Final Paper: The Induction Problem
Inductive reasoning is the idea that a conclusion is drawn from multiple premises. We tend to understand certain concepts by building up details from prior knowledge to reach a certain idea. However, this approach to learning has its limitations. Not all conclusions can be drawn from what we know because we aren’t always all knowing about certain concepts. We are also left with no room for drawing conclusions about the future because not all premises are consistent. Different philosophers have gone into detail to make it clear why problems of induction exist and they’ve offered up their own solutions. If exceptions are made to the process of inductive reasoning, or at least different understandings, many problems can be avoided.
To understand the problems behind induction in philosophy, it’s first necessary to comprehend what it actually is. Inductive reasoning is the process of concluding from detailed facts to reach general principles, often referred to as the scientific method (Trimmer). Through induction, we infer and interpret from what has been observed already to understand a greater idea of a whole. Because the process requires outside knowledge, this approach to comprehending general ideas requires evidence from the premises to reach a true conclusion. However, philosophical induction more so suggests a truth rather than guaranteeing it. It’s important that the evidence used in premises to reach conclusions is reliable because it is used to notice patterns to make probably conclusions. For example, there is question as to why many students have transferred out of a statistics class. For evidence, specified examples of students with failing grades are presented. Then the mathematics dep...
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...the premises will hold true in the future because it’s been an ongoing pattern. It’s reasonable to believe this has the greatest chance of predictive success and that doing otherwise is unreasonable, therefore there is justification in using inductive reasoning.
Inductive reasoning does have many flaws to it, but it is a method that we cannot avoid. Though it does not ensure one hundred percent accuracy, it does point out probable generalizations that typically lead to appropriate conclusions. Induction is unavoidable so it is necessary that all comprehend where one could go wrong with the process and how to go right. As long as it’s understood that induction does not produce perfect truths, the approach should continue to be used as regularly as it is. One cannot prevent the practice of inductive thinking, therefore we must recognize how it should be managed.
In this passage, Schulz explores the pros and cons of inductive reasoning, which she describes as “the strategy of guessing based on past experience.” She then goes on to say that this strategy is very helpful because it helps us come to conclusions much faster. Usually, these conclusions we come to are correct. However, when we make bad assumptions, inductive reasoning can be very dangerous. To extend her argument, Schulz discusses multiple examples of induction gone wrong. She talks about how stereotypes are formed due to leaping to conclusions, a bias of inductive reasoning. Along with this, Schulz brings up how induction is also responsible for the continuation of stereotypes. Also, Schulz brings up the fact that sometimes evidence can be looking us right in our faces, yet we will ignore it or distort it in order to hold on to our previously made conclusions. This is known as confirmation bias, which is “the tendency to give more weight to evidence that confirms our beliefs than to evidence that challenges them.” This is the downfall of inductive reasoning; it’s not perfect, and we’re bound to be wrong at least some of the
Hume’s problem of induction is that inductive reasoning is not, in fact, reasonable. That is, we are not justified in reasoning inductively. This is because he believes that, in order to justify induction, we must use some form of the Uniformity Principle. This Uniformity Principle (henceforth noted as UP) states “[t]hat instances, of which we have had no experience, must resemble those, of which we have had experience, and that the course of nature continues always uniformly the same” (Hume 89). He also believes that “we must provide one of two types of justification for UP: (a) Show that UP is the conclusion of a deductive argument, or (b) show that UP is based on experience” (Crumley 15). He shows that it is not possible to prove this principle deductively because of problems of circularity, and that to show that it is based on experience is to be similarly circular. That is, providing evidence for something and using this as a justification for a believe is precisely what induction is all about, and so one ends up justifying induction through induction. (Crumley 14-16)
...re some foundational beliefs that possess some degree of intrinsic justification, but as it was noted, accepting these beliefs as completely self-justifying is difficult to accept. Therefore, these foundational beliefs that possess a low degree of justification can rely on other minimally-justified beliefs for support, consequently creating a coherent foundation of sorts.
The hypothesis that is discussed by Nelson Goodman is an enumerative induction, which concludes that “all emeralds are green” since all the many emeralds we have observed prior to 2020 are green. Instinctively, this type of inductive argument looks like a good argument due to the fact that the premises are certain examples with the same properties of the conclusion. This hypothesis is confirmed by observations of green emeralds because based on our knowledge so far, all emeralds are green and no exception has shown up. In this case, the generalization of all emeralds being green is confirmed by its examples, which are green emeralds.
...et who is to determine the evidence and theory to determine whether it is ad hoc? More importantly, when interpreting this, no matter who does it, how will you get past induction when interpreting the theory and/or evidence?
Statistical Induction- is based on statistical information, it predicts something will happen with numerical probability.
Inductive reasoning is a process of applying logic in which conclusions are made from ideas, which are believed to be true most of the time. It is based on predictions and behavior.
