icegov2012 proceedings

Use of Agent-Based Modeling for e-Governance Research
Yushim Kim
Arizona State University
411 N. Central Ave. Ste. 400
Phoenix, AZ 85004
1-602-496-1157
ykim@asu.edu
ABSTRACT
This tutorial focuses on the basic functionalities of an emerging
simulation modeling technique (i.e., agent-based modeling), the
use of the tool for modeling a managerial issue, and the analysis
of simulation data for electronic governance (e-governance)
research. It also articulates potential questions that may be raised
in each step of modeling. The tutorial ends with perspectives on
critical subjects in ABM.
Categories and Subject Descriptors
I.6.5 [Simulation and Modeling]: Model Development
General Terms
Management, Performance
Keywords
Agent-Based Modeling, e-Governance
1. INTRODUCTION
The idea that the world we inhabit is complex is not new, and the
problems policy makers and public managers deal with are quite
challenging. Leveraging upon advances in information and
technology, electronic governance (e-governance) initiatives have
been proposed to provide better approaches to some of the
complexities that public managers are confronting. However, even
our most current knowledge is often limited in conceptualizing,
capturing, and analyzing the world and its problems in a way that
is useful for decision makers.
Modeling and simulation approaches are useful for researchers,
policy makers, and stakeholders in thinking through, defining, and
analyzing the world, system, or problems that concern the world
or a system. Computational tools and approaches can aid in
crystalizing the thought process related to complex problems [1].
This tutorial aims to help those who are interested in approaching
e-governance topics with simulation using computational tools by
articulating the basic steps required. The steps are discussed using
the example of a public service delivery model.
Permission to make digital or hard copies of part or all of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies
bear this notice and the full citation on the first page. Copyrights for
components of this work owned by others than ACM must be honored.
Abstracting with credit is permitted. To copy otherwise, to republish, to
post on servers or to redistribute to lists, requires prior specific permission
and/or a fee.
ICEGOV '12, October 22 - 25 2012, Albany, NY, USA
Copyright 2012 ACM 978-1-4503-1200-4/12/10...$15.00
Callie McGraw
Arizona State University
411 N. Central Ave. Ste. 400
Phoenix, AZ 85004
1-623-363-6275
callie.mcgraw@asu.edu
531
2. LEARNING OBJECTIVE
The objective of this tutorial is to introduce a way for researchers
and practitioners in e-governance to interact with an agent-based
modeling approach in order to better understand complex
problems and systems that they deal with. Attention is given to a
specific topic in public management on which the authors have
worked (i.e., fraud detection in public service deliveries) so the
audience can easily contextualize this approach. The expected
learning outcome is an increase in e-governance researchers’
interest in computational modeling.
3. AGENT-BASED MODELING (ABM)
3.1 Why ABM?
ABM has been popularized among social science disciplines with
the explosion of computational technologies. From a social
science research point of view, one of the most significant
features of ABM is not that researchers can work with a primitive
data type—numbers and characters—that is similar to other
computer programming languages, but that ABM allows
researchers to define other types of entities (e.g., individuals,
schools, or stores) and the interactions that are relevant to one’s
own context. This provides a unique opportunity to introduce not
only individuals but also important entities and objects necessary
for an autonomous decision-making unit in social simulations.
With ABM, researchers can frame their problems within large,
dynamic social processes. In doing so, they must carefully
consider social actors, interactions, interdependencies, and
processes among heterogeneous decision-making units. If
properly done, this can enhance the fidelity of simulation models
and aid in understanding the nature of complex social problems.
3.2 What is ABM?
Artificial worlds built as simulation models whose dynamics can
be observed, tested, and retested serve as flexible research and
decision support platforms. ABM has the unique benefit of
enabling the investigation of mechanisms at micro-levels that can
lead to dynamic patterns of social systems at the macro-level.
Thus, the goal of this approach is often stated as exploring social
processes and mechanisms—the rules, interactions, and
feedback—that can lead to aggregate systemic patterns.
Conceptually, ABM requires two components: agents and
interaction rules. ABM consists of ‘agents’ who can make
autonomous and adaptive decisions using local information and
‘rules’ that elicit social mechanisms and processes. ABM can
accommodate heterogeneous agent types and asymmetries in
social systems, and can be easily scaled to a larger system with
many agents.