “strong” categories used before.
Focus on Dynamics: It emphasizes how systems evolve over time and whether macro-level behaviors can be directly linked to micro-level rules.
Role of Parts: It carefully considers how macro systems are built from micro parts, distinguishing between local effects (nearby interactions) and nonlocal effects (widespread influences).
Relation to “Weak” and “Strong” Emergence:
Weak Emergence: Types 0, 1, and 2 fall under this category because macro properties can (in principle) be fully explained by the micro rules, even if it’s complex or surprising.
Strong Emergence: Type-3 corresponds to this. It includes genuinely new features at the macro level that aren’t derivable from micro-level rules, challenging the idea that “the whole is just the sum of its parts.”
This scheme offers more clarity and nuance, helping scientists and philosophers pinpoint exactly what kind of emergence they’re dealing with in different systems.
KT: Considering the transcript of Carroll's recent podcast episode on this topic, how does "emergence" relate to computer simulations?
GPT: In Sean Carroll’s podcast episode, he explains how “emergence” relates to computer simulations by illustrating that weak emergence (a common type of emergence) can often be demonstrated through computational models. Here’s the connection in simple terms:
1. Weak Emergence and Simulations:
Weak emergence occurs when higher-level behaviors in a system can be explained by the interactions of its smaller parts, even if this process is complex or counterintuitive.
Carroll highlights that if you have a good understanding of the microscopic rules (e.g., the behavior of atoms), you can simulate these rules on a computer to reproduce the higher-level behaviors. For instance:
The motion of a crumpled ball of paper can be predicted by simulating all the forces acting on its atoms.
Similarly, the temperature or pressure in a gas can be derived by modeling interactions among individual molecules.
2. Simulations Prove the Connection:
Running such simulations confirms that the macro-level patterns (like the trajectory of a paper ball or fluid dynamics) emerge logically from micro-level rules.
This process grounds weak emergence in something testable: if the simulation reproduces reality, the macro-behavior is indeed derivable from the micro-rules.
3. The Limit of Simulations for Strong Emergence:
Strong emergence, on the other hand, refers to macro-level behaviors that cannot, even in principle, be predicted by micro-level rules. In this case, simulations wouldn’t work because the higher-level properties would involve genuinely new principles not captured by the micro-level framework.
4. The Example of Mark Bedau’s Proposal:
Carroll mentions Mark Bedau’s approach to operationalizing weak emergence. Bedau suggests that if you can simulate a system using only the micro-rules and get accurate macro-predictions, you’ve demonstrated weak emergence. This is a practical way to evaluate the relationship between micro and macro levels.
In summary, computer simulations serve as a powerful tool to confirm weak emergence by showing how complex macro-level patterns can arise from micro-level rules. However, they cannot address strong emergence, which would require fundamentally new principles beyond the micro-level theory.
KT: A problem with computer simulations is that we are often in the position of having insufficient resources to carry out an effort such as Bedau proposes; such simulations would be prohibitively expensive.
GPT: You’re absolutely right. Simulating complex systems at the micro-level, as Bedau suggests, can be computationally expensive and resource-intensive. This is especially true for systems with vast numbers of interacting components, like modeling all atoms in a fluid or the brain’s neural activity.
Instead, scientists often use coarse-grained models, focusing only on the essential variables that influence macro-level behavior. These models approximate the system’s dynamics, making the problem more manageable while sacrificing some microscopic detail.
This highlights the importance of identifying what information is necessary to capture emergence effectively without needing exhaustive simulations.
KT: We've been discussing the emergence of phenomena at larger scales based on physics at smaller ones. In conducting a simulation of a coarse-grained model, our results could be in error if the model does not account for all relevant physics at smaller scales. How do scientists use "sub-grid" models to address this in simulations? Consider the "Computer Simulations in Science" article by Eric Winsberg in the Stanford Encyclopedia of Philosophy.
GPT: Sub-grid models address the limitations of coarse-grained simulations by incorporating small-scale processes that cannot be explicitly resolved within the simulation grid. In Eric Winsberg’s discussion, sub-grid modeling is described as a multiscale simulation technique often used in fields like climate science and fluid dynamics.
How Sub-Grid Models Work:
Coarse Graining: Simulations divide the system into discrete grid cells, each representing average properties (e.g., temperature, velocity).
Small-Scale Effects: Processes within these cells, such as turbulence or molecular interactions, are too fine-grained to model directly.
