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Extreme Event Agenda Setting and Decision Making
Michael A. Deegan
Rockefeller College, SUNY Albany
92 Willett Street
Apartment 2A
Albany, NY 12210
518.463.3876
Mdeegan@yahoo.com
Abstract
Extreme events are “potential” focusing events that can cause severe damage and
potential harms to many people in a very short period of time. A focusing event may create a
“window of opportunity” that policy makers can use to advance an issue on the national agenda.
However, once disaster issues reach the agenda, the resulting extreme event policy often focuses
on response and relief rather than mitigation and preparedness. Much of the previous literature
on this topic discusses these problems in terms of discrete “events” that have some degree of
influence on agenda setting and the policy process. This paper develops a continuous perspective
on the problem by using a system dynamics approach to explore how changing relationships
between various stakeholders in the system influence extreme event agenda setting and policy
making. This paper will also discuss the utility of such a decision aid for public administrators
who wish to have a better understanding of the policy process.
Organization of this paper
This paper has two main sections. The first section is an introduction to the problem.
Background information is provided on the problem focus. There is also a discussion of the
problem context, intended audiences for this paper, model purpose, model boundaries and
reference modes. Section one concludes with a brief discussion on the initial policy options
likely to be explored by policy makers.
In section two, the model is explained with series of causal loop diagrams. As the loops
are revealed, important stocks and flows are discussed, along with some mention of the pertinent
sectors in the model. In addition, there is some preliminary insights on conditions that may
“activate” certain loops and cause them to “dominate” the system.
This paper concludes with a brief discussion about the contributions this work will have
in the fields of system dynamics, political science and public administration. In addition, there is
some discussion of selected policy tests for the model. Finally, some basic insights are presented
based on preliminary runs of the mode, along with an outline for next steps of this research.
I. Introduction to the Problem
Problem focus
Extreme events are “potential” focusing events that can cause severe damage and
potential harms to many people in a very short period of time. A focusing event may create a
“window of opportunity” that policy makers can use to advance an issue on the national agenda
(Kingdon 1995). There is some debate as to how a “potential” focusing event becomes an actual
focusing event. However, once disaster issues reach the agenda, the resulting extreme event
policy often focuses on response and relief rather than mitigation and preparedness. As a result,
we enter endless cycles where administrators are cleaning up existing damage rather than using
resources to gain more knowledge on the problems to mitigate future damage. In fact, it would
be reasonable to suggest that “mitigation” policies created without proper knowledge of the
event may actually produce more damage than it prevents. One might intuitively recognize this
pattern of behavior as a product of “fixes that fail” structure. One purpose of this model is to find
points of leverage in the system, where policy efforts can be more effective, preventing future
damage rather than just clearing current damage.
Context
There are many examples of extreme events. Disasters are products of nature (e.g.,
hurricanes and earthquakes), human error (e.g., oil spills and nuclear reactor leaks), or even
deliberate acts of violence (e.g. the shootings at Columbine and the September 11" attacks). Any
one of these extreme events has the potential to be considered a focusing event, an event that
focuses our attention a specific problem underlying the cause of the damage associated with the
event. In reality, many of these disasters never illuminate the problem to the point where it stays
on the national agenda very long.
The problem becomes more complicated when one considers the fact that the agenda
setting process and policy making process appear to be very disconnected at times. For example,
there have been many issues that reached the top of the national agenda, where policies were
developed to resolve the issue, and yet the final policy selected did not directly address the issue.
Very seldom do public sector problems have clear-cut solutions, even when the problem has
been recognized by all stakeholders. Usually there are one or more interest groups that perceive
adverse affects by any change to current policy. These folks often prefer policies that maintain
the “status quo.”
Measures of administrative accountability and performance in the implementation phase
add another layer of complexity for extreme event policy problems. For example, a top level
administrator at the Federal Emergency Management Association (FEMA) is responsible for
coordinating response and relief efforts between the national government and the state and local
districts who have been “damaged” by an extreme event. Policy solutions will be ineffective if
the agency does not have enough capacity or knowledge on the underlying problems to deal with
the event.
