Agent-Based Models within spatial information science – possible applications and methods

1. Agent-Based Models within spatial information science – possible applications and methods Charlotte Bruun

2. Object-oriented programming • Et objekt er en software enhed der rummer attributter + metoder • Objekter "kommunikerer med hinanden gn. metoder • Agent-baseret modellering hænger uløseligt sammen med objekt-orienteret programmering • Distributed software crises • ”Computing hardware and networks get smaller, faster and cheaper, yet distributed software gets larger, slower and more expensive to develop! ”(fayad og Schmidt) • Genbrug af kode OG design (gn. Frameworks)

3. Terminologi • Class definitionen af et objekt • Superclass class som et objekt arver adfærd og variable fra • Subclass en class som arver adfærd og variable fra superclass • Instance et objekt er et instance af en class som er blevet skabt i hukommelsen • Instance variable en variable som er tilgængelig for alle funktioner i et objekt • Method en funktion – kaldes gennem objektet • Attributes = data = variable

4. 3 hovedprincipper • Encapsulation • Objekter gemmer deres funktioner (methods) og data. Begrænset brug af globale variable. Gør det lettere at udskifte dele, og teste enkeltdele. Begrænser utilsigtede ændringer af variable. • Inheritance • Hver subclass arver alle variable og metoder fra sin superclass. • Polimorphism • Multiple instances af samme class. Kopierne deler adfærd, men ikke state eller hukommelse.

5. Frameworks • Beskriver arkitekturen af et objektorienteret system. Typer af objekter og hvordan de interagerer • Fokuserer på design genbrug (modsat class library m. componenter. • Et framework er et skelet som tilpasses • Abstrakt klasse er en superclass m. virtuelle (tomme) metoder. Bruges til udformning af subclasses IKKE instances. (huskeseddel!) • Genbrugsdesignet er et set af abstrakte klasser + metoder til interaktion af instances (virtuelle).

6. adfærdsbeskrivelser • Fra dumme til superintelligente agenter afhængig af konteksten. • Typisk agenter der i en eller anden forstand lærer. • Agenter typisk begrænset i tid og rum - også hvad angår informationer • Goals?? Hvad er formålet med adfærden? • Metoder til adfærdsbeskrivelse: • Genetiske algoritmer • Neurale netværk • If then beslutnings regler

7. Genetiske algoritmer • Randomly generate initial population M(0) • Compute and save the fittness u(m) for each individual m in M(t) • Fitness function!!! • Define selection probabilities p(m) for each individual so that p(m) is proportional to u(m) • Generate M(t+1) by probabilistically selecting individuals from M(t) to produce offspring via genetic operators • Crossover (recombination) • mutation • Repeat step 2 until satisfying result is obtained

8. An agent-based architecture for the simulation of social reality in a cadastre - S. Bittner • Environment • Agents, land, system of documentation (cadastral system) • Agent • Inbox - messages sent to the agent • Internal state - goals (duty + objective) and beliefs • Outbox - messages sent by the agent • Decision rules • Update internal state based on inbox • Decide on actions to perform (duty (tax) + objective (buy/sell)) • Update beliefs based on outbox • Send outbox content to inbox of reciver

9. ABLOoM: Location behaviour, spatial patterns, and agent-based modelling - Otter, Veen og Vriend • Environment • Land use layer (land, natural area, sea), fixed • Attraction layer (agglomeration effects), non-fixed • Different for each type of agent • Agents: households and firms • Households have Preference for employment, neighbours service levels and environment. • Firms: industry, manufacturing, service -> requirement for inputs • Rules • Agents search the grid for optimal location (local or global)

10. Example of houshold rules (low-income) • Search for location with higest attraction • Set this value as target attraction • Search for employment opportunities • Choose location with target attraction closest to employment • If chosen location is vacant, move there - else nearest vacant • Update attraction of chosen location • Example of firm rules (heavy industry - natural ressource) • Search for location nearest to nature • If more locations - choose randomly (OBS! RANDOM) • If chosen location is vacant, move there - else nearest vacant • Update attraction of chosen location

11. Litteratur • Bittner, Steffen (2001), An agent-based architecture for the simulation of social reality in a cadastra, 4th AGILE conference. • Otter, H.S, A. van der Veen and H.J. de Vriend (2001), ABLOoM: location behaviour, spatial patterns and agent-based modelling, JASS vol.4 no.4 • Teran, O, J. Alvaraz, M. Ablan and M. Jaimes (2007), characterising emergence of landowners in a forest reserve, JASSS vol. 10 no.3 • Dibble, C. and P.G.Feldman (2004), The geoGraph 3D computational laboratory: network and terrain landscapes for RePast, JASSS vol 7 no.1 • The spatial dimension and social simulations: a review of three books, JASSS vol. 9 no. 4 • Hodgson, G. and T. Knudsen (forthcoming), The emergence of proporty rights enforcement in early trade: a behavioural model without reputational effects, Journal of economic behavior and organization. • Obs: JASS Journal of artificial societies and social simulation http://jasss.soc.surrey.ac.uk