Simulation for Policy Inquiry

238 Pages·2012·3.37 MB·English

by  Anand Desai (auth.)

Simulation for Policy Inquiry

Anand Desai
Editor
Simulation for Policy Inquiry
Editor
Anand Desai
John Glenn School of Public Affairs
Ohio State University
Columbus, OH, USA
ISBN 978-1-4614-1664-7 e-ISBN 978-1-4614-1665-4
DOI 10.1007/978-1-4614-1665-4
Springer New York Dordrecht Heidelberg London
Library of Congress Control Number: 2012937212
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Preface

The hope underlying successful policy inquiry has been that it would yield a useful
approach to achieving desired goals by selecting the best means from a set of well
thought out options. Much of such hope relies upon our ability to make sense of
data. Despite increasing sophistication in the methods employed to identify patterns
as well as to analyze, to synthesize, and to interpret data, this hope has yet to mate-
rialize. There are multiple reasons for this failure. First, there is the perennial prob-
lem of data quality and availability. Second, there is the dif fi culty of matching
methods to data in order to capture their inherent complexities and interdependen-
cies. Third, even if perfect data were available, they could only describe the past and
would not necessarily tell us much about the future unless we assume that the future
will be a repetition of the past. Even though some data patterns are robust and per-
sist into the future, when human affairs are concerned, history rarely repeats itself.
Human affairs, and therefore policy inquiry, tend to be messy and unpredictable.
The common thread that ties this collection of papers together is the belief that by
simulating possible worlds and studying how these arti fi cial worlds function, we
might attain useful insight into how the real world works.
Through the use of examples drawn from public policy contexts, the papers in this
book discuss how to develop simulations, illustrate model construction, analyze mul-
tiple scenarios, and discuss how simulations can offer research insights. Each of the
chapters discusses an application of simulation modeling in a policy context. For our
purposes, simulation is a tool for systematically capturing the contextual and temporal
complexity of policy concerns for the purposes of informing decisions and action.
Our hope for this collection is that it will serve as a primer to illustrate how, when,
where, and to what end simulations can be gainfully used in policy inquiry.
In this collection, we present three of the variety of available simulation tools:
Monte Carlo simulations, system dynamics, and agent-based models. Although our
objective is to illustrate policy modeling and simulation use, not to proselytize, our
agnosticism does not preclude the belief that simulation approaches are often better
suited to addressing certain problems and can yield more useful insights than other
approaches. In this respect, the view of simulation espoused in this collection is that
it is an art form with a scienti fi c and practical purpose. It is an art form in that the
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simulation is a representation of how the modeler views the object of study and
includes in that representation the biases and experience deemed relevant to informing
judgment and practice. It serves a scienti fi c purpose in providing an exploratory
environment for policy inquiry. However, the ultimate objective of any approach to
policy inquiry is a practical one: to inform decisions and practice.
The basic premise underlying this collection is that an inquiry can be assisted
and enhanced by computational methods of simulation. Thus, simulation-assisted
thinking is likely to enable an inquirer or a community of inquirers to better capture
and comprehend the complexity of the world of policy inquiry. If this complexity
can be even partially captured in representations of the real or imagined world that
simulations generate, then the hope is that manipulating the representations will
yield insights into ways to address the complex issues with which policy makers,
managers, and researchers grapple. To that end, this set of papers seeks to illuminate
how a subset of available computational tools assists the process of policy inquiry.
This collection has three parts and an introductory chapter. The introductory
chapter paints a picture in which the world of policy inquiry is emergent and com-
plex. It suggests that certain characteristics of simulation models make them ideally
suited for the pursuit of policy inquiry.
The point of departure for the simulations in Part I Chaps. 2 and 3 and Part II,
Chap. 4 is statistical analysis, the general linear model, in particular. In Part I, Chaps.
2 and 3, Monte Carlo simulations are used to generate data when none are
available.
In Chap. 2 , Seligman describes a Monte Carlo simulation to explore worker
investment patterns and timing and how their interaction in fl uences potential returns
from a de fi ned contribution retirement savings program. Because of the novelty of
such programs, the data series that exist are not long enough to study investment
behavior and returns from those investments upon retirement. He constructs syn-
thetic data to “travel out of the sample” to create hypothetical, but feasible situa-
tions to explore the wealth outcomes.
In Chap. 3 , Heidelberg and Richardson report on a case study of the Nurse-
Family Partnership Program in Louisiana. Here the problem that the authors address
is also one of how to take the data out of sample, not across time but to a different
context. In building a Monte Carlo simulation they make explicit the decisions and
assumptions made regarding data from one context being used in another and in
doing so, discuss the problems of drawing lessons from one context and applying
them to another. Acknowledging the uncertainty of not knowing what is transfer-
able, they choose to build scenarios to explore what might happen under different
sets of circumstances.
In Part II, the focus of producing policy relevant information shifts from study-
ing relationships among variables to studying interactions among entities or agents.
This shift requires an adjustment in how one thinks about a problem. In focusing on
entities—people, buildings, neighborhoods, households, policies—the problem for-
mulation takes us away from measures and the relationships among them and
instead toward thinking about interactions among heterogeneous entities and their
individual behavioral patterns over time and space. These interactions result in
Preface vii
emergent behavior that cannot be obtained through an averaging or aggregation
process, for example, our current state of knowledge is such that even a detailed
understanding of how individual birds fl y cannot help us fathom the V-formation of
