J Evol Econ (2002) 12: 539–562
c Springer-Verlag 2002
Evolutionary economics and the counterfactual
threat: on the nature and role of counterfactual
history as an empirical tool in economics
Robin Cowan1 and Dominique Foray2
1
2
MERIT, University of Maastricht, PB 616, 6200 MD Maastricht, The Netherlands
(e-mail: [email protected])
IMRI, Université Dauphine, 75775 Paris Cedex, France
(e-mail: [email protected])
Abstract. Counterfactual conditional statements are ubiquitous in any scientific
endeavour. This paper contains an analysis of the nature of counterfactual conditionals and the conditions under which they are considered assertable by scientists.
The paper then applies this analysis to the use of counterfactuals in evolutionary
economics, arguing that because evolutionary economics is inherently concerned
with historical processes it cannot avoid the use of counterfactual history as one of
its tools of empirical analysis. We discuss the strengths and pitfalls of counterfactual
history. We argue that because evolutionary economics starts from the foundation
that randomness may be inherent in any economic system, the very aspects of evolutionary economics that make counterfactual history a desirable empirical tool
also make that tool difficult to employ.
1 Introduction
This paper is concerned with a methodological question in economics. In particular
it examines the role and nature of counterfactual history as an empirical tool in
evolutionary analysis of economic phenomena. As a method, counterfactual history
has not met with universal approbation, and many economists look upon it with
considerable dis-favour. In 1983 the American Economic Review published a paper
by Preston McAfee in which he asks about American economic growth under
the counter-factual proposition that Columbus had not discovered the new world.
McAfee clearly means to ridicule the use of counter-factual history. Part of the
goal of the present paper is to explain both why counterfactual history is appealing
We thank the participants of the International Seminar on Evolutionary Economics as a Research
Programme in Stockholm, May 1997, for many helpful comments. We also thank Lorri Baier for many
helpful substantive and textual comments.
Correspondence to: R. Cowan
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R. Cowan and D. Foray
as a tool for certain approaches to economic analysis, and why it may appear
unappealing to someone deeply schooled in an axiomatic, deductive tradition.
Empirical analysis in any research field is entwined in theoretical analysis. That
is, empirical work depends on theory for concepts, definitions and hypotheses, all of
which are used as foundations for empirical investigation. Thus if theoretical work in
a field evolves and becomes more complex, the range of tools employed in empirical
analysis is also likely to evolve, and in general we would expect the toolbox to grow.
One of the changes in theoretical economics in recent years has been an increase in
the number of models producing multiple equilibria. Multiple equilibria have been
present in analysis almost since the birth of the discipline, but for many years, part
of the methodology of economics seemed to be to design models that had unique
equilibria. This is certainly sensible in many ways: it simplifies comparative statics
analysis, and it removes the need for detailed examination of the (difficult) dynamic
processes involved in equilibrium selection. But the growth of the literature of path
dependence, evolutionary modelling, game theoretic models and much modern
macro-economics has brought with it multiple equilibria, which in these fields are
generally accepted as a normal and untroubling outcome. The pervasiveness of this
result cannot help but affect methodology. For instance, when an equilibrium is
unique, the welfare question is restricted to whether this equilibrium can be made
better. This is often answered through a comparative statics exercise, which in
principle involves small adjustments to parameters, resulting in small changes to
equilibrium values: we can adjust a tax rate to move the current equilibrium to a
point very near and ask which of the two is preferred. But when multiple equilibria
are present, a second question is possible: would a different equilibrium be or have
been, better? This cannot necessarily be answered with a simple comparative statics
exercise.
Kaldor (1934) points out that there are two general types of multiple equilibria models, but that analysis of both will depend on analysis of the path to the
equilibrium. First, the (possibly single) equilibrium moves in response to events
along the path. Fisher (1983) for example, shows that if dis-equilibrium trades take
place, the final equilibrium value, even if there is enough convexity in the system
to guarantee that only one exists, cannot be predicted from the initial data. Simply,
in an Edgeworth Box, equilibrium lies somewhere in the lens formed by the initial
endowments and indifference curves. An out of equilibrium trade will create a new
“endowment” and thereby a new core. An out of equilibrium trade at a different
non-equilibrium price will create a different new “endowment” and thereby a different new core. Thus the set of possible equilibria will change if out-of-equilibrium
trades take place, and will change differently depending on the (almost certainly
unpredictable) prices at which those trades take place. Notice that for this view that
equilibria move in response to out-of equilibrium phenomena to make sense, time
must pass before the equilibrium is reached.1
Kaldor’s second reference is to cases in which there are several equilibria which
do not move in response to the path of adjustment. This is simply the case of multiple
equilibria for a given set of parameter values. Given the initial data, the analyst is
1
For a similar discussion of the same issue see David (1997).
Evolutionary economics and the counterfactual threat
541
able to predict, but can only narrow his predictions to some set of equilibria – the
market will evolve to one of a set of known equilibria, but which of them it will
be is not determined. In a static model, this exists when there are appropriate nonconvexities – perhaps some form of increasing returns. The “in principle” difficulty
this poses is how one of these equilibria is chosen – there must be some selection
mechanism. It is in the selection of the equilibrium that the path dependence emerges
or becomes important. With some exceptions, of course, it seems again that time is
going to play a central role here; the selection of an equilibrium is something that
will take place as a process evolves over time.
When multiple equilibria are present, there is no guarantee that they are welfare
equivalent. This raises the possibility of potential regret (see Strotz, 1956). Whether
or not potential regret has a place in a model will depend on the nature of the
selection mechanism. How do we get one equilibrium rather than another? For
some economists the answer to this question is “History.” Selection mechanisms are
inherently historical. Historical processes can have the feature of path dependence,
and this is commonly present in evolutionary models. Here is the moment at which
the Potential Regret result arises. Had history taken a different course in the past,
we would now be at a different, and better, position. The historicity present here
implies that explanations which address the issue of potential regret will necessarily
be historical. The questions “Is potential regret possible?”, or “Is regret actual in this
case?” effectively ask what would have been the case had history taken a different
course. This is a counterfactual question, equivalent in this regard to the question
“What would be the effect on employment if the tax rate were reduced by x%?”
This paper is concerned with the former type of counterfactual, and in particular
how historical concerns and the introduction of path dependence into economic
models changes the nature of counterfactuals.
The paper is organized as follows: We begin by addressing the role of counterfactual conditionals in science in general, and then turn to their relationship
with views of causation. This is followed by a discussion of two distinct views of
counterfactual conditionals, the branching view, and the non-actual-possible-world
view. The following sections bring these abstract considerations back to economics,
addressing both the difficulties raised for counterfactual analysis, and suggestions
regarding how to strengthen it.2
2 There is a difficulty of nomenclature here. It would be convenient to have a phrase that captures the
body of analysis for which historical processes are central. This includes work concerned with selection
mechanisms, evolutionary game theory, adjustment processes, path dependence, what is known tightly
as evolutionary economics, significant parts of modern macro-economics, and no doubt a variety of
other work. With apologies to the members of the Schumpeter Society, we will refer to this collection
as evolutionary. The reason is that much of economics that lies outside “Evolutionary Economics” has
evolutionary features – concern with processes, positive feedbacks, variety among the agents and so on
(see Nelson, 1995 for a characterisation of “Evolutionary Economics”), and it is the presence of these
features in the theory that demand the kind of historical counterfactual analysis that we are discussing
here. When we refer to Evolutionary Economics per se we will capitalize it.
