732AIDS PATIENT CARE and STDsVolume 21, Number 10, 2007.docx

732
AIDS PATIENT CARE and STDs
Volume 21, Number 10, 2007
© Mary Ann Liebert, Inc.
DOI: 10.1089/apc.2007.0012
Efficacy of Group Psychotherapy to Reduce Depressive
Symptoms among HIV-Infected Individuals:
A Systematic Review and Meta-Analysis
SETH HIMELHOCH, M.D., M.P.H., DEBORAH R. MEDOFF,
Ph.D.,
and GLORIA OYENIYI, B.A.
ABSTRACT
Depressed mood is highly prevalent among HIV-infected
individuals. Some but not all stud-
ies have found group psychotherapy to be efficacious in this
population. We performed a sys-
tematic review and meta-analysis of double-blinded,
randomized controlled trials to exam-
ine efficacy of group psychotherapy treatment among HIV
infected with depressive
symptoms. We used PubMed, the Cochrane database, and a
search of bibliographies to find
controlled clinical trials with random assignment to group
psychotherapy or control condi-
tion among HIV infected patients with depressive symptoms.
The principal measure of ef-

fect size was the standard difference between means on
validated depression inventories. We
identified 8 studies that included 665 subjects: 5 used cognitive
behavioral therapy (CBT), 2
used supportive therapy, and 1 used coping effectiveness
training. Three of the 8 studies re-
ported significant effects. The pooled effect size from the
random effects model was 0.38 (95%
confidence interval [CI]: 0.23–0.53) representing a moderate
effect. Heterogeneity of effect was
not found to be significant (p � 0.69; I2 � 0%). Studies
reporting use of group CBT had a
pooled effect size from the random effects model of 0.37 (95%
CI: 0.18–0.56) and was signif-
icant. Studies reporting the use of group supportive
psychotherapy had a pooled effect size
from the random effects model 0.58 (95% CI: �0.05–1.22) and
was nonsignificant. The results
of this study suggest that group psychotherapy is efficacious in
reducing depressive symp-
toms among, HIV-infected individuals. Of note, women were
nearly absent from all studies.
Future studies should be directed at addressing this disparity.
INTRODUCTION
DEPRESSED MOOD is highly prevalent amongindividuals
receiving medical care for
HIV.1 Individuals with HIV and depressive
disorders, compared to those with HIV alone,
have increased HIV related morbidity,2,3 and
among women a higher mortality.4,5 Although
highly active antiretroviral therapy (HAART)
has led to substantial reductions in morbidity
and mortality associated with HIV, studies

have shown that individuals with HIV and de-
pressive disorders are more likely to encounter
greater delays in being prescribed antiretro-
viral therapy,6 and have worse adherence to
taking antiretroviral medication.7 This is in
keeping with research that has shown that de-
pression itself is associated with poor adher-
ence to medical treatment.8
Recent studies, however, suggest that men-
Department of Psychiatry, University of Maryland School of
Medicine, Baltimore, Maryland.
GROUP PSYCHOTHERAPY TO REDUCE DEPRESSIVE
SYMPTOMS 733
tal health interventions may lead to improved
depressive and HIV-related outcomes.9,10 A
recent systematic review and meta-analysis
found that antidepressants are efficacious tar-
geting depression among those with HIV.11
However, antidepressant treatment may be as-
sociated with high dropout rates11 and may not
be acceptable to all patients.
Psychotherapeutic interventions have also
been used to alleviate psychosocial and inter-
personal difficulties and distress associated
with HIV. Several randomized control trial
studies have investigated the efficacy of group
therapy techniques to decrease psychological
distress, decrease social isolation, and improve
coping among HIV-infected people.12–19 Most

of these studies used interventions based on
cognitive behavioral theory and nearly all these
studies were conducted among men. Because
some, but not all, studies have found group
therapy interventions to be efficacious in de-
creasing distress among HIV-infected people,
we undertook a meta-analysis of randomized
controlled trials to examine whether depressive
symptoms respond to group psychotherapy
treatment among HIV-infected people.
MATERIALS AND METHODS
Search strategy and study inclusion criteria
Because the term AIDS was introduced in
1981 we searched MEDLINE, PSYCHINFO,
and Cochrane databases from 1981–2006 using
the key words: psychotherapy and adaptation,
psychological with HIV or AIDS and limited to
randomized control trials. In an effort to locate
both published and unpublished studies the
bibliographies of key reviews were examined.
Studies were included if they met the follow-
ing criteria: (1) prospective, double-blinded,
controlled trials with random assignment; (2)
report of outcomes of depressive symptoms;
(3) report of use of a psychotherapeutic inter-
ventions. The three authors independently
screened the titles and abstracts of each citation.
Data extraction
Data were independently extracted from the
studies by the three authors. Discrepancies

were resolved by formal review and then by
consensus. Our outcome of interest was de-
pressive symptoms. Depression inventories
that were specific for depressive symptoms
were abstracted. These inventories included
the Hamilton Depression Inventory (Ham-D),
Center for Epidemiogic Studies-Depression
(CES-D), and Beck Depression Inventory (BDI).
The standardized difference in means (Co-
hen d), the effect size, was calculated from
means and standard deviations from these
scales. When data on means or standard devi-
ations were lacking we contacted the authors
of the manuscripts. The one author contacted
did not respond to our inquiry for requested
information. We also compiled information re-
garding demographics, study characteristics,
and type of psychotherapy intervention re-
ported.
Quality of clinical trials
As variation in quality of clinical trials can
result in biased estimates of reported interven-
tion effectiveness, we evaluated the quality of
the clinical trials using a 15-item scale devel-
oped by Detsky et al.20 Each author indepen-
dently rated the quality of the clinical studies.
Discrepancies were resolved by formal review
and then by consensus.
Statistical analysis
We calculated effect sizes and pooled esti-
mates of effect across studies (Stat 8.0: metan

command) using analysis of variance models
for standardized mean differences (Cohen d).
A random effects model was used. We chose
to use a random effects model because it takes
into account both within and between-study
variation leading to a more conservative
weighting estimates. Heterogeneity, or the be-
tween study variation in outcomes, was mea-
sured using the Q statistic.21 The Q statistic is
considered to have a low power as a test of het-
erogeneity; therefore, heterogeneity was con-
sidered present with a p � 0.10. If heterogene-
ity was found to be present the I2 statistic was
used to describe the percentage of variation
due to heterogeneity across studies. In the ab-
sence of heterogeneity (i.e., Q statistic, p �
0.10), pooled results were reported. Publication
bias was evaluated using a funnel plot as well
as Eggers and Beggs tests.21
RESULTS
Search findings
We identified 18 randomized clinical tri-
als.12–19,22–31 Of these, 8 trials12–19 met inclusion
criteria (Fig. 1). These 8 trials included 665 pa-
tients randomly assigned to psychotherapy or
a parallel control arm (Table 1). Depression was
required at baseline for only one study14 and
two studies excluded those with major depres-
sion.15,17 With respect to the type of psycho-
therapeutic treatment all of the studies used a

group format. One study had two intervention
arms—a CBT group intervention and a sup-
portive therapy group intervention.14 Five of
the treatment interventions were described as
cognitive behavioral therapy (CBT),12–16 one
was described as coping effectiveness training
(CET),17 and two were described as supportive
psychotherapy.14,18 Finally one study reported
results that combined two treatment arms
(emotional expressive and CBT therapy) to-
gether.19 Length of treatment ranged between
7–15 sessions. The length of the intervention
ranged between 90 and 150 minutes. All inter-
ventions were directed at improving psycho-
logical distress and improving mood. Two
interventions were also directed at reducing
grief.16,18 Six trials occurred in the United
States, one trial occurred in Amsterdam19 and
one occurred in Hong Kong.13 With respect to
demographics all but one16 study was con-
ducted on men (Table 1). All studies were rated
as reflecting good quality.
Depressive symptom outcome
Three of the 8 studies reported significant ef-
fects. Of the 3 studies that found significant ef-
fects, one used cognitive behavioral treatment
intervention,16 one used supportive psycho-
therapy,14 and one reported the results of a
combination of emotional expressive and CBT
therapy.19 The pooled effect size from the ran-
dom effects model was 0.38 (95% CI: 0.23–0.53;
HIMELHOCH ET AL.734

FIG. 1. Flow diagram of randomized control trials included and
excluded in meta-analysis.
SYMPTOMS 735
Fig. 2) representing a small-moderate effect
size. Heterogeneity of effect was not found to
be significant (p � 0.69; I2 � 0% of variability
in effect sizes due to heterogeneity).
We were interested in investigating whether
intervention type (i.e., CBT versus non-CBT
group therapy interventions) moderated the ef-
fect between psychotherapy and depressive
symptoms. Studies reporting use of group CBT
had a pooled effect size from the random ef-
fects model of 0.37 (95% CI: 0.18–0.56]) and was
significant representing a moderate effect size.
Studies reporting the use of group supportive
psychotherapy had a pooled effect size from
the random effects model 0.58 (95% CI:
�0.05–1.22]) and was nonsignificant. In the one
study that used CET, the effect size from the
random effects model was 0.16 (95% CI:
�0.27–0.59]) and was not significant.
We were also interested in investigating
whether the focus of treatment (i.e., grief and
depressive symptoms versus depressive symp-
toms) moderated the effect between psy-
chotherapy and depressive symptoms. Studies
focusing on grief and depressive symptoms

had a pooled effect size from the random ef-
fects model of 0.34 (95% CI: 0.12–0.56]) and was
significant, representing a small to moderate ef-
fect size. Studies focusing on depressive symp-
toms had a pooled effect size from the random
effects model 0.42 (95% CI: 0.21–0.63) and was
significant representing a moderate effect size.
Finally we were interested in investigating
whether the exclusion of depression moderated
the effect between psychotherapy and depres-
sive symptoms. The two studies that excluded
participants with major depression were found
to have a pooled effect size from the random
effects model of 0.26 (95% CI: �0.10–0.61) and
was not significant. In contrast, those studies
that included participants with major depres-
sion had a pooled effect size from the random
effects model of 0.41 (95% CI: 0.24–0.48) and
was significant, representing a moderate effect
size.
Assessment of publication bias
The funnel plot was roughly symmetric. Eg-
ger’s test and Begg’s test were both nonsignif-
icant. Taken together these findings suggest the
relative absence of publication bias.
DISCUSSION
Our meta-analysis of randomized double-
blinded controlled trials of group psychother-
TABLE 1. CHARACTERISTICS OF THE GROUP THERAPY

