Presented at Society for Mining, Metallurgy & Exploration (SME) 2014 Annual Conference

1. 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, MO
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2. Presentation Outline
 Motivation & background
 Objectives
 Methodology
 Framework for Modeling Dynamic Community Acceptance
 Validation
 Conclusions & Future Work
2

3. Motivation
Source: http://www.youtube.com/watch?v=9L2q2H7VqJc
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4. 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.
Exploration
& permitting
Development
Exploitation
 Mine engineers and managers need all the tools
to understand the inter-relationship between
project & dynamic community acceptance
P roject characteristics, P t
P roject im pacts, I t
C om m unity dem ographics, D t
C om m unity acceptance, A t
Closure &
reclamation
f1 t
f2 P t
f3 P t , I t , t
4
f4 D t , I t , P t

5. 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).
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6. 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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7. 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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8. Agent Based Model
Agent Interactions
with Other Agents
Elements of Agent-Based Model:
 A set of agents, their attributes and
behavior
 A set of relationships and methods of
interaction: topology
Agent Attributes:
 Static: name, gender…
 Dynamic: memory, resources
Age
Methods:
 Behaviors
 Behaviors that modify behaviors
 Update rules for dynamic attributes
 Agent’s environment: Agents interact
with their environment, defined by a set
of common variables
Agent Interactions with
the Environment
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9. Agent Based Model
Agent Interactions
with Other Agents
Other Features:
 Agent Methods: Link the agent’s
situation with action or set of potential
actions
Agent Attributes:
 Static: name, gender…
 Dynamic: memory, resources
 Agents are autonomous: Being capable
of making independent decisions
Methods:
 Behaviors
 Behaviors that modify behaviors
 Update rules for dynamic attributes
Age
• Utility function vs. agent state
Agent Interactions with
the Environment
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10. 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
n
O dds ratio
exp
xi
p
b
xi
i
i 1
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11. Methodology
 The agent-based modeling
of local community
Step 1:
Read and define
model input data
Step 2:
Initialize the
agent's
environment
Step 3:
Initialize the
agents
acceptance done in
MATLAB 7.7 (2012).
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
Step 6:
Repeat steps 3, 4 and 5 for N
number of iterations
Agent supports
the project
Step 7:
Average the results and
Terminate the iteration
Is agent’s
Odds ratio > 1
YES
No
Agent does
not Support
the project
Step 8:
Report and analyse the
results to determine the
acceptance or rejection of
the project
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12. 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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13. 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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14. 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
-0.10
5
0.01
2200
Length of Residence (years)
Monthly Spending ($)
Source: Ivanova and Rolfe (2011)
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15. Interpreting Ivanova and Rolfe 2011 Data
Attributes and levels for the choice sets
Attributes
Levels
Additional annual costs to the $0 (base), $250, $500, $1,000
household
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
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16. Interpreting Ivanova and Rolfe 2011 Data
Attributes and levels for the choice sets
Attributes
Levels
Buffer for mine impacts close 1.
to town
2.
3.
Population in work camps
Moderate impacts from noise, vibration
and dust (base)
Slight impacts from noise, vibration and dust
No additional impacts
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
Respondents were presented with Options A, B & C and 43%, 32%, and 25%
chose A, B & C, respectively
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17. 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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18. Results and Discussion
Option A: Mean support over 100
iterations is 50%
Option B: Mean support over 100
iterations is 57%
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19. Model Results and Discussion
Option C: Mean support over 100 iterations is
48%
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20. 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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21. 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
 This research will facilitate better
understanding of community
acceptance for all stakeholders.
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