Philosophy

Sentiment analysis on online social network Vijaya Abhinandan  Department of Computer Science and Engineering Nalla Malla Reddy Engineering College ABSTRCT A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis. 1. Introduction and State of Art A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data[2].It is essential to employ tools able of analyzing social media mainly the characteristics of social media. The mining review which uses machine learning and semantic orientation has investigated [3]. The approach which is used to classify the movie review uses the supervised classification. A corpus is formed to represent the data in the documents and all the classifiers are trained using this corpus.Thus the proposed technique is more efficient. The machine learning approach uses supervised learning, the proposed semantic orientation approach uses "un-supervised learning" prior data is not required to mine the data. To investigate the effectiveness of classification of the documents machine learning techniques are used [4]. By experimenting machine learning techniques are much better than human produced baseline for sentiment analysis on a review data. This classification uses features which are based on unigrams and bigrams. Zhu[5] proposed aspect based opinion polling free from textual customer reviews. Opinion polling uses aspect based segmentation model which segment the multi-aspect sentence to single aspect units. A sentiment analyzer to extract opinions has proposed [6] related to a subject from online data documents. Sentiment analyzer uses natural language processing techniques. The sentiment analyzer finds out all the references on the subject and sentiment polarity of each reference is determined. The sentiment analysis conducted by the researches utilized the sentiment lexicon and sentiment pattern database for extraction and association purposes. Alekh Agarwal[7] proposed a machine learning method incorporating linguistic knowledge gathered through synonymy graphs, for effective opinion classification. This approach shows the degree of influence among relationships of documents have on their sentiment analysis. This is brought about by the use of graph-cut technique and opinion words got through synonym graphs of word net. The proposed approach also improves the accuracy of predictions in classification task. Experiments using the system have given results with the accuracy over 90 percent with an advantage of decreased in processing time, with the difference of minimum in the final accuracies. The proposed methodology from the authors resulted in the following conclusions: 1. Automated mining of linguistic information is possible, so demonstrated with the structure of links in word net. 2. Generic method of using graph-cut technique for efficient opinion classification. Ahmed Abbasi[8] proposed sentiment analysis methods which are used to classify the forum opinions in web which are in multiple languages. To enhance the performance of the classifier the entrophy weighted genetic algorithm is incorporated. Using movie review data set the experiments stated that the used techniques are efficient. Machine learning has many application for security and text application for many area [9-13] Anidya et al., [9] ranked the product reviews based on customer-oriented and manufacturer ranking mechanism. The expected helpfulness of the review is used for the ranking and also ranking is based on the expected effect on sale. The proposed methods identify the reviews which have the most impact. To all the customer reviews of a product Miniqing Hu[10] performed mining and summarization process. The process is carried out in three steps: 1. The product characteristics commented by the customer in the review are mined. Natural language processing and Data mining techniques are used for mining. 2. The opinions in the review are identified and the opinions are divided as positive or negative. Set of adjectives words called opinion words are identified. To identify the semantic orientation and opnion orientation of each sentence is decided WordNet is used. 3. Summarize the results. The main objective is to perform summary of a customer reviews of a product which are sold online. Qui[11] analyzed the problems which are related to opinion mining such as the expansion of lexicon and target of the opinion. Good, Bad, Excellent, Poor are the list of opinion words which are used to indicate positive and negative sentiments. Based on boot strapping the link between opinion word and targets relations are identified. Bootstrapping process is started using the initial opinion lexicon. Semi-supervised methods are used in this process. Lei Zhang[12] analyzed opinion words which are domain dependent. Sentiment context of each noun feature which is positive or negative. For identifying the noun product feature two steps are used which are positive or negative. Xiaowen Ding [13] proposed a holistic lexicon-based approach which uses External indications. This approach has the advantage that opinion words which are context dependent are easily handed. Linguistic Patterns are used in this algorithm to deal with special words, phrases. Sentiment classification: Sentiment classification is the classification task of naming an document as positive or negative opinions. Machine learning algorithms are used for sentiment classification. Machine Learning Algorithms: Machine learning algorithm is defined as a system which is able of getting and merging the knowledge automatically.The systems that learn from analytical observation, training, experience, and other means, results in a system that can reveal self-improvement, effectiveness and efficiency. Knowledge and a corresponding knowledge organization are usually used by a machine learning system to test the knowledge acquired, interpret and analyse. Supervised learning generates a function which connects input to expected outputs which are also called as labels. Semi-supervised learning generates an suitable function or classifier in which both lebelled and unlabelled examples are combined[16,18]. Sentiment