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R. Cowan and D. Foray
2 Counterfactuals in science
The prototypical theoretical result in mainstream economics is the comparative statics result: the number of patents filed will increase by 10% for every 25% increase
in R&D spending. These results are usually presented as theorems or propositions
derived from axioms. Similar results are presented in most sciences, though the degree of precision and extent of generality vary, both within and across disciplines.
What is key here is that these statements can all be re-cast explicitly as equivalent counter-factual conditionals, that is, conditionals in which the antecedents
are false.3 Indeed, Nelson Goodman (1983) argues convincingly that six types of
statements are equivalent, and that any statement of one type can be re-cast as a
statement of any of the other five types. The types are counter-factual conditionals,
scientific laws, dispositional statements (p.3), factual conditionals (p.4), statements
regarding possible worlds (p.34), and causal statements (Goodman is not explicit
about the last category but it is implicit in what he writes on pages 37 and 45). So
the following statements are equivalent: If R&D spending were 25% higher than
it is, patenting would be 10% higher. The ratio of the change in R&D spending to
the change in patents is 2.5:1. Patenting has the tendency to increase (in the ratio
1/2.5) as R&D spending increases. Since patenting increased by 10%, R&D spending must have increased by 25%. In a world like ours but with 25% more R&D
spending, we would observe 10% more patenting. Changes in patenting activity
are caused by (inter alia) changes in R&D spending.4
Counterfactual conditionals are ubiquitous in science. They can be, and are, used
to express causal relations. Further, they are part of the link between theoretical and
empirical work. Empirical work is successfully characterized as the investigation of
counterfactual conditional claims. Do the data show that if R&D spending increases
then patenting increases in turn?
3 Causation
Counterfactuals also appear in another place that is germane to this discussion.
Philosophical discussions of causation often employ counterfactual conditionals as
the key concept. Indeed for many years there were vigorous attempts to unpack causation formally in those terms. The appeal is obvious: “C causes E.” is very much
like the statement “If C occurs then E occurs.” The problems with this approach
3 This idea is not new in economics: Fogel (1973) fends off an attack on the new economic history
made by Genovese (1962) by presenting a long list of “hidden” counterfactuals in Genovese’s own
work.
4 McCloskey (1989, p.150) has a similar list regarding money supply and inflation; Cowan and Foray
(1997) illustrate using minimum wages and unemployment.
A claim that appears impossible to render sensibly in this way might be “If R&D spending goes up
25% next year, patenting will increase by 10%.” This appears not to lend itself to exposition as a
counterfactual, since the future has, as yet, no fact. The counterfactual arises when one notes that the
claim is implicitly contrasting a world in which R&D spending does not increase by 25% next year.
Now, it is clear that (at least) one of the two contrasting cases will be contrary to fact. The counterfactual
can be formed prospectively, even though we cannot know which of the cases will be fact, and which
counter to fact.
Evolutionary economics and the counterfactual threat
543
turned out to be insuperable when addressed purely in terms of material conditionals, so that endeavour has effectively been given up. Part of the difficulty was
a tension between logical analysis and physical analysis. To most non-Humeans,
causation does seem like a real feature of the world. If one thing did not cause another there would be no reason for the world not to be totally chaotic, and therefore
no reason, beyond observations of Humean constant conjunction, to believe that it
might not suddenly become so. This is what logical analysis has so much difficulty
capturing. So we can note that understanding the statement “If C occurs then E
occurs” as a causal statement involves understanding more than simply the formal
logic involved. It involves understanding things about C and E, about what links
them and how C “necessitates” the occurrence of E.
We can make this point a slightly different way. All counterfactual conditionals, when viewed as material conditionals or read purely truth-functionally, are
true. Logically, they can be written as ∼C & (C → E).5 Such a statement has the
truth value true. Thus, truth-functionally, “If R&D spending were higher than it is
patenting would be higher than it is.” and “If R&D spending were higher than it
is patenting would be lower than it is.” are both true, since R&D spending is not
higher than it is. This makes it sound as if the logical conclusion of scientific endeavour is “Anything can happen.” (which of course is not inconsistent with many
of the beliefs of the spiritual movement of the 1990s). But this is clearly not where
science is headed or wants to end up. How do we distinguish between these two
statements, accepting one and rejecting the other?
The first, brief part of the answer is that it cannot be done purely with logical
analysis, and no modern treatment of either counter-factuals or causation attempts
to do so. Peter Gallison (1987) shows convincingly just how much this is the case
for experimental physics. Experimenters must decide when they have arrived at
the correct result, and when errors introduced by faulty equipment, extraneous
forces, observation error and so on are not affecting the results in a significant way.
Gallison shows in a series of cases that while formal logical analysis is important,
it is probably not the most important factor in making that decision.
The longer answer has had two strands in the literature. A meta-linguistic analysis, (associated with writers such as Chisolm, Goodman, and Mackie) gives up
on truth and falsity and asks instead about “assertability”. We are willing to assert
that an increase in patenting follows an increase in R&D spending, but we are
unwilling to assert that a decrease in patenting would follow an increase in R&D
spending. This removes the onerous burden of logical proof, but leaves open the
question of what determines assertability. The other strand, following Stalnaker
(1968) or Lewis (1973 a,b) for example, retains truth and falsity, but attempts a
more sophisticated analysis of what determines it. For our purposes, it is not necessary to choose between these approaches, for the main problem is over-arching:
in Goodman’s words, it is “to define the circumstances under which a given counterfactual holds while the opposing conditional with the contradictory consequent
fails to hold.” (p.4).6 Meta-linguistic analysis is relatively clear that assertability
5
This should be read as “C is false, and (C implies E)”.
There may be exceptional cases in which both hold, but for our purposes we need not be concerned
with those cases. Lewis’ (1973b) theory allows for such a possibility.
6
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R. Cowan and D. Foray
follows from being able to infer the consequent from the antecedent plus some
additional premises. On the theories of Lewis and Stalnaker, truth or falsity of a
counterfactual follows from the same considerations as truth and falsity of a factual
conditional, but to evaluate the former we consider a non-actual possible world.
The two approaches have similar problems: which additional premises are considered; which possible world is examined.7 But having determined which premises or
which world, there is the issue of how to use the set-up to evaluate the conditional.
The consequent must somehow be linked to the antecedent. Purely formal links
will almost certainly fail to provide the answer in general, just as they do in evaluating many factual conditionals. Goodman is explicit that this link must be causal;
Lewis is less so. For Lewis a counterfactual conditional is true if the factual conditional is true in a closest possible world. How do we evaluate factual conditionals?
Gallison’s work shows that the answer to this question is complicated, but at the
very least non-contradiction of known causal laws is one of the strongest criteria.
Laws are included in descriptions of possible worlds, and a conditional which was
shown to be an instantiation of a causal law (or a system of them) would certainly
be judged true. Even if we do not go as far as Goodman (or Elster, see below) in
asserting that the consequent must follow from the antecedent by a causal law, our
ideas about causation will be central in determining how we evaluate counterfactual
conditionals.
