CAVES Project Meeting March 2007 ● CPM 1

South African Case StudyModel Report

Shah Jamal Alam, Ruth Meyer, Scott MossCentre for Policy Modelling, MMU

CAVES Project Meeting March 2007 ● CPM 2

Table of Contents

Declarative Model• Current state of the model• Experiences with Jess• Next steps

Dynamic Networks Analysis• Kolmogorov-Smirnov Test• Motifs

CAVES Project Meeting March 2007 ● CPM 3

Declarative Model – Description

Individuals, households and villages• Distributions extracted from empirical data (RADAR)

• Household size: Normal (7, 3)• Household head age: Normal (56.2, 12.6)• Marital status of household head: Empirical discrete,

different for female and male heads• Age difference between spouses: Normal (8.43, 6.576)• Type of household member: Empirical discrete

(child 0.653, grandchild 0.238, other 0.109)• Age of household member: Gamma (2.4, 0.086)• Number, age and gender of migrants: Empirical

discrete

Decisions / behaviour on individual and household level• Rules for individuals• Rules for households

CAVES Project Meeting March 2007 ● CPM 4

Declarative Model – Groups

Church• Importance rated very high according to RADAR data• 80% of population are member of a church• Implementation so far based on assumptions:

• 1-4 denominations per village, 1 church / denomination• Households randomly assigned to churches in their village• All members of a household belong to the same church

Stokvel (ROSCA)• Third highest in importance (if there is no other financial support like

SEF)• Provide means to save up for a particular purpose• Social aspect important: provide social support, enhance social status• Risk of default is low in small communities

• Defaulters are unlikely to be accepted as members into any other associations

• Formed between groups of friends, min. 3-8 Burial society• Second highest in importance, more formal than stokvels• Next to be implemented

CAVES Project Meeting March 2007 ● CPM 5

Declarative Model – Household Rules

• Household economy, modelled on a monthly scale, largely based on assumptions• Food expenses: 120 Rand / 100 Rand / 25 Rand • Income from state grants: 870 Rand pension / 200

Rand child grant• Income from jobs: 800 Rand / 200 Rand• Income from remittances: ?

• Households buy bulk food at the beginning of each month• Spend minimum of accumulated food expenses and

available cash• "Rich" households offer short-term employment ("piece

jobs")• if they can afford it and• if they need it (modelled stochastically, p = 0.15)

CAVES Project Meeting March 2007 ● CPM 6

Declarative Model – Individual Rules

Endorsements• Every agent endorses other agents with certain "labels"• Related to existing links

• Kinship: is-kin• Neighbourhood: is-neighbour• Groups like churches: same-church, same-denomination

• Related to behaviour of other agent• Reliable, trustworthy, honest, capable, recommended• Unreliable, untrustworthy, dishonest, incapable

• Labels are evaluated according to an individual's endorsement scheme• Resulting endorsement value is used in decisions

Friendship

Stokvels• Only household heads are members• When there is enough money left, household heads express a desire to

form a stokvel and ask other household heads amongst their friends• If there is consent between a certain number of friends, they start a

stokvel

CAVES Project Meeting March 2007 ● CPM 7

Declarative Model – Networks

Multi-layer network on several levels• Individual level

• Friendship• Based on endorsements and tags, evolves

dynamically• Acquaintanceship

• Based on group membership• Family (parent, child, sibling)

• Set at creation of person, based on empirical data• Household level

• Kinship• Based on small-world network

• Neighbourhood• Based on spatial location within village, assigned

randomly at creation

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Declarative Model – Visualisations

CAVES Project Meeting March 2007 ● CPM 9

Declarative Model – Visualisations

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Declarative Model – Friendship Network

• Assumptions used: Friends have• same gender• similar age (± 3 years for children, ± 8 years for adults)• similar interests/character traits• similar background (same church, neighbour…)

• Friendship network evolves from these• Agents evaluate all known other agents

• Compute similarity index based on tags• Compute endorsement value based on endorsement

scheme• Agents pick highest evaluated agents as friends

• Up to a maximal number of friends

Surprising effect: very low proportion of mutual links• Solutions tried:

• Special friendship endorsement scheme• Higher max. number of friends

CAVES Project Meeting March 2007 ● CPM 11

Experiences with Jess

Model implementation• Java/Repast for model framework• Jess for all cognition and decision processes• Java classes (Person, Household, Model…) as shadow facts • Per time step one run of the Jess engine

Too slow to be actually used

Problem: Re-computation of the Rete network

Solution: less Jess, more Java• Fewer rules

• Port procedural stuff to Java• Browse fact base from Java

• Fewer facts• Replace facts with fields in Java classes (slots in shadow

facts)

CAVES Project Meeting March 2007 ● CPM 12

Experiences with Jess – Example: Fewer facts

(defclass person Person) has slots name, gender, age, tag…(deftemplate known-person (slot owner) (slot known) (slot tick))

(defrule adult-similarity-identification "identify others with most similar and similar tags" (person (tag $?own-tag) (name ?person) (gender ?gender)

(age ?own-age &: (> ?own-age 12))) (model (tick ?tick)) (known-person (owner ?person) (known ?other) (tick ?t &:(<= ?t ?tick))) (person (gender ?gender) (name ?other) (age ?other-age &:(and (> ?other-age 12) (< (abs (- ?own-age ?other-age)) 8))) (tag $?other-tag)) (not (similarity-index (owner ?person)(other-person ?other)(tick ?t &: (< ?t ?tick)))) => (bind ?similarity (number-of-common-attributes ?own-tag ?other-tag)) (assert (similarity-index (other-person ?other) (similarity ?similarity) (owner ?person)

(tick ?tick))))

knownPersons

(knownPersons $?known-persons)

&: (member$ ?other ?known-persons))

Replace facts with field (slot)

CAVES Project Meeting March 2007 ● CPM 13

Next steps

Integration of further processes that influence social networks

• Burial societies• Marriage• Inheritance of (part of the) tags from parents• Spread of HIV/AIDS, if possible on a more individual

basis

Applying network measures

Improve visualisation and data collection• Discuss need with case study team