Audience
This paper should be interesting for four different groups of people:
System Dynamicists
Political Scientists
Public Administrators
Natural Scientists
AVY
There can be some valuable discussion among system dynamicists who observe these
“continuous vs. “event driven” debates regarding how a status quo belief is challenged by a
competing “policy”. Some aspects of this model were inspired by the system dynamics work
done by John Sterman on Kuhn’s (1970) theory of scientific revolutions. In fact, as will be
pointed out shortly, it is the unresolved “damage” in this model, which builds a public perception
that “status quo” policies are simply not working and it is time for change. A corollary to this
debate might be the debate between “punctuated equilibrium” and “evolutionary” explanations
of rivalry, which I think are also germane to theories on coalition building and agenda setting for
this paper.
Political scientists have running debates on the relative impact of different focusing events
and the prior conditions which must exist for a potential focusing event to become an actual
focusing event. In fact, an impetus for this research came to me by reading After Disaster by
Thomas Birkland (1997). This book operationalizes the term “focusing event” by observing
changes in Congressional testimony hearings and legislation for the periods before and after
several disasters over the last 30 years. The author identifies several “potential” focusing events
that became actual focusing events because of pre-existing conditions, including “coalition
strength” which accumulated over time. Although Birkland was specifically testing the effects of
extreme “events” on the political system, he often used a “continuous” perspective of the
conditions in the system to help explain these effects. My research picks up from this point. I
suggest that we begin with the continuous interactions between several key stakeholders in this
system to help us understand why certain groups gain and/or lose power over time after
unexpected “shocks” to the system. One goal for this research is to have a continuous perspective
help explain why certain extreme events “focus” our attention to policy issues on the national
agenda and other potential focusing events of equal magnitude have a relatively small impact.
Public Administrators are often unable to explore the most “effective” policies for several
reasons. One such reason is because these options are not politically feasible. There is a gap
between public administration and political science with respect to their focus on different
phases of the policy process for policy analysis. The literature discusses problem identification,
estimation, selection and implementation as four different phases in the policy process (Brewer
and DeLeon 1983). There appears to be some disconnect between how problems are identified
(i.e., the agenda setting process), how alternative solutions are developed and selected (i.e., the
policy making process) and how the policies are executed (i.e., the implementation process).
This research should resonate with policy analysts in public administration audience who desires
some understanding of how their actions influences the agenda setting and policy making
processes, and in turn will affect the implementation of policies in the future.
Finally, this paper should be of interest for natural scientists with some interest in public
policy. There is a basic assumption in this paper about how the level of “uncertainty” and our
relevant “knowledge” on an extreme event will affect the administration of such policies. There
are some disasters where knowledge in the natural scientists is sought to develop better policies.
The level of Congressional “commitment” may be signaled by which Congressional
Subcommittee maintains control over the policy issue. For example, earthquake policies rely on
science “experts” and these policies are located in a science and technology subcommittee. On
the other hand, hurricane policy resides in a public works subcommittee and use far less
scientific evidence to support current policy.'
' This observation is based on Congressional Hearing data collected between 1980 and 1999.
Model Purpose
There are three research goals:
1. To represent and test the theories on agenda setting and the policy-making processes.
2. To conduct research that bridges the gap between “continuous” and discrete “event”
perspectives by using a system dynamics approach.
3. To develop a decision tool for policy analysts and public administrators who wish to
understand how implementation decisions fits in with a much larger system of processes.
Model Boundaries
Temporal : The time horizon for this model is 20 years. It has been argued that extreme
event issues have agenda cycles, which peak when there is a perception that there is a problem
whose solution is within our reach. Intuitively, these cycles make sense if one considers national
election cycles.’ Presidents are restricted to a maximum of two and power in Congress (and thus
subcommitte control) changes with a similar period. One could make an argument that such
changes in power would result in changes to the agenda. A formal model can test how much
influence the President and/or Congress exerts on the national agenda.