Emphasizing the place of causation conforms well with the intuitions which
make counterfactual claims relatively common in everyday life. Asserting a counterfactual conditional is in essence making a claim about the regularity and coherence of the world. Causal forces connect events and provide the coherence that we
seek. The reliability of a counterfactual arises from the same source, namely the
coherent, consistent structure of events, even under Hume’s minimalist, “constant
conjunction” view of causation. To defend a counterfactual conditional is to explicate the causal connections among the events involved.8 If the causal account is
judged acceptable, the conditional is judged assertable. This brings us to the nature
of causation in economics.
7 Which additional premises to consider is determined by “co-tenability”. Lewis (1994, pp. 57 and
69) provides a definition which he claims captures the intent of metalinguistic theorists and also makes
the two theories identical.
8 Accounts of counterfactuals vary in their explicitness regarding the use of ideas about causation
in our evaluation of counterfactuals. Simon and Rescher (1966) are explicit that the acceptability of a
counterfactual is determined by the causal models we employ, through their version of modal orderings.
Stalnaker (1968), and similarly Lewis (1973a, 1973b), are not explicit that causal analysis is involved,
though Lewis makes an explicitly tight connection between causation and counterfactual conditionals in
that he seeks to provide a counterfactual analysis of causation. For both authors, though, the acceptability
of a counterfactual conditional is determined by asking whether the consequent is true in a world in
which the antecedent is true but which is minimally different from the actual world. Jackson’s (1977)
suggestion is to posit a world in which the antecedent is true and inter alia in which “causal laws . . . are
identical to ours” (p.7). Bennett (1984, p. 73), has a similar approach, though speaks of “laws” rather
than causal laws explicitly. Goodman’s exposition is very explicit regarding the causal under-pinnings
of counterfactuals, and it is clear that many authors feel there is a tight connection between the two.
Evolutionary economics and the counterfactual threat
545
1.1 Causation in economics
Within economics as a whole we can see that there are implicitly two notions of
causation. Traditionally, the fundamental goal in economics has been to explain
phenomena in terms of equilibria.9 Note that in this context, an equilibrium can
be dynamic, involving change, growth or even cycles in the variables of interest.
Something is explained when it is shown to be held in place by countervailing
forces. The economy (firm, market) exhibits behaviour X because there is no incentive to deviate from that behaviour, and in fact deviations are punished. The
explanation of X consists in finding the forces that impinge on X and showing that
they are in balance. How these forces came to impinge on X, or how they came into
balance is not generally considered part of the explanation. It is assumed that some
process would produce this result, but that process is rarely spelled out. Further, “in
abstracting from the actual course of adjustment to the initial shock one is assuming that the adjustment process has little effect on the final outcome.” (Hausman,
1990, p. 171), thus ruling out the processes described by Fisher (1983), mentioned
above. This implicit assumption connects well with uniqueness of equilibria. The
final result of an analysis is to find the effects of some independent variable on a
dependent one, R&D spending on patenting for example. Uniqueness implies that
the path to the equilibrium is in a very real sense unimportant. If this equilibrium
is the only place we can end up, and if we are in or close to equilibrium most of the
time (otherwise its usefulness as an explanatory tool would necessarily be called
into serious question) what difference does it make how we get there? It is not even
that interesting as a curiosity, since the path to equilibrium must be relatively short.
Causation, when seen this way, can be called “sustaining causation”, and from
this point of view understanding a phenomenon involves knowing what holds that
phenomenon in place, and why there is no deviation from it. There is another type
of causation, though, which we can call “originating causation”.10
The process by which an event, state or phenomenon comes to exist is central
to explanation in Evolutionary Economics, and must at the very least be considered
when a mechanism to select among equilibria is at play. The general goal of
explaining the movement of something over time, or to explain the existence of a
phenomenon in terms of how it came to be is common to all evolutionary sciences
(Nelson, 1995). Explanations of, for example, the location of an industry would
involve a dynamic model of location choice, in which choices made by firms this
period depend on the location choices of previous actors. The process might evolve
to a stationary distribution, but also might never do so, if the choices of later actors
sufficiently upset the choices of earlier actors that they provide incentives to change
9 See Reder (1982, p. 12) for example, who describes a dominant, Chicago, view as assuming that
“one may treat observed prices and quantities as good approximations to the long-run competitive
equilibrium values”.
10 See Cowan and Rizzo (1996) for a discussion of causation in economics and in particular on the
difference between sustaining and originating causes.
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location.11 The cause of the current distribution of activity is seen as historical. We
must look back at decisions made in the past, and trace their effects on following
decisions, as the choice of one actor affects the incentives of other actors. To understand a phenomenon, then, we must understand the process by which it came to
be. There is a sequence of events leading to this outcome, and we must be able to
trace this sequence, and the causal links between the events in it, before we “really
understand” why this phenomenon exists.
This is all part and parcel of the possible existence of multiple equilibria. When
multiple equilibria exist, the selection mechanism gains prominence. Selection
mechanisms (in the world) are inherently historical. If there are two possible outcomes, one cannot simply look at sustaining causes to explain which is selected –
by definition both are sustainable. One needs to address origins.
This discussion of causation impinges on counterfactual conditionals in a crucial way. We argued above that unpacking a counterfactual conditional involves
examining causal connections. This is the process by which a counterfactual is
judged – if the causal story is judged acceptable, or credible, the counterfactual
is deemed assertable. Clearly, then, the nature of causation will determine what
is unpacked during this enterprise. In a science in which causation is ahistorical,
examination of the counterfactual claim will not involve history. Contrarily, if causation is viewed as inherently historical, then examining a counterfactual claim will
necessarily involve history in one way or another.
We can make the same argument in a slightly different way. We pointed out
above that a counterfactual claim is implicitly a causal claim, but this is deeper than
simply a semantic identification. A claim about a counterfactual condition is about
the same thing as a causal claim. Thus the counterfactual will contain within it the
nature of causation. What this means is that if unique equilibria are derived from
accepted axioms, then simple counterfactual conditionals will be the norm. But if
multiple equilibria exist, and events on the path are important in determining which
is selected, and perhaps where it is located, then counterfactuals involving history
will be common. In the style of economics we have been calling evolutionary,
empirical work will involve historical excursus: hence the counterfactual history.
This discussion of causation makes contact with the solution of Conrad and
Meyer (1964) to the problem of counterfactual conditionals. As noted above, the
difficulty arises from logical problems with the counterfactual statements themselves. The solution, Conrad and Meyer suggest, is to look for statements that are
co-tenable with the counterfactual statement. Consider, for example, the statement,
“If there had been no Civil War, slavery in the southern US would have been dismantled peacefully.” There is no way to prove this analytically, nor is there a direct
empirical “proof”. But, following Conrad and Meyer’s exposition, this statement
would only be true if slavery were unprofitable at the time of the war. Thus we can
implicitly test this statement by testing the profitability of slavery. Notice, however,
that this approach makes several assumptions about the causal forces at work. It
11 The lack of an equilibrium is not necessarily considered either a failure or a problem to be addressed.
The role of equilibrium in economic analysis is something occasionally debated within Evolutionary
Economics. We hope, within the course of the argument presented in this paper, to remain agnostic on
that issue.