With inductive reasoning, jumping to conclusions is what the process calls for, but what Schulz is getting at is not the problem of jumping to conclusions; it is the problem of not overturning the false accusations of the assumption, thus creating stereotypes. Schulz expresses the frustration with the stubbornness behind stereotypes by exclaiming, “If the stereotypes we generate based on the small amount of evidence could be overturned by equally small amounts of counterevidence, this particular feature of inductive reasoning wouldn’t be terribly worrisome” (371). This problem that’s birthed from inductive reasoning is what Schulz wants us to “actively combat our inductive biases: to deliberately seek out evidence that challenges our beliefs, and to take seriously such evidence when we come across it”(373). Schulz wants us to challenge evidence when confronted rather than fall into the pitfalls of ignorant assumptions. Nearing the end of the chapter, Schulz warns that with attending to counterevidence is not hard, its conscious cultivation that’s the important key, without that key, “our strongest beliefs are determined by mere accidents of fate”(377). There is a threshold of new evidence above which our opinions would be amended, but what Schulz repeatedly brings us is that in many cases, that the threshold is not
I shall also expound Ayer's theory of knowledge, as related in his book. I will show this theory to contain logical errors, making his modified version of the principle flawed from a second angle.
In many aspects of our lives, the use of faith as a basis for knowledge can be found. Whether it is faith in the advice of your teacher, faith in a God or faith in a scientific theory, it is present. But what is faith? A definition of faith in a theory of knowledge context is the confident belief or trust in a knowledge claim by a knower, without the knower having conclusive evidence. This is because if a knowledge claim is backed up by evidence, then we would use reason rather than faith as a basis for knowledge . If we define knowledge as ‘justified true belief’, it can be seen that faith, being without justification, can never fulfill this definition, and so cannot be used as a reliable basis for knowledge. However, the question arises, what if a certain knowledge claim lies outside of the realm of reason? What if a knowledge claim cannot be justified by empirical evidence and reasoning alone, such as a religious knowledge claim? It is then that faith allows the knower to decide what is knowledge and what is not, when something cannot be definitively proved through the use of evidence. When assessing faith as a basis for knowledge in the natural sciences, the fact arises that without faith in the research done before us, it is impossible to develop further knowledge on top of it. Yet at the same time, if we have unwavering faith in existing theories, they would never be challenged, and so our progress of knowledge in the natural sciences would come to a standstill. Although I intend to approach this essay in a balanced manner, this essay may be subject to a small degree of bias, due to my own non-religious viewpoint.
...fore, I can conclude that my laptop will persist in the future. We can think that we justified our belief by providing these two premises as reasoning. However, we justified it though induction and Hume states that we have no reason in believing into the inductive argument. Our argument becomes a weak one, since the second premise is unsupported. The problem of induction raised by Hume is challenge to justified true belief account because it shows how our inductive argument about the future and unobserved does not provide a good support. Therefore, we cannot get a justified belief by applying inductive principle.
In this book, Samir Okasha kick off by shortly describing the history of science. Thereafter, he moves on scientific reasoning, and provide explanation of the distinction between inductive and deductive reasoning. An important point Samir makes, is the faith that humans put into the inductive reasoning
The principles of natural selection are suitable a metaphor for how knowledge within a discipline is developed. Natural selection is the process whereby organisms better adapted to their environment tend to survive and produce more offspring. The theory of its action was first fully proposed by Charles Darwin and is now believed to be the main process that brings about evolution. It is important to keep in mind that natural selection is different from evolution as evolution is the result of natural selection. The use of this metaphor signifies that only knowledge that is favored survives to be taught to the next generation and that only the best knowledge survives. In general, knowledge can be defined as justified true belief (Ichikawa). The
Inductive reasoning is logical reasoning where people have a lot of the information and use that to reach a conclusion. It is viewing the available data and figuring out what will be the results. For instance, from an online article, it demonstrates, “Inductive reasoning is a logical process in which multiple premises, all believed true or found true most of the time, are combined to obtain a specific conclusion” (Rouse, 2013). It shows that there are a lot of ideas to analyze and calculate what the possible outcomes will be. It can also be done by looking at patterns. When looking at patterns, it is important to study it to see what is recurring. This makes it possible to predict what will happen based on the knowledge that has been collected. Inductive reasoning is using information or events that have happened in the past to see what is in store for the future.
Perhaps the greatest endeavor that owes itself to induction is science. Its claim to be in the pursuit of truth, of empirical knowledge, is entirely dependent on the validity of inductive reasoning. As such, science has developed ways and means to guarantee the validity of its conclusions; this includes randomizing samples, choosing appropriately sized sample groups and the use of statistics to calculate whether something is merely possible or is probable. Each of these methods (and there may be more) needs to be examined.