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 o Class definitionen af et objekt o Superclass som et objekt arver adfærd og variable fra o Subclass en class som arver adfærd og variable fra superclass o Instance et objekt er et instance af en class som er blevet skabt i hukommelsen o Instance variable en variable som er tilgængelig for alle funktioner i et objekt o Method en funktion – kaldes gennem objektet o Attributes = data = variable
3 hovedprincipper o Encapsulation o 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. o Inheritance o Hver subclass arver alle variable og metoder fra sin superclass. o Polimorphism o Multiple 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
ABLOo. M: 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, 4 th AGILE conference. Otter, H. S, A. van der Veen and H. J. de Vriend (2001), ABLOo. M: 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 geo. Graph 3 D computational laboratory: network and terrain landscapes for Re. Past, 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
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