Evolutionary economics and the counterfactual threat
547
assumes that profitability is key to the continuance of an institution, and, more
tellingly, it assumes that the non-occurrence of the Civil War would not have affected the profitability of slavery, nor would it have set in play any other events that
would have over-whelmed the effects of the (non)profitability of slavery. Both of
these assumptions may be valid in this case, but by making them explicit we see
that the co-tenability solution runs the risk of being too static. It fits easily with the
view of counterfactuals as describing a world as similar as possible to the actual
world at the time under examination, but it may not fit so well if one’s view is
more historical, namely that the counterfactual world must be the outcome of some
feasible path from some point in the history of the actual world.
We turn now to a discussion of these two views of counterfactual statements.
4 Two views of counterfactuals
The philosophical literature contains (at least) two views of counterfactuals: counterfactuals as non-actual possible worlds; and counterfactuals as untaken branches
in the tree of history.
4.1 Possible worlds
David Lewis (1973 a,b) sees a counterfactual conditional as describing a possible
but not an actual world. There are many worlds which in principle could exist,
the actually existing world being only one of them. A counterfactual conditional
is merely describing another. One might be a world just like ours, but in which
R&D spending is 25% higher than it is in ours. Lewis’s view is that to do science
effectively, which of course involves examining counterfactual conditionals, this
alternative world should be as much like ours as is possible, and in fact he describes
a formal metric. His exposition is largely ahistorical since within it is no constraint
that it be possible to get to the alternative world from some point in our own actual
history. It is simply a world running parallel to our own in some alternative universe,
and which we examine when doing so might tell us something interesting about
our own.12
12
Though he does not develop it particularly, Lewis allows for a more historical version of his theory.
In effect, it involves putting non-simultaneous dates into the counterfactual: “If the roulette ball had
landed on red instead of black last week, then today . . . ”. Since roulette wheels are deterministic, the
antecedent involves a minor miracle if we are to keep the world the same up until the time at which the
ball landed. But following the landing of the ball, it seems that many major miracles could be necessary to
keep that possible world “just like” ours. So, Lewis (1974, p. 77) concludes, following the counterfactual
landing of the ball, we invoke existing causal (and other) laws, to trace out the consequences. What is
less clear is what to do if it would have taken a major miracle to change the colour. Should we go further
back in time, (to when the croupier got out of bed perhaps) to find a (counterfactual) historical path that
involves only a minor miracle? If this is the case, then this theory becomes very like a branching theory.
Other authors, (see Bennett, 1984, or Jackson, 1987 for example) develop possible worlds theories which
explicitly include temporal notions and thus share some of the features of explicit branching theories.
For a discussion of possible worlds theories, see Sanford (1989, Ch. 7, 9 and 10).
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4.2 Branching
There is a second view of counterfactuals, defended by Jon Elster (1978, Ch. 6),
which involves a branching view of history. History is seen as a tree, in which
each decision taken represents a branching point. We proceed up the tree, moving
higher and higher, never descending, as decisions are made and history unfolds.
A counterfactual analysis involves moving back down the tree to some branching
point, and examining a branch not taken. Sometime last year a decision was taken
about this year’s R&D budget. The analyst returns to the point at which that decision
was taken and asks “If at that point a different decision had been taken, how would
history have unfolded?”. Bring this alternative history up to today’s date and ask
how the alternative present differs from our own.
Given the historical nature of evolutionary economics, and the view that small
causes can beget large effects, it seems that the branching view of counterfactuals
is more apt.
But in its most general form, a counterfactual analysis amounts to postulating
that something is different than in fact it is, and examining what follows from this
difference. Two difficulties arise. What is it feasible to postulate as a difference?
How do we tell what follows? The key to addressing both of these difficulties
lies in there being theory that can be employed in the analysis. Theory confines
a counterfactual analysis in two ways. First, it restricts what can be postulated
as the counterfactual antecedent. Second, it determines what follows from those
postulates.13
4.3 Branching and the role of theory in counter-factual construction
The branching approach to counterfactual analysis consists in choosing a branching
point in our actual history, and tracing an alternative history that would follow
from a different branch having been taken. The branching view is common among
economic historians. When discussing the “new economic history”, Fogel (1973,
p. 139) states, “In order to determine what would have happened in the absence of a
given institution, the economic historian needs a set of general statements that will
13 The introduction of theory in this way runs the risk of introducing circularity of two types. If
the aim of the empirical work is to test the theory, in the strictest version of testing, then using the
theory to guide the counterfactual construction of history runs the risk of assuming what is to be tested.
This risk is not always as severe as it is made out to be. Often the basis of scientific judgement has
to do with internal consistency, which is not affected by this issue. The second source of circularity
is more abstract. It would undercut the very starting point of counterfactual analysis. Theories can be
and often are expressed as law-like statements. As Goodman has pointed out, any law-like statement is
equivalent to a counterfactual conditional. Thus any counterfactual analysis which employs theory to
guide and restrain it rests on further counterfactual claims (since whatever form the theory is expressed
in, it can be re-written as a set of counterfactuals). So to argue that counterfactual analysis in general is
acceptable because any counterfactual analysis must be grounded in theory fails as a defence of this form
of endeavour. It is equivalent to saying that counterfactual analysis in general is acceptable since any
counterfactual analysis must be grounded on a set of counterfactual conditionals. This is problematic
if one is concerned with defending the technique itself. We leave aside this concern, assuming that is it
better dealt with, and indeed has been dealt with, in the philosophical literature.
Evolutionary economics and the counterfactual threat
549
allow him to deduce a counterfactual situation from institutions and relationships
that actually existed.” Elster (1978, p.181) is more explicit, claiming that there
are three conditions that must be met to make any such counterfactual analysis
compelling. First, the antecedent of the counterfactual conditional must be feasible
in a static sense; that is, it must describe a state that is internally consistent.14 The
claim that had railroads not existed, GDP would only be slightly smaller, needs,
as one necessary condition, that a world without railroads does not contain logical
contradictions. Second, the antecedent, “railroads do not exist in 1890” must also
be “insertable”; there must be some feasible historical path from a real historical
state that would produce the state described in the counterfactual antecedent. Given
the actual history of the world to some point, 1840 say, it must be possible to trace a
feasible history which includes no logical or physical impossibilities from that point
to the one in which the antecedent – railroads do not exist in 1890 – obtains. Third,
the consequent, “GDP in 1890 is virtually the same as it is in our world containing
railroads”, must be linked to the antecedent by a (dynamic) theory. Theory enters
twice. It enters into insertability conditions – tracing out a history from a real point,
obeying all known laws, to reach the antecedent. It can also enter in the link between
antecedent and consequent if they are separated in time. It is clear that all three of
these conditions are governed by theoretical considerations: internal consistency
is a theoretical notion; the feasibility of a hypothetical history implies that it does
not contradict held theories of how the world works; and the connection between
antecedent and consequent is, if it is to be compelling, based on accepted causal
theories.