STUDIES
Baseline Number Group Depression
Number Age Male Caucasian depression group meetings:
outcome Type of control
Study randomized (mean) (%) (%) required meetings min/wk
measurea group
Goodkin 97 36.5 100 52.6 No 10 90 Hamilton Usual care
Sikkema 235 40.3 64 28.0 No 12 90 Hamilton Usual care
Kellyb 68 34.0 100 62.0 Yes 8 90 CES-D Usual care
Chanc 13 38.1 100 — No 7 120 CES-D Wait list
Chesney 84 39.0 100 82.0 Noe 10 90 CES-D HIV info/wait list
Mulderc,d 27 40.4 100 — No 15 150 BDI Wait list
Lutgendorf 40 36.7 100 62.5 Noe 10 135 BDI Wait list
Antoni 101 41.6 100 52.0 No 10 135 BDI Med adherence
aThe Ham-D is a 17-item scale clinician-rated depression scale
with a response range from 0–54. The CES-D is a
20-item subject-rated depression scale with a response range
from 0–60. The BDI is a 21-item subject-rated depres-
sion scale with a response range from 5–63.
bThis study had a CBT arm and a supportive therapy arm.
cThe Chan study was from Hong Kong and did not report on
race. The Mulder sample was from Amsterdam and
did not report on race.
dThe Mulder study had a CBT and an emotional expressive
therapy arm. However, the intervention results were
presented as a combination of both CBT and emotional
expressive therapy.
eThe Chesney study excluded participants with major
depression. The Lutgendorf study excluded participants with

Hamilton Depression Rating Scale for Depression in the
“moderate or greater severity level.”
CBT, cognitive behavioral therapy.
apy targeting depressive symptoms among
HIV-infected individuals found that group
psychotherapy is efficacious. The combined ef-
fect size was 0.38 (95% CI: 0.23–0.53) repre-
senting a small to moderate effect size. We did
not find any heterogeneity among the studies
and there did not appear to be publication bias.
A meta-analysis of group psychotherapy for
unipolar depression found that among 15 stud-
ies in which participants in the group psy-
chotherapy intervention were compared to un-
treated controls the pooled effect size was
1.03.32 The greater effect size found in the meta-
analysis among those treated for unipolar de-
pression may not be surprising. Those with
unipolar depression, in contrast to those with
depressive symptoms, are, on average more
likely to have a greater burden of depressive
symptoms and therefore have a greater proba-
bility of depressive symptom reduction which
would be reflected in a larger effect size.
In our meta-analysis, most studies used a
cognitive behavior group therapy intervention
to target depressive symptoms. The combined
effect size for cognitive behavior was 0.37 (95%
CI: 0.18–0.56) representing a moderate effect
size. Thus, cognitive behavioral therapy ap-

pears to be efficacious in targeting depressive
symptoms among HIV-infected individuals.
Less can be said about the other forms of
therapy used. For example, although support-
ive therapy seems to have a positive effect on
reducing distress and depression among HIV-
infected individuals, the limited number of
studies and the large variability in the results
of these studies makes it difficult to draw a
clear conclusion. Whether the focus of the in-
tervention was on grief and depressive symp-
toms or depressive symptoms alone, did not
appear to moderate the effect of the interven-
tion with respect to depressive symptoms.
Finally, the pooled results of the studies that
included participants with major depression
appeared to have a significant effect while
those that excluded participants with major de-
pression did not. As those with major depres-
sion, on average, are likely to have a greater
probability of depressive symptom reduction
than those without major depression, the dif-
ference we found may in fact reflect a floor ef-
fect.
Although the theoretical underpinnings of
the group therapy interventions included in the
meta-analysis were diverse they did share sev-
HIMELHOCH ET AL.736
FIG. 2. Forrest plot: Effect of group psychotherapy on
depressive symptom outcome stratified by type of group
intervention.

SYMPTOMS 737
eral features in common. First, all used a group
therapy format. Second, all sessions were at
least 90 minutes and occurred on average for
10 sessions. Third, each study used techniques
specifically tailored to improve coping strate-
gies and improve social support. Most, but not
all, also provided some form of relaxation train-
ing. These elements may represent common
components of successful group psychother-
apy for HIV-infected individuals with distress.
With respect to demographics it is interest-
ing to note that all but one of the studies was
conducted among men. These findings may in
part be result of the demographic nature of the
epidemic over time. In the late 1980s and early
1990s HIV was considered primarily a disease
of men.33 However, the emerging population
at risk for HV are now non-white and Hispanic
women. Providing effective interventions that
target depressive symptoms among women is
especially important as two prospective stud-
ies demonstrate that compared to nonde-
pressed women with HIV, women with depres-
sive symptoms are significantly at increased
risk of mortality.4,5 Furthermore, being a
woman is considered an independent risk fac-
tor for depression.34,35 Because some studies
suggest that mental health interventions may
in fact be protective 9 it is important to ensure

that women are accessing appropriate mental
health treatment. As the results of the meta-
analysis may not generalize to women, future
studies may be needed to address this dis-
parity.
Minorities appeared to be well represented
in most of the studies evaluated. Among the 5
studies that occurred in the United States, mi-
norities represented, on average, about half of
the participant sample.
There are several limitations to this study.
First, many of the studies occurred prior to the
HAART era and as such we were unable to ad-
dress whether or not adherence to HAART was
an important moderator of response. Studies
have shown that individuals with HIV and de-
pressive disorders, compared to those with
HIV alone, have worse adherence to taking an-
tiretroviral medication.6,36,37 However, studies
have also found that mental health treatment
increases the probability that individuals with
depression receive and utilize HAART.9,38,39
Thus, it is possible that interventions that re-
duce depressive symptoms may in fact im-
prove access to and adherence with HAART.
Future meta-analyses may be able to better ad-
dress this outcome.
Second, only a couple of studies provided in-
formation of CD4 counts or HIV disease sever-
ity and therefore we were unable to determine
the impact this may have had on treatment re-
sponse. As there did not appear to be any sig-

nificant heterogeneity in the studies investi-
gated, it is unclear whether severity of illness
would be important moderators to consider in
a meta-regression. Third, we acknowledge that
individuals enrolled in clinical trials may be
more adherent to interventions and may be dif-
ferent then patients seen in actual clinical prac-
tice. This may then limit the generalizability of
the findings of this meta-analysis.
Finally, we used a unit-free, standardized
score, the effect size, in order to combine the
results from several depression instruments. By
combining the results of the depression instru-
ments in this way we avoided the possibility of
selection bias (i.e., not including results in the
meta-analysis because they contained different
depression outcome measures) and increased
the overall power of our analysis. This method,
though, assumes that the different instruments
used in the meta-analysis, in fact, measure the
same construct (i.e., depression) and are simi-
larly responsive to symptom change. If these
assumptions are not met, there is a potential for
increased heterogeneity in the study results. As
our study used instruments that are frequently
used to measure depression and as we did not
find any heterogeneity in our study results we
believe that combing results from different de-
pression instruments did not violate the above
assumptions.
CONCLUSION
This study suggests that group therapy, and
particularly group cognitive behavioral ther-

apy may be efficacious in treating depressive
symptoms among those infected with HIV.
However, the underrepresentation of women
limits the generalizability of these findings. Be-
cause women may be at risk for depression and
are an emerging population at risk for HIV fu-
ture studies should be directed to remedy this
disparity.
REFERENCES
1. Bing EG, Burnam MA, Longshore D, et al. Psychiatric
disorders and drug use among human immunodefi-
ciency virus-infected adults in the United States. Arch
Gen Psychiatry 2001;58:721–728.
2. Leserman J, Petitto JM, Gu H, et al. Progression to
AIDS, a clinical AIDS condition and mortality: Psy-
chosocial and physiological predictors. Psychol Med
2002;32:1059–1073.
3. McDaniel JS, Fowlie E, Summerville MB, Farber EW,
Cohen-Cole SA. An assessment of rates of psychiatric
morbidity and functioning in HIV disease. Gen Hosp
Psychiatry 1995;17:346–352.
4. Ickovics JR, Hamburger ME, Vlahov D, et al. Mortal-
ity, CD4 cell count decline, and depressive symptoms
among HIV-seropositive women: Longitudinal anal-
ysis from the HIV Epidemiology Research Study.
JAMA 2001;285:1466–1474.
5. Cook JA, Grey D, Burke J, et al. Depressive symptoms

and AIDS-related mortality among a multisite cohort
of HIV-positive women. Am J Public Health 2004;94:
1133–1140.
6. Gordillo V, del Amo J, Soriano V, Gonzalez-Lahoz J.
Sociodemographic and psychological variables influ-
encing adherence to antiretroviral therapy. AIDS
1999;13:1763–1769.
7. Fairfield KM, Libman H, Davis RB, Eisenberg DM.
Delays in protease inhibitor use in clinical practice. J
Gen Intern Med 1999;14:395–401.
8. DiMatteo MR, Lepper HS, Croghan TW. Depression
is a risk factor for noncompliance with medical treat-
ment: Meta-analysis of the effects of anxiety and de-
pression on patient adherence. Arch Intern Med
2000;160:2101.
9. Himelhoch S, Treisman G, Moore RA, Gebo K. Does
presence of a mental disorder in AIDS patients affect
the initiation of anitretroviral treatment and duration
of therapy? J Acquir Immune Defic Syndr 2004;37:
1457–1463.
10. Yun LWH, Maravi M, Koayashi JS, Barton PL, David-
son AJ. Antidepressant treatment improves adhernce
to antiretorviral therapy among depressed HIV-in-
fected patients. J Acquir Immune Defic Syndr 2005;
38:432–438.
11. Himelhoch S, Medoff DR. Efficacy of antidepressant
medication among HIV� individuals with depres-
sion: A systematic review and meta-analysis. AIDS
Patient Care STDs 2005;19:813–822.