a fl ock of geese in fl ight.
In Chap. 4 , Rice focuses on food deserts, their creation, and their effects on
health outcomes. This fi rst chapter of Part II is different from the two chapters in
Part I in that it does not discuss an implementation of a simulation, but instead it
presents a comparison of a multilevel linear model with an agent-based simulation
model. Starting with a common conceptual model she discusses how asking similar
questions using two different model implementations provides different insights.
In Chap. 5 , Eckerd and Reames help us sort through the dynamics of urban
change. Their objective is to understand the gentri fi cation process and to determine
the circumstances under which displacement occurs during gentri fi cation. By devel-
oping an agent-based model of this process they wish to identify economic develop-
ment policies that would bring about positive change while helping those most in
need.
In Chap. 6 , Eckerd, Lufkin, Mattimore, Miller, and Desai explore the life cycle
costs of facilities. Using data on the costs of maintaining a US National Laboratory
infrastructure, they develop an agent-based model that incorporates risk of failure
and the effect of that risk on nearby facilities. The model serves as a decision sup-
port tool for facility managers as they grapple with the question of how to best
allocate scarce resources.
In Chap. 7 , Kim models the incidence of fraud in the Women, Infants and
Children program. She builds a spatial model of participant–vendor interactions to
study contact patterns. She compares model results with data to assess fi delity of
such methods to observed reality.
In Chap. 8 , Heidelberg studies the assumptions underlying home ownership and
models housing policy to explore whether home ownership affects the quality of a
neighborhood. In so doing, he puts to test the long held belief that home ownership
leads to improved neighborhood quality.
In Part III, the modeling tool of choice is system dynamics where differential and
integral equations form the basis of these models. General solutions to such equa-
tions were developed in the 1860s, decades before the invention of many of the
statistical tools commonly used in the social sciences. The core concept underlying
a system dynamics model is a stock. Associated concepts are fl ows and rates of fl ow
into and out of this stock. Changes in this stock due to the in fl ows and out fl ows
capture the dynamics of the system.
For most of us, the dynamics of stocks and fl ows are not obvious. From the
knowledge of fl oods, we know that a river crests long after the maximum rainfall
has ceased. In fact, the river crests and begins to ebb when the out fl ow of water
equals and eventually exceeds the in fl ow. Yet, this speci fi c understanding of fl oods
does not usually translate into a general understanding of the relationships among
rates of flo w, feedback, and stocks. Until such understanding becomes a common
feature of our thinking processes, system dynamics modeling offers a powerful formal
tool for comprehending systemic behaviors that at fi rst seem counterintuitive.
viii Preface
In Chap. 9 , Lufkin, Hightower, Landsbergen, and Desai offer a different model-
ing perspective on facilities management from the one discussed by Eckerd and his
colleagues in Chap. 6 . Their purpose in this chapter is to develop a tool that facility
managers can use to help them identify decision points and explore the conse-
quences of decisions regarding funding maintenance and repair of facilities. They
also discuss measures to capture the multiple facets of facility condition.
In Chap. 10 , Zhong, Lant, Jeng, and Kim take the classic model of the dynamics
of the spread and decline of infectious diseases and modify it to study risk commu-
nication and avoidance behavior of individuals during an in fl uenza outbreak. Their
simulation results suggest that communicating with the public regarding the under-
lying risks can be an effective strategy for slowing the spread of the disease.
In Chap. 11 , Hightower suggests that policy narratives are the basis for simula-
tions across modeling paradigms. Storytelling in general, and iterative storytelling
more speci fi cally, is the method by which policy intentions and options are trans-
lated into formal representations in computational models. Storytelling is an active
process that engages the practitioner and the modeler in cocreation and develop-
ment of simulations.
Simulation is not a spectator sport. People have to engage with it if they are to
get full advantage of the learning that simulation offers. In this collection the authors
make their assumptions explicit and offer rationales for the decision to use one
characterization rather than another. In some instances, the authors themselves are
unaware of their choices. Their decisions re fl ect habits of mind rather than consid-
ered deliberation over the choices available to them at each decision juncture in the
process of creating the simulations.
We welcome you to a collection of essays devoted to investigating the method
and merit of simulations in the context of public policy inquiry. We invite you to
re fl ect on the authors’ assumptions and question their choices. We challenge you to
speculate about how you would select your options, describe your assumptions, and
construct your own abstractions and representations of the problems you
encounter.
Columbus, OH, USA Anand Desai
Acknowledgments
Over the past half dozen years, a number of collaborations at the John Glenn School
of Public Affairs at The Ohio State University have led to the use of simulations in
public policy inquiry. Many of the chapter authors have been supported by funds not
only from the School but also from the National Science Foundation, the National
Institutes of Health and the Ohio Department of Job and Family Services. Lawrence
Livermore National Laboratory and Whitestone Research have established a fellow-
ship that has supported graduate students. We are indebted to them for this support,
however, we should also mention that the views expressed in this volume are those
of the authors and do not necessarily re fl ect the opinions of the funders.
There are a number of young scholars whose critical reading and commentary
have helped improve the contents of this volume. In particular, the contributions of
James Comeaux, Lisa Frazier, Lisa Gajary, Kristin Harlow, Hyungjo Hur, Stephen
Roll, Nicole Thomas, and Tyler Winslow are gratefully acknowledged. Thanks also
go to Yushim Kim who helped bring order during the fi nal stages of putting this
volume together. We are grateful to Jon Gurstelle at Springer US for his patience
and support of this project.
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