Analysis Tasks Sentiment classification consists of the polarity classification of a given document text. Positive, negative, neutral express the sentence level opinions. The sentiment analysis can be done at three levels which are: the document level, Sentence level and feature level. Document Level sentiment classification: In document level sentiment analysis main challenge is to extract informative text for inferring sentiment of the whole document. The learning methods can be confused because of objective statements are rendered by subjective statements and complicate further for document categorization task with confliciting sentiment[17]. Sentence level sentiment classification: The sentiment classification is a fine-grained level than document level sentiment classification in which polarity of the sentence can be given by three categories as positive,negative and neutral.The challenge faced by sentence level sentiment classification is the identification features indicating whether sentences are on-topic which is kind of co-reference problem.[17] Feature sentiment classification: Product features as product attributes of components. Analysis of such features for identifying sentiment of the document is called as feature based sentiment analysis. In this approach positive or negative opinion is identified from the already extracted features. It is a fine grained analysis model among all other models[16]. Text Classification The large volume of online text is available through the news feed from internet, mails, databases, websites and the digital libraries. The problem is to arrange text documents from large databases. Naive bayes classifier: The naïve bayes classifier is well known as probabilistic classifier which describes its application to text. The naive bayes classifier was build in order to incorporate the data which is unlabelled. The main aim of learning generative model is to estimation of the parameters which uses labelled training data. The algorithm uses estimated parameters for the classification of new documents by the calculation to which class it belongs to. The navie Bayesian classifier works as follows: By considering samples of a training set ,with the class labels T. C1,C2,C3,.............Ck are the classes .Each sample contains n-dimensional vector,X={x1,x2,x3................xn} which represents n measured values of the n attributes named as A1,A2,A3..........,An respectively. The classifier classifies the X which is the given sample which belongs to the class which has the highest probability. Thus X is belonged to the class C1 if and only. P(Ci/X) > P(Ci/X) for 1 ≤ j ≤ m, j ≠ i. The value of P(Ci/X) which is maximized is called as the maximum posterior hypothesis. By Bayes theorem, P(Ci/X) = P(X/Ci) P(Ci) P(X) P(X) is same for all classes value so only the value of P(X|Ci)P(Ci) needs to be maximized. The estimation of priori probabilities of the class P(Ci) is P(Ci) = freq(Ci, T)/|T| P(X|Ci) ≈ ∏ P(xk|Ci) n k=1 The expectation maximization algorithm oscillates between the steps of guessing the model which depends on probability of distribution over completions of missing data (E-step) and the re-estimation of the model parameters using these completions (M-step). E-step is used only to compute the expected statistics over completions rather than the probability distribution forming over completions. Where M-step consists of re-estimation of the model which can be maximization of the estimated log-likelihood the data.[51,52]. 2.Sentiment Analysis Methods: This section provides a brief description of the eight sentiment analysis methods investigated in this paper. These methods are the most popular in the literature (i.e., the most cited and widely used) and they cover diverse techniques such as the use of Natural Language Processing (NLP) in assigning polarity, the use of Amazon's Mechanical Turk (AMT) to create labeled datasets, the use of psychometric scales to identify mood-based sentiments, the use of supervised and unsupervised machine learning techniques, and so on. Validation of these methods also varies greatly, from using toy examples to a large collection of labeled data. Emoticons The simplest way to detect polarity (i.e., positive and negative affect) of a message is based on the emoticons it contains. Emoticons are popular in these days, to the extent that some (e.g. <3) are now included in English Oxford Dictionary [19]. Emoticons are primarily face-based and represent happy or sad feelings, although a wide range of non-facial variations exist: for instance, <3 represents a heart and expresses love or affection. To extract polarity from emoticons, we utilize a set of common emoticons from [20, 21, 22] as listed in Table 1. This table also includes the popular variations that express the primary polarities of positive, negative, and neutral. Messages with more than one emoticon were associated to the polarity of the first emoticon that appeared in the text, although we encountered only a small number of such cases in the data. As one may expect, the rate of OSN messages containing at least one emoticon is very low compared to the total number of messages that could express emotion. A recent work has identified that this rate is less than 10% [23]. Therefore, emoticons have been often used in combination with other techniques for building a training dataset in supervised machine learning techniques [24]. SentiStrength Machine-learning-based methods are suitable for applications that need content-driven or adaptive polarity identification models. Many key classifiers for identifying polarity in OSN data have been proposed [26,27,28].The most comprehensive work [28] compared a wide range of supervised and unsupervised classification methods, including simple logistic regression, SVM, J48 classification tree, JRip rule-based classifier, SVM regression, AdaBoost, Decision Table and Naive Bayes. The core classification of this work relies on the set of words in the LIWC dictionary [29], and the authors expanded this baseline by adding new features for the OSN context. The tool named SentiStrengh, which implements a combination of learning techniques that produces the best results and the best training model empirically obtained [28]. 