1. CAVES Project Meeting March 2007 ● CPM1 South African Case Study Model Report Shah Jamal Alam, Ruth Meyer, Scott Moss Centre for Policy Modelling, MMU

2. CAVES Project Meeting March 2007 ● CPM2 Table of Contents Declarative Model Current state of the model Experiences with Jess Next steps Dynamic Networks Analysis Kolmogorov-Smirnov Test Motifs

3. CAVES Project Meeting March 2007 ● CPM3 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

4. CAVES Project Meeting March 2007 ● CPM4 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

5. CAVES Project Meeting March 2007 ● CPM5 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)

6. CAVES Project Meeting March 2007 ● CPM6 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

7. CAVES Project Meeting March 2007 ● CPM7 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

8. CAVES Project Meeting March 2007 ● CPM8 Declarative Model – Visualisations

9. CAVES Project Meeting March 2007 ● CPM9 Declarative Model – Visualisations

10. CAVES Project Meeting March 2007 ● CPM10 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

11. CAVES Project Meeting March 2007 ● CPM11 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)

12. CAVES Project Meeting March 2007 ● CPM12 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)

13. CAVES Project Meeting March 2007 ● CPM13 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