12. Antoni MH, Carrico AW, Duran RE, et al. Ran-
domized clinical trial of cognitive behavioral stress
management on human immunodeficiency virus
viral load in gay men treated with highly active
antiretroviral therapy. Psychosom Med 2006;68:
143–151.
13. Chan I, Kong P, Leung P, Cognitive-behavioral group
program for Chinese heterosexual HIV-infected men
in Hong Kong. Patient Educ Couns 2005;56:78–84.
14. Kelly JA, Murphy DA, Bahr GR, et al. Outcome of
cognitive-behavioral and support group brief thera-
pies for depressed, HIV-infected persons. Am J Psy-
chiatry 1993;150:1679–1686.
15. Lutgendorf SK, Antoni MH, Ironson G, et al. Cogni-
tive-behavioral stress management decreases dys-
phoric mood and herpes simplex virus-type 2 anti-
body titers in symptomatic HIV-seropositive gay
men. J Consult Clin Psychol 1997;65:31–43.
16. Sikkema KJ, Hansen NB, Kochman A, Tate DC,
Difranceisco W. Outcomes from a randomized con-
trolled trial of a group intervention for HIV positive
men and women coping with AIDS-related loss and
bereavement. Death Stud 2004;28:187–209.
17. Chesney MA, Chambers DB, Taylor JM, Johnson LM,
Folkman S. Coping effectiveness training for men liv-
ing with HIV: Results from a randomized clinical trial
testing a group-based intervention. Psychosom Med
2003;65:1038–1046.
18. Goodkin K, Blaney NT, Feaster DJ, Baldewicz T,
Burkhalter JE, Leeds B. A randomized controlled clin-

ical trial of a bereavement support group intervention
in human immunodeficiency virus type 1-seroposi-
tive and -seronegative homosexual men. Arch Gen
Psychiatry 1999;56:52–59.
19. Mulder CL, Emmelkamp PM, Antoni MH, Mulder
JW, Sandfort TG, de Vries MJ. Cognitive-behavioral
and experiential group psychotherapy for HIV-in-
fected homosexual men: a comparative study. Psy-
chosom Med 1994;56:423–431.
20. Detsky AS, Naylor CD, O’Rourke K, McGeer AJ,
L’Abbe KA. Incorporating variations in the quality of
individual randomized trials into meta-analysis. J
Clin Epidemiol 1992;45:255–265.
21. Egger M, Smith GD, Altman DG, eds. Systematic Re-
views in Health Care Meta-Analysis in Context. Lon-
don: BMJ Books, 2001:3-475.
22. Markowitz JC, Kocsis JH, Fishman B, et al. Treatment
of depressive symptoms in human immunodeficiency
virus-positive patients. Arch Gen Psychiatry 1998;55:
452–457.
23. Targ EF, Karasic DH, Diefenbach PN, Anderson DA,
Bystritsky A, Fawzy FI. Structured group therapy and
fluoxetine to treat depression in HIV-positive per-
sons. Psychosomatics 1994;35:132–137.
24. Weber R, Christen L, Loy M, et al. Randomized,
placebo-controlled trial of Chinese herb therapy for
HIV-1-infected individuals. J Acquir Immune Defic
Syndr 1999;22:56–64.
25. Zisook S, Peterkin J, Goggin KJ, Sledge P, Atkinson

JH, Grant I. Treatment of major depression in HIV-
seropositive men. HIV Neurobehavioral Research
Center Group. J Clin Psychiatry 1998;59:217–224.
26. Ironson G, Weiss S, Lydston D, et al. The impact of
improved self-efficacy on HIV viral load and distress
in culturally diverse women living with AIDS: The
SMART/EST Women’s Project. AIDS Care 2005;17:
222–236.
HIMELHOCH ET AL.738
SYMPTOMS 739
27. Koopman C, Gore-Felton C, Marouf F, et al. Rela-
tionships of perceived stress to coping, attachment
and social support among HIV-positive persons.
AIDS Care 2000;12:663–672.
28. Inouye J, Flannelly L, Flannelly KJ. The effectiveness
of self-management training for individuals with
HIV/AIDS. J Assoc Nurses AIDS Care 2001;12:71–82.
29. Carrico AW, Antoni MH, Duran RE, et al. Reductions
in depressed mood and denial coping during cogni-
tive behavioral stress management with HIV-positive
gay men treated with HAART. Ann Behav Med 2006;
31:155–164.
30. Lechner SC, Antoni MH, Lydston D, et al. Cognitive-
behavioral interventions improve quality of life in
women with AIDS. J Psychosom Res 2003;54:253–261.

31. Antoni MH, Baggett L, Ironson G, et al. Cognitive-be-
havioral stress management intervention buffers dis-
tress responses and immunologic changes following
notification of HIV-1 seropositivity. J Consult Clin
Psychol 1991;59:906–915.
32. McDermut W, Miller IW, Brown RA. The efficacy of
group psychotherapy for depression: A meta-analy-
sis and review of the empirical literature. Clin Psy-
chol 2001;8:98.
33. HIV and AIDS—United States, 1981–2000. MMWR
Morb Mortal Wkly Rep 2001;50:430–434.
34. Regier DA, Narrow WE, Rae DS, Manderscheid RW,
Locke BZ, Goodwin FK. The de facto US mental and
addictive disorders service system. Epidemiologic
catchment area prospective 1-year prevalence rates of
disorders and services. Arch Gen Psychiatry 1993;50:
85–94.
35. Weissman MM, Bland RC, Canino GJ, et al. Cross-na-
tional epidemiology of major depression and bipolar
disorder. JAMA 1996;276:293–299.
36. Mugavero M, Ostermann J, Whetten K, et al. Barriers
to antiretroviral adherence: The importance of de-
pression, abuse, and other traumatic events. AIDS Pa-
tient Care STDs 2006;20:418–28.
37. Wagner GJ. Predictors of antiretroviral adherence as
measured by self-report, electronic monitoring, and
medication diaries. AIDS Patient Care STDs 2002;16:
599–608.
38. Cook JA, Cohen MH, Burke J, et al. Effects of de-

pressive symptoms and mental health quality of life
on use of highly active antiretroviral therapy among
HIV-seropositive women. J Acquir Immune Defic
Syndr 2002;30:401–409.
39. Turner BJ, Fleishman JA, Wenger N, et al. Effects of
drug abuse and mental disorders on use and type of
antiretroviral therapy in HIV-infected persons. J Gen
Intern Med 2001;16:625–633.
Address reprint requests to:
Seth Himelhoch, M.D., M.P.H.
Department of Psychiatry
Division of Services Research
737 Lombard Street, Room 516
Baltimore, MD 21201
E-mail: [email protected]
Public Administration and Information
Technology
Volume 10
Series Editor
Christopher G. Reddick
San Antonio, Texas, USA

[email protected]
More information about this series at
http://www.springer.com/series/10796
[email protected]
Marijn Janssen • Maria A. Wimmer
Ameneh Deljoo
Editors
Policy Practice and Digital
Science
Integrating Complex Systems, Social
Simulation and Public Administration
in Policy Research
2123
[email protected]
Editors
Marijn Janssen Ameneh Deljoo
Faculty of Technology, Policy, and Faculty of Technology,
Policy, and
Management Management
Delft University of Technology Delft University of Technology
Delft Delft

The Netherlands The Netherlands
Maria A. Wimmer
Institute for Information Systems Research
University of Koblenz-Landau
Koblenz
Germany
ISBN 978-3-319-12783-5 ISBN 978-3-319-12784-2 (eBook)
Public Administration and Information Technology
DOI 10.1007/978-3-319-12784-2
Library of Congress Control Number: 2014956771
Springer Cham Heidelberg New York London
© Springer International Publishing Switzerland 2015
This work is subject to copyright. All rights are reserved by the
Publisher, whether the whole or part of the
material is concerned, specifically the rights of translation,
reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microfilms or in any other
physical way, and transmission or information
storage and retrieval, electronic adaptation, computer software,
or by similar or dissimilar methodology
now known or hereafter developed.
The use of general descriptive names, registered names,
trademarks, service marks, etc. in this publication
does not imply, even in the absence of a specific statement, that
such names are exempt from the relevant
protective laws and regulations and therefore free for general
use.
The publisher, the authors and the editors are safe to assume
that the advice and information in this book
are believed to be true and accurate at the date of publication.
Neither the publisher nor the authors or the
editors give a warranty, express or implied, with respect to the

material contained herein or for any errors
or omissions that may have been made.
Printed on acid-free paper
Springer is part of Springer Science+Business Media
(www.springer.com)
[email protected]
Preface
The last economic and financial crisis has heavily threatened
European and other
economies around the globe. Also, the Eurozone crisis, the
energy and climate
change crises, challenges of demographic change with high
unemployment rates,
and the most recent conflicts in the Ukraine and the near East or
the Ebola virus
disease in Africa threaten the wealth of our societies in
different ways. The inability
to predict or rapidly deal with dramatic changes and negative
trends in our economies
and societies can seriously hamper the wealth and prosperity of
the European Union
and its Member States as well as the global networks. These
societal and economic
challenges demonstrate an urgent need for more effective and
efficient processes of
governance and policymaking, therewith specifically addressing
crisis management
and economic/welfare impact reduction.

Therefore, investing in the exploitation of innovative
information and commu-
nication technology (ICT) in the support of good governance
and policy modeling
has become a major effort of the European Union to position
itself and its Member
States well in the global digital economy. In this realm, the
European Union has
laid out clear strategic policy objectives for 2020 in the Europe
2020 strategy1: In
a changing world, we want the EU to become a smart,
sustainable, and inclusive
economy. These three mutually reinforcing priorities should
help the EU and the
Member States deliver high levels of employment, productivity,
and social cohesion.
Concretely, the Union has set five ambitious objectives—on
employment, innovation,
education, social inclusion, and climate/energy—to be reached
by 2020. Along with
this, Europe 2020 has established four priority areas—smart
growth, sustainable
growth, inclusive growth, and later added: A strong and
effective system of eco-
nomic governance—designed to help Europe emerge from the
crisis stronger and to
coordinate policy actions between the EU and national levels.
To specifically support European research in strengthening
capacities, in overcom-
ing fragmented research in the field of policymaking, and in
advancing solutions for
1 Europe 2020 http://ec.europa.eu/europe2020/index_en.htm
v

[email protected]
vi Preface
ICT supported governance and policy modeling, the European
Commission has co-
funded an international support action called eGovPoliNet2. The
overall objective
of eGovPoliNet was to create an international, cross-
disciplinary community of re-
searchers working on ICT solutions for governance and policy
modeling. In turn,
the aim of this community was to advance and sustain research
and to share the
insights gleaned from experiences in Europe and globally. To
achieve this, eGovPo-
liNet established a dialogue, brought together experts from
distinct disciplines, and
collected and analyzed knowledge assets (i.e., theories,
concepts, solutions, findings,
and lessons on ICT solutions in the field) from different
research disciplines. It built
on case material accumulated by leading actors coming from
distinct disciplinary
backgrounds and brought together the innovative knowledge in
the field. Tools, meth-
ods, and cases were drawn from the academic community, the
ICT sector, specialized
policy consulting firms as well as from policymakers and
governance experts. These
results were assembled in a knowledge base and analyzed in
order to produce com-
parative analyses and descriptions of cases, tools, and scientific

approaches to enrich
a common knowledge base accessible via www.policy-
community.eu.
This book, entitled “Policy Practice and Digital Science—
Integrating Complex
Systems, Social Simulation, and Public Administration in Policy
Research,” is one
of the exciting results of the activities of eGovPoliNet—fusing
community building
activities and activities of knowledge analysis. It documents
findings of comparative
analyses and brings in experiences of experts from academia
and from case descrip-
tions from all over the globe. Specifically, it demonstrates how
the explosive growth
in data, computational power, and social media creates new
opportunities for policy-
making and research. The book provides a first comprehensive
look on how to take
advantage of the development in the digital world with new
approaches, concepts,
instruments, and methods to deal with societal and
computational complexity. This
requires the knowledge traditionally found in different
disciplines including public
administration, policy analyses, information systems, complex
systems, and com-
puter science to work together in a multidisciplinary fashion
and to share approaches.
This book provides the foundation for strongly multidisciplinary
research, in which
the various developments and disciplines work together from a
comprehensive and
holistic policymaking perspective. A wide range of aspects for
social and professional