The importance of the role of theory here introduces what Elster (p. 184) considers to be the basic paradox of counterfactual analysis. The stronger is the theory
(that is to say the more it restricts what can happen) the better grounded is the
“deduction” of the consequent from the antecedent, and the tighter is the connection between the (actual) world at the point when counter-facts are inserted, and
the counterfactual state under discussion; the tighter the connection, for example
between the actual world of 1840 and the 1890-world-without-railroads. But on
the other hand, the stronger is the theory, the more restrictive are the conditions of
insertability, that is, the smaller is the set of antecedents that are consistent with
“what we know about the world”. Any counterfactual analysis must avoid both
risks of vagueness and risks of absurdity. An analysis is vague when the model
connecting consequent to antecedent is too loose, or not specified completely – it
allows too many things to happen. Absurdity arises when the antecedent runs awry
of a tight theory, that is, a theory that is highly specific about what can happen,
and so when used to examine the counterfactual antecedent, reveals a state that is
internally incoherent or absurd.15
The branching view of counterfactuals implies a stronger notion of consistency
than the possible worlds view. To logical and physical consistency the branching
14 This consistency condition is common to many theories of counterfactuals, Stalnaker (1968) being
perhaps most explicit about it.
15 One potential way around this paradox, which cannot always be employed, however, is to observe
that the theory determining whether the antecedent is insertable may be different from the theory used
to deduce the consequent, provided the theories are not inconsistent.
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R. Cowan and D. Foray
view adds historical (or originary causal) consistency as a requirement. Thus to
ask what would be the effect on GDP if railroads did not exist in 1890 is not
to ask about the world in the instance that railroads were vaporised on the first
of January 1890, since some of the remaining physical and institutional structures
would be historically incongruous. Historical consistency rules that out as an avenue
of inquiry, and demands instead that we consider the possibility that they were
not introduced in 1840, nor between then and 1890. Notice that this permits or
accommodates some looseness in the statement of the counterfactual antecedent.
Whether the antecedent is stated as “railroads do not exist in 1890”, “railroads were
not introduced in 1840, or any time up to 1890”, or “railroads were never invented”,
all demand the same analysis. The same theories apply in each case, assuming the
counterfactual consequent in each case refers to the state of GDP in 1890.
5 Counterfactuals in economics
One of the strengths of the axiomatic, deductive aspect of neo-classical economics,
and something which makes the counterfactual analysis used there seem innocuous,
is that the theory is very strong. Its strength places severe restrictions on what can
be assumed as antecedents. The primitives of the theory, utility functions and production functions, are tightly constrained by the analytical tradition and demands
of rigour: a small number of functional forms is acceptable, and all have similar
regularity properties. Thus to a great extent the only possible counterfactual antecedents have to do with changing parameter values in the models. In any model
with a unique equilibrium consequents follow very tightly from counterfactual antecedents. In fact the theory has been specifically constructed so that consequents
follow antecedents by the laws of formal logic or mathematics. Tighter connections
are not available even in principle.16 Further, when equilibrium is the focus of the
analysis the accepted form of causation is sustaining causation, from which it follows that comparative statics analysis tells a complete story (again see Hausman,
1990). We can place this in the context of Elster’s three conditions for counterfactual analysis. Because changes in parameter values are by definition exogenous to
the model, they lie outside the scope of economics, and so many changes are acceptable. The only constraints are internal, logical consistency, and that the change
not create a violation of the functional form regularity conditions that most models
contain. The “course of history” that produces the parameter changes is exogenous
to the analysis, and so an economist has little, if anything, to say about it. Further,
since the equilibria are typically unique, all possible histories will lead to the new
equilibrium. The link between consequent and antecedent is deductive, which is
the strongest kind of link we know. So if the tenets of the theory are accepted,
counterfactual analysis will be a very strong tool.
It is clear that any deductive science will be able to make very strong counterfactual claims, since theory provides a strong link between consequent and antecedent. If one agrees with the premises of the basic theory, one cannot argue with
16 This should not be read to be saying that therefore the theory is correct. It is simply to point out
that if the premises of the theory are accepted, then antecedents logically imply their consequents.
Evolutionary economics and the counterfactual threat
551
the counterfactual claims that are drawn from it. Some of evolutionary economics
fits this mould. Specifically, it is often possible to derive, using an evolutionary
model, results about patterns of behaviour. Technology choice models, for example, derive results about proportions of agents using different technologies.17 And
evolutionary game theory models typically have this feature.
6 Counterfactuals and evolutionary economics
Not all of evolutionary economics can be characterized this way though. Many of
the models yield probabilistic results. This is not surprising given the underlying
view that many processes are contingent (see Nelson, 1995). Probabilistic results
are not a difficulty when two conditions are satisfied. The first is that there is a
deducible relationship between the probability distribution and some exogenous
variable (a form of a comparative statics result); and second, that the phenomenon
under investigation is manifest in a way that permits estimation of the entire distribution. When both of these are fulfilled, the counterfactual conditional relating
changes in the parameter to changes in the probability distribution can be examined
since both are observable. It is common, though, that the second condition fails, and
that we cannot observe distributions, but rather a very small number of realisations
of the distributed variables. This is typically the case with potential regret results.
In the technology choice literature for example, a typical result which is provable
analytically is that with non-zero probability, the system will standardise on an
inferior technology. When turned into a counterfactual conditional referring to a
particular instance we get: “Had we standardized on gas-graphite nuclear power reactors, we would now be better off.” This claim seems much stronger than anything
present in the theory which only claims that if there are enough cases of technology
competitions, some of them will have the result that an inferior technology wins.
It makes no claims about nuclear power, nor about this particular realisation of its
development. If we were to confront the theory with the nuclear power case, what
would be involved?
On the “possible worlds” view of counterfactuals, we need to imagine a world
in which we simply replace all the light water reactors with gas-graphite reactors,
and ask whether it would be a better world. 18 This is the thought experiment of
a comparative statics analysis, and makes perfect sense if history does not matter.
A (formal, analytic) map from technology-type to net-benefits-from-use will be
17 See David and Greenstein (1990) or Foray (1989) for surveys of this literature. See Cowan and
Cowan (1998) or Bassanini and Dosi (1998) for more recent contributions that derive results deductively.
18 The answer would almost certainly be that it would not, since the current state of gas-graphite
reactors is not a particularly good one. Notice in general that if this is the view taken of the counterfactual
conditional, it will be rare to find examples of dominant inferior technologies. If there were one, why
would users not switch? Apart from co-ordination problems, which can in principle be solved by making
information public, there is no reason for rational users not to create the other possible world. Thus
there cannot be dominant inferior technologies. This is the approach taken by strident opponents of
path dependence and in our view involves applying a static possible worlds approach when a branching
(or dynamic possible worlds) approach is appropriate. (See for example the writings of Liebowitz and
Margolis, 1995 e.g.)
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enough to tell us whether gas-graphite would have been better. An ahistorical map
renders the possible worlds approach sensible.
On the branching view of counterfactuals, we must go back in history, probably to about 1960, and change several of the decisions made at that point, and
then re-write the history to the present. (See Cowan, 1990, for a detailed history of
this example). This is the approach taken if historical events do make a difference.
And this is the approach appropriate to evolutionary economics in a great many
instances. This follows from the presence of positive feedbacks in evolutionary
models. Changes in decisions can be magnified, and re-enforced by the system.
