Correspondence, abstraction, and realism

Science is generally concerned with two central semantic features of theories: truth of theoretical hypotheses and reliability of observational predictions. (Philosophers understand the concept of semantics as encompassing the relations between a sentence and the world: truth and reference. This understanding connects with the ordinary notion of semantics as meaning, in that the truth conditions of a sentence are thought to constitute the meaning of the sentence.) Truth involves a correspondence between hypothesis and the world; while predictions involve statements about the observable future behavior of a real system. Science is also concerned with epistemic values: warrant and justification. The warrant of a hypothesis is a measure of the degree to which available evidence permits us to conclude that the hypothesis is approximately true. A hypothesis may be true but unwarranted (that is, we may not have adequate evidence available to permit confidence in the truth of the hypothesis). Likewise, however, a hypothesis may be false but warranted (that is, available evidence may make the hypothesis highly credible, while it is in fact false). And every science possesses a set of standards of hypothesis evaluation on the basis of which practitioners assess the credibility of their theories–for example, testability, success in prediction, inter-theoretical support, simplicity, and the like.

The preceding suggests that there are several questions that arise in the assessment of scientific theories. First, we can ask whether a given hypothesis is a good approximation of the underlying social reality–that is, the approximate truth of the hypothesis. Likewise, we can ask whether the hypothesis gives rise to true predictions about the future behavior of the underlying social reality. Each of these questions falls on the side of the truth value of the hypothesis. Another set of questions concerns the warrant of the hypothesis: the strength of the evidence and theoretical grounds available to us on the basis of which we assign a degree of credibility to the hypothesis. Does available evidence give us reason to believe that the hypothesis is approximately true, and does available evidence give us reason to expect that the hypothesis’s predictions are likely to be true? These questions are centrally epistemic; answers to them constitute the basis of our scientific confidence in the truth of the hypothesis and its predictions.

It is important to note that the question of the approximate truth of the hypothesis is separate from that of the approximate truth of its predictions. It is possible that the hypothesis is approximately true but its predictions are not. This might be the case because the ceteris paribus conditions are not satisfied, or because low precision of estimates for exogenous variables and parameters leads to indeterminate predictive consequences. Therefore it is possible that the warrant attaching to the approximate truth of the hypothesis and the reliability of its predictions may be different. It may be that we have good reason to believe that the hypothesis is a good approximation of the underlying economic reality, while at the same time we have little reason to rely on its predictions about the future behavior of the system. The warrant of the hypothesis is high on this account, while the warrant of its predictions is low.

Whatever position we arrive at concerning the possible truth or falsity of a given economic hypothesis, it is plain that this cannot be understood as literal descriptive truth. Economic hypotheses are not offered as full and detailed representations of the underlying economic reality. For a hypothesis unavoidably involves abstraction, in at least two ways. First, the hypothesis deliberately ignores some empirical characteristics and causal processes of the underlying economic reality. Just as a Newtonian hypothesis of the ballistics of projectiles ignores air resistance in order to focus on gravitational forces and the initial momentum of the projectile, so an economic hypothesis ignores differences in consumption behavior among members of functional defined income groups. Likewise, a hypothesis may abstract from regional or sectional differences in prices or wage rates within a national economy. Daniel Hausman provides an excellent discussion of the scope and limits of economic theories in The Inexact and Separate Science of Economics.

Another epistemically significant feature of social hypotheses is the difficulty of isolating causal factors in real social or economic systems. Hypotheses are generally subject to ceteris paribus conditions. Predictions and counterfactual assertions are advanced conditioned by the assumption that no other exogenous causal factors intervene; that is, the assertive content of the hypothesis is that the economic processes under analysis will unfold in the described manner absent intervening causal factors. But if there are intervening causal factors, then the overall behavior of the system may be indeterminate. In some cases it is possible to specify particularly salient interfering causal factors (e.g. political instability). But it is often necessary to incorporate open-ended ceteris paribus conditions as well.

Finally, social theories and hypotheses unavoidably make simplifying or idealizing assumptions about the populations, properties, and processes that they describe. Consumers are represented as possessing consistent and complete preference rankings; firms are represented as making optimizing choices of products and technologies; product markets are assumed to function perfectly; and so on.