networking and multidisciplinary constituency building along
the axes of technol-
ogy, participative processes, governance, policy modeling,
social simulation, and
visualization are tackled in the 19 papers.
With this book, the project makes an effective contribution to
the overall objec-
tives of the Europe 2020 strategy by providing a better
understanding of different
approaches to ICT enabled governance and policy modeling, and
by overcoming the
fragmented research of the past. This book provides impressive
insights into various
theories, concepts, and solutions of ICT supported policy
modeling and how stake-
holders can be more actively engaged in public policymaking. It
draws conclusions
2 eGovPoliNet is cofunded under FP 7, Call identifier FP7-ICT-
2011-7, URL: www.policy-
community.eu
[email protected]
Preface vii
of how joint multidisciplinary research can bring more effective
and resilient find-
ings for better predicting dramatic changes and negative trends
in our economies and
societies.
It is my great pleasure to provide the preface to the book

resulting from the
eGovPoliNet project. This book presents stimulating research by
researchers coming
from all over Europe and beyond. Congratulations to the project
partners and to the
authors!—Enjoy reading!
Thanassis Chrissafis
Project officer of eGovPoliNet
European Commission
DG CNECT, Excellence in Science, Digital Science
[email protected]
Contents
1 Introduction to Policy-Making in the Digital Age . . . . . . . . . .
. . . . . . . 1
Marijn Janssen and Maria A. Wimmer
2 Educating Public Managers and Policy Analysts
in an Era of Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 15
Christopher Koliba and Asim Zia
3 The Quality of Social Simulation: An Example from Research
Policy Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 35
Petra Ahrweiler and Nigel Gilbert
4 Policy Making and Modelling in a Complex World . . . . . . . .
. . . . . . . . 57
Wander Jager and Bruce Edmonds

5 From Building a Model to Adaptive Robust Decision Making
Using Systems Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 75
Erik Pruyt
6 Features and Added Value of Simulation Models Using
Different
Modelling Approaches Supporting Policy-Making: A
Comparative
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 95
Dragana Majstorovic, Maria A.Wimmer, Roy Lay-Yee, Peter
Davis
and Petra Ahrweiler
7 A Comparative Analysis of Tools and Technologies
for Policy Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 125
Eleni Kamateri, Eleni Panopoulou, Efthimios Tambouris,
Konstantinos Tarabanis, Adegboyega Ojo, Deirdre Lee
and David Price
8 Value Sensitive Design of Complex Product Systems . . . . . . .
. . . . . . . . 157
Andreas Ligtvoet, Geerten van de Kaa, Theo Fens, Cees van
Beers,
Paulier Herder and Jeroen van den Hoven
ix
[email protected]
x Contents

9 Stakeholder Engagement in Policy Development: Observations
and Lessons from International Experience . . . . . . . . . . . . . . . .
. . . . . . 177
Natalie Helbig, Sharon Dawes, Zamira Dzhusupova, Bram
Klievink
and Catherine Gerald Mkude
10 Values in Computational Models Revalued . . . . . . . . . . . . .
. . . . . . . . . . 205
Rebecca Moody and Lasse Gerrits
11 The Psychological Drivers of Bureaucracy: Protecting
the Societal Goals of an Organization . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 221
Tjeerd C. Andringa
12 Active and Passive Crowdsourcing in Government . . . . . . . .
. . . . . . . . 261
Euripidis Loukis and Yannis Charalabidis
13 Management of Complex Systems: Toward Agent-Based
Gaming for Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 291
Wander Jager and Gerben van der Vegt
14 The Role of Microsimulation in the Development of Public
Policy . . . 305
Roy Lay-Yee and Gerry Cotterell
15 Visual Decision Support for Policy Making: Advancing
Policy
Analysis with Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 321
Tobias Ruppert, Jens Dambruch, Michel Krämer, Tina Balke,
Marco
Gavanelli, Stefano Bragaglia, Federico Chesani, Michela

Milano
and Jörn Kohlhammer
16 Analysis of Five Policy Cases in the Field of Energy Policy .
. . . . . . . . 355
Dominik Bär, Maria A.Wimmer, Jozef Glova, Anastasia
Papazafeiropoulou and Laurence Brooks
17 Challenges to Policy-Making in Developing Countries
and the Roles of Emerging Tools, Methods and Instruments:
Experiences from Saint Petersburg . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . 379
Dmitrii Trutnev, Lyudmila Vidyasova and Andrei Chugunov
18 Sustainable Urban Development, Governance and Policy:
A Comparative Overview of EU Policies and Projects . . . . . . . .
. . . . . 393
Diego Navarra and Simona Milio
19 eParticipation, Simulation Exercise and Leadership Training
in Nigeria: Bridging the Digital Divide . . . . . . . . . . . . . . . . . .
. . . . . . . . . 417
Tanko Ahmed
[email protected]
Contributors
Tanko Ahmed National Institute for Policy and Strategic Studies
(NIPSS), Jos,
Nigeria
Petra Ahrweiler EA European Academy of Technology and
Innovation Assess-

ment GmbH, Bad Neuenahr-Ahrweiler, Germany
Tjeerd C. Andringa University College Groningen, Institute of
Artificial In-
telligence and Cognitive Engineering (ALICE), University of
Groningen, AB,
Groningen, the Netherlands
Tina Balke University of Surrey, Surrey, UK
Dominik Bär University of Koblenz-Landau, Koblenz, Germany
Cees van Beers Faculty of Technology, Policy, and
Management, Delft University
of Technology, Delft, The Netherlands
Stefano Bragaglia University of Bologna, Bologna, Italy
Laurence Brooks Brunel University, Uxbridge, UK
Yannis Charalabidis University of the Aegean, Samos, Greece
Federico Chesani University of Bologna, Bologna, Italy
Andrei Chugunov ITMO University, St. Petersburg, Russia
Gerry Cotterell Centre of Methods and Policy Application in the
Social Sciences
(COMPASS Research Centre), University of Auckland,
Auckland, New Zealand
Jens Dambruch Fraunhofer Institute for Computer Graphics
Research, Darmstadt,
Germany
Peter Davis Centre of Methods and Policy Application in the

Social Sciences
Sharon Dawes Center for Technology in Government,
University at Albany,
Albany, New York, USA
xi
[email protected]
xii Contributors
Zamira Dzhusupova Department of Public Administration and
Development Man-
agement, United Nations Department of Economic and Social
Affairs (UNDESA),
NewYork, USA
Bruce Edmonds Manchester Metropolitan University,
Manchester, UK
Theo Fens Faculty of Technology, Policy, and Management,
Delft University of
Technology, Delft, The Netherlands
Marco Gavanelli University of Ferrara, Ferrara, Italy
Lasse Gerrits Department of Public Administration, Erasmus
University
Rotterdam, Rotterdam, The Netherlands
Nigel Gilbert University of Surrey, Guildford, UK

Jozef Glova Technical University Kosice, Kosice, Slovakia
Natalie Helbig Center for Technology in Government,
University at Albany,
Albany, New York, USA
Paulier Herder Faculty of Technology, Policy, and Management,
Delft University
Jeroen van den Hoven Faculty of Technology, Policy, and
Management, Delft
University of Technology, Delft, The Netherlands
Wander Jager Groningen Center of Social Complexity Studies,
University of
Groningen, Groningen, The Netherlands
Marijn Janssen Faculty of Technology, Policy, and
Management, Delft University
Geerten van de Kaa Faculty of Technology, Policy, and
Management, Delft
University of Technology, Delft, The Netherlands
Eleni Kamateri Information Technologies Institute, Centre for
Research &
Technology—Hellas, Thessaloniki, Greece
Bram Klievink Faculty of Technology, Policy and Management,
Delft University
Jörn Kohlhammer GRIS, TU Darmstadt & Fraunhofer IGD,

Darmstadt, Germany
Christopher Koliba University of Vermont, Burlington, VT,
USA
Michel Krämer Fraunhofer Institute for Computer Graphics
Germany
Roy Lay-Yee Centre of Methods and Policy Application in the
Social Sciences
Deirdre Lee INSIGHT Centre for Data Analytics, NUIG,
Galway, Ireland
[email protected]
Contributors xiii
Andreas Ligtvoet Faculty of Technology, Policy, and
Management, Delft Univer-
sity of Technology, Delft, The Netherlands
Euripidis Loukis University of the Aegean, Samos, Greece
Dragana Majstorovic University of Koblenz-Landau, Koblenz,
Germany
Michela Milano University of Bologna, Bologna, Italy
Simona Milio London School of Economics, Houghton Street,
London, UK

Catherine Gerald Mkude Institute for IS Research, University of
Koblenz-Landau,
Koblenz, Germany
Rebecca Moody Department of Public Administration, Erasmus
University
Rotterdam, Rotterdam, The Netherlands
Diego Navarra Studio Navarra, London, UK
Adegboyega Ojo INSIGHT Centre for Data Analytics, NUIG,
Galway, Ireland
Eleni Panopoulou Information Technologies Institute, Centre
for Research &
Technology—Hellas, Thessaloniki, Greece
Anastasia Papazafeiropoulou Brunel University, Uxbridge, UK
David Price Thoughtgraph Ltd, Somerset, UK
Erik Pruyt Faculty of Technology, Policy, and Management,
Delft University of
Technology, Delft, The Netherlands; Netherlands Institute for
Advanced Study,
Wassenaar, The Netherlands
Tobias Ruppert Fraunhofer Institute for Computer Graphics
Germany
Efthimios Tambouris Information Technologies Institute, Centre
for Research &
Technology—Hellas, Thessaloniki, Greece; University of
Macedonia, Thessaloniki,