3. Conclusion A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis. Future direction of analysis online social network will involve many application as mentioned in [36-41]. References: [1]Kim, P:"The Forrester Wave: Brand Monitoring"Forrester Wave,2006. [2]Pang, B., Lee,"Proceedings of the International Conference on Computational Linguistics 2008. [3] Lina Zhou,Pimwadee Chaovalit, "Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches", 2005. [4]P.S.Dodds and C.M.Danforth. Measuring the happiness of large-scale written expression: [5] Zhu, Jingbo Wang, Huizhen Zhu, Muhua Tsou,Benjamin K. Ma, Matthew, "Aspect-Based Opinion Polling from Customer Reviews". 2011. [6] Yi, J., T. Nasukawa, R. Bunescu, and W. Niblack:"Sentiment Analyzer: Extracting Sentiments about Given Topic using Natural Language Processing Techniques",,2003 [7]Alekh Agarwal & Pushpak Bhattacharyya,Sentiment analysis: "A new approach for effective use of linguistic knowledge and exploiting similarities in a set of documents to be classified", 2005. [8]Ahmed Abbasi,Hsinchun Chen, And Arab Salem,"Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums". [9] Yaqoob, I., et al., The rise of ransomware and emerging security challenges in the Internet of Things. Computer Networks, 2017. [10] Mujtaba, G., et al., Email Classification Research Trends: Review and Open Issues. IEEE Access, 2017. [11] Mujtaba, G., et al., Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection. PloS one, 2017. 12(2): p. e0170242. [12] Al-garadi, M.A., et al., Using online social networks to track a pandemic: A systematic review. Journal of biomedical informatics, 2016. 62: p. 1-11. [13] Zerdoumi, S., et al., Image pattern recognition in big data: taxonomy and open challenges: survey. Multimedia Tools and Applications, 2017: p. 1-31. [14]Anindyam Ghose, PanagiotisG.Ipeirotis: "Designing Novel Review Ranking Systems: Predicting Usefulness and Impact of Reviews". [15] Minqing Hu and Bing Liu, "Mining and Summarizing Customer Reviews". [16] Guang Qiu, Bing Liu, Jiajun Bu and Chun Chen. "Opinion Word Expansion and Target Extraction through Double Propagation". [17] Lei Zhang and Bing Liu. "Identifying Noun Product Features that Imply Opinions.",2011 [18]Xiaowen Ding. A Holistic Lexicon-Based Approach to Opinion Mining. [19]Wellman, Barry & S.D. Berkowitz, Social Structures: "A Network Approach. Cambridge: Cambridge University Press",1988. [20]Linton(1977)."A set of measures of centrality based on betweenness". [21 ]Zhong wu zhai, Bing Liu, Hua Xu & Hua Xu, "Clustering product features for opinion mining". [22 ]V.S.Jagtap & karishma pawar,"Anlaysis of different approaches to sentence-Level Sentiment Classification". [23]"Bing Liu. Sentiment Analysis and opinion Mining",2012. [24]Graphic symbol for love (and that exclamation) are added as words. [25] List of text emoticons: "The ultimate resource. ". [26]Msn messenger emoticons. "http: //". [27]Yahoo messenger emoticons. . [28] J.Park, V.Barash, C. Fink, and M. Cha: "Interpreting differences in emoticons across cultures". 2013. [29] J. Read. "Using emoticons to reduce dependency in machine learning techniques for sentiment classification". [30]Y.R.Tausczik & J. W. Pennebaker"Liwc and computerized text analysis methods",2010. [31]A.Bermingham&A.F.Smeaton:"Classifying Sentiment in Microblogs". [32]G.Paltoglou & M.Thelwall: "Unsupervised Sentiment Analysis in Social Media". [33]M.Thelwall:"Sentimentm strength detection in the social web with sentistrength". [34 ] Al-garadi, M.A., K.D. Varathan, and S.D. Ravana, Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network. Computers in Human Behavior, 2016. 63: p. 433-443. [35] Albrecht, C.C., C.O. Albrecht, and M. Zimbelman, Fraud examination. 2008: Cengage Learning. [36] Al-garadi, M.A., K.D. Varathan, and S.D. Ravana, Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method. Physica A: Statistical Mechanics and its Applications, 2017. 468: p. 278-288. [37] Berthon, P.R., et al., Marketing meets Web 2.0, social media, and creative consumers: Implications for international marketing strategy. Business horizons, 2012. 55(3): p. 261-271. [38] Khan, M.S., et al., Virtual community detection through the association between prime nodes in online social networks and its application to ranking algorithms. IEEE Access, 2016. 4: p. 9614-9624. [39] Al-Garadi, M.A., et al., Identifying the influential spreaders in multilayer interactions of online social networks. Journal of Intelligent & Fuzzy Systems, 2016. 31(5): p. 27212735. [40] Corley, C.D., et al., Text and structural data mining of influenza mentions in web and social media. International journal of environmental research and public health, 2010. 7(2): p. 596-615. [41] Yang, C.C., et al. Social media mining for drug safety signal detection. in Proceedings of the 2012 international workshop on Smart health and wellbeing. 2012. ACM.


In “Belief in the Face of Controversy,” Hilary Kornblith argues for a radical form of epistemic modesty: given that there has been no demonstrable cumulativeprogress in the history of philosophy – as there has been in formal logic, math, and science – Kornblith concludes that philosophers do not have the epistemic credibility to be trusted as authorities on the questions they attempt to answer. After reconstructing Kornblith's position, I will suggest that it requires us to adopt a different conception of philosophy's epistemic value. First, I will argue that ‘progress’ has a different meaning in logic, science and philosophy, and that to judge one of these disciplines by the standards appropriate to one of the others obscures the unique epistemic functions of all. Second, I will argue that philosophy is epistemically unique in that it is a non-relativistic but historically determined excavation of foundations. Finally, drawing on Frank Herbert's Dune, I will suggest that Kornblith leaves us with a choice between two epistemic ideals: the hyper-logical ‘Mentat,’ or the historically informed ‘pre-born.’