Conceptual: What is included in this model: There are three main sectors in this model:
1. The Agenda Sector: This includes all stakeholders (including policy entrepreneurs)
and activities who influence the Initiation phase of the policy process. That is, all
people who have the power to raise or lower an issue’s national agenda ranking.
2. The Policy Making Sector: This sector includes all relevant “decision makers” and
activities dealing with the Estimation and Selection phase of the policy process. In
theory, an issue must have a high ranking on the “Decisions Agenda” before any
policy may be passed. However, a high agenda ranking does not determine what type
of policy will result (whether it favors the status quo groups or advocates for change
groups)
3. The Administrative Sector: This sector includes all of the knowledge acquired and
decisions made by public administrators who face implementation challenges for any
given disaster policy.
Causal:
Endogenous: All policy and decision-making activity relative to a disaster policy.
Exogenous: All of the activity in other policy domains.*
Reference Modes — graphs of hypothesized data based on the literature
The graph over time below represents a reference mode based on my readings of the
agenda setting literature by the following prominent academic scholars:
1. John Kingdon
2. Cobb and Elder
? For the purpose of this discussion, “national” agenda will refer to the U.S. The literature does not suggest there is
reason to believe all political systems would respond to “shocks” in the same way.
* This may change as the model develops, but it has been a very clear boundary item for me thus far.
Baumgartner and Jones
Thomas Birkland
Deborah Stone
E.E. Shattschneider
Paul Sabatier
SS Go
The reference modes illustrate the behavior for three important stocks in the system. It is
important to be clear on these definitions.
Effective Policies: For the purpose of this discussion, effective policies are ‘policies
designed to mitigate damage.’ As we increase the number of effective policies, administrators
will be able to prevent damage and thus fewer people will be harmed in the future. The
effectiveness of any disaster policy certainly depends on the level of knowledge administrators
possess at the time of a disaster.
Long Term Damage: This is damage caused by extreme events. This is not necessarily a
measure of the event’s size. Rather it is an accumulation of damage over time. For example,
disasters that strike often but carry relatively minor damage may be just as problematic as large
“shocks” to the system. Thus Long Term Damage should be thought of as accumulated damage.
Decision Agenda: The decision agenda is where new legislation on disaster issues is
actively considered. This is the end result of a long battle between coalitions for and against
policy change, with the status quo trying to prevent issues from rising to the decision agenda’. I
prefer to think of the decision agenda as “decision agenda ranking.” The ranking can rise or fall
depending on several factors. These factors will explored in the next section of the paper.
“Tn fact, a more accurate statement would be that issues move from the systemic agenda to the institutional agenda
and then final to the decision agenda. Status quo coalitions try to block issues from reaching the decision agenda.
Reference Mode
0 JNU! Ae
i) 12 24 36 48 60 72 84 96 108 120
Time (Month)
Effective Policies : base policy
Long Term Damage : base policy
Decision Agenda : base policy
There has been competing theories to explain how and why agendas behave the way they
do. “Punctuated equilibrium” suggests that policies remain at a standstill for long periods of time
and then suddenly gain attention and undergo rapid change, finally returning to some equilibrium
level (Baumgartner and Jones 1993). Another purpose of this model will be to test the punctuated
equilibrium explanation.
The reference mode above tells a particular story. As damage accumulates, decision
makers eventually perceive the problem and the issue rises on the agenda. The scale of 0 to 100
could be though of as a percent of decision makers talking about the problem. The policies which
develop in response to extreme events do not show a long term commitment to mitigation and
research.’ Without a sustained commitment to a mitigation policy the cycle never appears to end.
We will always be clearing prior damage rather than preventing future damage. One final
comment on the reference mode. Notice how damage is never completely cleared away. This
represents the notion that our commitment to disaster relief and recovery is not perfectly
effective even when there is strong commitment. For example, the Federal Emergency
Management Agency (FEMA) website lists several disasters that still require relief funding.