Greece
Konstantinos Tarabanis Information Technologies Institute,
Centre for Research
& Technology—Hellas, Thessaloniki, Greece; University of
Macedonia, Thessa-
loniki, Greece
Dmitrii Trutnev ITMO University, St. Petersburg, Russia
Gerben van der Vegt Faculty of Economics and Business,
University of Groningen,
Groningen, The Netherlands
Lyudmila Vidyasova ITMO University, St. Petersburg, Russia
Maria A. Wimmer University of Koblenz-Landau, Koblenz,
Germany
Asim Zia University of Vermont, Burlington, VT, USA
[email protected]
Chapter 1
Introduction to Policy-Making in the Digital Age
Marijn Janssen and Maria A. Wimmer
We are running the 21st century using 20th century systems on
top of 19th century political structures. . . .
John Pollock, contributing editor MIT technology review
Abstract The explosive growth in data, computational power,
and social media

creates new opportunities for innovating governance and policy-
making. These in-
formation and communications technology (ICT) developments
affect all parts of
the policy-making cycle and result in drastic changes in the way
policies are devel-
oped. To take advantage of these developments in the digital
world, new approaches,
concepts, instruments, and methods are needed, which are able
to deal with so-
cietal complexity and uncertainty. This field of research is
sometimes depicted
as e-government policy, e-policy, policy informatics, or data
science. Advancing
our knowledge demands that different scientific communities
collaborate to create
practice-driven knowledge. For policy-making in the digital age
disciplines such as
complex systems, social simulation, and public administration
need to be combined.
1.1 Introduction
Policy-making and its subsequent implementation is necessary
to deal with societal
problems. Policy interventions can be costly, have long-term
implications, affect
groups of citizens or even the whole country and cannot be
easily undone or are even
irreversible. New information and communications technology
(ICT) and models
can help to improve the quality of policy-makers. In particular,
the explosive growth
in data, computational power, and social media creates new
opportunities for in-
novating the processes and solutions of ICT-based policy-

making and research. To
M. Janssen (�)
Faculty of Technology, Policy, and Management, Delft
University of Technology,
Delft, The Netherlands
e-mail: [email protected]
M. A. Wimmer
University of Koblenz-Landau, Koblenz, Germany
© Springer International Publishing Switzerland 2015 1
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI
10.1007/978-3-319-12784-2_1
[email protected]
2 M. Janssen and M. A. Wimmer
take advantage of these developments in the digital world, new
approaches, con-
cepts, instruments, and methods are needed, which are able to
deal with societal and
computational complexity. This requires the use of knowledge
which is traditionally
found in different disciplines, including (but not limited to)
public administration,
policy analyses, information systems, complex systems, and
computer science. All
these knowledge areas are needed for policy-making in the
digital age. The aim of
this book is to provide a foundation for this new
interdisciplinary field in which
various traditional disciplines are blended.

Both policy-makers and those in charge of policy
implementations acknowledge
that ICT is becoming more and more important and is changing
the policy-making
process, resulting in a next generation policy-making based on
ICT support. The field
of policy-making is changing driven by developments such as
open data, computa-
tional methods for processing data, opinion mining, simulation,
and visualization of
rich data sets, all combined with public engagement, social
media, and participatory
tools. In this respect Web 2.0 and even Web 3.0 point to the
specific applications of
social networks and semantically enriched and linked data
which are important for
policy-making. In policy-making vast amount of data are used
for making predictions
and forecasts. This should result in improving the outcomes of
policy-making.
Policy-making is confronted with an increasing complexity and
uncertainty of the
outcomes which results in a need for developing policy models
that are able to deal
with this. To improve the validity of the models policy-makers
are harvesting data to
generate evidence. Furthermore, they are improving their
models to capture complex
phenomena and dealing with uncertainty and limited and
incomplete information.
Despite all these efforts, there remains often uncertainty
concerning the outcomes of
policy interventions. Given the uncertainty, often multiple
scenarios are developed

to show alternative outcomes and impact. A condition for this is
the visualization of
policy alternatives and its impact. Visualization can ensure
involvement of nonexpert
and to communicate alternatives. Furthermore, games can be
used to let people gain
insight in what can happen, given a certain scenario. Games
allow persons to interact
and to experience what happens in the future based on their
interventions.
Policy-makers are often faced with conflicting solutions to
complex problems,
thus making it necessary for them to test out their assumptions,
interventions, and
resolutions. For this reason policy-making organizations
introduce platforms facili-
tating policy-making and citizens engagements and enabling the
processing of large
volumes of data. There are various participative platforms
developed by government
agencies (e.g., De Reuver et al. 2013; Slaviero et al. 2010;
Welch 2012). Platforms
can be viewed as a kind of regulated environment that enable
developers, users, and
others to interact with each other, share data, services, and
applications, enable gov-
ernments to more easily monitor what is happening and
facilitate the development
of innovative solutions (Janssen and Estevez 2013). Platforms
should provide not
only support for complex policy deliberations with citizens but
should also bring to-
gether policy-modelers, developers, policy-makers, and other
stakeholders involved
in policy-making. In this way platforms provide an information-

rich, interactive
[email protected]
1 Introduction to Policy-Making in the Digital Age 3
environment that brings together relevant stakeholders and in
which complex phe-
nomena can be modeled, simulated, visualized, discussed, and
even the playing of
games can be facilitated.
1.2 Complexity and Uncertainty in Policy-Making
Policy-making is driven by the need to solve societal problems
and should result in
interventions to solve these societal problems. Examples of
societal problems are
unemployment, pollution, water quality, safety, criminality,
well-being, health, and
immigration. Policy-making is an ongoing process in which
issues are recognized
as a problem, alternative courses of actions are formulated,
policies are affected,
implemented, executed, and evaluated (Stewart et al. 2007).
Figure 1.1 shows the
typical stages of policy formulation, implementation, execution,
enforcement, and
evaluation. This process should not be viewed as linear as many
interactions are
necessary as well as interactions with all kind of stakeholders.
In policy-making
processes a vast amount of stakeholders are always involved,
which makes policy-

making complex.
Once a societal need is identified, a policy has to be formulated.
Politicians,
members of parliament, executive branches, courts, and interest
groups may be
involved in these formulations. Often contradictory proposals
are made, and the
impact of a proposal is difficult to determine as data is missing,
models cannot
citizen
s
Policy formulation
Policy
implementation
Policy
execution
Policy
enforcement and
evaluation
politicians
Policy-
makers
Administrative
organizations
b

u
sin
esses
Inspection and
enforcement agencies
experts
Fig. 1.1 Overview of policy cycle and stakeholders
[email protected]
capture the complexity, and the results of policy models are
difficult to interpret and
even might be interpreted in an opposing way. This is further
complicated as some
proposals might be good but cannot be implemented or are too
costly to implement.
There is a large uncertainty concerning the outcomes.
Policy implementation is done by organizations other than those
that formulated
the policy. They often have to interpret the policy and have to
make implemen-
tation decisions. Sometimes IT can block quick implementation
as systems have
to be changed. Although policy-making is the domain of the
government, private
organizations can be involved to some extent, in particular in
the execution of policies.

Once all things are ready and decisions are made, policies need
to be executed.
During the execution small changes are typically made to fine
tune the policy formu-
lation, implementation decisions might be more difficult to
realize, policies might
bring other benefits than intended, execution costs might be
higher and so on. Typ-
ically, execution is continually changing. Evaluation is part of
the policy-making
process as it is necessary to ensure that the policy-execution
solved the initial so-
cietal problem. Policies might become obsolete, might not work,
have unintended
affects (like creating bureaucracy) or might lose its support
among elected officials,
or other alternatives might pop up that are better.
Policy-making is a complex process in which many stakeholders
play a role. In
the various phases of policy-making different actors are
dominant and play a role.
Figure 1.1 shows only some actors that might be involved, and
many of them are not
included in this figure. The involvement of so many actors
results in fragmentation
and often actors are even not aware of the decisions made by
other actors. This makes
it difficult to manage a policy-making process as each actor has
other goals and might
be self-interested.
Public values (PVs) are a way to try to manage complexity and
give some guidance.
Most policies are made to adhere to certain values. Public value

management (PVM)
represents the paradigm of achieving PVs as being the primary
objective (Stoker
2006). PVM refers to the continuous assessment of the actions
performed by public
officials to ensure that these actions result in the creation of PV
(Moore 1995). Public
servants are not only responsible for following the right
procedure, but they also have
to ensure that PVs are realized. For example, civil servants
should ensure that garbage
is collected. The procedure that one a week garbage is collected
is secondary. If it is
necessary to collect garbage more (or less) frequently to ensure
a healthy environment
then this should be done. The role of managers is not only to
ensure that procedures
are followed but they should be custodians of public assets and
maximize a PV.
There exist a wide variety of PVs (Jørgensen and Bozeman
2007). PVs can be
long-lasting or might be driven by contemporary politics. For
example, equal access
is a typical long-lasting value, whereas providing support for
students at universities
is contemporary, as politicians might give more, less, or no
support to students. PVs
differ over times, but also the emphasis on values is different in
the policy-making
cycle as shown in Fig. 1.2. In this figure some of the values
presented by Jørgensen
and Bozeman (2007) are mapped onto the four policy-making
stages. Dependent on
the problem at hand other values might play a role that is not
included in this figure.

[email protected]
Policy
formulation
Policy
implementation
Policy
execution
Policy
enforcement
and evaluation
efficiency
efficiency
accountability
transparancy
responsiveness
public interest
will of the people
listening

citizen involvement
evidence-based
protection of
individual rights
accountability
transparancy
evidence-based
equal access
balancing of interests
robust
honesty
fair
timelessness
reliable
flexible
fair
Fig. 1.2 Public values in the policy cycle
Policy is often formulated by politicians in consultation with
experts. In the PVM
paradigm, public administrations aim at creating PVs for society

and citizens. This
suggests a shift from talking about what citizens expect in
creating a PV. In this view
public officials should focus on collaborating and creating a
dialogue with citizens
in order to determine what constitutes a PV.
1.3 Developments
There is an infusion of technology that changes policy processes
at both the individual
and group level. There are a number of developments that
influence the traditional
way of policy-making, including social media as a means to
interact with the public
(Bertot et al. 2012), blogs (Coleman and Moss 2008), open data
(Janssen et al. 2012;
Zuiderwijk and Janssen 2013), freedom of information (Burt
2011), the wisdom
of the crowds (Surowiecki 2004), open collaboration and
transparency in policy
simulation (Wimmer et al. 2012a, b), agent-based simulation
and hybrid modeling
techniques (Koliba and Zia 2012) which open new ways of
innovative policy-making.
Whereas traditional policy-making is executed by experts, now
the public is involved
to fulfill requirements of good governance according to open
government principles.
[email protected]

