An Agent-Based Approach to Evaluating the Effect of

Dynamic Age Changes on Community Acceptance of

Mining Projects

1

Mark K. 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

Presentation Outline

Background

Methodology

Validation

Case Study

Conclusions & Future Work

2

Background

3

Why Local Communities Opposing Mining?

General Causes:

Social and cultural change

Economic change

Socio Environmental change

The process of change

(Davis & Franks, 2011)

Background

4

Effects of Local Community Conflicts on Mining Based on 64 Studied

Cases:

(Davis & Franks, 2011)

Background Literature

5

(IFC, 2007) : Stakeholder Engagement Principle

(Que & Awuah-Offei , 2014): Framework for Mining Community Consultations

(ICMM , 2012): Community Development Toolkit

(Thomson & Boutilier , 2011): Social License to Operate

(Ivanova and Rolfe , 2011): Assessing Development Options in Mining

Community Using Stated Preference Techniques

The existing work done by other researchers have focused

more on static and qualitative approach to evaluating

community acceptance of mining project

Background Literature (Work done by Ivanova & Rolfe)

6Source: (Ivanova and Rolfe, 2011)

R

Results of the Survey:

43% of the respondent preferred Option A

32% of the respondent chose Option B

25% of the respondent selected Option C

Motivation

Lack of community acceptance leads to costof mining.

The local community’s degree of acceptanceis a complicated function of demographicsand mine characteristics over time (Ivanova& Rolfe, 2011; Que & Awuah-Offei, 2014)

A challenge to quantify community supporthas been a concern (Davis & Franks, 2011).

Mine engineers and managers need the toolsto understand the inter-relationship betweenproject & dynamic community acceptance

7

Exploration & permitting

Development

Exploitation

Closure & reclamation

Objectives

To present an agent-based model

(ABM) framework for estimating local

community acceptance of mining

project.

Using the ABM framework to evaluate

dynamic local community acceptance

of a mining project as a function of

demographic factors such as age

The hypothesis for this study is to

quantitatively predict dynamic

community acceptance of a mining

project using Agent-Based Model

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MethodologyAgent Based Model (ABM)

Elements of Agent-Based Model:

Agents are computational entities that make

decisions based on their relationship with

other agents and environment

Agent’s environment: Agents interact with

their environment, defined by a set of

common variables

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

(Macal & North, 2010)

Odds Ratio = > 1: means an agent accepts the option (proposal)

Odds Ratio < 1: means an agent does not accept the option (proposal)

Modeling Community Acceptance of Mining Project

Using ABM

Agent: Individuals in the community older than 18

Environment: variables to describe the status quo and proposed action

Agent’s Autonomy: Utility function based on discrete choice modeling

Odds Ratio =

10

1 1

expn n

p b o m

i i j ji ji j

x x a a

Flowchart

The agent-based modeling of dynamic local community acceptance built in

MATLAB 7.7 (2012).

11

Validation

Modeling Framework was validated using data contained in Ivanova and Rolfe (2011)

The data was analyzed to define values for agent’s attributes and environment

attributes

The validation was based on the following 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 attributes: Housing cost; water restrictions; population in camps; mine

impacts; additional household costs; infrastructure improvement

Number of Iterations: 100

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Validation Input Data- (Agent Attributes)

Agent’s Attributes Coefficient, 𝛃 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

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Source: (Ivanova and Rolfe ,2011)

Validation Input Data –(Environment Attributes)Environment

Attributes

Option A Option B Option C Coefficient

𝛃

Base case

𝑋𝑏Proposal

𝑋𝑝Base case

𝑋𝑏Proposal

𝑋𝑝Basecase

𝑋𝑏Proposal

𝑋𝑝

Housing Pricing 2 2 2 2 2 1 0.284 **

Water Restrictions1 1 1 2 1 3 0.218*

Population in

Camps2 2 2 3 2 2 1.583**

Buffer for mine

impacts1 1 1 2 1 2 0.248**

Additional

household cost0 0 0 250 0 1000 0.001***

Infrastructure

Improvement2 2 2 2 2 2 0.025***

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Validation- Example

Environment

Attributes

Option B Coefficient

𝛃

Proposal

𝑋𝑝Base case

𝑋𝑏

Housing

Pricing2 2 0.284 **

Water

Restrictions2 1 0.218*

Population in

Camps3 2 1.583**

Buffer for mine

impacts2 1 0.248**

Additional

household cost250 0 0.001***

Infrastructure

Improvement2 2 0.025***

15Odds Ratio =

Agent’s

Attributes

Coefficient

𝛃Median

Age (years) 0.037 *** 38

Gender 1.24 *** 0.5

Enjoy Living

in the

community

(years)

0.21* 0.5

Number of

Children0.26*** 2

Length of

Residence

(years)-0.10 * 5

Monthly

Spending ($)-0.01** 2200

Results and Discussion (Framework)

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Results of the Survey:

43% of the respondent preferred Option A

32% of the respondent chose Option B

25% of the respondent selected Option C

Results & Discussion(Framework)

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 utility function obtained by

Ivanova & Rolf ‘s work (Ivanova & Rolf, 2011) using odds

ratio

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Case Study

This was carried out using already built modeling

framework.

The evaluation was achieved by varying birth rate,

mortality rate and length of residence

The results were compared to option A results (status quo)

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Input Data

Birth and mortality rates were increased and decreased by 10%

Increasing the percentage of new entrants (>5years) by 10%

Increasing the percentage of new entrants (>5years) by 20%

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Results and Discussion

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Results and Discussion

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The results show that over the five years, there is only a

marginal change in support, decreasing from 46 to 44%.

There is very slight change in support as the birth and death

rates are increased.

Increasing the number of new entrants reduces the level of

support more than the other two factors.

Conclusions & Future Work

An agent based model (ABM) framework for estimating local community

acceptance of mining has been successfully demonstrated

This study indicates that age and associated demographics on their own do not

significantly affect the acceptance of mining project in the model

This work has successfully used odds ratio to model utility function

It is therefore recommended that future work will incorporate mine

characteristics and environmental aspects that change over time

It is expected that this work would assist investors and stakeholders to

understand drivers of community acceptance early in project planning and

design

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