Epstein has argued that an explanation\'s capacity to make predictions should play a minor role in its evaluation . This view contradicts centuries of scientific practice and, at least, decades of philosophy of science. We argue that the view is not only unfounded but seems to arise from a mistaken fear that ABM models are in need of defense against the criticism that they don\'t necessarily forecast events in the natural or social world.ABM, Agent Based Model, Modeling, Prediction, Explanation, Philosophy of Science

Nicholas S. Thompson

Patrick Derr

English

Research Papers in Economics

©Copyright JASSS
 
Nicholas S. Thompson and Patrick Derr (2009)
Contra Epstein, Good Explanations Predict
Journal of Artificial Societies and Social Simulation 12 (1) 9
<http://jasss.soc.surrey.ac.uk/12/1/9.html>
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Received: 24-Nov-2008    Accepted: 21-Dec-2008    Published: 31-Jan-2009
Abstract
Epstein has argued that an explanation's capacity to make predictions should play a minor
role in its evaluation . This view contradicts centuries of scientific practice and, at least,
decades of philosophy of science. We argue that the view is not only unfounded but seems to
arise from a mistaken fear that ABM models are in need of defense against the criticism that
they don't necessarily forecast events in the natural or social world.
Keywords:
ABM, Agent Based Model, Modeling, Prediction, Explanation, Philosophy of Science
1.1
In a recent contribution to JASSS, Joshua Epstein (2008) argues that predictions are a
dispensable product of scientific explanation and, that models should not be evaluated on the
basis of their power to generate predictions. These views appear to contradict one of the
most fundamental tenets of ordinary science: that respectable scientific theories lead to
expectations of discovery. Is Epstein declaring war against the foundations of contemporary
philosophy of science? If he is, then, given his eminence among computational scientists and
the wide distribution of his views on this subject (JSSS, Second World Congress on Social
Simulation, and addresses at George Mason University, the Institute of Medicine, the
University of Michigan and the Santa Fe Institute), such a declaration would seem to demand a
response.
1.2
Epstein's stated motivation for writing his piece is to defend the modeling enterprise against
hypothetical critics who suggest that: (1) modeling is alienated from commonsense thought
and ordinary science, (2) that a model must be realistic in all its particulars, and (3) that a
model's chief purpose is fortune telling. We agree with Epstein that these suggestions are
unworthy. Modeling is the systematic deployment of the human capacity for metaphor and is
central to all scientific activity. Models don't stand or fall on their detailed verisimilitude, but
on their capacity to capture the essence what is already known about a phenomenon and to
generate expectations concerning what more might be discovered if the scientist were to look
where the model pointed.1.3
However unworthy the hypothetical criticisms may be, the arguments that Epstein brings
against them are more unworthy still. "Explanation," he asserts, "does not imply prediction."
Three examples are offered in support of this heterodox assertion:
1.  "Plate tectonics surely explains earthquakes, but does not permit us to predict the time
and place of their occurrence. "
2.  "Electrostatics explains lightning, but we cannot predict when or where the next bolt will
strike. "
3.  "[E]volution is accepted as explaining speciation, but we cannot even predict next year's
flu strain."
1.4
These examples do not support the conclusion that predictive power is an inessential feature
of good explanations. In fact, in each example, the explanation is strong just insofar as it can
make predictions and weak insofar as it cannot. Plate tectonics both explains and predicts
roughly where and when earthquakes occur but can neither explain nor predict that an
earthquake of specific power will occur in a specific place at a specific time. Precisely the
same point holds for the other examples—which, collectively, serve to confirm, not
undermine, Carl Hempel's sixty-year old "Symmetry Thesis" concerning explanatory and
predictive power (Hempel 1948). That these theories do not tell us everything about the
timing, location and character of these events does not imply that giving expectations about
the future is irrelevant to scientific explanation. And, indeed, if theory in each of the relevant
literatures could be strengthened to the point where such predictions could be confidently
made, scientists in those fields would be overjoyed. Their joy would arise not from the
practical utility of such predictions, but primarily because a theory that can make more
precise predictions is widely regarded as a better theory.
1.5
Although Epstein's comments on prediction and explanation are sweeping, they seem to be
directed in particular towards a defense of Agent Based Modeling. No such defense is
necessary. Agent based models (ABMs) are scientific models in good standing. They are
simple systems that help us understand more complex phenomena. Models are chosen or
designed to be in accord with what is already known about the phenomenon we are trying to
explain in ways that our theories tell us are important. In ways designated as unimportant by
our theories, they may be in discord with previous knowledge. Models that are inconsistent
with theoretically important elements of the phenomenon under investigation are not likely to
be well-regarded. Honest scientists may disagree—and do disagree frequently (Thompson
and Derr 1995)—about whether a particular discordant detail is theoretically important. What
scientists almost universally agree on, however, is that a good model implies the existence of
theoretically significant properties or behaviors of the modeled phenomenon that were not
known or anticipated until the model was applied. The prediction of such theoretically
significant properties or behaviors, and of the circumstances under which they can be
observed, is what demonstrates a model's heuristic power (Hesse 1963).
1.6
Models may be natural or contrived—i.e., they may be systems with which we are already
familiar—as when we use fruit-fly genetics to explain properties of human inheritance—or
