Agent Based Modeling Framework for
Community Acceptance of Mining Projects
Mark Boateng,
PhD Student, Department of Mining & Nuclear Engineering
Missouri S&T, Rolla, MO
Dr. Kwame Awuah-Offei
Associate Professor, Department of Mining & Nuclear Engineering
Missouri S&T, Rolla, MO1
Presentation Outline
Motivation & background
Objectives
Methodology
Framework for Modeling Dynamic Community Acceptance
Validation
Conclusions & Future Work
2
Motivation
3Source: http://www.youtube.com/watch?v=9L2q2H7VqJc
Motivation
The local community’s acceptance of a project
is crucial for success.
The local community’s degree of acceptance is
a complicated function of demographics and
mine characteristics over the project life cycle.
Mine engineers and managers need all the tools
to understand the inter-relationship between
project & dynamic community acceptance
4
Exploration & permitting
Development
Exploitation
Closure & reclamation
1
2
3
4
Project characteristics,
P roject im pacts,
C om m unity dem ographics, , ,
C om m unity acceptance, , ,
P t f t
I t f P t
D t f P t I t t
A t f D t I t P t
Background Literature
1. Understanding of the relationship between
mines and community acceptance
Assessing and addressing impacts of mining on
the community:
Ivanova et al. (2007); Petkova et al. (2009).
Handling and Promoting and maintaining
sustainable development:
Estves (2007); Temeng et al. (2009); Guaerra
(2002); Tuck et al. (2005).
5
Background Literature
2. Agent-Based Modeling:
Overview and some applications:
North and Macal (2007); Valbuena et al. (2008); Delre et al.(2007); Torres
(2006); Gilbert (2007)
3. Discrete Choice Modeling to motivate the agent utility function:
Que and Awuah-Offei (2013)
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Objective
To present an agent-based model (ABM) for
estimating degree of community acceptance of
a mining project.
To present an ABM framework for estimating
dynamic degree of community acceptance
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Agent Based Model
Elements of Agent-Based Model:
A set of agents, their attributes and
behavior
A set of relationships and methods of
interaction: topology
Agent’s environment: Agents interact
with their environment, defined by a set
of common variables
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Age
Agent Interactions
with Other Agents
Agent Interactions with
the Environment
Agent Attributes:
Static: name, gender…
Dynamic: memory, resources
Methods:
Behaviors
Behaviors that modify behaviors
Update rules for dynamic attributes
Agent Based Model
Other Features:
Agent Methods: Link the agent’s
situation with action or set of potential
actions
Agents are autonomous: Being capable
of making independent decisions
• Utility function vs. agent state
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Age
Agent Interactions
with Other Agents
Agent Interactions with
the Environment
Agent Attributes:
Static: name, gender…
Dynamic: memory, resources
Methods:
Behaviors
Behaviors that modify behaviors
Update rules for dynamic attributes
Methodology
Agent: Individuals in the community older than 18
Topology: Being in the same community interacting (no social interaction…yet)
Environment: variables to describe the status quo and proposed action
Agent’s Autonomy: Utility function based on discrete choice modeling
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1
O dds ratio exp
n
p b
i i i
i
x x
Methodology
The agent-based modeling
of local community
acceptance done in
MATLAB 7.7 (2012).
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Step 1:
Read and define
model input data
Step 2:
Initialize the
agent's
environment
Step 4:
Evaluate the odds ratio
to determine agent's
highest utility
Step 5:
Repeat the odds ratio evaluation
for the number of agents and
deduce the % in support or
against the project
Is agent’s
Odds ratio > 1
NoAgent does
not Support
the project
YESAgent supports
the project
Step 3:
Initialize the
agents
Step 6:
Repeat steps 3, 4 and 5 for N
number of iterations
Step 7:
Average the results and
Terminate the iteration
Step 8:
Report and analyse the
results to determine the
acceptance or rejection of
the project
Framework for Modeling Dynamic Community
Acceptance of Mining Projects
Use the current model as a basis for dynamic simulations.
Dynamic simulations achieved by changing demographics and
environment over time
Manage computational efficiency
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Validation
Data contained in Ivanova and Rolfe (2011) was used to validate the modeling
framework
The data was analyzed to define values for agent’s attributes and environment attributes
Model Assumptions:
Agent utility depends on the following attributes and environment variables
Agent attributes: age, gender, enjoys living in community, no. of children,
length of residence, monthly spending
Environment variables: Housing cost; water restrictions; population in camps;
mine impacts; additional household costs; infrastructure improvement
Number of Iterations: 100
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Agent Characteristics
Agent’s Characteristics Median
Age (years) 0.037 38
Gender 1.24 0.5
Enjoy Living in the community
(years) 0.21 0.5
Number of Children 0.26 2
Length of Residence (years)-0.10 5
Monthly Spending ($) 0.01 2200
14Source: Ivanova and Rolfe (2011)
Interpreting Ivanova and Rolfe 2011 Data
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Attributes Levels
Additional annual costs to the
household
$0 (base), $250, $500, $1,000
Housing and rental prices 1. 25% increase
2. No change (base)
3. 25% decrease
Level of water restrictions 1. Some for households, town parks and
gardens are drier than now (base)
2. None for households, town parks and gardens
are drier than now
3. None for households, town parks and gardens
are greener than now
Attributes and levels for the choice sets
Interpreting Ivanova and Rolfe 2011 Data
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Attributes Levels
Buffer for mine impacts close
to town
1. Moderate impacts from noise, vibration
and dust (base)
2. Slight impacts from noise, vibration and dust
3. No additional impacts
Population in work camps 1. No more housing and 5000 in work camps
2. 1000 in housing and 4000 in work camps
(base)
3. 4000 in housing and 1000 in work camps
Attributes and levels for the choice sets
Respondents were presented with Options A, B & C and 43%, 32%, and 25%
chose A, B & C, respectively
Simulation Input
Environment
Attributes
Option A Option B Option C
Housing Pricing 2 2 2 2 2 1 0.284
Water Restriction 1 1 1 2 1 3 0.218
Population in Camps 2 2 2 3 2 2 1.583
Mine Impacts 1 1 1 2 1 2 0.248
Additional
household cost 0 0 0 250 0 1000 -0.001
Infrastructure
Improvement 2 2 2 2 2 2 0.025
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Results and Discussion
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Option A: Mean support over 100
iterations is 50%
Option B: Mean support over 100
iterations is 57%
Model Results and Discussion
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Option C: Mean support over 100 iterations is
48%
Further Discussions
The model appears to perform well when only demographic factors play a role.
Model confirms Option B is preferred to Option C.
Option A (status quo) is preferred to Option C.
Model appears to validate the percentage of the community in support of
mining (43% & 48% when compared to Options B and C, respectively)
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Conclusions & Future Work
Agent-based model of local community acceptance of mining project has been
developed & validated
The proposed framework would facilitate modeling dynamic community
acceptance
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This research will facilitate better
understanding of community
acceptance for all stakeholders.
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