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

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)

Terminologi oClassdefinitionen af et objekt oSuperclassclass som et objekt arver adfærd og variable fra oSubclassen class som arver adfærd og variable fra superclass oInstanceet objekt er et instance af en class som er blevet skabt i hukommelsen oInstance variableen variable som er tilgængelig for alle funktioner i et objekt oMethoden funktion – kaldes gennem objektet oAttributes= data = variable

3 hovedprincipper oEncapsulation oObjekter 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. oInheritance oHver subclass arver alle variable og metoder fra sin superclass. oPolimorphism oMultiple instances af samme class. Kopierne deler adfærd, men ikke state eller hukommelse.

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).

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

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

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

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)

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

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