Again, though, if an analytic map from technology-type to benefits-from-use existed, the counterfactual conditional would be strong again, even if history were
involved in that mapping. What renders this attempt infeasible, from the point of
view of many evolutionary economists, is the importance of randomness, coupled
with positive feedbacks. When these are both present, at the level of analysis typically conducted by economists any outcome is under-determined by the data, even
in an equilibrium model. A central tenet of Evolutionary Economics (and common
to much of evolutionary economics) is that there are many sources of indeterminacy in any economy: the identities of interacting agents, which will necessarily
be beneath the vision of the analyst; the process of learning, which is often unpredictable; the presence or details of innovation which are, again, unpredictable, are
all examples. This indeterminacy implies that model outcomes (at least the details
of them) are under-determined, and further, that the actual determination of them
is historical. This indeterminacy weakens the link between antecedent and consequent in the counterfactual. Evaluating it is no longer just a matter of formal logic,
it now involves judgement.
It is important to notice that under-determination notwithstanding, history is
not unconstrained—that would make investigation impossible. But history is only
loosely constrained, since even in a world governed by causal laws, the presence of
randomness (or even simply ignorance on the part of the analyst, which means he
cannot predict everything) implies that within those laws many things are possible.
We argued above that law-like statements can be re-expressed as counterfactuals, and that thereby the investigation of law-like statements will be, implicitly at
least, an investigation of counterfactuals. Analysts investigate, therefore, the causal
link between antecedent and consequent of the counterfactual conditional. If causation is viewed historically, so will be the counterfactual and so the investigation of
it. But if history is only loosely constrained, many things can happen, which means
that in all probability there is some link between consequent and antecedent. But
“many things can happen” does not mean that everything happens with equal probability. Nonetheless, the fact that many things have non-trivial probabilities implies
that tracing a tight link between an event (or counter-event) and its consequences
may be problematic, the more so as the two are removed from each other in time.
History unfolds by choosing among alternative possibilities. That is, history makes
a choice about what today is like. With every possible today, there is associated a set
of possible tomorrows. If agents are learning (often in an unpredictable way) and
agents are innovating (in an unpredictable way) and interacting with other agents
(in an unpredictable way), the choice of today’s state does not place strong restric-
Evolutionary economics and the counterfactual threat
553
tions on the set of tomorrow’s possible states. This means that tracing the effects
of a (counterfactual) event becomes less secure as the tracing occurs over longer
times.
The most compelling counterfactual analyses exist when the theory on which
they are based is tightest and most restrictive about what can take place. One of
the intuitive strengths of evolutionary economics is the premise that in a system
as complicated and complex as any modern economy must be, any theory that
too tightly constrains what might happen will leave out very important aspects of
economic activity. But this makes counterfactual analysis very difficult. If history
matters, then counterfactual analysis will often be historical. Without severe theoretical constraints on what history can produce, though, the analysis runs the risk
of being far from compelling.
We can summarise this section briefly. To accept a counterfactual conditional
we demand an argument that it is possible to “infer” the counterfactual consequent
from the counterfactual antecedent plus auxiliary conditions. This inference must
be causal, so causes and the causal structure must be understood (Goodman, 1978).
Hausman points out that for a comparative statics analysis to be coherent, the
economist must believe that there is a process that would connect the states before
and after the change in the exogenous variable. He argues that for a variety of reasons
this process is not of particular interest in general. But, as Tunzelman (1990, p. 296)
argues, counterfactual histories “represent an empirical working out of comparative
statics”, that is, they are an explicit rendering of Hausman’s process. Evolutionary
economics contains a general belief that the process between initial and final states
is important, due to the under-determined nature of outcomes, and so counterfactual
history will be an important part of explanation. To come to the same conclusion
from a different direction, we note that virtually all sciences embrace some form
of causality, and use that notion as part of their explanations and to support their
predictions. For Evolutionary Economics, since it is generally accepted that “cause”
refers to originating or genetic causes and that the causal structure is temporal or
historical, the argument supporting the counterfactual conditional will necessarily
be temporal or historical. But one of the underlying beliefs of many evolutionary
economists is that outcomes are under-determined; in general any basic data present
more than one possible outcome. Thus theory does not tell us what will happen, it
only restricts us to a set of possibilities.
7 Evolutionary economics and counterfactual history
The discussion in the previous section seems to be driving toward the conclusion
that since empirical counterfactual analysis is so difficult to perform convincingly
in evolutionary economics, this branch of economics is fated to be either nonempirical or mere story-telling and therefore highly speculative, and presumably
of little practical value. In this section we argue that we need not be driven to this
conclusion. We present four different routes away from it – three “in practice”, and
one “in principle”.
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7.1 Weak counterfactuals and appreciative theorizing
Counterfactual analysis can be used to buttress theory. The aim of appreciative
theorizing is less to formalise general laws, which could be used in predicting
the future, than to use general, well-understood theoretical concepts to understand
either the past or the present. 19 Evolutionary economics takes almost as a precept
the idea that understanding is necessarily historical. Thus to understand the present
we must know and understand the history that brought us here. As time passes,
the economy is continually eliminating future branches, and thereby producing
the present. Understanding this process involves showing either that there were no
branching points, and thus the present state is inevitable given the starting point, or
identifying the branching points (those at which there was a non-trivial probability
of taking a different route), and showing why the economy followed the route it
did.20
We refer to this exercise as one of weak counterfactual history, since it involves
minimal knowledge or assertions about the non-actual branches. In this regard it
is only necessary to argue that the branches not taken would lead to a different
present.21 Even more generally, weak counterfactual analysis is restricted to understanding the major events and chains of decisions which, coupled with processes
that magnified rather than damped their effects, can be considered as having played
a role in disconnecting some sub-regions of the tree from the branches followed by
actual history.
Advantages of a weak counterfactual argument are clear. Accepting its goals
renders Elster’s conditions less important, and thus permits economists to treat a
large number of problems in those terms. Drawbacks also seem clear however.
Weak counterfactual analysis does not give welfare results, and in a sense can only
argue that the actual outcome is not the only possible one. This does permit the
claim of potential regret, but cannot assert more; it will not make a case for actual
regret (see Foray, 1997). For that we need something stronger.
7.2 Strong counterfactual analysis
Appreciative theorising, or appreciative analysis more generally, is sometimes considered not enough. In particular, economists are often concerned about making
welfare comparisons between two different states. This exercise involves knowing
something about the nature of both states. It is difficult enough to know about the actual present. But to compare it to a counter-factual present can be extremely tricky.
If, following the arguments given above, we adopt the branching view, rather than
the non-actual possible worlds view of counterfactuals, this comparison is made
19
For an example see Malerba et al. (1999).
It is immediately clear that such an approach excludes one view of counterfactuals, namely counterfactuals as non-actual possible worlds. The point of the exercise is to show that certain historical
events contributed to forming the present state, and that had different branches been taken at some point
in the past, we would be at a different present.
21 It is possible that this argument may be difficult in certain cases, but in general to have identified a
branching point is to have identified a moment when one future is eliminated.
20
Evolutionary economics and the counterfactual threat
555
more difficult as we must compare our state not simply with another state having
minimal variations from it, but rather with the state that would exist now following
some counterfactual change at some point in the past.
The obvious difficulty of the exercise is that strong counterfactual analysis consists of (re)-vitalising something that did not happen.22 In contrast to weak counterfactual argument, the restrictions discussed above must be given much more
consideration in the analysis. The theory or principles employed in strong counterfactual argument must, therefore, i) constrain the world of potential developments;
and ii) use strong laws on the basis of which both to construct and to develop the
alternative paths “selected” from the set of possible paths.