Given, then, that hypotheses abstract from reality, in what sense does it make sense to ask whether a hypothesis is true? We must distinguish between truth and completeness, to start with. To say that a description of a system is true is not to say that it is a complete description. (A complete description provides a specification of the value of all state variables for the system–that is, all variables that have a causal role in the functioning of the system.) The fact that hypotheses are abstractive demonstrates only that they are incomplete, not that they are false. A description of a hockey puck’s trajectory on the ice that assumes a frictionless surface is a true account of some of the causal factors at work: the Newtonian mechanics of the system. The assumption that the surface of the ice is frictionless is false; but in this particular system the overall behavior of the system (with friction) is sufficiently close to the abstract hypothesis (because frictional forces are small relative to other forces affecting the puck). In this case, then, we can say two things: first, the Newtonian hypothesis is exactly true as a description of the forces it directly represents, and second, it is approximately true as a description of the system as a whole (because the forces it ignores are small).

This account takes a strongly realist position on social theory, in that it characterizes truth in terms of correspondence to unobservable entities, processes, or properties. The presumption here is that social systems generally–and economic systems in particular–have objective unobservable characteristics which it is the task of social science theory to identify. The realist position is commonly challenged by some economists, however. Milton Friedman’s famous argument for an instrumentalist interpretation of economic theory (Essays in Positive Economics) is highly unconvincing in this context. The instrumentalist position maintains that it is a mistake to understand theories as referring to real unobservable entities. Instead, theories are simply ways of systematizing observable characteristics of the phenomena under study; the only purpose of scientific theory is to serve as an instrument for prediction. Along these lines, Friedman argues that the realism of economic premises is irrelevant to the warrant of an economic theory; all that matters is the overall predictive success of the theory. The instrumentalist approach to the interpretation of economic theory, then, is highly unpersuasive as an interpretation of the epistemic standing of economic hypotheses. Instead, the realist position appears to be inescapable: we are forced to treat general equilibrium theory as a substantive empirical hypothesis about the real workings of competitive market systems, and our confidence in general equilibrium hypotheses is limited by our confidence in the approximate truth of the general equilibrium theory.

Polling and social knowledge

Here’s a pretty interesting graphic from

As you can see, the graph summarizes a large number of individual polls measuring support for the two major party candidates from January 1 to October 26. The site indicates that it includes all publicly available polls during the time period. Each poll result is represented with two markers — blue for Obama and red for McCain. The red and blue trend lines are “trend estimates” based on local regressions for the values of the corresponding measurements for a relatively short interval of time (the site doesn’t explicitly say what the time interval is). So, for example, the trend estimate for August 1 appears to be approximately 47%:42% for the two candidates. As the site explains, 47% is not the average of poll results for Obama on August 1; instead, it is a regression result based on the trend of all of Obama’s polling results for the previous several days.

There are a couple of things to observe about this graph and the underlying methodology. First, it’s a version of the “wisdom of the crowd” idea, in that it arrives at an estimate based on a large number of less-reliable individual observations (the dozen or so polling results for the previous several days). Each of the individual poll results has an estimate-of-error which may be in the range of 3-5 percentage points; the hope is that the aggregate result has a higher degree of precision (a narrower error bar).

Second, the methodology attempts to incorporate an estimate of the direction and rate of movement of public opinion, by incorporating trend information based on the prior several days’ polling results.

Third, it is evident that there is likely to be a range of degrees of credibility assigned to the various component polls; but the methodology doesn’t assign greater weight to “more credible” polls. Ordinary readers might be inclined to assign greater weight to a Gallup poll or a CBS poll than a Research2000 or a DailyKos poll; but the methodology treats all results equally. Likewise, the critical reader might assign more credibility to a live phone-based poll than an internet-based or automated phone poll; but this version of the graph includes all polls. (On the website it is possible to filter out internet-generated or automated phone polling results; this doesn’t seem to change the shape of the results noticeably.)

There is also a fundamental question of validity and reliability that the critical reader needs to ask: how valid and reliable are these estimates for a particular point in time? That is, how likely is it that the trend estimate of support for either candidate on a particular day is within a small range of error of the actual value? I assume there is some statistical method for estimating probable error for this methodology, though it doesn’t appear to be explained on the website. But fundamentally, the question is whether we have a rational basis for drawing any of the inferences that the graph suggests — for example, that Obama’s lead over McCain is narrowing in the final 14 days of the race.

