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South African Case Study Model Report Shah Jamal Alam, Ruth Meyer, Scott Moss Centre for Policy Modelling, MMU CAVES Project Meeting March 2007 ● CPM 1 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 2 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 3 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 4 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 5 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 6 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 CAVES Project Meeting March 2007 ● CPM 7 Declarative Model – Visualisations CAVES Project Meeting March 2007 ● CPM 8 Declarative Model – Visualisations CAVES Project Meeting March 2007 ● CPM 9 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 10 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 11 Experiences with Jess – Example: Fewer facts (defclass person Person) has slots name, gender, age, tag… knownPersons (deftemplate known-person (slot owner) (slot known) (slot tick)) Replace facts with field (slot) (defrule adult-similarity-identification "identify others with most similar and similar tags" ) (person (tag $?own-tag) (name ?person) (gender ?gender) (knownPersons $?known-persons) (age ?own-age &: (> ?own-age 12))) (model (tick ?tick)) (known-person (owner ?person) (known ?other) (tick ?t &:(<= ?t ?tick))) (person (gender ?gender) (name ?other) &: (member$ ?other ?known-persons)) (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))) CAVES Project Meeting March 2007 ● CPM 12 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 CAVES Project Meeting March 2007 ● CPM 13