Epistemology Internet Encyclopedia of Philosophy. Epistemology is the study of knowledge. Airline E Ticket Issuing Training Manual on this page. Casper The Interactive Adventure more. Epistemologists concern themselves with a number of tasks, which we might sort into two categories. First, we must determine the nature of knowledge that is, what does it mean to say that someone knows, or fails to know, something This is a matter of understanding what knowledge is, and how to distinguish between cases in which someone knows something and cases in which someone does not know something. While there is some general agreement about some aspects of this issue, we shall see that this question is much more difficult than one might imagine. Second, we must determine the extent of human knowledge that is, how much do we, or can we, knowA Bayesian network, Bayes network, belief network, Bayesian model or probabilistic directed acyclic graphical model is a probabilistic graphical model a type of. How can we use our reason, our senses, the testimony of others, and other resources to acquire knowledge Are there limits to what we can knowFor instance, are some things unknowable Is it possible that we do not know nearly as much as we think we do Should we have a legitimate worry about skepticism, the view that we do not or cannot know anything at all While this article provides on overview of the important issues, it leaves the most basic questions unanswered epistemology will continue to be an area of philosophical discussion as long as these questions remain. Table of Contents. Kinds of Knowledge. The Nature of Propositional Knowledge. Belief. Truth. Justification. The Gettier Problem. The No False Belief Condition. AcL.jpg' alt='2Nd Edition Introduction Reasoning Definition' title='2Nd Edition Introduction Reasoning Definition' />BibMe Free Bibliography Citation Maker MLA, APA, Chicago, Harvard. Mormon tithing facts, scriptures, history, controversies, and contradictions. The No Defeaters Condition. Causal Accounts of Knowledge. The Nature of Justification. Internalism. Foundationalism. Coherentism. Externalism. The Extent of Human Knowledge. Sources of Knowledge. Nd Edition Introduction Reasoning Definition' title='2Nd Edition Introduction Reasoning Definition' />Skepticism. Cartesian Skepticism. Humean Skepticism. Numerical vs. Qualitative Identity. Humes Skepticism about Induction. Conclusion. References and Further Reading 1. Kinds of Knowledge. The term epistemology comes from the Greek episteme, meaning knowledge, and logos, meaning, roughly, study, or science, of. Logos is the root of all terms ending in ology such as psychology, anthropology and of logic, and has many other related meanings. The word knowledge and its cognates are used in a variety of ways. One common use of the word know is as an expression of psychological conviction. For instance, we might hear someone say, I just knew it wouldnt rain, but then it did. While this may be an appropriate usage, philosophers tend to use the word know in a factive sense, so that one cannot know something that is not the case. This point is discussed at greater length in section 2b below. Even if we restrict ourselves to factive usages, there are still multiple senses of knowledge, and so we need to distinguish between them. One kind of knowledge is procedural knowledge, sometimes called competence or know how for example, one can know how to ride a bicycle, or one can know how to drive from Washington, D. C. to New York. Another kind of knowledge is acquaintance knowledge or familiarity for instance, one can know the department chairperson, or one can know Philadelphia. Epistemologists typically do not focus on procedural or acquaintance knowledge, however, instead preferring to focus on propositional knowledge. A proposition is something which can be expressed by a declarative sentence, and which purports to describe a fact or a state of affairs, such as Dogs are mammals, 227, It is wrong to murder innocent people for fun. Note that a proposition may be true or false that is, it need not actually express a fact. Propositional knowledge, then, can be called knowledge that statements of propositional knowledge or the lack thereof are properly expressed using that clauses, such as He knows that Houston is in Texas, or She does not know that the square root of 8. In what follows, we will be concerned only with propositional knowledge. Propositional knowledge, obviously, encompasses knowledge about a wide range of matters scientific knowledge, geographical knowledge, mathematical knowledge, self knowledge, and knowledge about any field of study whatever. Any truth might, in principle, be knowable, although there might be unknowable truths. One goal of epistemology is to