# The Many Ways of Knowing

_NB: This is an excerpt from my forthcoming book,_ [Fundamental Uncertainty](https://www.fundamentaluncertainty.com/)_. I’m posting it now because I’m writing a post for next week where I’d like to reference it._

What does it mean to say “I know”?

This might seem like a strange question to ask since knowing is such a fundamental and intuitive activity. It’s hard to imagine being a person and not knowing things. In fact, the only time in our lives when we aren’t swimming in a sea of knowledge is when we’re newborns, and we quickly wade in by learning the voice, face, and touch of our caregivers. Within days we’re able to tell them apart from strangers, and our long relationship with knowledge begins. So if we want to understand what it means to “know”, we’re going to have to spend some time exploring how we use this almost invisible word.

When we say to each other that we “know” something, we generally mean that we’re able to recall an idea, hold it in our mind, reason about it, and say things based on understanding it. Consider these examples of things I claim to know:

*   I know my friend Eric.
    
*   I know how to tie my shoes.
    
*   I know how to speak English.
    
*   I know that Paris is the capital of France.
    
*   I know what it’s like to ride a rollercoaster.
    
*   I know that if I eat too much food I’ll get a stomach ache.
    

Although all of these sentences begin “I know,” the knowledge expressed in each is not the same. Knowing that Paris is the capital of France is knowing a propositional fact. When I say that I know my friend Eric, though, I’m not claiming to state a proposition, but rather that I can recognize him by sight and sound and am familiar with his patterns of behavior. There’s also no propositional fact about what it’s like to experience the thrill of riding a roller coaster: it’s a lived experience that simply is. Rather than using “know” to mean many things, perhaps it would be useful to have different words for these different forms of knowledge.

The ancient Greeks did exactly that. They used multiple words to break down knowing into several categories, including:

*   _**episteme**_: things you know because you reasoned them from evidence, like knowing that water boils at 100 degrees celsius because you boiled a pot of water using a thermometer to see when it started to boil
    
*   _**doxa**_: things you know because others told you, like knowing what happened at a party you didn’t attend because your friend tells you
    
*   _**mathema**_: things you know because you were educated in them, like knowing how to spell words because you were taught them in school
    
*   _**gnosis**_: things you know through direct experience, like how it feels to jump in a lake
    
*   _**metis**_: practical wisdom, usually collectively constructed from many people’s experiences, and shared with others, often starting at a young age, like how you know to look both ways before crossing the street
    
*   _**techne**_: procedural knowledge that comes from doing, like the “muscle memory” of how to ride a bicycle
    

These categories aren’t perfectly distinct, though, because the same information can be known multiple ways. For example, Ada recently saw Ed wearing a traffic cone on his head. Later, she told Grace about it. As a result, Ada had gnosis of Ed’s appearance, while Grace had doxa of it. And because nothing is ever simple, Grace also eventually saw Ed wearing the traffic cone on his head and gained gnosis of her own in addition to her pre-existing doxa.

Does this mean Grace now knows more than Ada does? Maybe, if the combination of gnosis and doxa provides a deeper understanding than gnosis alone can. Or maybe gnosis trumps doxa and the value of doxastic knowledge is lost once gnosis is gained. Whatever the answer, that we can ask this question at all shows that the lines between different kinds of knowledge aren’t always clear. Perhaps that’s why English and many other languages have collapsed these distinctions into a single word for knowing.

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Yet, sometimes we need to make distinctions between different ways of knowing, as political science professor James C. Scott does in his book _Seeing Like a State_. In it, he examines how modern and historical governments have differed in their treatment of knowledge. He then goes on to explore how those differences have had significant impacts on people’s lives.

Prior to the modern era, states placed a high value on metis. They frequently saw themselves as the defenders of tradition, often to the point of killing those who dared to challenge the established order. Modern states, in contrast, often throw tradition out in favor of rational, scientifically-grounded episteme. By prioritizing episteme over metis, modern states have created a bounty of benefits for the people living in them, including more reliable food supplies, better medicine, and increased access to what were previously luxury goods. But, as Scott explains, these benefits aren’t guaranteed, and sometimes overturning tradition leads to disastrously worse outcomes.

In the middle half of the 20th century, there were numerous attempts to modernize agriculture. Many of them ended in failure. Between the 1920s and the 1970s, Russia, China, and other communist countries saw catastrophic famines resulting from forced collectivization, unrealistic production targets, and bad agricultural science. On this last point, it was the misguided theories of Soviet scientist Trofim Lysenko that magnified what might have been survivable food shortages into widespread, mass starvation.

Lysenko trusted his own episteme over everything else. He claimed that his methods were more rigorous than established science, rejected the accumulated practical knowledge of farmers, and dismissed all critics as ideologically impure. He treated his own reasoning as the only valid path to truth. When communist leaders implemented his ideas alongside their political programs, the resulting crop failures led directly to the deaths of tens of millions of people.

Over-reliance on episteme was not a problem unique to communist countries, though. In the United States, the Dust Bowl and associated food scarcity of the 1930s was the direct result of careless industrialization of farms in the 1920s. Early agricultural science thought it could achieve higher crop yields simply by working the land harder, and for about a decade this was true. Using mechanized plows and harvesting equipment, farmers converted a hundred million acres of Great Plains prairie from low productivity cattle pasture to bountiful fields of wheat and corn.

Scientific farming was a boon to the people of the United States, right up until drought hit in the 1930s. The intensive agriculture of the previous decade had damaged the natural ability of the land to protect itself, and overworked fields crumbled to dust as they baked under cloudless skies. The situation only began to turn around when rain finally returned in 1939, but it took decades for the land to fully recover, and even longer for farmers and scientists to develop and widely adopt sustainable farming practices that work in the Great Plains.

Do we know better now? We like to think so. Certainly we’re less naive because we tell each other about the failures of the past (metis) and learn about them in school (mathema). Politicians no longer propose that we solve all our problems with science, and we know that episteme doesn’t have a monopoly on the truth. Yet even as we’ve learned from our mistakes, we risk overcorrecting and forgetting the power of episteme. In fact, many would say we still don’t value episteme highly enough and too often ignore scientific results that offer us clear ways to improve the world.

But it’s not that we need to spend more effort finding the right balance between different forms of knowledge. Rather, we need to integrate all our knowledge together, whatever the source. When we privilege one way of knowing over another, we risk incomplete understanding that leaves out things we could have known. We must instead strive to update our beliefs in proportion to all the evidence we have available, and by so doing build the most accurate models of the world that we can.

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