On emergence again

How did this come about?

I was chatting with chatGPT about strong emergence and it asked me what I think about it. I explained that I am torn as I am physicist (so tend to be deterministic) but also witness the need for new categories and relationships between them which cannot be computed using lower level concepts. chatGPT recommended to look at Epistemological Emergence. Here are some details about it provided by new friend (version 4.o). I find this concept particularly useful for scientists like myself.

ChatGTP on epistemological emergence

Epistemological emergence is a concept that acknowledges the emergence of new properties, behaviors, or patterns in complex systems while maintaining that these emergent phenomena are ultimately rooted in the underlying deterministic laws. This concept is distinct from strong ontological emergence, which posits that emergent properties are fundamentally new and irreducible. Here’s a deeper look into epistemological emergence:

Key Aspects of Epistemological Emergence

Predictability and Complexity:

- Practical Unpredictability: Even if a system is governed by deterministic laws, the complexity of interactions can make it practically impossible to predict higher-level behaviors. This unpredictability is not due to the absence of underlying laws but to the immense computational complexity.

- Simplified Models: In many cases, higher-level models are used because they simplify the understanding and prediction of complex systems, even if these models are, in theory, derivable from lower-level laws.

Levels of Explanation:

- Multiple Levels: Epistemological emergence recognizes that different levels of explanation (e.g., physical, chemical, biological, psychological) are useful and necessary. Each level provides insights that are not easily gleaned from lower levels.

- Context-Dependence: Higher-level phenomena often depend on the specific context and interactions at that level, which may not be easily inferable from lower-level descriptions.

Autonomy of Disciplines:

- Interdisciplinary Cooperation: This concept supports the idea that different scientific disciplines can operate with a degree of autonomy while still being consistent with each other. For instance, biology can develop theories about life processes without constantly referring back to physics.

- Effective Theories: Higher-level sciences develop their own effective theories that are robust and predictive within their domains, even though these theories are grounded in the underlying physical laws.

Examples of Epistemological Emergence

Thermodynamics and Statistical Mechanics:

- Temperature and Pressure: Concepts like temperature and pressure emerge from the collective behavior of particles in a gas. While these properties can be derived from statistical mechanics, they provide a more practical and comprehensible way to describe and predict the behavior of gases.

Biology and Chemistry:

- Life Processes: Biological phenomena such as metabolism, growth, and reproduction can be understood through biochemical processes. However, the complexity of interactions at the biological level means that biologists often use higher-level descriptions and models.

Cognitive Science and Neuroscience:

- Mental States: Cognitive phenomena such as perception, memory, and decision-making emerge from neural processes. While these can, in principle, be explained by neuroscience, cognitive science provides useful models and theories to understand these phenomena.

Philosophical Implications

Reductionism vs. Holism:

- Pragmatic Reductionism: Epistemological emergence supports a form of pragmatic reductionism, where lower-level explanations are acknowledged but higher-level models are used for practical understanding.

- Holistic Understanding: It also aligns with a holistic approach, recognizing that higher-level properties are important and meaningful in their own right.

Scientific Realism:

- Real but Derived: Emergent properties are considered real and significant, even if they are ultimately derived from underlying laws. This view allows for a realist interpretation of emergent phenomena without invoking ontological novelty.

Explanatory Pluralism:

- Multiple Valid Explanations: Different explanatory frameworks are valid and necessary to fully understand complex systems. This pluralism respects the contributions of various scientific disciplines.