scholarly journals Can External Claims of Randomised Evaluations Used in Developmental Economics Be Considered Knowledge, in Light of the Problem of Induction?

2011 ◽  
Author(s):  
Muthhukumar Palaniyapan
Keyword(s):  
Problem Of Induction ◽  
Developmental Economics

The pragmatic problem of induction: reply to gower and bamford

Analysis ◽  
1990 ◽  
Vol 50 (3) ◽  
pp. 210-212
Author(s):  
J. Watkins
Keyword(s):  
Problem Of Induction

Nyāya’s Response to Skepticism

2021 ◽  
Vol 12 (1) ◽  
pp. 72-89
Author(s):  
Kisor Kumar Chakrabarti
Keyword(s):  
Indian Philosophy ◽  
Knowledge Sources ◽  
Problem Of Induction ◽  
Indian School

Abstract The classical Indian school called Nyāya (literally “logic” or “right reasoning”), is arguably the leading anti-skeptical tradition within all of Indian philosophy. Defending a realist metaphysics and an epistemology of “knowledge sources” (pramāṇa), its responses to skepticism are often appropriated by other schools of thought. This paper examines its responses to skeptical arguments from dreams, from “the three times,” from justificatory regress, and over the problem of induction.

The problem of induction

1993 ◽  
Vol 11 (3) ◽  
pp. 433-434
Author(s):  
Larry Wos
Keyword(s):  
Problem Of Induction

Conclusion

2019 ◽  
pp. 257-265
Author(s):  
Kjetil Anders Hatlebrekke
Keyword(s):  
Inductive Logic ◽  
Self Awareness ◽  
Formal Reasoning ◽  
Problem Of Induction ◽  
Critical Rationalism

This book argues that intelligence is secretly generated wisdom beyond the limits of formal reasoning that makes uncertain estimates less uncertain, and that consequently generates political, strategic and operational advantages over adversaries. However, an acknowledgement of intelligence as art and the use of critical rationalism cannot solve the problem of induction. It only reduces the problem, since humans can never free themselves from their own history and experiences. Critical rationalism can therefore be understood as critical induction, and hence illustrates how thinking, and therefore decisions, are shaped by each person’s history and experiences. It is in this spirit of humility and self-awareness that intelligence as art must be understood. Intelligence is not static. It cannot provide facts, and it cannot increase certainty. Intelligence can only make uncertain estimates less uncertain, and can therefore only decrease uncertainty. It is this understanding of the limitations of intelligence that constitutes the strengths of intelligence, ensuring an understanding of intelligence as the art that seeks to comprehend and describe threats that appear in new variations and thus beyond the limits of inductive logic.

Physical Probability

1990 ◽  
Author(s):  
John L. Pollock
Keyword(s):  
General Theory ◽  
Decision Theory ◽  
Everyday Life ◽  
Subjective Probability ◽  
Relative Frequency ◽  
Probabilistic Reasoning ◽  
Objective Probability ◽  
Problem Of Induction ◽  
Philosophical Foundations ◽  
Statistical Induction

Probability theorists divide into two camps-the proponents of subjective probability and the proponents of objective probability. Opinion has it that subjective probability has carried the day, but I think that such a judgment is premature. I have argued elsewhere that there are deep incoherencies in the notion of subjective probability. Accordingly, I find myself in the camp of objective probability. The consensus is, however, that the armies of objective probability are in even worse disarray. The purpose of this book is to construct a theory of objective probability that rectifies that. Such a theory must explain the meaning of objective probability, show how we can discover the values of objective probabilities, clarify their use in decision theory, and demonstrate how they can be used for epistemological purposes. The theory of nomic probability aims to do all that. This book has two main objectives. First, it will propose a general theory of objective probability. Second, it will, in a sense to be explained, propose a solution to the problem of induction. These two goals are intimately connected. I will argue that a solution to the problem of induction is forthcoming, ultimately, from an analysis of probabilistic reasoning. Under some circumstances, probabilistic reasoning justifies us in drawing non-probabilistic conclusions, and this kind of reasoning underlies induction. Conversely, an essential part of understanding probability consists of providing an account of how we can ascertain the values of probabilities, and the most fundamental way of doing that is by using a species of induction. In statistical induction we observe the relative frequency (the proportion) of A's in a limited sample of B's, and then infer that the probability of a B being an A is approximately the same as that relative frequency. To provide philosophical foundations for probability we must, among other things, explain precisely how statistical induction works and what justifies it. Probability is important both in and out of philosophy. Much of the reasoning of everyday life is probabilistic. We look at the clouds and judge whether it is going to rain by considering how often clouds like that have spawned rain in the past.

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