You have just come downstairs in the morning. You look at the clock and note it is 8.50 (we’ll use the example of it being an analogue clock). This is indeed the time. However, the clock actually stopped working at 8.50pm last night and it just so happens that you are looking at it exactly 12 hours later. So did you know the correct time when you looked at the clock? The instinctive response is that of course you didn’t. You are looking at a broken clock, therefore you didn’t actually know the time, even though by chance it was correct. However, the JTB definition would have said that you did. The time was correct (true), you believe it to be true, and looking at a clock is a very reasonable (justified) way to know what time it is. Therefore, you have a justified true belief. I think from this you can get a good idea of what the Gettier cases show. In essence, they show that, with a bit of luck, you can actually end up with numerous examples of justified true belief, that we intuitively feel are clearly not knowledge. Indeed, there is even a formula that we can create to describe what would constitute a Gettier case:
- Take a belief that is justified but would normally be false
- Modify the details of the case so that, by luck, it is actually true
The first is the problem of skepticism. By working through some of the problems that Gettier Cases pose, we may actually end up finding that we don’t know very much at all. One answer to the Gettier problem is to make our true belief infallible. This is also problematic. I am not planning on going deep down the rabbit hole of skepticism in this series, but infallibility is quite a high bar to clear for something as important to our daily lives as knowledge. Indeed, some forms of radical skepticism (Descartes’ thought experiment is what springs to my mind) would suggest that we can ‘know’ almost nothing. The presence of hallucinations and illusions, the fallibility of our senses, and the innate urge to find patterns where they may not exist, mean we may not actually be well connected to the reality that we think we are. The Matrix might actually be true! If this is hard to believe (which I find that it is), what is harder is where you draw the line between what is possible (we may be in the Matrix if we really think it all through), and what is probable and useful. But that means we have to find some nondescript threshold, somewhere away from infallibility, that defines when we truly know something.
The second point, following on from this first conclusion, is that of luck. If luck can play such a role in how things play out, where does this leave our system of knowledge. Isn’t this just an excellent breeding ground for the retrospective attribution of justification that we see in the different pseudosciences? If my horoscope uses broad enough language, it can be true enough times to make me stop and think. And if justification doesn’t actually help us separate what we know, then the pseudoscientific stories that get retrofitted are as useful to defining knowledge as any others (I am almost certainly overstating the point here though). So to summarise, it is actually a bit of an issue when we want to start to look at the different methods of getting to the truth as there almost seems to be some need to set a threshold for the concept of knowledge to make sense.
A couple of examples include the no-false lemmas and no-defeaters arguments. These appear to be centred around the confidence of the justification process. A lemma is a term for a false assumption, and a defeater is a piece of information that exists (although not known to the believer) that would ‘defeat’ their justification. The proponents of these responses have suggested mitigating against these factors by adding them as a fourth component of JTB, thereby patching it. These seem to perform fairly well in some of the Gettier cases that have been described. However, greater exploration can lead us back to the spiral of problems that we see in skepticism when we have to decide where we want to set our thresholds. What counts as a false assumption? How wide do we have to cast our net to make sure that there are no defeaters? If we think about this broad enough we end up back with the problem that we may not actually know anything with adequate confidence.
The reliabilist theory comes up against similar problems. This theory replaces the concept of justification with the component that the true belief must be formed from a reliable process. This seems like another reasonable definition, but comes apart again with some simple examples-the lottery example probably being the best. Using a reliabilist approach, we would be able to say that we know that we haven’t won the lottery every time that we buy a ticket, because the odds would say that this is a reliable route to a belief (>99.999%). However, it runs against our intuitions so say that we know this even before the numbers have been drawn. This leads us to think that there is something different from pure probability that goes into determining whether we know something.
Links & References
- Lacewing, M. Philosophy for AS and A level: Epistemology and Moral Philosophy. Routledge. 2017.
- Epistemology. The Basics of Philosophy. https://www.philosophybasics.com/branch_epistemology.html
- Stanford Encyclopedia of Philosophy. Epistemology. 2020. https://plato.stanford.edu/entries/epistemology/
- Wireless Philosophy. Philosophy - Epistemology. Youtube. 2016. https://www.youtube.com/watch?v=r_Y3utIeTPg&list=PLtKNX4SfKpzUxuye9OdaRfL5fbpGa3bH5&index=1
- University of Edinburgh. Introduction to Philosophy. Coursera.
- Hetherington, S. Gettier problems. Internet Encyclopaedia of Philosophy. https://iep.utm.edu/gettier/