Also, the skills and capabilities of crowds can be explored and
can lead to better and
more transparent democratic policy decisions. All these
developments can be used for
enhancing citizen’s engagement and to involve citizens better in
the policy-making
process. We want to emphasize three important developments.
1.3.1 The Availability of Big and Open Linked Data (BOLD)
Policy-making heavily depends on data about existing policies
and situations to
make decisions. Both public and private organizations are
opening their data for use
by others. Although information could be requested for in the
past, governments
have changed their strategy toward actively publishing open
data in formats that are
readily and easily accessible (for example,
European_Commission 2003; Obama
2009). Multiple perspectives are needed to make use of and
stimulate new practices
based on open data (Zuiderwijk et al. 2014). New applications
and innovations can
be based solely on open data, but often open data are enriched
with data from other
sources. As data can be generated and provided in huge
amounts, specific needs for
processing, curation, linking, visualization, and maintenance
appear. The latter is
often denoted with big data in which the value is generated by
combining different
datasets (Janssen et al. 2014). Current advances in processing
power and memory
allows for the processing of a huge amount of data. BOLD
allows for analyzing

policies and the use of these data in models to better predict the
effect of new policies.
1.3.2 Rise of Hybrid Simulation Approaches
In policy implementation and execution, many actors are
involved and there are a
huge number of factors influencing the outcomes; this
complicates the prediction
of the policy outcomes. Simulation models are capable of
capturing the interdepen-
dencies between the many factors and can include stochastic
elements to deal with
the variations and uncertainties. Simulation is often used in
policy-making as an
instrument to gain insight in the impact of possible policies
which often result in
new ideas for policies. Simulation allows decision-makers to
understand the essence
of a policy, to identify opportunities for change, and to evaluate
the effect of pro-
posed changes in key performance indicators (Banks 1998; Law
and Kelton 1991).
Simulation heavily depends on data and as such can benefit
from big and open data.
Simulation models should capture the essential aspects of
reality. Simulation
models do not rely heavily on mathematical abstraction and are
therefore suitable
for modeling complex systems (Pidd 1992). Already the
development of a model
can raise discussions about what to include and what factors are
of influence, in this
way contributing to a better understanding of the situation at
hand. Furthermore,

experimentation using models allows one to investigate
different settings and the
influence of different scenarios in time on the policy outcomes.
[email protected]
The effects of policies are hard to predict and dealing with
uncertainty is a key
aspect in policy modeling. Statistical representation of real-
world uncertainties is
an integral part of simulation models (Law and Kelton 1991).
The dynamics asso-
ciated with many factors affecting policy-making, the
complexity associated with
the interdependencies between individual parts, and the
stochastic elements asso-
ciated with the randomness and unpredictable behavior of
transactions complicates
the simulations. Computer simulations for examining,
explaining, and predicting so-
cial processes and relationships as well as measuring the
possible impact of policies
has become an important part of policy-making. Traditional
models are not able to
address all aspects of complex policy interactions, which
indicates the need for the
development of hybrid simulation models consisting of a
combinatory set of models
built on different modeling theories (Koliba and Zia 2012). In
policy-making it can
be that multiple models are developed, but it is also possible to
combine various

types of simulation in a single model. For this purpose agent-
based modeling and
simulation approaches can be used as these allow for combining
different type of
models in a single simulation.
1.3.3 Ubiquitous User Engagement
Efforts to design public policies are confronted with
considerable complexity, in
which (1) a large number of potentially relevant factors needs to
be considered, (2) a
vast amount of data needs to be processed, (3) a large degree of
uncertainty may exist,
and (4) rapidly changing circumstances need to be dealt with.
Utilizing computational
methods and various types of simulation and modeling methods
is often key to
solving these kinds of problems (Koliba and Zia 2012). The
open data and social
media movements are making large quantities of new data
available. At the same time
enhancements in computational power have expanded the
repertoire of instruments
and tools available for studying dynamic systems and their
interdependencies. In
addition, sophisticated techniques for data gathering,
visualization, and analysis have
expanded our ability to understand, display, and disseminate
complex, temporal, and
spatial information to diverse audiences. These problems can
only be addressed from
a complexity science perspective and with a multitude of views
and contributions
from different disciplines. Insights and methods of complexity
science should be

applied to assist policy-makers as they tackle societal problems
in policy areas such
as environmental protection, economics, energy, security, or
public safety and health.
This demands user involvement which is supported by
visualization techniques and
which can be actively involved by employing (serious) games.
These methods can
show what hypothetically will happen when certain policies are
implemented.
[email protected]
1.4 Combining Disciplines in E-government Policy-Making
This new field has been shaped using various names, including
e-policy-making,
digital policy science, computational intelligence, digital
sciences, data sciences,
and policy informatics (Dawes and Janssen 2013). The essence
of this field it that it
is
1. Practice-driven
2. Employs modeling techniques
3. Needs the knowledge coming from various disciplines
4. It focused on governance and policy-making
This field is practice-driven by taking as a starting point the
public policy problem and
defining what information is relevant for addressing the
problem under study. This

requires understanding of public administration and policy-
making processes. Next,
it is a key to determine how to obtain, store, retrieve, process,
model, and interpret the
results. This is the field of e-participation, policy-modeling,
social simulation, and
complex systems. Finally, it should be agreed upon how to
present and disseminate
the results so that other researchers, decision-makers, and
practitioners can use it.
This requires in-depth knowledge of practice, of structures of
public administration
and constitutions, political cultures, processes and culture and
policy-making.
Based on the ideas, the FP7 project EgovPoliNet project has
created an inter-
national community in ICT solutions for governance and policy-
modeling. The
“policy-making 2.0” LinkedIn community has a large number of
members from dif-
ferent disciplines and backgrounds representing practice and
academia. This book
is the product of this project in which a large number of persons
from various dis-
ciplines and representing a variety of communities were
involved. The book shows
experiences and advances in various areas of policy-making.
Furthermore, it contains
comparative analyses and descriptions of cases, tools, and
scientific approaches from
the knowledge base created in this project. Using this book,
practices and knowl-
edge in this field is shared among researchers. Furthermore, this
book provides the
foundations in this area. The covered expertise include a wide

range of aspects for so-
cial and professional networking and multidisciplinary
constituency building along
the axes of technology, participative processes, governance,
policy-modeling, social
simulation, and visualization. In this way eGovPoliNet has
advanced the way re-
search, development, and practice is performed worldwide in
using ICT solutions
for governance and policy-modeling.
Although in Europe the term “e-government policy” or “e-
policy,” for short, is
often used to refer to these types of phenomena, whereas in the
USA often the term
“policy informatics” is used. This is similar to that in the USA
the term digital
government is often used, whereas in Europe the term e-
government is preferred.
Policy informatics is defined as “the study of how information
is leveraged and efforts
are coordinated towards solving complex public policy
problems” (Krishnamurthy
et al. 2013, p. 367). These authors view policy informatics as an
emerging research
space to navigate through the challenges of complex layers of
uncertainty within
[email protected]
governance processes. Policy informatics community has
created Listserv called

Policy Informatics Network (PIN-L).
E-government policy-making is closely connected to “data
science.” Data science
is the ability to find answers from larger volumes of
(un)structured data (Davenport
and Patil 2012). Data scientists find and interpret rich data
sources, manage large
amounts of data, create visualizations to aid in understanding
data, build mathemat-
ical models using the data, present and communicate the data
insights/findings to
specialists and scientists in their team, and if required to a
nonexpert audience. These
are activities which are at the heart of policy-making.
1.5 Overview of Chapters
In total 54 different authors were involved in the creation of
this book. Some chapters
have a single author, but most of the chapters have multiple
authors. The authors rep-
resent a wide range of disciplines as shown in Fig. 1.2. The
focus has been on targeting
five communities that make up the core field for ICT-enabled
policy-making. These
communities include e-government/e-participation, information
systems, complex
systems, public administration, and policy research and social
simulation. The com-
bination of these disciplines and communities are necessary to
tackle policy problems
in new ways. A sixth category was added for authors not
belonging to any of these
communities, such as philosophy and economics. Figure 1.3
shows that the authors

are evenly distributed among the communities, although this is
less with the chapter.
Most of the authors can be classified as belonging to the e-
government/e-participation
community, which is by nature interdisciplinary.
Foundation The first part deals with the foundations of the
book. In their Chap. 2
Chris Koliba and Asim Zia start with a best practice to be
incorporated in public
administration educational programs to embrace the new
developments sketched in
EGOV
IS
Complex Systems
Public Administration and
Policy Research
Social Simulation
other (philosophy, energy,
economics, )
Fig. 1.3 Overview of the disciplinary background of the authors
[email protected]
this chapter. They identify two types of public servants that

need to be educated.
The policy informatics include the savvy public manager and
the policy informatics
analyst. This chapter can be used as a basis to adopt
interdisciplinary approaches and
include policy informatics in the public administration
curriculum.
Petra Ahrweiler and Nigel Gilbert discuss the need for the
quality of simulation
modeling in their Chap. 3. Developing simulation is always
based on certain as-
sumptions and a model is as good as the developer makes it.
The user community is
proposed to assess the quality of a policy-modeling exercise.
Communicative skills,
patience, willingness to compromise on both sides, and
motivation to bridge the
formal world of modelers and the narrative world of policy-
makers are suggested as
key competences. The authors argue that user involvement is
necessary in all stages
of model development.
Wander Jager and Bruce Edmonds argue that due to the
complexity that many
social systems are unpredictable by nature in their Chap. 4.
They discuss how some
insights and tools from complexity science can be used in
policy-making. In particular
they discuss the strengths and weaknesses of agent-based
modeling as a way to gain
insight in the complexity and uncertainty of policy-making.
In the Chap. 5, Erik Pruyt sketches the future in which different
systems modeling

schools and modeling methods are integrated. He shows that
elements from policy
analysis, data science, machine learning, and computer science
need to be combined
to deal with the uncertainty in policy-making. He demonstrates
the integration of
various modeling and simulation approaches and related
disciplines using three cases.
Modeling approaches are compared in the Chap. 6 authored by
Dragana Majs-
torovic, Maria A. Wimmer, Roy Lay-Yee, Peter Davis,and Petra
Ahrweiler. Like in
the previous chapter they argue that none of the theories on its
own is able to address
all aspects of complex policy interactions, and the need for
hybrid simulation models
is advocated.
The next chapter is complimentary to the previous chapter and
includes a com-
parison of ICT tools and technologies. The Chap. 7 is authored
by Eleni Kamateri,
Eleni Panopoulou, Efthimios Tambouris, Konstantinos
Tarabanis, Adegboyega Ojo,
Deirdre Lee, and David Price. This chapter can be used as a
basis for tool selecting
and includes visualization, argumentation, e-participation,
opinion mining, simula-
tion, persuasive, social network analysis, big data analytics,
semantics, linked data
tools, and serious games.
Social Aspects, Stakeholders and Values Although much
emphasis is put on mod-
eling efforts, the social aspects are key to effective policy-

making. The role of values
is discussed in the Chap. 8 authored by Andreas Ligtvoet,
Geerten van de Kaa, Theo
Fens, Cees van Beers, Paulien Herder, and Jeroen van den
Hoven. Using the case of
the design of smart meters in energy networks they argue that
policy-makers would
do well by not only addressing functional requirements but also
by taking individual
stakeholder and PVs into consideration.
In policy-making a wide range of stakeholders are involved in
various stages
of the policy-making process. Natalie Helbig, Sharon Dawes,
Zamira Dzhusupova,
Bram Klievink, and Catherine Gerald Mkude analyze five case
studies of stakeholder
[email protected]
engagement in policy-making in their Chap. 9. Various
engagement tools are dis-
cussed and factors identified which support the effective use of
particular tools and
technologies.
The Chap. 10 investigates the role of values and trust in
computational models in
the policy process. This chapter is authored by Rebecca Moody
and Lasse Gerrits. The
authors found that a large diversity exists in values within the
cases. By the authors