Parameterization: Sub-grid models replace these processes with simplified mathematical representations. For example, turbulence effects are approximated by “eddy viscosity” models, while in climate models, cloud formation might be tied to local humidity.
This approach balances computational feasibility and accuracy by estimating unresolved physics without directly simulating every microscopic interaction.
KT: There's also the situation in which a coarse-grained model requires ever more refinement in space or time to follow the development of a system. A sub-grid model for smaller scales is necessitated simply by a desire to continue the simulation at larger scales, even when there is no new small-scale physics to incorporate.
GPT: You’re correct—sub-grid models are often employed not because new small-scale physics is introduced, but because the system’s evolution requires finer spatial or temporal resolution. In such cases, coarse-grained models struggle to capture the necessary details as the dynamics intensify or develop.
When smaller scales become relevant to the larger-scale simulation, sub-grid models approximate these unresolved scales. For instance, in fluid dynamics, as turbulence develops, finer-scale features affect the overall flow. Rather than fully resolving these scales (computationally prohibitive), sub-grid models, like eddy viscosity or other parameterizations, estimate their influence.
This approach allows the simulation to continue evolving at large scales without exceeding computational limits. The goal is to maintain fidelity while balancing resolution and efficiency.
KT: Suppose we aim to use a simulation to demonstrate emergence of a phenomenon, but sub-grid models must be included in the simulation due to computational limits. What risks are introduced by the sub-grid models?
GPT: Using sub-grid models in simulations to demonstrate emergence introduces several risks. One key issue is that sub-grid models involve approximations that may oversimplify or misrepresent small-scale processes. This can affect the accuracy of the simulation’s larger-scale emergent behavior.
Another risk is parameter sensitivity. Sub-grid models often rely on parameters that may not be well-constrained or universal. If these parameters are tuned incorrectly, the emergent phenomenon might appear inaccurately or even artificially.
Sub-grid models can also introduce spurious dynamics. Since they are not derived directly from the underlying physics, their approximations might produce artifacts that interfere with genuine emergent patterns. This undermines confidence in the observed outcomes.
Finally, the presence of sub-grid models complicates claims about emergence, as one cannot easily distinguish whether the observed phenomenon arises from the system’s intrinsic dynamics or is an artifact of the sub-grid approximations. This can weaken the explanatory power of the simulation.
KT: On the other hand, proceeding without sub-grid models also introduces epistemic risks when seeking emergence.
GPT: Proceeding without sub-grid models introduces significant epistemic risks when seeking to demonstrate emergence. Without them, small-scale processes that influence the larger-scale behavior may be ignored or misrepresented due to the coarse resolution of the simulation. This omission can result in inaccurate or incomplete predictions about the emergent phenomenon.
By neglecting sub-grid dynamics, the simulation risks producing artifacts or failing to capture key interactions that drive emergence. For example, in fluid dynamics, unresolved turbulence can disrupt predictions of flow behavior, obscuring whether the observed patterns truly emerge from the system’s natural dynamics.
Another risk is oversimplification. Ignoring small-scale effects might lead to models that are computationally simpler but do not adequately represent the complexity required for accurate emergent behavior. This creates a gap between the simulated system and the target system, potentially invalidating conclusions about emergence.
In both cases, whether including or excluding sub-grid models, careful validation and sensitivity analysis are crucial to ensure that the simulation’s results are robust and meaningful.
KT: One might say something similar with respect to the output of AI language models.
GPT: Absolutely, similar epistemic risks apply to the output of AI language models. These models are trained on vast datasets and use complex, high-level approximations of language dynamics, analogous to how sub-grid models approximate small-scale physics. This introduces risks in how their outputs are interpreted.
When language models generate responses, they rely on statistical patterns and learned associations, not an underlying “ground truth.” This can lead to artifacts, such as plausible-sounding but incorrect or misleading statements. Like sub-grid models, AI outputs may capture broad patterns but fail to account for finer details or rare cases, introducing epistemic gaps.
Additionally, the model’s “parameters” (e.g., weights and biases) are tuned during training, much like the parameters in sub-grid models. If the training data or tuning processes are biased or incomplete, the output risks reinforcing inaccuracies or systemic biases, akin to a sub-grid model producing spurious dynamics.
Just as simulations require careful validation to ensure emergent phenomena are genuine and not artifacts, interpreting AI output requires critical evaluation to distinguish meaningful insights from limitations or errors of the model. In both cases, the complexity of the system highlights the challenges of relying on approximations to understand complex phenomena.
KT: A conclusion has emerged: Let the users of models beware.