Some of the disasters have remained on the list for nearly 10 years. This suggests that damage
caused by these events can leave residual problems that we must deal with even years after the
initial impact of event is long gone. This becomes another reason why good mitigation
“prevention” policies would be preferred over relief and recovery “fire fighting” policies.
Initial Policy Options
Earlier, I stated that one purpose of this model is to develop a tool that administrators and
policy analysts can use to understand the sources of opposition to different policy solutions.
Currently, I am considering the following policy options. These policy options are consistent
with the way administrators discuss the issues when they testify before Congressional.
* Claire Rubin’s disaster timeline connects disasters with resulting policies. She concludes that American disaster
policies have been historically more “reactive” than “proactive” (before September 11".
Mitigation: These are long term policies implemented well before the next event and
they are designed to prevent future damages. There are three types of mitigation policy:
1.Structural
2.Non-structural
3.Insurance
Preparedness: These are policies implemented right before the damage hits. These
policies are most effect for disasters that give administrators some before they strike (i.e., for
hurricanes but not for earthquakes).
Response: These policies are probably the most well-known and often, they are the most
important. These policies are in effect the day of the event and when they are effective, they can
prevent substantial damage to infrastructures. There is also a key information piece to this policy.
Administrators are often forced to rely heavily on judgments based on the information at hand.
These judgments are made from multiple fallible indicators (cues) about disasters
(environments), which are rare and thus difficult to predict (even if information was perfect).
Relief & Recovery: This is the policy that comes into effect to clean up the damage
after all other options have been exhausted. Policies which change directly after a major event
often have a disproportionate level of relief and recovery policies. This behavior, while
politically attractive, creates problems for administrators.
Il: Model Presentation
Causal Loop Diagrams
This section presents the model through series of causal loop diagrams that illustrate key
“stories” in the literature. Throughout this presentation, key stocks in the system are defined,
along with some discussion of how these variables relate to the sectors in the model.
A quick look at the entire model....
RI
Congress willingness to
‘Status Quo Coalition
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A first look at the mental model, represented by this causal loop diagram, shows ten main
balancing loops and three reinforcing loops. These loops will be discussed in more detail in a
moment. Also, there are four important stocks identify in this diagram, which represent the main
accumulations in the system. This section continues with a brief description of each loop,
followed by an explanation of “new” variables introduced in each loop. Balancing loops are
identified with the letter “B” followed by a number. Reinforcing loops are identified with the
letter “R” followed by a number. Extreme Events are referred to as “XE” in the decision and
policy sciences literature.
The Media Influence
Much of the literature dealing with agenda setting and the policy process discusses the
importance of media perception and “causal stories” in forcing issues on the agenda (Stone
1997). Media coverage” is not defined as an accumulation in the system. Rather, as the literature
suggests, the coverage responds to events in a way that influences public perception of disaster
“problems.” In After Disaster, the author discussed specific qualities of different events, which
made some of them inherently more attractive news stories than others (Birkland 1997). The
classic example in the book compares news coverage of a nuclear reactor leak with news
coverage of an oil spill. Suppose you are the managing editor of the New York Times and you
have to decide which is the more interesting story, an oil spill or reactor leak. The oil spill is
potentially a more “attractive” story because of the visual power of the event, even though the
reactor leak is potentially more harmful to a wider scope of the population. The decision is likely
to be based on the strength of an event’s “story” relative to other stories available to you. There
are not many pictures one can take of a nuclear power plant building before the story gets old
and slides off the front page. Oil spills, on the other hand, are easily photographed and carry a
large “symbolic value.”
B10: “Is there a problem?”
