Agent Based Modeling Framework for
Community Acceptance of Mining Projects

Mark Boateng,
PhD Student, Department of Minin...
Presentation Outline
 Motivation & background
 Objectives
 Methodology
 Framework for Modeling Dynamic Community Accep...
Motivation

Source: http://www.youtube.com/watch?v=9L2q2H7VqJc

3
Motivation
 The local community’s acceptance of a project
is crucial for success.
 The local community’s degree of accep...
Background Literature
1.

Understanding of the relationship between

mines and community acceptance
 Assessing and addres...
Background Literature
2. Agent-Based Modeling:
 Overview and some applications:
 North and Macal (2007); Valbuena et al....
Objective
 To present an agent-based model (ABM) for
estimating degree of community acceptance of

a mining project.
 To...
Agent Based Model
Agent Interactions
with Other Agents

Elements of Agent-Based Model:
 A set of agents, their attributes...
Agent Based Model
Agent Interactions
with Other Agents

Other Features:
 Agent Methods: Link the agent’s
situation with a...
Methodology

 Agent: Individuals in the community older than 18
 Topology: Being in the same community interacting (no s...
Methodology
 The agent-based modeling
of local community

Step 1:
Read and define
model input data

Step 2:
Initialize th...
Framework for Modeling Dynamic Community
Acceptance of Mining Projects
 Use the current model as a basis for dynamic simu...
Validation
 Data contained in Ivanova and Rolfe (2011) was used to validate the modeling
framework
 The data was analyze...
Agent Characteristics
Agent’s Characteristics

Median

Age (years)

0.037

38

Gender

1.24

0.5

Enjoy Living in the comm...
Interpreting Ivanova and Rolfe 2011 Data
Attributes and levels for the choice sets
Attributes

Levels

Additional annual c...
Interpreting Ivanova and Rolfe 2011 Data
Attributes and levels for the choice sets
Attributes

Levels

Buffer for mine imp...
Simulation Input
Environment
Attributes

Option A

Option B

Option C

Housing Pricing

2

2

2

2

2

1

0.284

Water Res...
Results and Discussion

Option A: Mean support over 100
iterations is 50%

Option B: Mean support over 100
iterations is 5...
Model Results and Discussion

Option C: Mean support over 100 iterations is
48%
19
Further Discussions
 The model appears to perform well when only demographic factors play a role.
 Model confirms Option...
Conclusions & Future Work
 Agent-based model of local community acceptance of mining project has been

developed & valida...
22
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An Agent Based Modeling Framework for Community Acceptance of Mining Projects

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

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An Agent Based Modeling Framework for Community Acceptance of Mining Projects

  1. 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 1
  2. 2. Presentation Outline  Motivation & background  Objectives  Methodology  Framework for Modeling Dynamic Community Acceptance  Validation  Conclusions & Future Work 2
  3. 3. Motivation Source: http://www.youtube.com/watch?v=9L2q2H7VqJc 3
  4. 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. 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). 5
  6. 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) 6
  7. 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 7
  8. 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 8
  9. 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 9
  10. 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 10
  11. 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 11
  12. 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 12
  13. 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 13
  14. 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) 14
  15. 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 15
  16. 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 16
  17. 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 17
  18. 18. Results and Discussion Option A: Mean support over 100 iterations is 50% Option B: Mean support over 100 iterations is 57% 18
  19. 19. Model Results and Discussion Option C: Mean support over 100 iterations is 48% 19
  20. 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) 20
  21. 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. 21
  22. 22. 22
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