Finally, there is the narrative that we can extract from the graph, and it tells an interesting story. From January through March candidate Obama has a lead over candidate McCain; but of course both candidates are deeply engaged in their own primary campaigns. At the beginning of April the candidates are roughly tied at 45%. From April through September Obama rises slowly and maintains support at about 48%, while McCain falls in support until he reaches a low point of 43% in the beginning of August. Then the conventions take place in August and early September — and McCain’s numbers bump up to the point where Obama and McCain cross in the first week of September. McCain takes a brief lead in the trend estimates. His ticket seems to derive more benefit from his “convention bump” than Obama does. But in the early part of September the national financial crisis leaps to center stage and the two candidates fare very differently. Obama’s support rises steeply and McCain’s support falls at about the same rate, opening up a 7 percentage point gap in the trend estimates by the middle of October. From the middle of October the race begins to tighten; McCain’s support picks up and Obama’s begins to dip slightly at the end of October. But the election looms — the trend estimates tell a story that’s hard to read in any way but “too late, too little” for the McCain campaign.

And, of course, it will be fascinating to see where things stand a week from today.

Here is the explanation that the website offers of its methodology:

[quoting from]
“Where do the numbers come from?

When you hold the mouse pointer over a state, you see a display of the latest “trend estimate” numbers from our charts of all available public polls for that race. The numbers for each candidate correspond to the most recent trend estimate — that is the end point of the trend line that we draw for each candidate. If you click the state on the map, you will be taken to the page on that displays the chart and table of polls results for that race.

In most cases, the numbers are not an “average” but rather regression based trendlines. The specific methodology depends on the number of polls available.

  • If we have at least 8 public polls, we fit a trend line to the dots represented by each poll using a “Loess” iterative locally weighted least squares regression.
  • If we have between 4 and 7 polls, we fit a linear regression trend line (a straight line) to best fit the points.
  • If we have 3 polls or fewer, we calculate a simple average of the available surveys.

How do regression trend lines differ from simple averages?

Charles Franklin, who created the statistical routines that plot our trend lines, provided the following explanation last year:

Our trend estimate is just that, an estimate of the trends and where the race stands as of the latest data available. It is NOT a simple average of recent polling but a “local regression” estimate of support as of the most recent poll. So if you are trying to [calculate] our trend estimates from just averaging the recent polls, you won’t succeed.

Here is a way to think about this: suppose the last 5 polls in a race are 25, 27, 29, 31 and 33. Which is a better estimate of where the race stands today? 29 (the mean) or 33 (the local trend)? Since support has risen by 2 points in each successive poll, our estimator will say the trend is currently 33%, not the 29% the polls averaged over the past 2 or 3 weeks during which the last 5 polls were taken. Of course real data are more noisy than my example, so we have to fit the trend in a more complicated way than the example, but the logic is the same. Our trend estimates are local regression predictions, not simple averaging. If the data have been flat for a while, the trend and the mean will be quite close to each other. But if the polls are moving consistently either up or down, the trend estimate will be a better estimate of opinion as of today while the simple average will be an estimate of where the race was some 3 polls ago (for a 5 poll average– longer ago as more polls are included in the average.) And that’s why we estimate the trends the way we do.”

What is social scientific knowledge?

The social and behavioral sciences endeavor to describe, explain, and interpret the range of the social and behavioral facts that surround us. To refer to this body of findings as “science” is to claim a set of epistemic values about the nature of the methods of inquiry and evaluation that are used to arrive at and assess the conclusions offered about this domain. The label “science” brings with it a set of presuppositions about rigor, evidence, generalizability, logical analysis, objectivity, cumulativeness, and the likelihood that the assertions that are made are true.

Consider a few assumptions that are often made about scientific knowledge—some valid and some not. Science is based on a set of rationally justified methods of inquiry and testing. Scientific knowledge progresses, in scope, in detail of understanding, and in reliability. Science is performed by specialists, working within equally exacting communities of peers and competitors and subject to a demanding set of standards of evaluation—peer-reviewed journals, university review processes, national laboratories, and international associations and conferences. The result of these processes of testing and evaluation, we expect, is an expanding body of hypotheses, experimental findings, observations, theories, and explanations that have substantial credibility—and substantially higher credibility than the writings of casual observers of a given range of phenomena. We come to know the nature of the world better through the institutions and methods of science.