- Starting with Damage: As Damage caused by the disaster event increases, the media
develops a perception that there could be a problem here. As media perception increases,
there will be more stories suggesting there is a problem needing our attention. As the number
stories suggesting there is a problem increases and public perception grows (not explicitly
drawn here) the issue will climb up the agenda. This will result in action by Congress,
resulting in new policies to address the issue. Ultimately, more policies to address the issue
will reduce the future damage. This creates a balancing loop.
- So then my task becomes to find out, ‘exactly what type of damage will keep an issue alive?’
B9: ”Which solution is ‘perceived to be’ the best?”
- On the other end, there is competition for media attention on the solutions proposed by
different coalition groups. The media perception of the solution can influence policy. Again, this
creates a balancing loop.
storigs and, testimony
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While the media perception deals with the short run dynamics in the system, | think the
more interesting explanations deal with how coalitions develop over time and how they influence
the agenda.
° In the case of the Exxon Valdez, there were camera crews taking photographs of birds covered in oil. These images
became symbols the media used to keep stories alive, creating a public perception that a problem existed.
B1: “Balancing the Damage”
Starting again from Damage: As damage increases, there will be more people harmed. Some
fraction of these people will form a coalition to change the existing policy. These folks will
create “causal stories” suggesting that there is a problem. If the public (and the media) agrees
with their version of the story, the issue will reach the agenda (Schattschneider 1975). Again,
completing the main balancing loop as more new policies are created to reduce future damage
caused by the next event.
storie testimony
q “te Se Ba
roblem
Agenda Ranking
Policy Change Advocacy of XE Policy
Coalition
New Policies to
| address XE
4e1) J issues
frequency
People Harmed of XE
by Damage
Cumulative eA y
Damage f+ Severity of
next XE
New Stocks to discuss
Cumulative Damage — this is the same as the Long Term Damage mentioned earlier. I am
suggesting here that there are two components of damage: severity and frequency. If we can not
prevent the frequency of the events perhaps we can reduce the severity of the damage.
Policy Change Advocacy Coalition- | think the best way to think about a coalition as a stock is to
think about their strength. We could see that it does help to have a lot of people in your coalition.
But what makes it strong? This question is addressed in the next diagram.
B2: “Get by with a little help from my friends”
Starting from Damage: As damage increases there will be a group of people called Policy
Entrepreneurs’ who observe the damage and will eventually become “inspired” to join the cause.
These players build strength in your coalition. As the strength builds you will be able to now not
only push stories to the public but you will have more direct testimony in from of Congress. As
these two things build, your issue will rise to the top of the agenda. The result (potentially) will
be better policies and less future harm — completing the balancing loop.
( swing
Agenda Ranking
Policy Change Advocacy of XE Policy
Coalition
eX ez) 2
NEA
eine eraues New Policies to
+ . i | address XE
issues
frequency
of XE
Cumulative +
Damage FR. Severity of
New Stocks to Discuss
Policy Entrepreneurs: To keep the diagram coherent, I decided not to box this stock. In part,
because I am learning that some of these folks do not stay with the cause for very long.
Agenda Ranking: Again it is not boxed. In the current model there are two agendas (Institutional
and Decision as discussed in the literature) that are very important. I represent those stocks as
one concept here. We could think of this agenda as a scale from 0 to 100 for example. If the issue
ranking was high enough on the agenda (over a threshold of 50 for example) then there would a
guarantee that some new policy would result.
’ These are folks who know something about the issue and understand the political process very well. You will need
these people if you wish to advance your issue on the agenda.
B3: “Building a stifling status quo”
Starting from New Policies: There will always be some group of people who fear a change to
status quo (from Prospect Theory). In fact, other groups of people might be directly harmed by a
new policy. These people form a dominant status quo coalition (Sabatier 1988). The status quo
attempts to keep items off the agenda (Cobb and Elder 1983). The literature suggests that they
probably have some desired “goal” they seek for the issue’s agenda ranking.