Philosophy

Disagreement and Philosophical Progress

© LOGOS & EPISTEME, VI, 1 (2015): 115-127 DISAGREEMENT AND PHILOSOPHICAL PROGRESS Brent ABLES ABSTRACT: In "Belief in the Face of Controversy," Hilary Kornblith argues for a radical form of epistemic modesty: given that there has been no demonstrable cumulative progress in the history of philosophy - as there has been in formal logic, math, and science - Kornblith concludes that philosophers do not have the epistemic credibility to be trusted as authorities on the questions they attempt to answer. After reconstructing Kornblith's position, I will suggest that it requires us to adopt a different conception of philosophy's epistemic value. First, I will argue that 'progress' has a different meaning in logic, science and philosophy, and that to judge one of these disciplines by the standards appropriate to one of the others obscures the unique epistemic functions of all. Second, I will argue that philosophy is epistemically unique in that it is a non-relativistic but historically determined excavation of foundations. Finally, drawing on Frank Herbert's Dune, I will suggest that Kornblith leaves us with a choice between two epistemic ideals: the hyper-logical 'Mentat,' or the historically informed 'pre-born.' KEYWORDS: disagreement, epistemic modesty, epistemic authority, Hilary Kornblith 1. Introduction: The Mentat Ideal In Frank Herbert's popular series of science fiction novels, The Dune Chronicles, humankind has done away with complex machinery and conditioned itself to fill the void. Instead of computers, there are 'Mentats.' A Mentat is a human being with extraordinary logical capacities, able to solve extremely complex problems in a short time and make predictions with a very high degree of empirical accuracy. They receive information as input, and their output is truth. Mentats are still human, however; they marry, exhibit loyalty to their authorities, and have complex emotions. But if they begin to get carried away by those emotions, they need only remind themselves to "function as a Mentat" and their dominant rational side reasserts itself.1 We can probably agree on two facts about Mentats. The first is that they seem like an excellent ideal for the philosopher to uphold. To be able to stop what one is doing, set aside emotions and passions, and function as a Mentat is surely a skill that any honest seeker of truth would want to cultivate. In contemporary 1 Frank Herbert, Dune (New York: Penguin, 1965), 17. Brent Ables 116 analytic epistemology, the paradigm of the epistemic agent is often something very like a Mentat. It is supposed that we receive evidence as input, process that evidence in accordance with the best methods and standards that we have available to us, and form true beliefs as output. Even if it is acknowledged that real-world circumstances might interfere with this process, it is taken by epistemologists of disagreement to be a suitable norm. The second fact is that Mentats belong in science fiction. As much as we would like to be able to cordon off error and the illusions of what William James called our passional nature, we know from experience that willpower and practice cannot reliably accomplish this. Look at the methodology of contemporary science, which demands of any individual researcher a full explanation of the procedures and materials she employs so that her experimental results can be replicated and verified by other scientists. Or take the academic process of peer review, which would hardly be necessary if we could all trust our qualified peers to reach proper conclusions given the same data that we have. These kinds of institutional checks are necessary because academics and scientists can be dishonest, but more fundamentally because we recognize that in the collective human search for truth, even the brightest and most honest individuals among us can - and often do - get things wrong. The Mentat is an artifact of imaginative fiction because the ideal is simply too good to be true. The epistemology of disagreement takes this constitutive imperfection of the rational animal as its starting point. If we were all capable of functioning as Mentats on cue, then substantive philosophical disagreements - indeed, any disagreements whatsoever that go beyond expressions of personal preference - would be systematically resolvable in short order. All the participants would have to do is feed each other the evidence and arguments available, and one of the parties would recognize their error. But this isn't, of course, what happens in the actual practice of philosophy. What happens is that we ask ourselves and each other a varied but limited number of fundamental questions, draw out problems and further questions from that basis, and then argue interminably about those questions and problems at conferences, in journals and private conversations, and even in our heads. As a matter of fact, to paraphrase James, we find ourselves disagreeing. What does this disjunct between the Mentat model and the messy human reality mean for philosophy? What conclusions ought we to draw about a practice whose results seem so apparently at odds with its ideals? Can these conclusions tell us what to do in the case of substantive disagreement with our colleagues? What is the philosophical significance of such disagreement? In "Belief in the Face of Disagreement and Philosophical Progress 117 Controversy," Hilary Kornblith confronts these questions head-on and ends up with some rather pessimistic conclusions about the epistemic authority of philosophy.2 My goal in this essay shall be to evaluate these conclusions and to determine, not just what philosophy can tell us about disagreement, but what disagreement can tell us about philosophy. 2. Kornblith: The Question of Progress Kornblith begins his essay by delimiting the scope of the epistemological problem of disagreement. This reduction is necessary since, at first glance, disagreement is as ubiquitous as belief itself: from politics to religion to simple matters of geographical fact, it seems there is almost no topic about which people do not disagree. Not all disagreements are epistemologically significant, however. In some cases, I may have evidence that others do not have and know (or be reasonably certain) that if they had the same evidence, they would believe as I do. In other situations, I may have good reasons to doubt someone's judgment even if we have the same evidence. If I disagree about a simple addition problem with a child learning arithmetic, clearly it would not be proper for me to defer to the child's belief - no matter how certain she may be. Thus, in cases where I have good reasons to doubt my peer's judgment or their access to relevant evidence, disagreement poses no problem. Conviction itself is no guarantee of truth, or even a reliable indication of it. The epistemologically significant cases of disagreement are those concerning what Kornblith, following Gary Gutting, calls epistemic peers. If you and I are equally intelligent and well-educated, have access to and familiarity with the same evidence, and are both sincere about our beliefs, then we are epistemic peers. These three requirements are jointly necessary, and none alone is sufficient. If we add the proviso that the same interlocutor might be an epistemic peer with regard to one intellectual domain and not another, then we have the idealized interpersonal scenario presupposed by epistemologists of disagreement: a dialogue between humans doing their best to function as Mentats. To be sure, not all writers who make use of the idea of epistemic peerhood use it the same way. One of the more interesting complications of the idea is raised by Adam Elga.3 In addition to the criteria already discussed, Elga adds that epistemic peers must have broadly similar beliefs with regard to the topic under dispute. If you and I are both intelligent and generally well-educated, and we find 2 Hilary Kornblith, "Belief in the Face of Controversy," in Disagreement, ed. Richard Feldman and Ted A. Warfield (New York: Oxford University Press, 2010), 29-52. 