In addition to these reasonably valid assumptions about scientific knowledge, there is another group of more questionable ideas that derive from assumptions drawn from the natural sciences. Science permits generalizations; it permits us to systematize otherwise apparently separate domains of phenomena (planetary motion, the tides; rational choice theory, behavior of the family) and to demonstrate that apparently heterogeneous sets of phenomena are in fact governed by the same general laws. Science permits predictions; if the fundamentals are thus-and-so, then the compounds will behave thusly. Science aims at unification: the discovery of unitary systems of forces and entities whose aggregate properties represent the whole of nature.

Notice that these latter expectations are derived from the successes and specific characteristics of certain of the natural sciences. And this marks the first of many opportunities for error in the philosophy of social science. There is no reason to expect that the social domain possesses the underlying nature and orderliness that would make it possible to achieve some of these characteristics (in particular, uniformity, generalizability, unification, simplicity). Consider some other areas of possible empirical research—for example, animal behavior. We should not expect there to be comprehensive theories of animal behavior. Instead, we should expect many threads of research, corresponding to many dimensions of animal behavior: cognition, memory, instinct, social behaviors, migratory behavior. And these many strands of research would reach out to different kinds of causal backgrounds: evolutionary biology, neurophysiology, intra-group learning. Likewise with the domain of social behavior. There is no single unified “theory of human motivation”—whether rational choice theory, social psychology, or any other unified theory. And this is so, because there is no unified reality of motivation and action; rather, there is a heterogeneous range of motives, errors, impulses, commitments, and habits that together constitute “dispositions to behave.”

What underwrites the claim of truthfulness for scientific knowledge? What gives us a rational basis for believing that the results of the socially constructed activities of science lead to true hypotheses about the nature and workings of the phenomena that scientific inquiry considers? There is, first, the basic argument of empiricism: we can observe some features of the world and establish certain statements as being probable. And we can use a collection of tools of inference to establish credibility of other non-observational statements (deductive and inductive logic, statistics, the experimental method, causal modeling).

This simple empiricist epistemology underwrites the strongest claims for veridicality and justification for the social sciences. The discovery of empirical facts about the social world is possible but challenging; this is what much of social science methodology attempts to under-gird. And hypotheses about the causal relationships that exist among social entities and processes can be tested using a variety of methods of inference that themselves possess strong epistemic justification. We have learned from the writings of philosophers of science since the 1960s to emphasize corrigibility and anti-foundationalism in our interpretation of scientific knowledge; but a coherentist epistemology and a perspective of causal realism provides a philosophically powerful grounding for social science knowledge. (See articles in the Stanford Encyclopedia of Philosophy on coherence epistemology and scientific realism by Kvanvig and Boyd).

In addition, in some areas of the natural sciences, there is the fact that cumulative scientific research leads to the invention of technologies that work as they were designed to do: new materials are invented in the electronics industry, new designs are created for large structures (buildings, aircraft, electron microscopes), and these materials and artifacts perform as expected on the basis of the underlying theories. So scientific theories of materials, structures, and natural systems are supported by the effectiveness of the technologies that they give rise to. If the theories and hypotheses were fundamentally untrue about the parts of the natural world that they describe, then we would expect the technologies to fail; the technologies do not fail; so we have some additional reason to believe the scientific theories that underlay these technologies. (This is a version of Richard Boyd’s argument of methodological realism.)

Is there anything analogous to the relationship between the natural sciences and technology, for the social and behavioral sciences? On the whole, there is not. Social predictions are notoriously unreliable; public policies based on social-science theory commonly give rise to unanticipated consequences; and the twists and turns of deliberate social processes (war, alliance, efforts to address global warming) continue to surprise us. This unpredictability in the social world derives from the nature of social action. Human behavior and social processes are plainly subject to an open-ended range of causes, motives, and influences. The construction of various areas of science with the goal of understanding and explaining this multiplicity is therefore a profoundly challenging task.

Here, then, is a very elliptical description of a plausible interpretation of social science epistemology: There are empirical foundations for knowledge in the form of social observation (empiricism); there are social causes that influence social behavior, processes, and outcomes (causal realism); there is no a priori reason to expect strong generalizations across social phenomena, “regulating” the social world; and there is no reason to expect unified master theories of social phenomena, suggesting instead a preference for theories of the middle range.

