Dani Rodrik’s blind spot (II)16 December, 2015 at 18:53 | Posted in Economics, Theory of Science & Methodology | 1 Comment
As I argued in a previous post, Dani Rodrik’s Economics Rules describes economics as a more or less problem-free smorgasbord collection of models. Economics is portrayed as advancing through a judicious selection from a continually expanding library of models, models that are presented as “partial maps” or “simplifications designed to show how specific mechanisms work.”
But one of the things that’s missing in Rodrik’s view of economic models is the all-important distinction between core and auxiliary assumptions. Although Rodrik repeatedly speaks of ‘unrealistic’ or ‘critical’ assumptions, he basically just lumps them all together without differentiating between different types of assumptions, axioms or theorems. In a typical passage, Rodrik writes (p. 25):
Consumers are hyperrational, they are selfish, they always prefer more consumption to less, and they have a long time horizon, stretching into infinity. Economic models are typically assembled out of many such unrealistic assumptions. To be sure, many models are more realistic in one or more of these dimensions. But even in these more layered guises, other unrealistic assumptions can creep in somewhere else.
Modern mainstream (neoclassical) economists ground their models on a set of core assumptions (CA) — basically describing the agents as ‘rational’ actors — and a set of auxiliary assumptions (AA). Together CA and AA make up what I will call the ur-model (M) of all mainstream neoclassical economic models. Based on these two sets of assumptions, they try to explain and predict both individual (micro) and — most importantly — social phenomena (macro).
The core assumptions typically consist of:
CA1 Completeness — rational actors are able to compare different alternatives and decide which one(s) he prefers
CA2 Transitivity — if the actor prefers A to B, and B to C, he must also prefer A to C.
CA3 Non-satiation — more is preferred to less.
CA4 Maximizing expected utility — in choice situations under risk (calculable uncertainty) the actor maximizes expected utility.
CA4 Consistent efficiency equilibria — the actions of different individuals are consistent, and the interaction between them result in an equilibrium.
When describing the actors as rational in these models, the concept of rationality used is instrumental rationality – choosing consistently the preferred alternative, which is judged to have the best consequences for the actor given his in the model exogenously given wishes/interests/ goals. How these preferences/wishes/interests/goals are formed is typically not considered to be within the realm of rationality, and a fortiori not constituting part of economics proper.
The picture given by this set of core assumptions (rational choice) is a rational agent with strong cognitive capacity that knows what alternatives he is facing, evaluates them carefully, calculates the consequences and chooses the one — given his preferences — that he believes has the best consequences according to him.
Weighing the different alternatives against each other, the actor makes a consistent optimizing (typically described as maximizing some kind of utility function) choice, and acts accordingly.
Beside the core assumptions (CA) the model also typically has a set of auxiliary assumptions (AA) spatio-temporally specifying the kind of social interaction between ‘rational actors’ that take place in the model. These assumptions can be seen as giving answers to questions such as
AA1 who are the actors and where and when do they act
AA2 which specific goals do they have
AA3 what are their interests
AA4 what kind of expectations do they have
AA5 what are their feasible actions
AA6 what kind of agreements (contracts) can they enter into
AA7 how much and what kind of information do they possess
AA8 how do the actions of the different individuals/agents interact with each other.
So, the ur-model of all economic models basically consist of a general specification of what (axiomatically) constitutes optimizing rational agents and a more specific description of the kind of situations in which these rational actors act (making AA serve as a kind of specification/restriction of the intended domain of application for CA and its deductively derived theorems). The list of assumptions can never be complete, since there will always unspecified background assumptions and some (often) silent omissions (like closure, transaction costs, etc., regularly based on some negligibility and applicability considerations). The hope, however, is that the ‘thin’ list of assumptions shall be sufficient to explain and predict ‘thick’ phenomena in the real, complex, world.
But in Rodrik’s model depiction we are essentially given the following structure,
A1, A2, … An
where a set of undifferentiated assumptions are used to infer a theorem.
This is, however, to vague and imprecise to be helpful, and does not give a true picture of the usual mainstream modeling strategy, where — as I’ve argued in a previous post — there’s a differentiation between a set of law-like hypotheses (CA) and a set of auxiliary assumptions (AA), giving the more adequate structure
CA1, CA2, … CAn & AA1, AA2, … AAn
CA1, CA2, … CAn
(AA1, AA2, … AAn) → Theorem,
more clearly underlining the function of AA as a set of (empirical, spatio-temporal) restrictions on the applicability of the deduced theorems.
This underlines the fact that specification of AA restricts the range of applicability of the deduced theorem. In the extreme cases we get
CA1, CA2, … CAn
where the deduced theorems are analytical entities with universal and totally unrestricted applicability, or
AA1, AA2, … AAn
where the deduced theorem is transformed into an untestable tautological thought-experiment without any empirical commitment whatsoever beyond telling a coherent fictitious as-if story.
Not clearly differentiating between CA and AA means that Rodrik can’t make this all-important interpretative distinction, and so opens up for unwarrantedly “saving” or “immunizing” models from almost any kind of critique by simple equivocation between interpreting models as empirically empty and purely deductive-axiomatic analytical systems, or, respectively, as models with explicit empirical aspirations. Flexibility is usually something people deem positive, but in this methodological context it’s more troublesome than a sign of real strength. Models that are compatible with everything, or come with unspecified domains of application, are worthless from a scientific point of view.