The first principle is relatively easy to illustrate. Consider the hypothesis that
environmental problems would be less severe if the internal combustion engine
did not dominate personal mobility technology. There are, currently, four serious
possible automobile propulsion sources: internal combustion; lead-acid, or more
advanced batteries; fuel cells; and hybrid battery-internal combustion technology.
An argument that internal combustion is an inferior technology (on the environmental criterion) could only be built using as competing technologies steam and
lead-acid batteries. This follows from the fact that the important events determining the outcome of the competition took place at the turn of the century, and the
fuel cell technology did not then exist as an automobile propulsion source. Thus
the important branching point in the history was a three-way branching in which
the internal combustion branch was taken. This is related to Elster’s insertability
criterion. The fuel cell option is not insertable at the crucial point in time.23 But this
is, rather than a weakness, a strength of the analysis. A small set of options implies
that a small number of branches need be traced to address the issue of whether the
present state is an optimal one. Bifurcation points with few branches make the case
for regret stronger, by deriving it from a point with relatively few options.
The second condition, that counterfactual history be based on strong laws, is
more difficult to illustrate. A causal story is simplest and most compelling when
there is a single causal factor that clearly dominates all other considerations. In
this case a relatively tight link between antecedent and consequent can be drawn,
because the construction of the chain of events is tightly controlled and constrained.
22 It is worth pointing out that facts about paths not chosen are often available. When this is the
case, analysis is considerably facilitated. In many cases of apparent total technological standardization,
for example, small islands of the non-standard technology can be found. These islands can be used as
sources of information about how the technology might have developed had it been more extensively
used. Another case might be a particular industry that had different trajectories in different countries or
regions: the cotton industry in Texas and Mexico, which were very similar to each other at one point,
adopted different strategies for pest control with dramatically different results (Cowan and Gunby,
1996); for a similar case in ferrous casting in France and Germany, see Foray and Grübler (1989). Thus
counterfactual analysis is not necessarily “analysis without facts”.
23 It is possible that on a different history the fuel cell could have been available in 1900. But to make
this counterfactual analysis would involve going back yet further in time to find a point at which research
on fuel cells could have been taken up. Elster uses this notion to create a measure of difference – how
far back in history do we have to go to trace a feasible path to the counterfactual present we wish to
examine?
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The law must be strong in two senses: it must be general (that is to say, robust)
and important. Generality is straightforward. The law must cope with a variety of
spatial and temporal contexts. Many macro-and micro-economic laws are general
in this sense.24
The other dimension, importance, is less simple. We need a law “important
enough” to cause the counterfactual construction we base on it to be, in effect,
isolated from the influence of other laws. It must be over-ridingly strong. It clearly
follows that the strength of a law cannot be dissociated from the context of the
enquiry. Network effects, for example, can create forces which underlie a strong
law in the field of telecommunications technologies. But these same forces will
not be “important enough” to build a counterfactual history about, for example, regional polarisation, since it is not plausible that the dynamics generated by network
externalities could be isolated from the effects of transportation costs, infrastructure externalities, local labour markets and so on. In this dimension, “importance”
implies that the ceteris paribus clause can be plausibly invoked.
There is another problem which must be faced, namely the risk of circularity.
Any economic system, agent or phenomenon will have many forces acting in or on
it. The discussion immediately above had to do with identifying and examining the
effects of the strongest forces. This implies that forces can be identified as primary
or secondary. Concentrating attention on primary forces (of which one hopes there
are few) connected to strong laws will not generate misleading results. The identification of primary and secondary forces is one typically done in the abstract, or
theoretically, and here lies the possibility of circularity, or perhaps assumption of
the result. Evolutionary theory, in common with other theories of complex systems,
typically has the feature that positive feedbacks, and the interconnections between
different parts of a system, can magnify what appear to be secondary considerations
into primary ones. Unfortunately, determining whether or not this is a relevant consideration can only be done using counterfactual history. So again the very feature
that makes counterfactual history a vital tool makes it a tricky one to use. We can see
this in the discussion of Robert Fogel’s (1964) work on US railroads. One central
issue of Paul David’s (1975) criticism is that Fogel ignored this possibility. Fogel
made a priori judgements about which forces were primary and which secondary,
and then showed that when the primary ones were considered, US railroads had a
certain effect on GNP. One interpretation of David’s arguments is that if the forces
considered secondary were put into a complete counterfactual history, they would
have been acknowledged to be as important as the primary ones.
7.3 Stronger theory to contstrain counterfactual analysis
We have just argued that whether or not a law is strong depends on context. This
might suggest that a general change in context could have the effect of increasing
the strength of laws generally. In other words, it may be possible to make strong
counterfactual history more compelling by looking at theory slightly differently,
reconsidering what questions are feasible, what count as respectable answers, or
24
On the issue of robustness of economic laws, see Kindleberger (1989).
Evolutionary economics and the counterfactual threat
557
what phenomena it is appropriate to analyse, if these have the effect of making the
theory in general stronger. If, for example, the theory is seen to be more deductive,
and in particular more deterministic, this reduces the “anything can happen” problem. But because of the underlying tenets of evolutionary economics, to adopt such
a strategy implies thinking about economics problems in a slightly different way.
Neo-classical economics attempts to make predictions of the behaviour of economic agents, either as individuals or as aggregations. In models of perfect competition all agents are infinitesimally small, and thus as individuals have no influence
on outcomes, so deducing market outcomes is facilitated by there being few “public” effects of individual actions. At another extreme, we observe models with few
enough agents that all can be traced and the non-anonymity is tractable. Evolutionary models, though, often have a large but finite number of heterogeneous agents
in them, and these agents are affected by each others’ actions. When this is true,
a detailed prediction of agent behaviour is simply not possible.25 It is this large
population of heterogeneous agents, interacting with and affecting each other, that
causes the serious under-determination of evolutionary models. Even if there is no
ontological uncertainty, epistemological uncertainty can be extreme.
Whether or not under-determination is important, though, is crucially dependent
on the level of analysis. Where in the world it will rain today is certainly underdetermined by my knowledge of the weather system. That it will rain somewhere is
not. And with a small amount of research one could almost certainly predict fairly
accurately how much rain will fall today. Thus we can reduce considerably the
degree of under-determination simply by changing the level or nature of analysis.
While the details of the actions of individual agents may be under-determined,
general patterns of behaviour may not be. This was an idea present in the work of
Hayek (1967, for example), and based on roughly the same arguments – there is too
much information present in the economy to be processed by any analyst. Any agent
will have more or different information than any analyst, thus the goal of predicting
detailed agent-level behaviour is un-reachable. There will be general tendencies,
however, which are predictable, due, in effect, to the central limit theorem.
Consider models of firm location. A firm chooses a location based in part on
the locations of other firms. Locating near another firm on the one hand permits
encroaching on its market, but on the other reduces one’s monopoly power. In early
models of such a system, (Eaton and Lipsey 1975, is a good example) the question
was where firms would locate in a certain space. It quickly became apparent, though,
that in many models attempts to expand beyond very small numbers of firms, or
beyond one dimension, created intractable problems, partly because of the economic
process involved. (Note that the goal was not to ask where a specific firm, Firm
A, would locate but rather a weaker one, whether there would be any firm at all at
some location X). The question was un-answerable, possibly even in principle. A
different question, though, and one which involves a different level of analysis, is
whether firms will cluster or spread out. This is of course related to the central issue
in Hotelling’s (1929) original paper. It is also possible to ask how large clusters will
25 It is inconceivable that the analyst could get enough information, and even if he could get the
information, he would not be able to process it.