3 See Adam Elga, "Reflection and Disagreement," Nous 41, 3 (2007): 478-502. Brent Ables 118 ourselves disagreeing about whether the animal that runs in front of us is a squirrel or a chipmunk, then I can disagree you with in this case and still consider you my epistemic peer. It may be that neither of us is especially well-qualified to discuss the physiology of small furry animals, but we nevertheless both have certain beliefs about other facts regarding chipmunks and squirrels: that they have tails and fur, they can climb trees, etc. The question could be resolved simply by reference to an illustrated encyclopedia. If, on the other hand, you are an idealist in the tradition of Berkley and I a strict materialist, then our disagreement about what we see is going to be less amenable to resolution. When you remark on the cute notion in God's mind that illusorily appeared to dart by, I might respond by saying that all I saw was a bundle of furry flesh traversing a plot of space-time. According to Elga, this disagreement is epistemically benign: because we are bringing such radically different sets of beliefs to the table, then our ground for considering each other as epistemic peers dissolves under us. Once we set aside all our differences pertaining to the question at hand, there remains no fact of the matter that would allow me to judge my faculty of judgment against yours. And without shared standards for comparison, the concept of epistemic peerhood is meaningless. Kornblith rejects Elga's additional criterion for epistemic peerhood. He argues that even in cases where beliefs appear to diverge so radically, there are nonetheless still common standards that both parties can appeal to. You and I may disagree about the metaphysical constitution of the critter in front of us, but we both agree that it is not a duck or a lacrosse stick; we both agree that it appeared to run from right to left; we both agree that it appeared to have four legs. Thus, I still have shared grounds for considering you my epistemic peer. Now, Elga might be willing to concede the point in this case. But surely, he might respond, there are other plausible cases where our relevant clusters of belief really do differ radically and irreconcilably. Kornblith's rebuttal attempts to draw a line between degrees of belief-similarity, and where there are degrees there are exceptions to any fixed rule. But a stronger rebuttal to Elga would be to insist that epistemic peerhood doesn't have anything to do with the content of beliefs at all. If you were not already my epistemic peer prior to our sighting the furry animal, then surely you wouldn't become one upon agreeing with me about what we have seen; conversely, if you were already my peer, then I ought not revoke this status when I discover that where I saw a squirrel running you saw a lacrosse stick being thrown. Certainly some response to the discrepancy is warranted, but as Kornblith suggests in the case of disagreements regarding perception, I have no more reason prima facie to think it is your error than mine. Generalizing the point, epistemic Disagreement and Philosophical Progress 119 peerhood is a matter of our trusting the judgmental faculties of another, not endorsing their particular conclusions. It is a sign of methodological competence, not empirical adequacy. For now, however, let us accept Kornblith's understanding of epistemic peerhood. The question of his essay is: what ought we as peers to do when we genuinely disagree with each other? Before examining Kornblith's answer, we note two important facts about the question itself. First, it is not a descriptive question but a normative one. As much as philosophers like to appeal to thought experiments and counterexamples to grease the axles of intuition, no appeals to juries or dinner bill calculations can answer the question on their own. Values must enter the discussion if it is to go anywhere at all. The second fact is that we can't appeal to some kind of latent but abstract error in calculation to come down on one side or the other. In other words, I can't answer the question by saying that if I am right then I should hold my ground, but if I am wrong then I should suspend judgment. No doubt this is true, but it evades the terms of the question inasmuch as both parties initially take their views to be right. The symmetry of epistemic peerhood must not be broken. How, then, does Kornblith advise us to act in the case of genuine disagreement with epistemic peers? Let's take the first form of the example discussed above. If I see a chipmunk running across the ground and you see a squirrel, clearly one of us cannot be right. The animal can't be both chipmunk and squirrel, so barring other relevant possibilities (the animal might be a groundhog) the law of contradiction demands that we come down on one side or another. Nevertheless, neither of us can support our belief with any immediate evidence beyond the simple attestation of perception. Thus, Kornblith concludes, the rational thing for us to do is to suspend judgment until we can consult the encyclopedia. Similarly, if you and I add up a dinner bill and come up with two different sums, one of us must be wrong; the logical thing to do in this case would be to suspend immediate judgment and recalculate the bill separately. What about philosophical disagreement? The widespread presence of disagreement in philosophy is evident from a look at any elementary ethics or metaphysics textbook. But what differentiates philosophy from simple perceptual or mathematical cases, according to Kornblith, is that disagreements in perception or math will or can be easily resolvable. All we have to do is look closer, or redo the calculations. But when philosophers disagree, they usually do so after having examined and considered all the relevant arguments. If they are truly peers, an externalist can't simply tell the internalist to look closer at the arguments to be convinced, nor can she appeal to some impartial third party as an authority (since