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)

6

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

8

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

9

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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