systems that we devise—as when we use tinker-toy like constructions to understanding the
chemistry of compounds. Agent Based Models are wonderful examples of contrived models.
Consider, as an example, the Schelling model of neighborhood segregation (Schelling 1978;
as realized by Wilensky 1998). Like a tinker-toy model of a molecule, almost everything about
it is artificial. Its neighbors are not subdivisions with streets, and automobiles and fences,
and its agents are not human beings with lawns and lawnmowers and garden parties and
backyard grilles. Its 'races" are not multiculturally and multiethnicly defined but a simple
distinction between red and green turtles. Its neighborhood is a simple grid of patches
occupied by "turtles"—agents that are capable of making a decision of whether to move or
not given the constituency of the 8 patches that surround the patch on which they are living.The model tests a theory that desire to live in segregated neighborhoods is a necessary or
even sufficient condition for neighborhoods to be segregated. It shows that, on the contrary,
neighborhoods can become segregated even when people prefer to live in integrated
neighborhoods and that neighborhoods can be integrated when people would prefer to live in
segregated neighborhoods. The model thus challenges the ideological assumption that
neighborhood integration will necessarily be achieved by making people a bit more tolerant.
Its positive implication is that that the problem of neighborhood segregation can arise simply
from the desire of people to have just a few of their own kind living next door.
1.7
How can this counter intuitive result come about? Is it just an artifact of all the artificialities in
the contrived model? Or does the model point to some fundamental flaw in our thinking
about segregation? Modelers would rightfully claim the latter.
1.8
Let's consider, for purposes of simplicity a community that is dedicated to racial integration
consisting of 50 percent black and 50 percent white inhabitants. An idealistic law is passed
which says the inhabitants must distribute themselves randomly across the community.
However, the law makes one concession to human failing. A family that is unhappy where it is
living may move by rejoining the pool of unassigned families to be reassigned at random to
the next available opening. Now, one imagines that after the first settlement, everybody
would have approximately 50% white neighbors and 50% black neighbors. And, indeed, that
would usually be the case. But randomness being what it is, it would be also the case that
some white residents would find themselves "isolated" in black neighborhoods and some
black residents would find themselves "isolated" in white neighborhoods. If these people feel
isolated enough to move, then the dynamics of the model show that the community will move
steadily toward segregation.
1.9
How does the model suggest we should desegregate our communities: one need only bring
together people who desire to live in segregated ones! The very same dynamic that produces
segregated neighborhoods from people who want to live in integrated ones, now produces
integrated neighborhoods from people who would prefer to live in segregated ones.
Randomness being what it is, most people are settled in neighborhoods that contain "too
many" of the other kind. And when they return to the pool to be reassigned, they are likely to
be reassigned to neighborhoods just as integrated as the ones they left. Consequently, almost
everybody is living in integrated neighborhoods and almost everybody is trying to move out.
1.10
In what sense is such a model predictive? It suggests to me that, on the whole, integrated
neighborhoods should consist of nasty intolerant people trying to escape and segregated
neighborhoods should consist of nice tolerant people who are embarrassed to live there, but
not embarrassed enough to move. We are not sociologists, but we suspect that research
would confirm that expectation. At a more general level, the result warns us that social
outcomes are not straightforward reflections of individual preferences. The result is therefore
an appropriate caution against the view that bad (or good) things happen in society because
bad or good people will them.
1.11
We hope that Joshua Epstein will qualify or even withdraw his remarks on the relation between
prediction and explanation. Already computer science experts have echoed in our hearing his
sentiment that the theoretical importance, precision, number, and accuracy of predictions
flowing from an explanation are no grounds for its evaluation. We fear it will not be long
before students in our classes with start arguing this case, and, unfortunately, arguing it on
the highest authority.
 AcknowledgementsN.S.T. never writes on philosophical matters without consulting Gillian Barker, Eric Charles,
and Caleb Thompson, in addition to his present collaborator. There is no greater kindness
than the willingness to comment on a colleague's unfinished work, particularly given the fact
that N.S.T. is often stubborn and argumentative in the face of good advice. He would like
particularly to thank Caleb Thompson for pointing out that the disconnect between individual
goals and social outcomes, which is so often demonstrated by ABM's, is the central concern of
Tolstoy's War and Peace, particularly its epilogue. Finally, he is grateful to his FRIAM
colleagues in Santa Fe for their help in thinking about these issues and, in particular, to Owen
Densmore of the Redfish Group for bringing the problem to his attention.
 References
EPSTEIN, J M (2008) Why Model? Journal of Artificial Societies and Social Simulation vol. 11,
no. 4 12; http://jasss.soc.surrey.ac.uk/11/4/12.html
HESSE, M (1963) Models and analogies in science. London: Publisher.
HEMPEL, C G (1948) Studies in the logic of explanation. Philosophy of Science, 15, 135-175.
SCHELLING, T (1978). Micromotives and Macrobehavior. New York: Norton.
THOMPSON, N S and Derr, P G (1995). On the use of mental terms in behavioral ecology and
sociobiology. Behavior and Philosophy, Volume 23, Number 3/Volume 24, Number 1, Winter
1995/1996, 31-37.
WILENSKY, U (1998). NetLogo Segregation model.
http://ccl.northwestern.edu/netlogo/models/Segregation. Center for Connected Learning and
Computer-Based Modeling, Northwestern University, Evanston, IL.
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