Piecemeal empirical assessment of social theories

The philosophy of science devotes a large fraction of its wattage to this question: what is the logic of empirical confirmation for scientific beliefs? (A good short introduction is Samir Okasha, Philosophy of Science: A Very Short Introduction.) In the natural sciences this question became entangled with the parochial fact about the natural sciences, that scientific theories postulated unobservable entities and processes and that the individual statements or axioms of a theory could not be separately confirmed or tested. So a logic of confirmation was developed according to which theories are empirically evaluated as wholes; we need to draw out a set of deductive or probabilistic consequences of the theory; observe the truth or falsity of these consequences based on experiment or observation; and then assign a degree of empirical credibility to the theory based on the success of the observational consequences. This could be put as a slogan: “No piecemeal confirmation of scientific beliefs!”

This is the familiar hypothetico-deductive model of confirmation (H-D), articulated most rigorously by Carl Hempel and criticized and amended by philosophers such as Karl Popper, Nelson Goodman, Norwood Hanson, and Imre Lakatos. These debates constituted most of the content of the evolution of positivist philosophy of science into post-positivist philosophy of science throughout the 1960s and 1970s.

I don’t want to dive into this set of debates, because I am interested in knowledge in the social sciences; and I don’t think that the theory-holism that this train of thought depends upon actually has much relevance for the social sciences. The H-D model of confirmation is approximately well suited — but only to a certain range of scientific areas of knowledge (mathematical physics, mostly). But the social sciences are not theoretical in the relevant sense. Social science “theories” are mid-level formulations about social mechanisms and structures; they are “theories of the middle range” (Robert Merton, On Theoretical Sociology). They often depend on formulations of ideal types of social entities or organizations of interest — and then concrete empirical investigation of specific organizations to determine the degree to which they conform or diverge from the ideal-typical features specified by the theory. And these mid-level theories and hypotheses can usually be empirically investigated fairly directly through chains of observations and inferences.

This is not a trivial task, of course, and there are all sorts of challenging methodological and conceptual issues that must be addressed as the researcher undertakes to consider whether the world actually conforms to the statements he/she makes about it. But it is logically very different from the holistic empirical evaluation that is required of the special theory of relativity or the string theory of fundamental physics. The language of hypothesis-testing is not quite right for most of the social sciences. Instead, the slogan for social science epistemology ought to be, “Hurrah, piecemeal empirical evaluation!”

I want to argue, further, that this epistemological feature of social knowledge is a derivative of some basic facts about social ontology: social processes, entities, and structures lack the rigidity and law-governedness that is characteristic of natural processes, entities, and structures. So general, universal theories of social entities that cover all instances are unlikely. But second, it is a feature of the accessibility of social things: we interact with social entities in a fairly direct manner, and these interactions permit us to engage in scientific observation of these entities in a way that permits the piecemeal empirical investigation that is highlighted here. And we can construct chains of observations and inferences from primary observations (entries in an archival source) to empirical estimates of a more abstract fact (the level of crop productivity in the Lower Yangzi in 1800).

Let’s say that we were considering a theory that social unrest was gradually rising in a region of China in the nineteenth century because of a gradual shift in the sex ratios found in rural society. The connection between sex ratios and social unrest isn’t directly visible; but we can observe features of both ends of the equation. So we can gather population and family data from registries and family histories; we can gather information about social unrest from gazettes and other local sources; and we can formulate subsidiary theories about the social mechanisms that might connect a rising male-female ratio to the incidence of social unrest. In other words — we can directly investigate each aspect of the hypothesis (cause, effect, mechanism), and we can put forward an empirical argument in favor of the hypothesis (or critical of the hypothesis).

This is an example of what I mean by “piecemeal empirical investigation”. And the specific methodologies of the various social and historical sciences are largely devoted to the concrete tasks of formulating and gathering empirical data in the particular domain. Every discipline is concerned to develop methods of empirical inquiry and evaluation; but, I hold, the basic logic of inquiry and evaluation is similar across all disciplines. The common logic is piecemeal inquiry and evaluation.

(I find Tom Kelly’s article on “Evidence” in the Stanford Encyclopedia of Philosophy to be a better approach to justification in the social sciences than does the hypothetico-deductive model of confirmation, and one that is consistent with this piecemeal approach to justification. Kelly also reviews the essentials of H-D confirmation theory.)

What kind of knowledge can philosophy offer?