B4: “The dominating agenda of the status quo”
Starting from Agenda Ranking: In the same way we can talk about policy entrepreneurs there,
there will be a group of folks who can work the system on behalf of the status quo. Thus, as the
issue rises on the agenda, the status quo will mobilize. The status quo can attempt to divert the
issue on the agenda by creating alternate “causal stories” to suggest that the issue is not a
problem at all. Also, these folks are conflicted by whether or not to “address” the issue with
Congressional testimony. If they decide to do nothing then only one version of the story is out
there. If they speak up then the issue’s agenda ranking will rise.
‘Status Quo Coalition
7 eigen esti
2 sete
a : = al
B4
Jeera panning a People harmed by
of XE Policy michange to paey
New Policies to
ae address XE
\ issues
New stock to discuss
Status quo Coalition: The strength of the status quo probably rests more in its numbers. In some
ways we could be lethargic at times. However, the sheer size will control much of the agenda
once it perceives a threat to its position.
R1: “Preserving the status quo makes the problem worse”
Starting from Damage: This is the first reinforcing loop of the model. If the status quo perceives
damage building it may choose to mobilize before the Advocates for Change have a chance to
mobilize. Their efforts to create alternate stories would probably succeed and new policies would
never be developed. Without these “solutions” we must rely on fading knowledge of these
complex problems and so the damage continues. As long as the status quo continues to mobilize,
perceiving potential harms by another group the agenda will shut down and there is no reason
that public awareness would be raised. Here is where it would take a very large “extreme event”
to shock the system off this cycle.
Status Quo Coalition
HE Sean BEY
Problem Ti
Agenda Ranking
of XE Policy
perceived
harms to sq +
New Policies to
address XE
issues
Cumulative [+ J
Damage fgt_ Severity of
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New Variable
Perceived harms to status quo: This idea is taken from prospect theory, which states the we are
risk averse to potential gains and risk seeking towards potential losses. As a result, there is a
tendency for people to desire the status quo position because it provides some stability.
R2: “The damaging death spiral”
Starting from Damage: How much damage can you bear? As the damage increases, the pressure
to leave home will increase. As they leave, there will be fewer harmed, resulting in a smaller
coalition for change. Basically, this creates a “death spiral” for the change coalition, as the
coalition won’t be strong enough to keep the agenda or the policy alive. The result will be more
and more damage until the population either moves out or in the worst case scenario, it literally
dies off if the damage is too severe
‘cee —
. a 7
Agenda Ranking
Policy Change Advocacy of XE Policy
Coalition
New Policies to
7 address XE
er issues
People Harmed \ of XE
nN Cumulative [+ ,
Re ae Damage Kq—t_ Severity of
next XE
pressure to PA
leave home
New Variable
Pressure to leave home: This is pretty straight-forward. If you have to keep rebuilding your
home because you live in a hazardous area then perhaps one day you decide it is better to just
leave. Another way to think of it is that maybe either market forces or the government steps in
and pressures you to move (e.g., with high mandatory insurance rates) .
R3: “Watered down solutions are not helping either”
Starting from Damage: At the same time as we fight the agenda, there is another battle emerging:
the policy solution. Even if the agenda rank is high, the status quo group can still propose their
own “solutions” to weaken their opposition’s position. As Damage increases and the status quo
mobilizes there will be “modified” solutions to the problem being developed by their people.
They will try to organize think tanks or research groups to show their position in favorable light.
As in the death spiral, a compromise with “watered down” policies only reinforce the real
problems in the system.
Status Quo Coalition
New Policies to
address XE |
issues
Cumulative
Damage Severity of
next XE
New variable
Modified solutions proposed by status quo: While there may be some good solutions proposed
by the status quo, we can expect that their interests will not be for a drastic change to the way we
currently solve problems. In fact, the word modified probably fits very well if we think of any
new policy as some type of negotiation between interest groups.
BS: “New policies that may work”
- Starting from Damage: Increasing cumulative damage mobilizes the advocacy coalition. The
advocates for policy change come up with their own solutions to the problem. Some fraction of
the proposed policy solutions will be accepted to help alleviate the damage caused by these
events. But how effective will these policies really be?