Brent Ables 120 she and her peer are as authoritative as anyone else on the subject at hand). There may be no impartial way to resolve the dispute, even if we accept that both sides cannot be right. Granted, then, that philosophical disagreements differ importantly from more mundane cases, what should philosophers do in the case of genuine disagreement? Kornblith considers Thomas Kelly's answer that we must simply turn to the arguments and believe what they demonstrate, rejecting it for reasons similar to the ones considered above.4 Pragmatically speaking, a disagreement might well be clarified or even resolved if both parties lay out all the relevant arguments, explicitly asses their cogency and their reasons for finding the arguments cogent, and so on. But since beliefs may still (and often do still) differ after such careful analysis by both parties, the question remains as to how the disputants ought to react to the persistent disagreement. The way Kornblith develops his own answer to the question is interesting, and worth retracing. His most significant move is to expand the discourse beyond the two-party scenario often presupposed by epistemologists of disagreement, shifting the focus of the discussion to the tenability of dissent within a community. His guiding example here is Kelly's discussion of the Newcomb Problem, introduced into decision theory by Robert Nozick in 1969 and debated ever since.5 Kelly tells us that when the problem was first introduced, opinion among decision theorists was split more or less evenly, but that over the next three decades consensus shifted in favor of the two-box option. According to Kelly, these facts about the distribution of opinion should have no bearing on which decision we make now; we must stick to the arguments and make the most rational decision possible. For we can always imagine possible worlds in which the distribution of opinion had developed in the opposite way, or where it remained evenly split. In general, the line between possible disagreement and actual agreement is "an extremely contingent and fragile matter."6 Thus, the lack of disagreement in a field - in a word, consensus - is no guide for making difficult decisions. Kornblith challenges Kelly on his own grounds. According to Kornblith, if we look at the actual history of formal areas of philosophy like logic and decision theory (along with mathematics), we have every reason to think that consensus in such fields is a reliable guide for making decisions. In their infancy, to be sure, 4 See Thomas Kelly, "The Epistemic Significance of Disagreement," Oxford Studies in Epistemology 1 (2005): 167-196. 5 Kelly, "The Epistemic Significance of Disagreement," 182-185. 6 Kelly, "The Epistemic Significance of Disagreement," 181. Disagreement and Philosophical Progress 121 these fields gave rise a wide variety of opinions among practitioners. Kornblith notes that the uncertainty correspondent to this diversity extended to arguments as much as it did to results; it wasn't until these fields developed to a certain extent that we were able to recognize what concepts had resonance and what methods worked. But once they did develop, the passage of time brought with it increased sophistication and, concomitantly, increased consensus. From this, Kornblith draws a rather strong conclusion: Even among experts, of course, convergence of opinion is no guarantee of truth, but one would have to be a radical skeptic about mathematics, logic, probability and decision theory to think that convergence of opinion is not, at this point in the history of these fields, evidence of truth. And at this point in the history of these fields, I think it is fair to say, radical skepticism is no longer a rational option.7 In other words, it is not only irrational to think that mathematics and decision theory do not provide us with truth, it is irrational to think that the bare fact of consensus in these fields does not serve as evidence for their truth. It is not far from this conclusion to the strong normative one that wherever there is a consensus in controversial matters, one ought to believe what the experts believe. Kornblith admits that "convergence of opinion is no guarantee of truth" and encourages us to attend to the relevant arguments or evidence as closely as possible, but despite these apparent concessions, his normative prescription remains the same: whether you are a novice or expert, if there is a consensus, you must go with the consensus. Contra Kelly, it is of no import that we can imagine other possible worlds where the consensus might have turned out differently. Kornblith argues that we can certainly picture a world where Gödel's incompleteness theorem was not discovered by Gödel or wasn't discovered at all, but it would be difficult to picture a world where Gödel published his theorem and it was rejected by the mathematical community at large. Possible, but difficult, for it would require us to rescind our basic faith in the competence of mathematicians in a way that belies the actual progress made within the mathematical community over time. The simple fact that we can imagine worlds where the experts get everything wrong doesn't have any bearing on the fact that in this world, they tend to get things right over time. According to Kornblith, the basic normative principle operative in the restaurant bill case is the same in the cases of mathematics, logic, and decision theory. If I go out to dinner with 17 mutually distrustful friends who all want to 7 Kornblith, "Belief in the Face of Controversy," 40-41. Brent Ables 122 add up the bill separately, and everyone but me calculates the same sum, the obvious thing for me to do in this case is to accept that I have made a mistake. As Kornblith tells us, Things are no different if we move from dividing the bill at a restaurant to solving a problem in decision theory.8 Increased complexity doesn't change the fact that the experts know best. But we ask again: what about philosophy? It's hard to think that Kornblith hasn't strayed from his focus a bit. In fact, he's effectively made his argument already; the case against the epistemic authority of philosophy is made entirely by analogy to fields like mathematics, decision theory, and the natural sciences. If the philosophical community was like the mathematical and scientific communities in the sense that there has been undeniable progress within the community over time, then we would be able to treat philosophers as reliable experts in their field and impart the same epistemic authority to them we do to individual scientists. In that case, where there was a roughly even divide on some controversial philosophical issue, we would be rationally compelled to suspend judgment on the issue. "But," Kornblith tells us, surely it is not reasonable to believe that the philosophical community is like the mathematical or scientific communities in relevant respects. We don't have a long history of steady progress on issues, and, as a result, the case for deferring to community opinion is thereby weakened.9 And the case for the epistemic authority of philosophers - a case, we note, that Kornblith never bothers to make - is also thereby weakened. For where we see no track record of success in the field, we have no reason to trust the authority of the practitioners within that field. The conclusions that Kornblith draws from this argument are merciless. Because of the lack of progress in philosophy, the only conclusion we can reasonably reach is that there is no basis for opinion here on anyone's part at all.10 By Kornblith's standards, it would seem to be as irrational to hold a genuine belief about a philosophical matter - any philosophical matter - as it is to question whether consensus in logic is really evidence that logicians are converging on the truth. To hold such opinions would be as presumptuous as it would be to believe that my calculations were correct and those of my 17 dinner companions were 8 Kornblith, "Belief in the Face of Controversy," 43. 