196 ALTERNATIVE VIEWS OF HERMENEUTICS

of that information as trustworthy’ (Lehrer 1986, p. 5). There is a normative aspect in the epistemic account of justification which is missing from the psychological.5 If we take the purpose of a belief to be that of making a true affirmation, then epistemic justification addresses the question of whether, given the available information, a subject should hold a belief.6 Sellars explains that:

in characterizing an episode or state as that of knowing, we are not giving an empirical description of that episode or state; we are placing it in the logical space of reasons, of justifying and being able to justify what one says.

(Sellars 1963, p. 169)

It is not enough that agents’ expectations accurately correspond to predictions derived from the economist’s probability functions. For subjects in RE models to know something, their expectations must be in a ‘logical space of reasons’.

EPISTEMIC ANALYSIS OF RE

According to the traditional definition of knowledge, a proposition must be true, it must be believed, and the belief must be justifiable.7 This definition of knowledge can also be applied to agents in RE models, but the specification of belief, truth, and justification depends on the type of model being analysed. The first type, hypothetical models, are purely theoretical and are not intended to represent an actual economy. Empirical models are the second type, and are designed to represent aspects of a real economy.

In an RE model of a purely hypothetical economy, agents’ beliefs would consist of the numeric values they expect the model to generate. These beliefs or expectations are true if the expected values correspond to the results of equations that represent their economy’s structure. If challenged, the agents in these models could justify their beliefs by arguing that their expectations-formation processes were designed by the model-building economist to be rational. The equations which generate predicted values correspond to the equations that describe the way their economy works. It is assumed that the economist has not omitted anything from the agents’ decision-making functions which would make their expectations more accurate. This is a plausible assumption because we expect the economist to have complete knowledge of all the relevant data for a hypothetical world.

Now consider an RE model designed to represent an actual economy. Because of the differences between non-empirical and empirical models, changes in the specification of belief, truth, and justification are required. Unlike the case involving an imaginary economy, beliefs or expectations in an empirical model would refer to events in an economy external to the model. Truth would be defined as a correspondence between empirical values that are produced by the model’s expectation-formation equations and the quantified measures of actual

THE ECONOMICS OF RATIONALITY 197

economic phenomena.8 Beliefs would be justified by agents in the empirical model if they could demonstrate that the beliefs were produced by processes that made use of all relevant data. In the case of hypothetical models, this meant that all the pertinent data regarding the model had to be taken into account. For empirical models the situation is somewhat different.

The economist constructing an empirical model is not at liberty to specify an arbitrary set of probability density functions and he cannot directly observe these functions in the economy. Of course, this is not a requirement unique to RE theorists. Economists are generally expected to provide reasons for accepting empirical models as accurate representations of the economy. The question this chapter addresses is whether economists can do this in such a way as to claim that agents in empirical RE models have rational expectations.

If challenged to defend the rationality of their expectations, RE agents in empirical models could offer a two-part argument. First, their expectations are generated exclusively by the mathematical functions supplied by the economist and, second, the formulas they use are the product of a rational study of the economy. If the economist has been rational in specifying the mathematical functions, and if he has designed the model correctly, then agents in these models have a basis for justifying their rational expectations.9 As Brian Loasby observes: ‘Modern theories of “rational expectations” require agents to believe what the analyst knows to be true’ (Loasby 1982, p. 113). RE agents could argue that their expectations are justified because the economist has knowledge of the economy, and their expectation-formation functions fully incorporate this knowledge.

The problem of epistemic regress

The preceding examination of belief, truth, and justification provides an account of what it means for RE agents to have knowledge of the economy. Despite the fact that this analysis makes the description of knowledge clearer in some respects, it also introduces significant ambiguities. An important issue to be resolved is the definition of the concept ‘rational economist’. If the standard of rationality applied to agents is applied to economists, then economists are rational when they make the best use of all relevant data. Such data could be divided into two categories. First, the rational economist must be aware of all information available about the economy. This would include empirical data that are regularly used in economic models as well as any other data that might be relevant. A second category of information concerns the techniques used to specify probability functions and construct analytic models. Given the RE definition of rationality, the rational economist should know about all the relevant econometric techniques for identifying systematic patterns in the economy, and should be aware of all the possible mathematical and statistical procedures for incorporating these patterns into an economic model. It is obvious that these are

198 ALTERNATIVE VIEWS OF HERMENEUTICS

impossibly strict demands. No economist could ever meet the requirements of rationality that agents in RE models are assumed to satisfy.

The practical problems of satisfying the RE standard of rationality are enough to warrant scepticism with regard to the knowledge possessed by economists and agents in RE models, but these difficulties are similar to those raised by the previously described psychological studies. If this were the only conclusion from an epistemic analysis of RE models, then nothing new would be added to the standard critique. Epistemic analysis discloses another problem. The new complication is an infinite regress of the arguments used to justify beliefs. The following sections of this paper focus on the implications of this logical problem.

Assume for a moment that there exists an economist who is rational in the sense that he is aware of all relevant data. Such an economist must still use his judgement: he must decide how to use this information. In evaluating the economic data he must decide such things as: which measures of economic phenomena to include in his model; which time periods to include in the statistical tests; what level of aggregation to employ, etc. In addition, there is a vast array of mathematical and statistical methods for identifying systematic patterns in the data. These options introduce a new level of complexity into the definition of rationality for the economist.

At one level, economists can refer to empirical observations and formal techniques to defend their beliefs about the economy, but at another level this justification must itself be defended. Philosopher James Jason notes that: ‘in any epistemology one needs some principle governing the use of justification techniques…. Otherwise, any proof technique, any logic would be acceptable’ (Jason 1986, p. 51). A rational economist must therefore be more than psychologically aware of economic data and techniques of model-building. He or she must also be rational in choosing the data and techniques that best identify systematic patterns in the economy. For this normative judgement to be rational, it too must be based on all relevant data. For example, there are numerous types of interest rates and these are measured in a number of ways. Economists constructing macroeconomic models must decide which measure of the interest rate best identifies systematic processes. Rational economists will not only be aware of the data and analytical tools available, they will also be able to justify their choice of economic variables and estimating techniques.

In his book on rational expectations, Steven Sheffrin provides an example of the problems encountered in correctly identifying which techniques and data to use. He compares the work of Robert Barro and David Small, and notes that a ‘key difficulty of any work of this nature is the identification of forecast equations for money growth with agents’ actual expectations’. Sheffrin observes that the debtate between these two economists over the best way to eliminate systematic error ‘illustrates the difficulty of ascertaining exactly what a “rational” agent should know’ (Sheffrin 1983, p. 61). The decision that all ‘systematic error’ has been eliminated presents a logical difficulty, since arguments justifying this decision must be made, and these arguments rest on