important explanatory factors were found including (1) the role
of the designer of
the model, (2) the number of different actors (3) the level of
trust already present,
and (4) and the limited control of decision-makers over the
models.
Bureaucratic organizations are often considered to be inefficient
and not customer
friendly. Tjeerd Andringa presents and discusses a
multidisciplinary framework con-
taining the drivers and causes of bureaucracy in the Chap. 11.
He concludes that the
reduction of the number of rules and regulations is important,
but that motivating
workers to understand their professional roles and to learn to
oversee the impact of
their activities is even more important.
Crowdsourcing has become an important policy instrument to
gain access to
expertise (“wisdom”) outside own boundaries. In the Chap. 12,
Euripids Loukis
and Yannis Charalabidis discuss Web 2.0 social media for
crowdsourcing. Passive
crowdsourcing exploits the content generated by users, whereas
active crowdsourcing
stimulates content postings and idea generation by users.
Synergy can be created by
combining both approaches. The results of passive
crowdsourcing can be used for
guiding active crowdsourcing to avoid asking users for similar
types of input.
Policy, Collaboration and Games Agent-based gaming (ABG) is
used as a tool

to explore the possibilities to manage complex systems in the
Chap. 13 by Wander
Jager and Gerben van der Vegt. ABG allows for modeling a
virtual and autonomous
population in a computer game setting to exploit various
management and leadership
styles. In this way ABG contribute to the development of the
required knowledge on
how to manage social complex behaving systems.
Micro simulation focuses on modeling individual units and the
micro-level pro-
cesses that affect their development. The concepts of micro
simulation are explained
by Roy Lay-Yee and Gerry Cotterell in the Chap. 14. Micro
simulation for pol-
icy development is useful to combine multiple sources of
information in a single
contextualized model to answer “what if” questions on complex
social phenomena.
Visualization is essential to communicate the model and the
results to a variety
of stakeholders. These aspects are discussed in the Chap. 15 by
Tobias Ruppert,
Jens Dambruch, Michel Krämer, Tina Balke, Marco Gavanelli,
Stefano Bragaglia,
Federico Chesani, Michela Milano, and Jörn Kohlhammer. They
argue that despite
the significance to use evidence in policy-making, this is
seldom realized. Three
case studies that have been conducted in two European research
projects for policy-
modeling are presented. In all the cases access for nonexperts to
the computational
models by information visualization technologies was realized.

[email protected]
Applications and Practices Different projects have been
initiated to study the best
suitable transition process towards renewable energy. In the
Chap. 16 by Dominik
Bär, Maria A. Wimmer, Jozef Glova, Anastasia
Papazafeiropoulou,and Laurence
Brooks five of these projects are analyzed and compared. They
please for transferring
models from one country to other countries to facilitate
learning.
Lyudmila Vidyasova, Andrei Chugunov, and Dmitrii Trutnev
present experiences
from Russia in their Chap. 17. They argue that informational,
analytical, and fore-
casting activities for the processes of socioeconomic
development are an important
element in policy-making. The authors provide a brief overview
of the history, the
current state of the implementation of information processing
techniques, and prac-
tices for the purpose of public administration in the Russian
Federation. Finally, they
provide a range of recommendations to proceed.
Urban policy for sustainability is another important area which
is directly linked
to the first chapter in this section. In the Chap. 18, Diego
Navarra and Simona Milio

demonstrate a system dynamics model to show how urban policy
and governance in
the future can support ICT projects in order to reduce energy
usage, rehabilitate the
housing stock, and promote sustainability in the urban
environment. This chapter
contains examples of sustainable urban development policies as
well as case studies.
In the Chap. 19, Tanko Ahmed discusses the digital divide
which is blocking
online participation in policy-making processes. Structuration,
institutional and
actor-network theories are used to analyze a case study of
political zoning. The
author recommends stronger institutionalization of ICT support
and legislation for
enhancing participation in policy-making and bridging the
digital divide.
1.6 Conclusions
This book is the first comprehensive book in which the various
development and disci-
plines are covered from the policy-making perspective driven by
ICT developments.
A wide range of aspects for social and professional networking
and multidisciplinary
constituency building along the axes of technology,
participative processes, gover-
nance, policy-modeling, social simulation, and visualization are
investigated. Policy-
making is a complex process in which many stakeholders are
involved. PVs can be
used to guide policy-making efforts and to ensure that the many
stakeholders have

an understanding of the societal value that needs to be created.
There is an infusion
of technology resulting in changing policy processes and
stakeholder involvement.
Technologies like social media provides a means to interact
with the public, blogs
can be used to express opinions, big and open data provide
input for evidence-based
policy-making, the integration of various types of modeling and
simulation tech-
niques (hybrid models) can provide much more insight and
reliable outcomes, gam-
ing in which all kind of stakeholders are involved open new
ways of innovative policy-
making. In addition trends like the freedom of information, the
wisdom of the crowds,
and open collaboration changes the landscape further. The
policy-making landscape
is clearly changing and this demands a strong need for
interdisciplinary research.
[email protected]
References
Banks J (1998) Handbook of simulation: principles,
methodology, advances, applications, and
practice. Wiley, New York
Bertot JC, Jaeger PT, Hansen D (2012) The impact of polices on
government social media usage:
Issues, challenges, and recommendations. Gov Inform Q 29:30–

40
Burt E (2011) Introduction to the freedom of information
special edition: emerging perspectives,
critical reflections, and the need for further research. Inform
Polit 16(2):91–92.
Coleman S, Moss G (2008) Governing at a distance—politicians
in the blogosphere. Inform Polit
12(1–2):7–20.
Davenport TH, Patil DJ (2012) Data scientist: the sexiest job of
the 21st century. Harv Bus Rev
90(10):70–76
Dawes SS, Janssen M (2013) Policy informatics: addressing
complex problems with rich data, com-
putational tools, and stakeholder engagement. Paper presented
at the 14th annual international
conference on digital government research, Quebec City,
Canada
De Reuver M, Stein S, Hampe F (2013) From eparticipation to
mobile participation: designing a
service platform and business model for mobile participation.
Inform Polit 18(1):57–73
European_Commission (2003) Directive 2003/98/EC of the
European Parliament and of the coun-
cil of 17 November 2003 on the re-use of public sector
information. http://ec.europa.eu/
information_society/policy/psi/rules/eu/index_en.htm. Accessed
12 Dec 2012
Janssen M, Estevez E (2013) Lean government and platform-
based governance—doing more with

less. Gov Inform Quert 30(suppl 1):S1–S8
Janssen M, Charalabidis Y, Zuiderwijk A (2012) Benefits,
adoption barriers and myths of open data
and open government. Inform Syst Manage 29(4):258–268
Janssen M, Estevez E, Janowski T (2014) Interoperability in
big, open, and linked data—
organizational maturity, capabilities, and data portfolios.
Computer 47(10):26–31
Jørgensen TB, Bozeman B (2007) Public values: an inventory.
Adm Soc 39(3):354–381
Koliba C, Zia A (2012) Governance Informatics: using
computer simulation models to deepen
situational awareness and governance design considerations
policy informatics. MIT Press,
Cambridge.
Krishnamurthy R, Bhagwatwar A, Johnston EW, Desouza KC
(2013) A glimpse into policy in-
formatics: the case of participatory platforms that generate
synthetic empathy. Commun Assoc
Inform Syst 33(Article 21):365–380.
Law AM, Kelton WD (1991) Simulation modeling and analysis
2nd ed. McGraw-Hill, New York
Moore MH (1995) Creating public value: strategic management
in government. Harvard University
Press, Cambridge
Obama B (2009) Memorandum for the Heads of executive
Departments and Agencies: trans-
parency and open government. Retrieved February 21, 2013,

from http://www.whitehouse.gov/
the_press_office/Transparency_and_Open_Government
Pidd M (1992) Computer simulation in management science, 3rd
ed. John Wiley, Chichester
Slaviero C, Maciel C, Alencar F, Santana E, Souza P (2010)
Designing a platform to facilitate
the development of virtual e-participation environments. Paper
presented at the ICEGOV ’10
proceedings of the 4th international conference on theory and
practice of electronic governance,
Beijing
Stewart JJ, Hedge DM, Lester JP (2007) Public policy: an
evolutionary approach 3rd edn. Cengage
Learning, Wadsworth
Stoker G (2006) Public value management: a new narrative for
networked governance? Am Rev
Public Adm 3(1):41–57
Surowiecki J (2004) The wisdom of crowds: why the many are
smarter than the few and how
collective wisdom shapes business economies, societies and
nations. Doubleday
Welch EW (2012) The rise of participative technologies in
government. In: Shareef MA, Archer N,
Dwivedi YK, Mishra A, Pandey SK (eds) Transformational
government through eGov practice:
socioeconomic, cultural, and technological issues. Emerald
Group Publishing Limited
[email protected]

http://ec.europa.eu/information_society/policy/psi/rules/eu/inde
x_en.htm.
http://ec.europa.eu/information_society/policy/psi/rules/eu/inde
x_en.htm.
http://www.whitehouse.gov/theprotect LY1extunderscore
pressprotect LY1extunderscore office/Transparencyprotect
LY1extunderscore andprotect LY1extunderscore Openprotect
LY1extunderscore Government
http://www.whitehouse.gov/theprotect LY1extunderscore
pressprotect LY1extunderscore office/Transparencyprotect
LY1extunderscore andprotect LY1extunderscore Openprotect
LY1extunderscore Government
Wimmer MA, Furdik K, Bicking M, Mach M, Sabol T, Butka P
(2012a) Open collaboration in
policy development: concept and architecture to integrate
scenario development and formal
policy modelling. In: Charalabidis Y, Koussouris S (eds)
Empowering open and collaborative
governance. Technologies and methods for online citizen
engagement in public policy making.
Springer, Berlin, pp 199–219
Wimmer MA, Scherer S, Moss S, Bicking M (2012b) Method
and tools to support stakeholder
engagement in policy development the OCOPOMO project. Int J
Electron Gov Res (IJEGR)
8(3):98–119
Zuiderwijk A, Janssen M (2013) A coordination theory
perspective to improve the use of open data
in policy-making. Paper presented at the 12th conference on
Electronic Government (EGOV),