9 Kornblith, "Belief in the Face of Controversy," 44. 10 Kornblith, "Belief in the Face of Controversy," 45. Disagreement and Philosophical Progress 123 wrong. By all means, Kornblith tells us, we should continue to swap arguments like low-value baseball cards and hold our little conferences to make noise out of our precious beliefs, but only so long as at the end of the day, we remind ourselves that it's all much ado about nothing. Kornblith calls this curious defeatism "epistemic modesty." Others might call it a good reason to not do philosophy. 3. Lessons From the History of Philosophy A natural first reaction to Kornblith's argument would be to reject the premise that philosophy hasn't made the same progress as the other disciplines discussed. No doubt progress of some kind has occurred in math and science, but surely it has transpired in philosophy as well. Suppose we take Plato and Aristotle to mark the beginning of systematic philosophy. Very few philosophers still believe in immaterial Forms that subsist independently of the material world, and even fewer believe that because of the superior constitution of their soul, philosophers ought to rule as kings. Aristotle's defense of the institution of slavery is no longer tenable, nor are his conclusions about the inferiority of women. Aside from some general platitudes about virtue and intellectual rigor, in fact, there is little in the philosophy of the ancient Greeks that many rational people endorse today. Is this not progress? As appealing as this approach seems on first glance, I think it is perhaps the weakest response to Kornblith. The kind of progress Kornblith has in mind isn't just a matter of there being a different set of beliefs now than there was two millennia ago, or even a century ago. As Kornblith says, even fashion exhibits such shifts in consensus. Progress implies cumulative change that converges on a fixed point. But there's certainly no track record of fixed conclusions that we can point to as evidence of progress in philosophy. Even in ethics, the domain that philosophers most often try to reserve for themselves against the encroachment of modern science, what we see isn't linear progress so much as perpetual shifts in disagreement. Philosophers might not fancy themselves to be kings anymore, but they're not much help in telling us who ought to rule instead. If we accept Kornblith's definition of progress, then, philosophy has exhibited little to none. But where does this definition come from? We know Kornblith's answer: it is simply the name for the successive advance towards truth evident in formal philosophy, math, and natural science. But does it really make sense to talk about progress in logic and progress in physics as if they were the same phenomenon? Philosophers of science like Kuhn and Lakatos have served the valuable function of reminding us that progress in physics is not nearly as clear-cut as we like to think, and there is an important sense in which theories Brent Ables 124 and observational data are incommensurable from one research program to the next. But suppose we accept for the sake of argument that progress in physics is clear-cut: what relation does the ability to predict and experimentally verify the behavior of natural objects have to progress in logic? Logicians make no predictions and conduct no experiments. There is nothing in the realm of nature that confirms the law of noncontradiction, or demonstrates that the fallacy of ambiguity is a fallacy. The only thing that logical progress and scientific progress seem to have in common is that they are marked by increasing consensus. But if the bare fact of expert consensus can't be proof of progress just on its own, it certainly can't serve to indicate truth either. Perhaps consensus in logic - and mathematics, for that matter - is nothing more than the inevitable outcome of a large group of intelligent people finding the most agreeable ways to manipulate symbols of their own making. Maybe such a view is mistaken, but no one is going to demonstrate that with an experiment in the lab. If I claim that water flows upwards, on the other hand, a simple experiment could prove me wrong. Physics and logic don't share methods, objects, arguments, or procedures of confirmation and falsification; why should they share the same standard of progress? The point here isn't just that Kornblith is glossing over some very important details - although he is - but rather that he is strewing loaded terms across such a diverse terrain that they cease to have any useful meaning at all. I doubt that the truth of physics has much in common with the truth of logic, if indeed the latter really gives us what ought to be called truth. Moreover - and this is the important point - even if the sense of truth in those fields was in some important sense congruent, I see no reason at all to think that the further extension to fields like metaphysics or epistemology is valid. Kornblith is committing his own fallacy of ambiguity here, and doing so with one of the most notoriously slippery terms of our language. Philosophers can't even agree on what truth is; what gives Kornblith the epistemic authority to throw around the term as if we all, philosophers and scientists alike, had come to a consensus about what it meant? This lacuna in Kornblith's argument wouldn't be so important if he weren't basing his entire argument on what amounts to an analogy between philosophy and other disciplines. But, as we have emphasized above, this is precisely what he is doing. This move seems especially egregious since, in the same article, Kornblith has stated that disagreement in perceptual or mathematical cases seems importantly different than disagreement in philosophy. In math or science (where conclusions are ultimately supported by reports of perceptual observation), there is typically an accepted method of confirmation that we can turn to in the case of Disagreement and Philosophical Progress 125 disagreement. But in philosophy the path from disagreement to resolution is rarely so apparent. I'd happily go a step further: when it comes to