- (rif
Status Quo Coalition
stories and testimom
fo Show there sa” qi
problem +
People harmed by | ~\
a change to policy 4 R3 )
Agenda Ranking
Policy Change Advocacy of XE Policy
Coalition
Modified, solutions
proposed by status
que
Policy Entrep
reneurs New Policies to
~ Ne address XE | 2
eo issues
frequency
People Harmed J of XE
by Damage
4 | Cumulative [+ y
Damage Fgit. Severity of
7 next XE
pressure to 7
leave home
(@)
New solutions
by advocates
New stock to discuss
New solutions by advocates: These are solutions proposed by people who want to eliminate the
damage. This is a bit of a tricky concept and perhaps the wording I use here does not explain the
variable as precisely as I need to discuss. We are not really talking about number of solutions or
number of policies but we are trying to get a measure of “effective policies.” You may then ask,
“How can we have effective policies in a model that doesn’t show how we learn about
problems?” This loop could be renamed “we have to do something, anything!” My intuition is
that when we send up solutions not grounded in anything learned about the problem we will seek
“Relief” policies rather “Mitigation” policies.
BS5a: “Knowledge is Power”
- Starting from New Policies: Perhaps one of the driving leverage points in the model will rest
here in this loop. How much can we learn about the problem? If the current policies are
ineffective and the frequency and/or severity of the of the damage continues. There will be an
increase in growth of relevant knowledge. (This leads to better judgments - Lens Model). This
helps us create better policies to address the real problems we are trying to mitigate. Thus, as
better solutions are proposed, less damage will accumulate, which is basically the goal for these
folks. This avoids a trap where we are merely “putting out fires” with relief instead of preventing
them with mitigation.
- (rif
Status Quo Coalition
stpries and, testimony
Shot here re >
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Policy Change Advocacy of XE Policy cane lel AC,
Coalition ¥
Modified, solutions
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wes | address XE | 7
XB1) issues
= frequency
People Harmed of XE
age —
4 e Poa a
‘ * Cumulative [+
\ AR2) Damage +/ Severity of
po a 7 next XE
Pressure to + 7 +
leave home mats
@} gee, os
New solutions
by advocates
New Stock to discuss
Knowledge on XE: This is an important stock in the model. As knowledge accumulates (and
maybe | should say relevant knowledge since literature from every discipline suggests that these
are tough problems to solve. However, just because problems are difficult does not mean they
are not worth studying. I would suggest just the opposite should be true. The severity of damage
can always be mitigated with creative analysis.
‘This variable should be boxed as a stock.
B6&B7: “Competing versions of the truth”
- Start from New Policies: There are two groups who have competing versions of how
something should be resolved. They have two very different goals for the strength of “new
policies to address XE” - Status quo would like that strength to be close to (if not) zero. That
is, the status quo would prefer no additional “new” policies. The Change Coalition seeks new
policies that are strong enough to mitigate the damage they are perceiving at the moment. In
the end these are two balancing loops seeking different goals
- Policy solutions need to be sold just as “causal stories”.
- The solution perceived to be the best will win.
- The other group will feel pressure to come up with better solutions.
- Or.... If | group dominates this arena - the other’s power will be diminished
Status Quo Coalition
+4
Policy Change Advocacy
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New Policies to
es address XE
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Penge SaGhtion +
New Variable
Perceived success: Both groups have essentially the same variable with different goals to
influence these perceptions on “success.” Therefore, we can measure success in relative terms to
some goal they have for the policy.
B8: “The political stream”
- Starting form New Policies: As new policies are created, there is initially an appearance that
“we may be able to solve this’ and so the willingness would increase. However, after some point,
especially when the recent memory of dealing with this issue is fresh, the policy solutions will
probably favor status the status quo. That is, Congress may ask the question, “haven’t we already
dealt with this?” and if the answer is “yes,” this would decrease perception that a problem needs
to be addressed. This loop represents how Congress perceives the window of opportunity.