Koblenz
Zuiderwijk A, Helbig N, Gil-García JR, Janssen M (2014)
Innovation through open data—a review
of the state-of-the-art and an emerging research agenda. J Theor
Appl Electron Commer Res
9(2):I–XIII.
[email protected]
Chapter 2
Educating Public Managers and Policy Analysts
in an Era of Informatics
Christopher Koliba and Asim Zia
Abstract In this chapter, two ideal types of practitioners who
may use or cre-
ate policy informatics projects, programs, or platforms are
introduced: the policy
informatics-savvy public manager and the policy informatics
analyst. Drawing from
our experiences in teaching an informatics-friendly graduate
curriculum, we dis-
cuss the range of learning competencies needed for traditional
public managers and
policy informatics-oriented analysts to thrive in an era of
informatics. The chapter
begins by describing the two different types of students who
are, or can be touched
by, policy informatics-friendly competencies, skills, and
attitudes. Competencies
ranging from those who may be users of policy informatics and
sponsors of policy

informatics projects and programs to those analysts designing
and executing policy
informatics projects and programs will be addressed. The
chapter concludes with
an illustration of how one Master of Public Administration
(MPA) program with a
policy informatics-friendly mission, a core curriculum that
touches on policy infor-
matics applications, and a series of program electives that
allows students to develop
analysis and modeling skills, designates its informatics-oriented
competencies.
2.1 Introduction
The range of policy informatics opportunities highlighted in this
volume will require
future generations of public managers and policy analysts to
adapt to the oppor-
tunities and challenges posed by big data and increasing
computational modeling
capacities afforded by the rapid growth in information
technologies. It will be up
to the field’s Master of Public Administration (MPA) and
Master of Public Policy
(MPP) programs to provide this next generation with the tools
needed to harness the
wealth of data, information, and knowledge increasingly at the
disposal of public
C. Koliba (�)
University of Vermont, 103 Morrill Hall, 05405 Burlington, VT,
USA
A. Zia
University of Vermont, 205 Morrill Hall, 05405 Burlington, VT,

USA
© Springer International Publishing Switzerland 2015 15
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI
10.1007/978-3-319-12784-2_2
[email protected]
16 C. Koliba and A. Zia
administrators and policy analysts. In this chapter, we discuss
the role of policy infor-
matics in the development of present and future public
managers and policy analysts.
Drawing from our experiences in teaching an informatics-
friendly graduate curricu-
lum, we discuss the range of learning competencies needed for
traditional public
managers and policy informatics-oriented analysts to thrive in
an era of informatics.
The chapter begins by describing the two different types of
students who are, or can
be touched by, policy informatics-friendly competencies, skills,
and attitudes. Com-
petencies ranging from those who may be users of policy
informatics and sponsors of
policy informatics projects and programs to those analysts
designing and executing
policy informatics projects and programs will be addressed. The
chapter concludes
with an illustration of how one MPA program with a policy
informatics-friendly
mission, a core curriculum that touches on policy informatics

applications, and a
series of program electives that allows students to develop
analysis and modeling
skills, designates its informatics-oriented competencies.
2.2 Two Types of Practitioner Orientations to Policy
Informatics
Drawn from our experience, we find that there are two “ideal
types” of policy infor-
matics practitioner, each requiring greater and greater levels of
technical mastery of
analytics techniques and approaches. These ideal types are:
policy informatics-savvy
public managers and policy informatics analysts.
A policy informatics-savvy public manager may take on one of
two possible roles
relative to policy informatics projects, programs, or platforms.
They may play instru-
mental roles in catalyzing and implementing informatics
initiatives on behalf of their
organizations, agencies, or institutions. In the manner, they may
work with technical
experts (analysts) to envision possible uses for data,
visualizations, simulations, and
the like. Public managers may also be in the role of using policy
informatics projects,
programs, or platforms. They may be in positions to use these
initiatives to ground
decision making, allocate resources, and otherwise guide the
performance of their
organizations.
A policy informatics analyst is a person who is positioned to
actually execute

a policy informatics initiative. They may be referred to as
analysts, researchers,
modelers, or programmers and provide the technical assistance
needed to analyze
databases, build and run models, simulations, and otherwise
construct useful and
effective policy informatics projects, programs, or platforms.
To succeed in either and both roles, managers and analysts will
require a certain set
of skills, knowledge, or competencies. Drawing on some of the
prevailing literature
and our own experiences, we lay out an initial list of potential
competencies for
consideration.
[email protected]
2 Educating Public Managers and Policy Analysts in an Era of
Informatics 17
2.2.1 Policy Informatics-Savvy Public Managers
To successfully harness policy informatics, public managers
will likely not need to
know how to explicitly build models or manipulate big data.
Instead, they will need
to know what kinds of questions that policy informatics projects
or programs can
answer or not answer. They will need to know how to contract
with and/or manage
data managers, policy analysts, and modelers. They will need to
be savvy consumers
of data analysis and computational models, but not necessarily

need to know how to
technically execute them. Policy informatics projects, programs,
and platforms are
designed and executed in some ways, as any large-scale,
complex project.
In writing about the stages of informatics project development
using “big data,”
DeSouza lays out project development along three stages:
planning, execution, and
postimplementation. Throughout the project life cycle, he
emphasizes the role of
understanding the prevailing policy and legal environment, the
need to venture into
coalition building, the importance of communicating the broader
opportunities af-
forded by the project, the need to develop performance
indicators, and the importance
of lining up adequate financial and human resources (2014).
Framing what traditional public managers need to know and do
to effectively
interface with policy informatics projects and programs requires
an ability to be a
“systems thinker,” an effective evaluator, a capacity to integrate
informatics into
performance and financial management systems, effective
communication skills,
and a capacity to draw on social media, information technology,
and e-governance
approaches to achieve common objectives. We briefly review
each of these capacities
below.
Systems Thinking Knowing the right kinds of questions that
may be asked through

policy informatics projects and programs requires public
managers to possess a “sys-
tems” view. Much has been written about the importance of
“systems thinking” for
public managers (Katz and Kahn 1978; Stacey 2001; Senge
1990; Korton 2001).
Taking a systems perspective allows public managers to
understand the relationship
between the “whole” and the “parts.” Systems-oriented public
managers will possess
a level of situational awareness (Endsley 1995) that allows them
to see and under-
stand patterns of interaction and anticipate future events and
orientations. Situational
awareness allows public mangers to understand and evaluate
where data are coming
from, how best data are interpreted, and the kinds of
assumptions being used in
specific interpretations (Koliba et al. 2011). The concept of
system thinking laid out
here can be associated with the notion of transition management
(Loorbach 2007).
Process Orientations to Public Policy The capacity to view the
policy making and
implementation process as a process that involves certain levels
of coordination
and conflict between policy actors is of critical importance for
policy informatics-
savvy public managers and analysts. Understanding how data
are used to frame
problems and policy solutions, how complex governance
arrangements impact policy
implementation (Koliba et al. 2010), and how data visualization
can be used to

[email protected]
18 C. Koliba and A. Zia
facilitate the setting of policy agendas and open policy windows
(Kingdon 1984) is
of critical importance for public management and policy
analysts alike.
Research Methodologies Another basic competency needed for
any public manager
using policy informatics is a foundational understanding of
research methods, par-
ticularly quantitative reasoning and methodologies. A
foundational understanding of
data validity, analytical rigor and relevance, statistical
significance, and the like are
needed to be effective consumers of informatics. That said,
traditional public man-
agers should also be exposed to qualitative methods as well,
refining their powers of
observation, understanding how symbols, stories, and numbers
are used to govern,
and how data and data visualization and computer simulations
play into these mental
models.
Performance Management A key feature of systems thinking as
applied to policy
informatics is the importance of understanding how data and
analysis are to be
used and who the intended users of the data are (Patton 2008).
The integration of
policy informatics into strategic planning (Bryson 2011),

performance management
systems (Moynihan 2008), and ultimately woven into an
organization’s capacity to
learn, adapt, and evolve (Argyis and Schön 1996) are critically
important in this
vein. As policy informatics trends evolve, public managers will
likely need to be
exposed to uses of decision support tools, dashboards, and other
computationally
driven models and visualizations to support organizational
performance.
Financial Management Since the first systemic budgeting
systems were put in place,
public managers have been urged to use the budgeting process
as a planning and eval-
uation tool (Willoughby 1918). This approach was formally
codified in the 1960s
with the planning–programming–budgeting (PPB) system with
its focus on plan-
ning, managerial, and operational control (Schick 1966) and
later adopted into more
contemporary approaches to budgeting (Caiden 1981). Using
informative projects,
programs, or platforms to make strategic resource allocation
decisions is a necessary
given and a capacity that effective public managers must
master. Likewise, the pol-
icy analyst will likely need to integrate financial resource flows
and costs into their
projects.
Collaborative and Cooperative Capacity Building The
development and use of pol-
icy informatics projects, programs, or platforms is rarely, if
ever, undertaken as

an individual, isolated endeavor. It is more likely that such
initiatives will require
interagency, interorganizational, or intergroup coordination. It
is also likely that
content experts will need to be partnered with analysts and
programmers to com-
plete tasks and execute designs. The public manager and policy
analyst must both
possess the capacity to facilitate collaborative management
functions (O’Leary and
Bingham 2009).
Basic Communication Skills This perhaps goes without saying,
but the heart of any
informatics project lies in the ability to effectively
communicate findings and ideas
through the analysis of data.
[email protected]
2 Educating Public Managers and Policy Analysts in an Era of
Informatics 19
Social Media, Information Technology, and e-Governance
Awareness A final com-
petency concerns public managers’ capacity to deepen their
understanding of how
social media, Web-based tools, and related information
technologies are being em-
ployed to foster various e-government, e-governance, and
related initiatives (Mergel
2013). Placing policy informatics projects and programs within
the context of these
larger trends and uses is something that public managers must

be exposed to.
Within our MPA program, we have operationalized these
capacities within a four-
point rubric that outlines what a student needs to do to
demonstrate meeting these
standards. The rubric below highlights 8 of our program’s 18
capacities. All 18 of
these capacities are situated under 1 of the 5 core competencies
tied to the accred-
itation standards of the Network of Schools of Public Affairs
and Administration
(NASPAA), the professional accrediting association in the USA,
and increasingly in
other countries as well, for MPA and MPP programs. A
complete list of these core
competencies and the 18 capacities nested under them are
provided in Appendix of
this chapter.
The eight capacities that we have singled out as being the most
salient to the role
of policy informatics in public administration are provided in
Table 2.1. The rubric
follows a four-point scale, ranging from “does not meet
standard,” “approaches
standard,” “meets standard,” and “exceeds standard.”
2.2.2 Policy Informatics Analysts
A second type of practitioner to be considered is what we are
referring to as a “policy
informatics analyst.” When considering the kinds of
competencies that policy infor-
matics analysts need to be successful, we first assume that the
basic competencies

732AIDS PATIENT CARE and STDsVolume 21, Number 10, 2007.docx

732AIDS PATIENT CARE and STDsVolume 21, Number 10, 2007.docx

Recommended

Recommended

More Related Content

Similar to 732AIDS PATIENT CARE and STDsVolume 21, Number 10, 2007.docx

Similar to 732AIDS PATIENT CARE and STDsVolume 21, Number 10, 2007.docx (20)

More from blondellchancy

More from blondellchancy (20)

Recently uploaded

Recently uploaded (20)

732AIDS PATIENT CARE and STDsVolume 21, Number 10, 2007.docx