determine the criteria for knowledge so that we can know what can or cannot be known, in other words, the study of epistemology fundamentally includes the study of meta epistemology what we can know about knowledge itself. We can also distinguish between different types of propositional knowledge, based on the source of that knowledge. Non empirical or a priori knowledge is possible independently of, or prior to, any experience, and requires only the use of reason examples include knowledge of logical truths such as the law of non contradiction, as well as knowledge of abstract claims such as ethical claims or claims about various conceptual matters. Empirical or a posteriori knowledge is possible only subsequent, or posterior, to certain sense experiences in addition to the use of reason examples include knowledge of the color or shape of a physical object or knowledge of geographical locations. Some philosophers, called rationalists, believe that all knowledge is ultimately grounded upon reason others, called empiricists, believe that all knowledge is ultimately grounded upon experience. A thorough epistemology should, of course, address all kinds of knowledge, although there might be different standards for a priori and a posteriori knowledge. We can also distinguish between individual knowledge and collective knowledge. Social epistemology is the subfield of epistemology that addresses the way that groups, institutions, or other collective bodies might come to acquire knowledge. The Nature of Propositional Knowledge. Having narrowed our focus to propositional knowledge, we must ask ourselves what, exactly, constitutes knowledge. What does it mean for someone to know somethingWhat is the difference between someone who knows something and someone else who does not know it, or between something one knows and something one does not know Since the scope of knowledge is so broad, we need a general characterization of knowledge, one which is applicable to any kind of proposition whatsoever. Epistemologists have usually undertaken this task by seeking a correct and complete analysis of the concept of knowledge, in other words a set of individually necessary and jointly sufficient conditions which determine whether someone knows something. Belief. Let us begin with the observation that knowledge is a mental state that is, knowledge exists in ones mind, and unthinking things cannot know anything. Further, knowledge is a specific kind of mental state. While that clauses can also be used to describe desires and intentions, these cannot constitute knowledge. Rather, knowledge is a kind of belief. If one has no beliefs about a particular matter, one cannot have knowledge about it. For instance, suppose that I desire that I be given a raise in salary, and that I intend to do whatever I can to earn one. Suppose further that I am doubtful as to whether I will indeed be given a raise, due to the intricacies of the universitys budget and such. Given that I do not believe that I will be given a raise, I cannot be said to know that I will. Only if I am inclined to believe something can I come to know it. Similarly, thoughts that an individual has never entertained are not among his beliefs, and thus cannot be included in his body of knowledge. Bayesian network Wikipedia. A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, Bayes network, belief network, Bayesian model or probabilistic directed acyclic graphical model is a probabilistic graphical model a type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph DAG. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Tapestry Patterns Download. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Formally, Bayesian networks are DAGs whose nodes represent random variables in the Bayesian sense they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies nodes that are not connected there is no path from one of the variables to the other in the Bayesian network represent variables that are conditionally independent of each other. Each node is associated with a probability function that takes, as input, a particular set of values for the nodes parent variables, and gives as output the probability or probability distribution, if applicable of the variable represented by the node. For example, if mdisplaystyle m parent nodes represent mdisplaystyle mBoolean variables then the probability function could be represented by a table of 2mdisplaystyle 2m entries, one entry for each of the 2mdisplaystyle 2m possible combinations of its parents being true or false. Similar ideas may be applied to undirected, and possibly cyclic, graphs such as Markov networks. Efficient algorithms exist that perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables e. Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. ExampleeditSuppose that there are two events which could cause grass to be wet either the sprinkler is on or its raining. Also, suppose that the rain has a direct effect on the use of the sprinkler namely that when it rains, the sprinkler is usually not turned on. Then the situation can be modeled with a Bayesian network shown to the right. All three variables have two possible values, T for true and F for false. The joint probability function is PrG,S,RPrGS,RPrSRPrRdisplaystyle PrG,S,RPrGS,RPrSRPrRwhere the names of the variables have been abbreviated to G Grass wet yesno, S Sprinkler turned on yesno, and R Raining yesno. The model can answer questions like What is the probability that it is raining, given the grass is wet by using the conditional probability formula and summing over all nuisance variables PrRTGTPrGT,RTPrGTST,FPrGT,S,RTS,RT,FPrGT,S,Rdisplaystyle PrRTGTfrac PrGT,RTPrGTfrac sum Sin T,FPrGT,S,RTsum S,Rin T,FPrGT,S,RUsing the expansion for the joint probability function PrG,S,Rdisplaystyle PrG,S,R and the conditional probabilities from the conditional probability tables CPTs stated in the diagram, one can evaluate each term in the sums in the numerator and denominator. For example,PrGT,ST,RTPrGTST,RTPrSTRTPrRT0. PrGT,ST,RT PrGTST,RTPrSTRTPrRT 0. Then the numerical results subscripted by the associated variable values are. PrRTGT0. 0. TTT0. TFT0. TTT0. 2. 88. TTF0. TFT0. 0. TFF8. PrRTGTfrac 0. TTT0. TFT0. TTT0. 2. 88TTF0. TFT0. 0TFFfrac 8. If, on the other hand, we wish to answer an interventional question What is the probability that it would rain, given that we wet the grass the answer would be governed by the post intervention joint distribution function PrS,RdoGTPrSRPRdisplaystyle PrS,RtextdoGTPrSRPR obtained by removing the factor PrGS,Rdisplaystyle PrGS,R from the pre intervention distribution. As expected, the probability of rain is unaffected by the action PrRdoGTPrRdisplaystyle PrRtextdoGTPrR. If, moreover, we wish to predict the impact of turning the sprinkler on, we have. PrR,GdoSTPrRPrGR,STdisplaystyle PrR,GtextdoSTPrRPrGR,STwith the term PrSTRdisplaystyle PrSTR removed, showing that the action has an effect on the grass but not on the rain. These predictions may not be feasible when some of the variables are unobserved, as in most policy evaluation problems. The effect of the action doxdisplaystyle textdox can still be predicted, however, whenever a criterion called back door is satisfied. It states that, if a set Z of nodes can be observed that d separates3 or blocks all back door paths from X to Y then PrY,ZdoxPrY,Z,XxPrXxZdisplaystyle PrY,ZtextdoxPrY,Z,XxPrXxZ. A back door path is one that ends with an arrow into X. Sets that satisfy the back door criterion are called sufficient or admissible. For example, the set Z  R is admissible for predicting the effect of S  T on G, because Rd separate the only back door path S  R  G. However, if S is not observed, there is no other set that d separates this path and the effect of turning the sprinkler on S  T on the grass G cannot be predicted from passive observations. We then say that PG  doS  T is not identified. This reflects the fact that, lacking interventional data, we cannot determine if the observed dependence between S and G is due to a causal connection or is spurious apparent dependence arising from a common cause, R. Simpsons paradoxTo determine whether a causal relation is identified from an arbitrary Bayesian network with unobserved variables, one can use the three rules of do calculus14 and test whether all do terms can be removed from the expression of that relation, thus confirming that the desired quantity is estimable from frequency data. Using a Bayesian network can save considerable amounts of memory, if the dependencies in the joint distribution are sparse. For example, a naive way of storing the conditional probabilities of 1. If the local distributions of no variable depends on more than three parent variables, the Bayesian network representation only needs to store at most 1. One advantage of Bayesian networks is that it is intuitively easier for a human to understand a sparse set of direct dependencies and local distributions than complete joint distributions. Inference and learningeditThere are three main inference tasks for Bayesian networks. Inferring unobserved variableseditBecause a Bayesian network is a complete model for the variables and their relationships, it can be used to answer probabilistic queries about them. For example, the network can be used to find out updated knowledge of the state of a subset of variables when other variables the evidence variables are observed. This process of computing the posterior distribution of variables given evidence is called probabilistic inference.