> an
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Congress willingness to
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Congress Willingness to change policy: Kingdon discusses willingness to hear new testimony
and coalition efforts to find the right venue (Kingdon 1995). Prospect theory suggests that we are
risk averse to potential gains and risk seeking towards potential losses. Thus, willingness to hear
about change is naturally low. In addition, if new (even poor) policy has just been made, there is
a perceived lower incentive for Congress to change the status quo policy, especially if they think,
‘haven’t we just dealt with this?’ This explanation must be tested with the formal model. There
are sunk costs involved in making a change to a policy. Once people invest their time in a
particular program or policy, they may be unwilling to change the course, even if they are headed
down a dead end.
Putting All of the Pieces Together..
testimony
pere Isa
1S see an
Status Quo Coalition
os
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om
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Preliminary Insights from the Causal Loop Diagrams ”
The three reinforcing loops make things problematic for any coalition desiring change to
the current policy. As I stated earlier, I think loop BSa will be an important loop in this model.
How can administrative agencies learn more about the problem, gain knowledge, develop more
effective policies, and convince Congress that these policies are worth pursuing — all in the face
of a status quo (for which the agency helped create) - who wants to keep new policies off the
agenda? The challenge for administrators comes as they try to create a wealth of “knowledge” on
the particular event in times when the status quo still dominates the agenda. At what point do
administrators sacrifice resources, which can be used for short term policy “relief,” in order to
further their knowledge and develop better long term “mitigation” policies. What policy mix will
work best in the long run? Can we afford to lower today’s “capacity” for preventing damage if it
may (potentially) guarantee long term benefits. This appears to be a major problem with disaster
policies. In fact, any policy where there is great uncertainty about the benefits faces a natural
tendency for the status quo.'°
° The model is currently a work in progress and will be available upon request.
'° If we agree with the insights from Kahneman and Tversky's Prospect Theory.
Discussion for further research
First, in the causal loop presentation, | illustrated several unintended consequences and a
few key points of leverage. I plan to test these ideas and develop policy recommendations for
several types of disasters, showing how and/or if the model would have a behavioral change
when a large event “shocks” the system. My intuition tells me that we will not see a behavior
change unless we are in the most extreme cases. Second, the literature suggests that “potential”
focusing events need just the right set of conditions in order to become actual focusing events.
The model will test “shocks” to the system under all possible conditions to determine how large
and at precisely what time extreme events are most likely to becoming focusing events. Finally, I
would like to test the previous theories on agenda setting and policy making by including an
administrative agenda that would be acceptable to policy analysts and the political scientists in
terms of efficiency and political viability respectively.
References:
Baumgartner, F. and B. D. Jones (1993). Agendas and Instability in American Politics. Chicago,
University of Chicago Press.
Birkland, T. A. (1997). After Disaster: Agenda Setting, Public Policy and Focusing Events.
Washington, D.C., Georgetown University Press.
Brewer, G. and P. DeLeon (1983). The Foundations of Policy Analysis. Chicago, Dorsey Press.
Cobb, R. W. and C. D. Elder (1983). Participation in American Politics: The Dynamics of
Agenda-Building. Baltimore, Johns Hopkins University Press.
Kingdon, J. W. (1995). Agendas, Alternatives and Public Policies. New York, Harper Collins.
Kuhn, T. (1970). The Structure of Scientific Revolution. Chicago, IL, Chicago University Press.
Sabatier, P. A. (1988). "An Advocacy Coalition Framework of Policy Change and the Role of
Policy-Oriented Learning Therein." Policy Sciences 21: 129-168.
Schattschneider, E. E. (1975). The Semisovereign People. Hinsdale, Ill, The Dryden Press.
Stone, D. (1997). Policy Paradox: The art of policy and decision making. New York, W.W.
Norton.