Let us say that knowledge is “a set of true statements based on compelling reasons.” (This is the same as the familiar definition of knowledge as “justified true belief”.) Philosophers offer a variety of claims, and they offer arguments for their positions. But do they offer knowledge about anything? Is it possible to say that “Philosophical statement P is true”, or only that “Philosophical statement P is supported by strong and compelling reasons”? Are philosophical topics within the domain of things concerning which we can have knowledge at all?

We know what kind of knowledge empirical science provides: factual knowledge about the world and inferences or theories supported by empirical observation. We also know what kind of knowledge is offered by mathematics and logic : deductive knowledge derived from a set of axioms in one or another of the fields of mathematics. And we can specify the nature of the knowledge provided by linguistics and semantics: expository knowledge of the meanings of various words and phrases in ordinary usage. Putting these areas of inquiry very crudely, we might summarize them as “inductive knowledge,” “deductive knowledge,” and “semantic knowledge.” But what does philosophy add to our knowledge and understanding of human experience and knowledge?

Notice that knowledge and truth are interrelated. Truth, in turn, has to do with correspondence and reference. Statements refer to things and properties; statements are true or false depending on whether the things to which the statement refers in fact possesses the properties attributed to it. So a statement cannot be a piece of knowledge if it does not permit of correspondence to some independent set of facts.

Now consider the kinds of reasoning and statements that occur within philosophy.

First, philosophy offers assertions based on rigorous analysis of concepts and conceptual relationships. This exercise looks a bit like the linguistics/ semantics option above; but philosophers offer “value-added” by providing rational reconstruction of the concepts they consider. They reconstruct and improve upon the concepts of everyday language.

Second, philosophy may provide constructive analysis and further development of the methods and conceptual systems of various disciplines, including the empirical sciences. Here the philosopher is purporting to offer substantial findings that will improve upon the epistemic characteristics of existing scientific practices. Crudely, a deeper understanding of the logic of evolutionary explanation and the mathematics of natural selection is a philosophical effort, and it is a substantive contribution to the philosophy of biology and to biological theory. Here, we must ask whether there is a credible basis for the contribution. In what respect does the philosopher possess a distinctive basis for providing rational assessment and recommendations about the structure and practice of empirical science? Typically, philosophers would respond by saying that philosophical argumentation about the methods of science gains credibility through the understanding of rationality and reasoning that philosophers possess.

Third, philosophy can construct ethical theories that possess some degree of justification, beyond ordinary moral opinions. When John Rawls asks, “What is justice?”, he begins with some ordinary associations with the concept, but then builds out a theory of justice that goes far beyond ordinary language. And he offers a range of philosophical justifications in support of his theory of “justice as fairness”.

A major problem in answering the key question, what is the nature of philosophical knowledge, is the fact that we ordinarily divide knowledge into “empirical/contingent” and “formal/necessary” (or what Quine calls the “analytic/synthetic” distinction). But philosophers seem to advance their theories in a way that implies they are neither empirical nor purely formal. Kant puts this point in the form of a category of knowledge that is “synthetic a priori” — that is, knowledge that is based on purely philosophical reasoning but that is nonetheless not purely formal. (Kant’s claim that the statement “reality is spatially organized in Euclidean three-dimensional space” is true synthetic a priori asserts that the statement is necessarily true but is not a truth of logic.)

One possible stance that we might take is to restrict the concept of “knowledge” to statements about empirical states of affairs (where truth and falsity are possible), but to then postulate other categories of belief that have a degree of rational credibility without correspondence to the empirical world. On this approach, empirical statements, suitably supported, can constitute knowledge of the empirical world. Philosophical statements — statements about scientific method, the nature of beauty, or ethical principles — can be offered with rational justification but do not have the referential structure that would permit them to be “true” and do not count as knowledge. If we take this approach, then we may be justified in accepting the proposition “Justice is fairness,” but we would not be justified in thinking it is “true.” And therefore the statement is not an example of “knowledge”.

This approach sounds a bit parallel to the position of early twentieth-century logical positivism, which maintained the verificationist theory of meaning — the meaning of a sentence is the set of conditions that establish its truth or falsity — with the result that theology, philosophy, or ethics are strictly speaking meaningless. However, the two views are not the same, because this view permits that philosophical discourse is meaningful and rational; it simply denies that the statements that philosophers make either correspond or fail to correspond with facts about the world.

The conclusion to this line of thought is somewhat startling: philosophy does not offer knowledge at all. However, it does provide opinions and statements that are founded on good reasons, and we have rational grounds for believing these statements.

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