Understanding versus Knowledge
A response to Julian who posed the question: What is Understanding?
The difference between understanding and mere knowledge is that understanding is always connected to your core beliefs whereas knowledge is simply true belief. Knowledge may or may not be connected to one's core beliefs but understanding always must connect to the core beliefs and forms a coherent web of beliefs with it. This is why knowledge may sometimes overload but understanding never does. Knowledge that passes into understanding always coheres with the bedrock of core belief. Knowledge that doesn’t may exist in a state of free float without being connected to a wider web of belief as in the Quinean sense.1
Understanding is not simply the correct answer to a particular question, but increases the coherence of our entire belief system. This view has been developed in the context of traditional epistemology (Harman, Lehrer) as well as the philosophy of science (Thagard, Kitcher). In the latter context, the terms “explanatory unification” and “consilience” have been introduced to promote the idea that good explanations necessarily tend to produce a more unified body of knowledge. The more unified the body of knowledge, the more understanding has been generated. An epistemic agent can increase their store of knowledge without necessarily contributing to their understanding if it is not well connected or structured according to core beliefs.
What are core beliefs?
Core beliefs are beliefs that an auto-intentional agent must have in order to minimally interact with the world. For one thing, an auto-intentional agent must have self-representation. Next, an AIA must form beliefs about its wants (in other words, values). It must be able to represent its value (i.e. self-worth) in relation to other agents. Beliefs about self and beliefs about what self values basically correspond to the disciplines known as ontology and axiology. Many beliefs are then downstream of this. These are core beliefs because any worldview would need to have these basic components in place to be viable.
Understanding is more epistemically valuable than knowledge. Knowledge is merely true belief. But understanding is true belief that is relevant for the agent to have. Understanding does not have to be as accurate as knowledge. The criteria for what counts as true can be looser and the model would still count as understanding while it might not pass the truth criteria for strict knowledge. There's a criteria of usefulness or actionability that's by definition necessary to understanding that may or may not be possessed by knowledge. Therefore, knowledge can be useless despite being true, as in the case of trivia. Understanding, however, is useful regardless of its accuracy. Angela Potochnik argues that idealization is central to science and, relatedly, that science does not track truth directly but rather aims to support human cognitive and practical ends.2 For example, the rudimentary atomic models—though they are not strictly true—may still count as understanding even though we know that they are merely pedagogical tools that do not reflect the reality of atoms perfectly. These models are in a sense, good enough. Another example would be Newtonian mechanics or frictionless planes. These are idealizations that break down when a fundamental assumption is not met. Yet, we still teach high schoolers about imaginary planes with impossible properties and we still teach Newtonian mechanics instead of jumping straight to quantum mechanics even though the models of the latter completely supersede the former. The simplicity of these untrue abstractions are sufficiently useful for generating understanding about engineering that we continue to find them useful enough (so long as our calculations do not have to deal with near-lightspeed conditions.)
All naturalistic sciences presuppose causal explanation as the gold standard for producing knowledge. Obviously, a physicist working in a naturalistic science where the primary objects of study are physical phenomena, would consider causal models to be the most relevant to her understanding. But for those dealing with the actions of goal-oriented agents, a teleological model might be more relevant. The physical sciences—physics and chemistry—abstract away the notion of goal-orientedness or purpose from their models of reality because it is convenient. This has become the base for which naturalism would launch from. However, evolutionary scientists dealing with agents capable of self-replication and, therefore, agents amenable to selectionist pressures might find this reductionist causal approach to be inadequate to producing understanding of purposive agents. They, therefore, cannot afford to make the same naturalistic assumptions without great cost to their understanding.
Agency
Since knowledge can be useless and often leads to choice paralysis due to the agent being inundated by irrelevant but true beliefs, understanding is much more valuable to an epistemic agent. This has implications because as Jenann Ishmael3 claims, knowledge is the enemy of agency. Understanding on the other hand, always enhances agency by furnishing the agent with information whose purpose is relevant with the agent’s aims.
Grimm, Stephen, "Understanding", The Stanford Encyclopedia of Philosophy (Summer 2021 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/sum2021/entries/understanding/>
Angela Potochnik, Idealization and the Aims of Science (2017)
Jenann Ismael "A Participatory Universe in the realist mode" - Mind and Agency Conference <
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