philosophy, I'm not so sure that resolutions or solutions always exist. Here Kornblith might remount his attack. If philosophical problems can't be solved or, what amounts to the same, if we don't know what it would mean to solve them then surely philosophy never had any epistemic authority to begin with. What good is a problem that can't be solved, or a theory that can't be confirmed? I think the correct answer is that they do a great deal of good, or in any case, have a significant impact. As any philosopher in our dying western Lyceum will readily tell you, the study of philosophy has all kinds of practical benefits at the introductory level: it sharpens critical thinking skills, encourages close reading, makes us question assumptions and assumptions behind assumptions, and perhaps even casts doubt on the idea that holding a fixed set of beliefs is really the most rewarding and productive way to employ one's rational capacities. We need not stop at philosophy 101, however. Philosophy also has influence on the world-historical scale. Hegel is typically lambasted as the caricature of an abstruse, hopelessly abstract metaphysician, but without Hegel there would have been no Marx and the twentieth century would have turned out very differently. If we look at Eastern philosophers like Confucius, the 'real world' applications are even deeper and more pervasive. Moreover, philosophy has served as the cradle for virtually every other major intellectual discipline. Without Aristotle, there would have been no political theory, aesthetic criticism, psychology, physics, biology, optics, etc. Without Plotinus, there would be no God as Christians understand that concept in the West today. If Kornblith wants to deny the epistemic authority of philosophers on the ground that they don't do what the scientists do, he would do well to remember that without philosophy there would be no science as we know it. In sum, Kornblith introduces the dimension of history into the epistemology of disagreement without bothering to make the slightest effort to understand what makes the history of philosophy unique, or valuable, or epistemically significant. If he had done so, he would have realized that philosophy doesn't make progress like physics does because it is a discipline concerned with foundations, and each new epoch in human history brings with it different requirements for such foundations. Cartesian mechanism was both product and mirror of its age, as was Aquinas' scholasticism, Nietzsche's aesthetic anti-nihilism, Quine's holism, and Kornblith's own blithe scientism. One need not be a relativist to understand that different eras in history give rise to different Brent Ables 126 problems and accept different kinds of answers, and one would be myopic to deny that philosophy has accomplished the task of formulating and propagating such questions for longer and at a deeper level than any other intellectual enterprise launched by humankind. The movement of philosophy does not proceed from point A to point B, but from point A to the ground beneath it; if it so happens that it digs all the way through and ends up on the other side of the world speaking a foreign language, ought we to deny its epistemic authority on the grounds that it didn't take the path we thought it would? 4. Conclusion: Mentats and the Pre-born The world of Dune is a veritable epistemological funhouse. In addition to the Mentats, Herbert gives us a very different - and much stranger - model of superhuman intellectuality. The 'pre-born,' as Herbert names them, are human beings who are exposed to a certain type of drug (the melange spice) while still in the womb and thereupon come immediately to mature consciousness. But that's not all: once exposed to the drug, they are imbued with the complete memories of every ancestor in their genetic line. They carry these memories with them for their entire lives, and as such, are able to draw upon an unimaginably vast reservoir of historical experience to guide them along their own paths. Because they have (vicariously) seen it all before, the pre-born are impeccable judges of human behavior, and exhibit a kind of stoic remove from the complications of everyday life. They don't have the problem-solving prowess of the Mentat, but they have something better: the ability to understand why and how problems arise in the first place, and to judge their significance for the human condition as it stands in their historical moment. Perhaps it is for this reason that it is the preborn who always end up on the throne, and Mentats who end up serving them. Few educated people in our own world, aside from radical skeptics and the credulously religious, are likely to deny the epistemic authority of scientists and mathematicians. The ability to function as a Mentat might be beyond the power of any living human, but the factor of time and the contributions of the community make up for this lack: scientific disciplines accomplish collectively and diachronically what the Mentat accomplishes individually and synchronically. I proposed at the start of this essay that the Mentat ideal is also a suitable model for the philosopher. For scientifically-minded, results-oriented thinkers like Kornblith, it might well be. But if my objections against Kornblith in this essay have been successful, then I hope to have laid the ground for a different kind of ideal. Those adhering to this ideal would shift their attention to the historical foundations and precedents for what comes to be. They would pay more attention Disagreement and Philosophical Progress 127 to the genetic causes and conceptual roots of contemporary problems than they would to finding the quickest solution to those problems. They would find an altogether more suitable ideal in the pre-born. This divide between the Mentats and the pre-born might be taken to map onto the divide between analytic and continental philosophy in interesting ways, but within the confines of this essay I can't pursue this idea. My concern is to suggest that although they may adopt different methods, deal with different problems in different ways, and ultimately come up with different results, neither of these ideals intrinsically holds any more epistemic authority than the other. There's nothing wrong with holding some philosophy up to the standards set by modern mathematics and science, so long as the philosophy in question is similar enough to those fields in relevant ways. But there is also nothing wrong with practicing philosophy as an autonomous discipline, judging it by its own standards and in accordance with its own historical successes and failures. If we do the latter, questions of progress and consensus simply don't matter as much as Kornblith thinks they do. The practical virtues of philosophy persist unperturbed in either case, so as far as I can tell there is no normative basis for choosing one over the other. But whichever way we go, thousands of years of endlessly fascinating ideas clamor to be acknowledged, heeded and heard anew. The question is whether we want to hear them.