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be, and how close together they will be. Here the questions do not refer specifically
to firm behaviour, but rather to the properties of the system as a whole, and in
particular the pattern or distribution of location. It is possible to address these
questions analytically. By changing the unit of analysis we have recovered strong
theory and the analytical results that are produced using deductive tools, yet the
models underlying these results retain the evolutionary processes inherent in the
economic decisions made by firms.
A second approach to making evolutionary theory tighter is to make the analysis statistical. The “interacting agents literature” models large numbers of (heterogeneous) agents who interact both within and outside the market. Population
properties evolve as agents respond both to their own past actions and to the actions
of other agents. Here, theory seeks to make predictions about statistical properties
of populations rather than about the properties of individual agents (this literature is
reviewed in Kirman, 1996, and Durlauf, 1996). Schelling (1978) refers to analysis
which explores the relationships between behavioural characteristics of individuals
and those of the aggregate. Developing counterfactual arguments is dependent on
our ability to calculate aggregate data as determined by individual actions. This
is indeed possible for some classes of social and economic interactions. Schelling
uses a simple example to illustrate: “if we know that every driver, on his own, turns
his lights on at sundown, we can guess from our helicopter we shall see all the
car lights in a local area going on at about the same time.” (Schelling, p. 13). In
more complex cases, however, agents’ behaviour or agents’ choices depend on the
behaviour or the choices of other agents; and those situations often do not permit
any simple summation or extrapolation to the aggregates. Then counterfactual constructions must look at the system of interactions between individuals and their
neighbourhoods.
Both approaches, by changing the focus of analysis, produce strong theory
and analytical results. They both reduce the historicity present in the abstract research, and permit counterfactual analysis which is more like comparative statics
in appearance. Tight theory and tight counterfactual conditionals are possible.
7.4 A different criterion by which to judge the success of an analysis?
There are occasions in which strengthening the theory to make its predictions more
precise in one of these ways is the right approach. But there may be phenomena
for which it is not appropriate, and some looseness in the theory, and consequent
looseness in the counterfactual empirical claims is either desirable or necessary.
When this is the case, the arguments given above indicate a problem for empirical
evolutionary research. It may fall prey to the fairly common view that while the
theory is pretty good, empirical evidence is frequently weak. But this may be
too hasty. One can only judge empirical endeavours of an approach by standards
appropriate to them; standards to judge success must be consistent with the task
undertaken.
Intuitive arguments against counterfactual history by and large suppose that it
tends to be based on theory which is so loose that it allows anything to happen. If one
changes the past one of course can change the present. Any counterfactual claim
Evolutionary economics and the counterfactual threat
559
can be asserted given the weakness of the theory involved. Therefore, because the
results are “loose” and are based heavily on the judgement of the analyst, they are
of little (or no) value. But this argument is to say that “this apple makes bad orange
juice.” Much of economics has opted for an axiomatic approach involving deductive
theory. Counterfactual conditionals are deduced under standard assumptions, and
empirical investigation involves checking the values of parameters (traditionally,
checking only that they are of the right sign). Predictions about behaviour are
arrived at deductively, and are supported by finding an elasticity in the right range.
Is the measured elasticity of patenting with respect to R&D spending large or small
or negative? Answering this question can corroborate the theory with which the
question was generated. The argument is of the form, “If assumptions 1 through n
hold, then the elasticity of patenting with respect to R&D is large.” Measuring the
value of the elasticity can provide very strong indications regarding the theory and
whether the theory is valuable in understanding innovation.26
When counterfactual conditionals are not (formally) deduced from axioms or
assumptions, or when the analysis generates multiple or possibly infinite equilibria,
this simple argument form is not available. Knowing an elasticity neither proves
nor disproves the theory, nor does it permit one to predict the future. The latter
follows from the under-determination typical of the approach we are considering.
If one opts for a non-deductive theory, empirical analysis will be of a different
nature, and it follows that the criteria by which it is judged will also be different. In
particular, the assertability of a counterfactual necessarily rests on a different type
of argument. A simple statistical test will not, in general, provide the empirical
arguments that buttress a theory, simply because under the tenets of evolutionary
theory, the counterfactuals that emerge are necessarily historical. To reject evolutionary theory on the grounds that it provides no identifiable statistical tests but
rather merely the opportunity to “tell stories”, involves first justifying the criteria
by which it is being judged.
Loosely, what empirical work does is to confront theory with “the facts”. But
even Popper admits that the notion of a brute fact, that is, a fact that exists independent of some held theory, is not tenable (see Hollis, 1994, p. 76; or Feyerabend,
1988, p. 155). All facts or observations are theory-dependent; what counts as or
constitutes a fact will depend crucially on the theories one holds. This implies that
the criteria we use to judge a theory on empirical grounds will also be theorydependent.27
To argue that the theory is weak because it provides no testable hypotheses,
where the definition of “testable hypotheses” follows from the milieu of deductive
theory, demands first a justification of the use of deductive theory. This has yet to be
26 Recall the stricture that in fact knowing the elasticity does not prove the theory. The only strong
statement we can make is that if the elasticity is not large the theory is wrong. This suggests that even on
its own criteria, if strictly applied, the empirical evidence in support of much economic theory is fairly
weak.
27 We are not arguing here that it is therefore impossible to compare theories (see Kuhn, 1962), rather
we are arguing that assertions about the strength or weakness of empirical evidence for a theory must
take into account what it is possible to produce as evidence, and how that sort of evidence ought to be
judged.
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R. Cowan and D. Foray
provided in economics (or, arguably, anywhere else for that matter; see Feyerabend,
1988). To reject particular instances of the analysis as having failed to pass the test
of empirical evidence involves accepting the standards of evidence that are implied
by the theory itself. If you accept the theory, then you must accept its definition of
a testable hypothesis. The idea that you can separate the two was given up with the
death of the theory-observation distinction.
Arrow (1995, p. 21), suggests that “The theory is pretty goood, the empirical
evidence may be by definition pretty hard to come by, not just as a practical matter.”
Indeed, the argument above suggests that creating a robust empirical case for an
evolutionary model will be a challenge due to the very nature of the theory. This
may make the empirical evidence for the theory appear weak. And perhaps by the
standards of axiomatic, deductive analysis the empirical evidence is weak. But if
you admit that the “theory is pretty good”, you must accept that the theory itself
implies that empirical evidence will take a certain form, from which it follows that
certain standards apply when judging that evidence. The standards developed to
judge empirical evidence when the underlying theory is axiomatic and deductive do
not necessarily apply to other types of theory. Different standards do. In the context
of a path-dependent, evolutionary economics, whether or not a counterfactual conditional is accepted as empirical evidence depends not on the statistical significance
of parameter estimates but rather on whether an historical, causal explanation is
compelling, and thus whether the counterfactual is assertable. The evidence looks
much stronger when judged on its own grounds.
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